diff --git a/.github/workflows/publish_asr_worker.yml b/.github/workflows/publish_asr_worker.yml index 40783c13..5ebca764 100644 --- a/.github/workflows/publish_asr_worker.yml +++ b/.github/workflows/publish_asr_worker.yml @@ -52,8 +52,8 @@ jobs: uses: astral-sh/setup-uv@v7 with: version: "0.10.8" - - name: Install Python 3.11 - run: uv python install 3.11 + - name: Install Python 3.12 + run: uv python install 3.12 - name: Build run: cd workers/asr-worker && uv build - name: Publish diff --git a/.github/workflows/publish_caul.yml b/.github/workflows/publish_caul.yml new file mode 100644 index 00000000..c7da902b --- /dev/null +++ b/.github/workflows/publish_caul.yml @@ -0,0 +1,87 @@ +name: Publish caul + +on: + workflow_dispatch: + push: + tags: + - 'caul-*' + - '!caul-core-*' + +jobs: + create-release: + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - uses: actions/checkout@v6 + - name: Create GH release + run: gh release create "$tag" --generate-notes + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + tag: ${{ github.ref_name }} + + publish-caul-to-pypi: + runs-on: ubuntu-latest + permissions: + id-token: write + contents: read + environment: + name: pypi + url: https://pypi.org/p/caul + steps: + - uses: actions/checkout@v6 + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + version: "0.10.8" + - name: Install Python 3.12 + run: uv python install 3.12 + - name: Set version from tag + run: | + if [ "$GITHUB_REF_TYPE" != "tag" ]; then + echo "must be run from a caul-* tag, got $GITHUB_REF_TYPE $GITHUB_REF_NAME" >&2 + exit 1 + fi + cd caul + uv version --frozen "${GITHUB_REF_NAME#caul-}" + - name: Build + run: cd caul && uv build + - name: Publish + run: cd caul && uv publish + + sync-caul-version: + permissions: + contents: write + needs: + - publish-caul-to-pypi + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + with: + # main, not the tag: the tag may point at a commit whose + # pyproject.toml doesn't have this version yet + ref: main + token: ${{ secrets.GITHUB_TOKEN }} + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + version: "0.10.8" + - name: Sync pyproject.toml version with tag + run: | + git config user.name "github-actions[bot]" + git config user.email "github-actions[bot]@users.noreply.github.com" + version="${GITHUB_REF_NAME#caul-}" + cd caul + uv version --frozen "$version" + git add pyproject.toml + if git diff --cached --quiet; then + echo "pyproject.toml already at $version, nothing to sync" + else + git commit -m "release(caul): \`caul==$version\`" + git pull --rebase + git push + fi + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: false diff --git a/.github/workflows/publish_caul_core.yml b/.github/workflows/publish_caul_core.yml new file mode 100644 index 00000000..e790841f --- /dev/null +++ b/.github/workflows/publish_caul_core.yml @@ -0,0 +1,86 @@ +name: Publish caul-core + +on: + workflow_dispatch: + push: + tags: + - 'caul-core-*' + +jobs: + create-release: + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - uses: actions/checkout@v6 + - name: Create GH release + run: gh release create "$tag" --generate-notes + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + tag: ${{ github.ref_name }} + + publish-caul-core-to-pypi: + runs-on: ubuntu-latest + permissions: + id-token: write + contents: read + environment: + name: pypi + url: https://pypi.org/p/caul-core + steps: + - uses: actions/checkout@v6 + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + version: "0.10.8" + - name: Install Python 3.12 + run: uv python install 3.12 + - name: Set version from tag + run: | + if [ "$GITHUB_REF_TYPE" != "tag" ]; then + echo "must be run from a caul-core-* tag, got $GITHUB_REF_TYPE $GITHUB_REF_NAME" >&2 + exit 1 + fi + cd caul-core + uv version --frozen "${GITHUB_REF_NAME#caul-core-}" + - name: Build + run: cd caul-core && uv build + - name: Publish + run: cd caul-core && uv publish + + sync-caul-core-version: + permissions: + contents: write + needs: + - publish-caul-core-to-pypi + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + with: + # main, not the tag: the tag may point at a commit whose + # pyproject.toml doesn't have this version yet + ref: main + token: ${{ secrets.GITHUB_TOKEN }} + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + version: "0.10.8" + - name: Sync pyproject.toml version with tag + run: | + git config user.name "github-actions[bot]" + git config user.email "github-actions[bot]@users.noreply.github.com" + version="${GITHUB_REF_NAME#caul-core-}" + cd caul-core + uv version --frozen "$version" + git add pyproject.toml + if git diff --cached --quiet; then + echo "pyproject.toml already at $version, nothing to sync" + else + git commit -m "release(caul-core): \`caul-core==$version\`" + git pull --rebase + git push + fi + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: false diff --git a/.github/workflows/test_asr_worker.yml b/.github/workflows/test_asr_worker.yml index 75967705..d94df7c4 100644 --- a/.github/workflows/test_asr_worker.yml +++ b/.github/workflows/test_asr_worker.yml @@ -7,6 +7,8 @@ on: paths: - 'datashare-python/**' - 'workers/asr-worker/**' + - 'caul/**' + - 'caul-core/**' # TODO: leverage some caching here jobs: diff --git a/.github/workflows/test_caul.yml b/.github/workflows/test_caul.yml new file mode 100644 index 00000000..5e3866a9 --- /dev/null +++ b/.github/workflows/test_caul.yml @@ -0,0 +1,59 @@ +name: Test caul + +on: + push: + branches: [ 'main' ] + paths: + - 'caul/**.py' + - 'caul/pyproject.toml' + - 'caul/uv.lock' + - 'caul-core/**.py' + - 'caul-core/pyproject.toml' + - 'caul-core/uv.lock' + pull_request: + paths: + - 'caul/**.py' + - 'caul/pyproject.toml' + - 'caul/uv.lock' + - 'caul-core/**.py' + - 'caul-core/pyproject.toml' + - 'caul-core/uv.lock' + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - uses: astral-sh/ruff-action@v4.1.0 + with: + args: "--version" # skips test by displaying the version + version: "0.16.1" + - name: Check formatting + run: ruff format --config qa/ruff.toml --check caul caul-core + - name: Lint test + run: ruff check --config qa/ruff.toml caul caul-core + + test: + runs-on: ubuntu-latest + env: + PYTHON_VERSION: 3.12 + ASTRAL_VERSION: 0.11.6 + steps: + - uses: actions/checkout@v6 + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + version: ${{ env.ASTRAL_VERSION }} + python-version: ${{ env.PYTHON_VERSION }} + enable-cache: true + working-directory: caul + - name: Install ffmpeg + run: sudo apt-get update && sudo apt-get install -y ffmpeg + - name: Run tests + run: | + cd caul + uv run --frozen --dev --extra preprocessing --extra nemo --extra pyannote --extra fireredasr2 --extra faster-whisper --extra cpu python -m pytest -vvv --cache-clear --show-capture=all -r A tests -m "not no_ci" + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true diff --git a/caul-core/.python-version b/caul-core/.python-version new file mode 100644 index 00000000..24ee5b1b --- /dev/null +++ b/caul-core/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/caul-core/README.md b/caul-core/README.md new file mode 100644 index 00000000..c7733178 --- /dev/null +++ b/caul-core/README.md @@ -0,0 +1,3 @@ +# Core caul objects + +Separates core configs and objects for separate import without having to pull in heavier `caul` dependencies. \ No newline at end of file diff --git a/caul-core/caul_core/__init__.py b/caul-core/caul_core/__init__.py new file mode 100644 index 00000000..f1b655a2 --- /dev/null +++ b/caul-core/caul_core/__init__.py @@ -0,0 +1,161 @@ +from .asr_pipeline import ASRPipeline +from .asr_task import ( + ASRInput, + Audio, + AudioID, + Batcher, + IdentifiedAudio, + InferenceRunner, + InputItem, + Postprocessor, + Preprocessor, + SampleRate, +) +from .config import ( + ASRPipelineConfig, + BaseBatcherConfig, + BasePreprocessorConfig, + BatcherConfig, + ConstantSizeBatcherConfig, + FasterWhisperInferenceRunnerConfig, + FireRedASR2InferenceRunnerConfig, + InferenceRunnerConfig, + MaxDurationBatcherConfig, + ParakeetInferenceRunnerConfig, + ParakeetPostprocessorConfig, + ParakeetPreprocessorConfig, + ParakeetTrtInferenceRunnerConfig, + PostprocessorConfig, + PreprocessorConfig, + TrtLlmDecoderConfig, + TrtLlmEncoderConfig, + WhisperTrtInferenceRunnerConfig, + WhisperTrtPreprocessorConfig, +) +from .constants import ( + DEFAULT_BATCH_SIZE, + DEFAULT_LARGE_FILE_THRESHOLD_BYTES, + DEFAULT_MAX_FRAMES, + DEFAULT_SAMPLE_RATE, + FIREREDASR2_INFERENCE_MAX_FRAMES, + FIREREDASR2_MODEL_HUB_PREFIX, + PARAKEET_INFERENCE_MAX_DURATION_S, + PARAKEET_INFERENCE_MAX_FRAMES, + PARAKEET_MODEL_REF, + PARAKEET_TRT_MODEL_REF, + SILERO_VAD_MODEL, + WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR, + WHISPER_TRT_ENCODER_INPUT_FEATURES, + WHISPER_TRT_ENCODER_INPUT_LENGTHS, + WHISPER_TRT_ENCODER_POSITION_IDS, + WHISPER_TRT_EOT_TOKEN, + WHISPER_TRT_HOP_LENGTH, + WHISPER_TRT_MAX_FRAMES, + WHISPER_TRT_MAX_MEL_PADDING_LEN, + WHISPER_TRT_MAX_NEW_TOKENS, + WHISPER_TRT_N_FFT, + WHISPER_TRT_N_MELS, + WHISPER_TRT_PAD_TOKEN_ID, + WHISPER_TRT_PATTERN_STR, + WHISPER_TRT_PREPROCESSOR_CLAMP_MIN, + WHISPER_TRT_PREPROCESSOR_LOG_RANGE_MAX_SHIFT, + WHISPER_TRT_PREPROCESSOR_LOG_RANGE_NORMALIZER, + WHISPER_TRT_PROMPT_PREFIX, + WHISPER_TRT_SPECIAL_TOKENS, + WHISPER_TRT_WORLD_SIZE, + TorchDevice, +) +from .objects import ( + ASRModel, + ASRResult, + AudioMetadata, + BatcherType, + Error, + FasterWhisperModel, + FireRedASR2ModelRef, + FireRedASR2ModelTag, + FSProcessedSegment, + MemoryProcessedSegment, + ProcessedAudioSegment, + SegmentIndex, + SegmentMetadata, + VadModelRef, +) + +__all__ = [ + "DEFAULT_BATCH_SIZE", + "DEFAULT_LARGE_FILE_THRESHOLD_BYTES", + "DEFAULT_MAX_FRAMES", + "DEFAULT_SAMPLE_RATE", + "FIREREDASR2_INFERENCE_MAX_FRAMES", + "FIREREDASR2_MODEL_HUB_PREFIX", + "PARAKEET_INFERENCE_MAX_DURATION_S", + "PARAKEET_INFERENCE_MAX_FRAMES", + "PARAKEET_MODEL_REF", + "PARAKEET_TRT_MODEL_REF", + "SILERO_VAD_MODEL", + "WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR", + "WHISPER_TRT_ENCODER_INPUT_FEATURES", + "WHISPER_TRT_ENCODER_INPUT_LENGTHS", + "WHISPER_TRT_ENCODER_POSITION_IDS", + "WHISPER_TRT_EOT_TOKEN", + "WHISPER_TRT_HOP_LENGTH", + "WHISPER_TRT_MAX_FRAMES", + "WHISPER_TRT_MAX_MEL_PADDING_LEN", + "WHISPER_TRT_MAX_NEW_TOKENS", + "WHISPER_TRT_N_FFT", + "WHISPER_TRT_N_MELS", + "WHISPER_TRT_PAD_TOKEN_ID", + "WHISPER_TRT_PATTERN_STR", + "WHISPER_TRT_PREPROCESSOR_CLAMP_MIN", + "WHISPER_TRT_PREPROCESSOR_LOG_RANGE_MAX_SHIFT", + "WHISPER_TRT_PREPROCESSOR_LOG_RANGE_NORMALIZER", + "WHISPER_TRT_PROMPT_PREFIX", + "WHISPER_TRT_SPECIAL_TOKENS", + "WHISPER_TRT_WORLD_SIZE", + "ASRInput", + "ASRModel", + "ASRPipeline", + "ASRPipelineConfig", + "ASRResult", + "Audio", + "AudioID", + "AudioMetadata", + "BaseBatcherConfig", + "BasePreprocessorConfig", + "Batcher", + "BatcherConfig", + "BatcherType", + "ConstantSizeBatcherConfig", + "Error", + "FSProcessedSegment", + "FasterWhisperInferenceRunnerConfig", + "FasterWhisperModel", + "FireRedASR2InferenceRunnerConfig", + "FireRedASR2ModelRef", + "FireRedASR2ModelTag", + "IdentifiedAudio", + "InferenceRunner", + "InferenceRunnerConfig", + "InputItem", + "MaxDurationBatcherConfig", + "MemoryProcessedSegment", + "ParakeetInferenceRunnerConfig", + "ParakeetPostprocessorConfig", + "ParakeetPreprocessorConfig", + "ParakeetTrtInferenceRunnerConfig", + "Postprocessor", + "PostprocessorConfig", + "Preprocessor", + "PreprocessorConfig", + "ProcessedAudioSegment", + "SampleRate", + "SegmentIndex", + "SegmentMetadata", + "TorchDevice", + "TrtLlmDecoderConfig", + "TrtLlmEncoderConfig", + "VadModelRef", + "WhisperTrtInferenceRunnerConfig", + "WhisperTrtPreprocessorConfig", +] diff --git a/caul-core/caul_core/asr_pipeline.py b/caul-core/caul_core/asr_pipeline.py new file mode 100644 index 00000000..6f46c318 --- /dev/null +++ b/caul-core/caul_core/asr_pipeline.py @@ -0,0 +1,134 @@ +import logging +from collections.abc import Iterable +from contextlib import ExitStack +from copy import copy +from pathlib import Path +from types import TracebackType +from typing import TYPE_CHECKING, Self + +from icij_common.pydantic_utils import safe_copy + +from .asr_task import ASRTask, InferenceRunner, Postprocessor, Preprocessor +from .config import ASRPipelineConfig +from .constants import TorchDevice +from .objects import ASRResult + +if TYPE_CHECKING: + import numpy as np + import torch + +logger = logging.getLogger(__name__) + + +class ASRPipeline: + def __init__( + self, tasks: list[ASRTask], device: TorchDevice = TorchDevice.CPU + ) -> None: + self._device = device + self._tasks = tasks + self._exit_stack = ExitStack() + + @classmethod + def from_config(cls, config: ASRPipelineConfig) -> Self: + tasks = [ + Preprocessor.from_config(config.preprocessing), + InferenceRunner.from_config(config.inference, device=config.device), + Postprocessor.from_config(config.postprocessing), + ] + return cls(tasks, device=config.device) + + def __enter__(self): + for t in self._tasks: + self._exit_stack.enter_context(t) + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: + self._exit_stack.__exit__(exc_type, exc_val, exc_tb) + + @property + def tasks(self) -> list[ASRTask]: + return copy(self._tasks) + + @property + def device(self) -> TorchDevice: + return self._device + + @device.setter + def device(self, device: TorchDevice = TorchDevice.CPU) -> None: + self._device = device + for t in self._tasks: + t.device = device + + def process( + self, + inputs: "Iterable[np.ndarray | torch.Tensor | str]" + " | np.ndarray | torch.Tensor | str", + languages: list[str] | None = None, + tensor_output_dir: str | Path | None = None, + *, + skip_errors: bool = False, + ) -> Iterable[ASRResult]: + """Run inputs sequentially through all the pipeline tasks""" + output = inputs + for task in self._tasks: + output = task.process_results( + output, + output_dir=tensor_output_dir, + languages=languages, + skip_errors=skip_errors, + ) + yield from output + + @classmethod + def parakeet(cls, device: TorchDevice = TorchDevice.CPU) -> Self: + config = ASRPipelineConfig.parakeet(device) + return cls.from_config(config) + + @classmethod + def parakeet_trt( + cls, + model_path: str | Path, + engine_path: str | Path, + device: TorchDevice = TorchDevice.CPU, + ) -> Self: + config = ASRPipelineConfig.parakeet_trt(device) + config = safe_copy( + config, + update={"model_path": model_path, "engine_path": engine_path}, + ) + return cls.from_config(config=config) + + @classmethod + def fireredasr2( + cls, + device: TorchDevice = TorchDevice.CPU, + *, + tmp_dir_fallback: bool = False, + ) -> Self: + config = ASRPipelineConfig.fireredasr2( + tmp_dir_fallback=tmp_dir_fallback, device=device + ) + return cls.from_config(config) + + @classmethod + def faster_whisper(cls, device: TorchDevice = TorchDevice.CPU) -> Self: + config = ASRPipelineConfig.faster_whisper(device=device) + return cls.from_config(config) + + @classmethod + def whisper_trt( + cls, + encoder_path: str, + *, + decoder_path: str, + device: TorchDevice = TorchDevice.CPU, + ) -> Self: + config = ASRPipelineConfig.whisper_trt( + encoder_path=encoder_path, decoder_path=decoder_path, device=device + ) + return cls.from_config(config=config) diff --git a/caul-core/caul_core/asr_task.py b/caul-core/caul_core/asr_task.py new file mode 100644 index 00000000..b0e0a50c --- /dev/null +++ b/caul-core/caul_core/asr_task.py @@ -0,0 +1,136 @@ +import gc +from abc import ABC, abstractmethod +from collections.abc import Iterable +from contextlib import AbstractContextManager +from pathlib import Path +from types import TracebackType +from typing import TYPE_CHECKING + +from icij_common.registrable import FromConfig, RegistrableFromConfig +from overrides import final + +from .config import BaseBatcherConfig +from .constants import TorchDevice +from .objects import ASRResult, Error, ProcessedAudioSegment + +if TYPE_CHECKING: + import numpy as np + import torch + + +class ASRTask[I, O](AbstractContextManager, ABC): + """Generic ASR task""" + + def __init__(self, device: TorchDevice = TorchDevice.CPU) -> None: + self._device = device + + @abstractmethod + def process(self, inputs: Iterable[I], *args, **kwargs) -> Iterable[O | Error]: + """Process inputs""" + + @final + def process_results( + self, inputs: Iterable[I], *args, skip_errors: bool = False, **kwargs + ) -> Iterable[O]: + for r in self.process(inputs, *args, **kwargs): + if not skip_errors and isinstance(r, Error): + raise RuntimeError(f"error: {r}") + yield r + + def __enter__(self): + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: ... + + @property + def device(self) -> TorchDevice: + return self._device + + @device.setter + def device(self, device: TorchDevice) -> None: + self._device = device + + +type Audio = "np.ndarray | torch.Tensor | str | Path" +InputItem = Audio # for backward comp +type AudioID = str +type IdentifiedAudio = tuple[AudioID, Audio] +type ASRInput = "Iterable[Audio | IdentifiedAudio]" +SampleRate = Iterable[int] | int + + +class Preprocessor(ASRTask, RegistrableFromConfig): + @abstractmethod + def process( + self, + inputs: ASRInput, + sample_rates: SampleRate | None = None, + output_dir: Path | None = None, + **kwargs, + ) -> Iterable[tuple[ProcessedAudioSegment, ...] | Error]: + """Process inputs""" + + +class InferenceRunner(ASRTask, RegistrableFromConfig): + """Abstract for ASR inference""" + + def __init__(self, device: TorchDevice = TorchDevice.CPU): + self._device = device + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: + import torch + + self._model = None + gc.collect() + if self._torch_device.type == torch.device(TorchDevice.GPU).type: + torch.cuda.empty_cache() + + @abstractmethod + def process( + self, inputs: Iterable[tuple[ProcessedAudioSegment, ...]], *args, **kwargs + ) -> Iterable[ASRResult | Error]: ... + + @property + def _torch_device(self) -> "torch.device": + import torch + + return torch.device(self.device) + + @property + def device(self) -> TorchDevice: + return self._device + + @device.setter + def device(self, device: TorchDevice) -> None: + self._device = device + + +class Postprocessor(ASRTask, RegistrableFromConfig): + def process( + self, inputs: Iterable[ASRResult], *args, **kwargs + ) -> Iterable[ASRResult | Error]: ... + + +class Batcher[I, O, C](RegistrableFromConfig): + def __init__(self, items: Iterable[I | Error], config: C): + self._items = items + self._config = config + + @abstractmethod + def batch(self) -> Iterable[tuple[O, ...] | Error]: ... + + @classmethod + def _from_config( + cls, config: BaseBatcherConfig, *, items: Iterable[I | Error] + ) -> FromConfig: + return cls(config=config, items=items) diff --git a/caul-core/caul_core/config.py b/caul-core/caul_core/config.py new file mode 100644 index 00000000..088a41f9 --- /dev/null +++ b/caul-core/caul_core/config.py @@ -0,0 +1,393 @@ +from abc import ABC +from contextlib import suppress +from pathlib import Path +from typing import TYPE_CHECKING, ClassVar, Self + +from icij_common.pydantic_utils import make_enum_discriminator, tagged_union +from icij_common.registrable import RegistrableConfig +from pydantic import Discriminator, Field + +from .constants import ( + DEFAULT_BATCH_SIZE, + DEFAULT_LARGE_FILE_THRESHOLD_BYTES, + DEFAULT_MAX_FRAMES, + DEFAULT_SAMPLE_RATE, + FASTER_WHISPER_APPEND_PUNCTUATIONS_DEFAULT, + FASTER_WHISPER_BEAM_SIZE_DEFAULT, + FASTER_WHISPER_BEST_OF_DEFAULT, + FASTER_WHISPER_CLIP_TIMESTAMPS_DEFAULT, + FASTER_WHISPER_COMPRESSION_RATIO_THRESHOLD_DEFAULT, + FASTER_WHISPER_COMPUTE_TYPE_DEFAULT, + FASTER_WHISPER_CONDITION_ON_PREVIOUS_TEXT_DEFAULT, + FASTER_WHISPER_LARGE_V3_TURBO_SUPPRESSED_TOKENS, + FASTER_WHISPER_LENGTH_PENALTY_DEFAULT, + FASTER_WHISPER_LOG_PROB_THRESHOLD_DEFAULT, + FASTER_WHISPER_MAX_INITIAL_TIMESTAMP_DEFAULT, + FASTER_WHISPER_MULTILINGUAL_DEFAULT, + FASTER_WHISPER_NO_REPEAT_NGRAM_SIZE_DEFAULT, + FASTER_WHISPER_NO_SPEECH_THRESHOLD_DEFAULT, + FASTER_WHISPER_PATIENCE_DEFAULT, + FASTER_WHISPER_PREPEND_PUNCTUATIONS_DEFAULT, + FASTER_WHISPER_PROMPT_RESET_ON_TEMPERATURE_DEFAULT, + FASTER_WHISPER_REPETITION_PENALTY_DEFAULT, + FASTER_WHISPER_SUPPRESS_BLANK_DEFAULT, + FASTER_WHISPER_TEMPERATURES_DEFAULT, + FASTER_WHISPER_WITHOUT_TIMESTAMPS_DEFAULT, + FASTER_WHISPER_WORD_TIMESTAMPS_DEFAULT, + FIREREDASR2_AED_LENGTH_PENALTY_DEFAULT, + FIREREDASR2_BEAM_SIZE_DEFAULT, + FIREREDASR2_DECODE_MAX_LEN_DEFAULT, + FIREREDASR2_EOS_PENALTY_DEFAULT, + FIREREDASR2_INFERENCE_MAX_FRAMES, + FIREREDASR2_NBEST_DEFAULT, + FIREREDASR2_RETURN_TIMESTAMP_DEFAULT, + FIREREDASR2_SOFTMAX_SMOOTHING_DEFAULT, + FIREREDASR2_USE_HALF_DEFAULT, + PARAKEET_INFERENCE_MAX_DURATION_S, + PARAKEET_INFERENCE_MAX_FRAMES, + PARAKEET_MODEL_REF, + WHISPER_TRT_DECODER_BATCH_SIZE, + WHISPER_TRT_DECODER_BEAM_WIDTH, + WHISPER_TRT_DECODER_CROSS_ATTENTION, + WHISPER_TRT_DECODER_DEBUG_MODE, + WHISPER_TRT_DECODER_GPT_ATTENTION_PLUGIN, + WHISPER_TRT_DECODER_HAS_POSITION_EMBEDDING, + WHISPER_TRT_DECODER_HAS_TOKEN_TYPE_EMBEDDING, + WHISPER_TRT_DECODER_HIDDEN_SIZE, + WHISPER_TRT_DECODER_NUM_HEADS, + WHISPER_TRT_DECODER_NUM_HIDDEN_LAYERS, + WHISPER_TRT_DECODER_PAGED_KV_CACHE, + WHISPER_TRT_DECODER_REMOVE_INPUT_PADDING, + WHISPER_TRT_DECODER_VOCAB_SIZE, + WHISPER_TRT_DTYPE, + WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR, + WHISPER_TRT_MAX_FRAMES, + WHISPER_TRT_MAX_MEL_PADDING_LEN, + WHISPER_TRT_N_MELS, + WHISPER_TRT_PROMPT_PREFIX, + WHISPER_TRT_RETURN_TIMESTAMPS, + TorchDevice, +) +from .objects import ASRModel, BaseModel, BatcherType, FasterWhisperModel + +if TYPE_CHECKING: + with suppress(ImportError): + from tensorrt_llm.runtime import ModelConfig + + +class _BaseConfig(BaseModel, RegistrableConfig, ABC): ... + + +# TensorRT LLM +class TrtLlmEncoderConfig(BaseModel): + downsampling_factor: int = WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR + + +class TrtLlmDecoderConfig(BaseModel): + batch_size: int = WHISPER_TRT_DECODER_BATCH_SIZE + beam_width: int = WHISPER_TRT_DECODER_BEAM_WIDTH + num_heads: int = WHISPER_TRT_DECODER_NUM_HEADS + num_kv_heads: int = WHISPER_TRT_DECODER_NUM_HEADS + hidden_size: int = WHISPER_TRT_DECODER_HIDDEN_SIZE + vocab_size: int = WHISPER_TRT_DECODER_VOCAB_SIZE + num_hidden_layers: int = WHISPER_TRT_DECODER_NUM_HIDDEN_LAYERS + gpt_attention_plugin: str = WHISPER_TRT_DECODER_GPT_ATTENTION_PLUGIN + remove_input_padding: bool = WHISPER_TRT_DECODER_REMOVE_INPUT_PADDING + paged_kv_cache: bool = WHISPER_TRT_DECODER_PAGED_KV_CACHE + has_position_embedding: bool = WHISPER_TRT_DECODER_HAS_POSITION_EMBEDDING + dtype: str = WHISPER_TRT_DTYPE + cross_attention: bool = WHISPER_TRT_DECODER_CROSS_ATTENTION + has_token_type_embedding: bool = WHISPER_TRT_DECODER_HAS_TOKEN_TYPE_EMBEDDING + debug_mode: bool = WHISPER_TRT_DECODER_DEBUG_MODE + + def to_model_config(self) -> "ModelConfig": + from tensorrt_llm.llmapi.kv_cache_type import ( + KVCacheType, + ) + from tensorrt_llm.runtime import ( + ModelConfig, + ) + + return ModelConfig( + max_batch_size=self.batch_size, + max_beam_width=self.beam_width, + num_heads=self.num_heads, + num_kv_heads=self.num_kv_heads, + hidden_size=self.hidden_size, + vocab_size=self.vocab_size, + cross_attention=self.cross_attention, + num_hidden_layers=self.num_hidden_layers, + gpt_attention_plugin=self.gpt_attention_plugin, + remove_input_padding=self.remove_input_padding, + kv_cache_type=( + KVCacheType.PAGED if self.paged_kv_cache else KVCacheType.CONTINUOUS + ), + has_position_embedding=self.has_position_embedding, + dtype=self.dtype, + has_token_type_embedding=self.has_token_type_embedding, + ) + + +class BaseBatcherConfig(_BaseConfig): + registry_key: ClassVar[str] = Field(frozen=True, default="type") + type: ClassVar[BatcherType] + + +class ConstantSizeBatcherConfig(BaseBatcherConfig): + type: ClassVar[str] = Field(default=BatcherType.CONSTANT_SIZE) + + batch_size: int = DEFAULT_BATCH_SIZE + + +class MaxDurationBatcherConfig(ConstantSizeBatcherConfig): + type: ClassVar[str] = Field(default=BatcherType.MAX_DURATION) + + batch_size: int = 0 + max_duration_s: float = PARAKEET_INFERENCE_MAX_DURATION_S + + +BatcherConfig = tagged_union( + [ConstantSizeBatcherConfig, MaxDurationBatcherConfig], + lambda t: t.type.default.value, +) + +batcher_discriminator = make_enum_discriminator("type", BatcherType) + + +class BasePreprocessorConfig(_BaseConfig): + registry_key: ClassVar[str] = Field(frozen=True, default="model") + model: ClassVar[ASRModel] + + max_frames: int = DEFAULT_MAX_FRAMES + sample_rate: int = DEFAULT_SAMPLE_RATE + large_file_threshold_bytes: int = DEFAULT_LARGE_FILE_THRESHOLD_BYTES + + batcher: BatcherConfig = Field( + default_factory=ConstantSizeBatcherConfig, + discriminator=Discriminator(batcher_discriminator), + ) + + +class BaseInferenceRunnerConfig(_BaseConfig): + registry_key: ClassVar[str] = Field(frozen=True, default="model") + model: ClassVar[ASRModel] + + tmp_dir_fallback: bool = False + + +class BasePostprocessorConfig(_BaseConfig): + registry_key: ClassVar[str] = Field(frozen=True, default="model") + model: ClassVar[ASRModel] + + +# Parakeet +class ParakeetPreprocessorConfig(BasePreprocessorConfig): + model: ClassVar[str] = Field(default=ASRModel.PARAKEET) + + max_frames: int = PARAKEET_INFERENCE_MAX_FRAMES + + +class ParakeetInferenceRunnerConfig(BaseInferenceRunnerConfig): + model: ClassVar[str] = Field(frozen=True, default=ASRModel.PARAKEET) + model_name: str = PARAKEET_MODEL_REF + + +class ParakeetTrtInferenceRunnerConfig(BaseInferenceRunnerConfig): + model_path: Path | str = None + engine_path: Path | str = None + model: ClassVar[str] = Field(frozen=True, default=ASRModel.PARAKEET_TRT) + + +class ParakeetPostprocessorConfig(BasePostprocessorConfig): + model: ClassVar[str] = Field(frozen=True, default=ASRModel.PARAKEET) + + +# Faster Whisper + + +class FasterWhisperPreprocessorConfig(BasePreprocessorConfig): + model: ClassVar[str] = Field(default=ASRModel.FASTER_WHISPER) + + +class FasterWhisperInferenceRunnerConfig(BaseInferenceRunnerConfig): + model: ClassVar[str] = Field(frozen=True, default=ASRModel.FASTER_WHISPER) + + whisper_model_name: FasterWhisperModel = FasterWhisperModel.MEDIUM + + compute_type: str = FASTER_WHISPER_COMPUTE_TYPE_DEFAULT + word_timestamps: bool = FASTER_WHISPER_WORD_TIMESTAMPS_DEFAULT + beam_size: int = FASTER_WHISPER_BEAM_SIZE_DEFAULT + best_of: int = FASTER_WHISPER_BEST_OF_DEFAULT + patience: int = FASTER_WHISPER_PATIENCE_DEFAULT + length_penalty: int = FASTER_WHISPER_LENGTH_PENALTY_DEFAULT + repetition_penalty: int = FASTER_WHISPER_REPETITION_PENALTY_DEFAULT + no_repeat_ngram_size: int = FASTER_WHISPER_NO_REPEAT_NGRAM_SIZE_DEFAULT + log_prob_threshold: float = FASTER_WHISPER_LOG_PROB_THRESHOLD_DEFAULT + no_speech_threshold: float = FASTER_WHISPER_NO_SPEECH_THRESHOLD_DEFAULT + compression_ratio_threshold: float = ( + FASTER_WHISPER_COMPRESSION_RATIO_THRESHOLD_DEFAULT + ) + condition_on_previous_text: bool = FASTER_WHISPER_CONDITION_ON_PREVIOUS_TEXT_DEFAULT + prompt_reset_on_temperature: float = ( + FASTER_WHISPER_PROMPT_RESET_ON_TEMPERATURE_DEFAULT + ) + temperatures: list[float] = FASTER_WHISPER_TEMPERATURES_DEFAULT + initial_prompt: str | None = None + prefix: str | None = None + suppress_blank: bool = FASTER_WHISPER_SUPPRESS_BLANK_DEFAULT + suppress_tokens: list[int] = FASTER_WHISPER_LARGE_V3_TURBO_SUPPRESSED_TOKENS + without_timestamps: bool = FASTER_WHISPER_WITHOUT_TIMESTAMPS_DEFAULT + max_initial_timestamp: float = FASTER_WHISPER_MAX_INITIAL_TIMESTAMP_DEFAULT + prepend_punctuations: str = FASTER_WHISPER_PREPEND_PUNCTUATIONS_DEFAULT + append_punctuations: str = FASTER_WHISPER_APPEND_PUNCTUATIONS_DEFAULT + multilingual: bool = FASTER_WHISPER_MULTILINGUAL_DEFAULT + max_new_tokens: int | None = None + clip_timestamps: list[float] = FASTER_WHISPER_CLIP_TIMESTAMPS_DEFAULT + hallucination_silence_threshold: float | None = None + hotwords: str | None = None + + +class FasterWhisperPostprocessorConfig(BasePostprocessorConfig): + model: ClassVar[str] = Field(frozen=True, default=ASRModel.FASTER_WHISPER) + + +# FireRedASR2s + + +class FireRedASR2PreprocessorConfig(BasePreprocessorConfig): + model: ClassVar[str] = Field(default=ASRModel.FIREREDASR2_AED) + + max_frames: int = FIREREDASR2_INFERENCE_MAX_FRAMES + + +class FireRedASR2InferenceRunnerConfig(BaseInferenceRunnerConfig): + model: ClassVar[str] = Field(frozen=True, default=ASRModel.FIREREDASR2_AED) + + use_half: bool = FIREREDASR2_USE_HALF_DEFAULT + beam_size: int = FIREREDASR2_BEAM_SIZE_DEFAULT + nbest: int = FIREREDASR2_NBEST_DEFAULT + decode_max_len: int = FIREREDASR2_DECODE_MAX_LEN_DEFAULT + softmax_smoothing: float = FIREREDASR2_SOFTMAX_SMOOTHING_DEFAULT + aed_length_penalty: float = FIREREDASR2_AED_LENGTH_PENALTY_DEFAULT + eos_penalty: float = FIREREDASR2_EOS_PENALTY_DEFAULT + return_timestamp: bool = FIREREDASR2_RETURN_TIMESTAMP_DEFAULT + + +class FireRedASR2PostprocessorConfig(BasePostprocessorConfig): + model: ClassVar[str] = Field(frozen=True, default=ASRModel.FIREREDASR2_AED) + + +# Whisper TRT + + +class WhisperTrtPreprocessorConfig(BasePreprocessorConfig): + model: ClassVar[str] = Field(frozen=True, default=ASRModel.WHISPER_TRT) + + n_mels: int = WHISPER_TRT_N_MELS + mel_filters_dir: str | None = None + dtype: str = WHISPER_TRT_DTYPE + max_frames: int = WHISPER_TRT_MAX_FRAMES + + +class WhisperTrtInferenceRunnerConfig(BaseInferenceRunnerConfig): + model: ClassVar[str] = Field(frozen=True, default=ASRModel.WHISPER_TRT) + + encoder_config: TrtLlmEncoderConfig = Field(default_factory=TrtLlmEncoderConfig) + decoder_config: TrtLlmDecoderConfig = Field(default_factory=TrtLlmDecoderConfig) + + encoder_path: str + decoder_path: str + + prompt_prefix: str = WHISPER_TRT_PROMPT_PREFIX + return_timestamps: bool = WHISPER_TRT_RETURN_TIMESTAMPS + max_mel_padding_len: int = WHISPER_TRT_MAX_MEL_PADDING_LEN + + +class WhisperTrtPostprocessorConfig(BasePostprocessorConfig): + model: ClassVar[str] = Field(frozen=True, default=ASRModel.WHISPER_TRT) + + +PreprocessorConfig = tagged_union( + BasePreprocessorConfig.__subclasses__(), lambda t: t.model.default.value +) + +InferenceRunnerConfig = tagged_union( + BaseInferenceRunnerConfig.__subclasses__(), lambda t: t.model.default.value +) + +PostprocessorConfig = tagged_union( + BasePostprocessorConfig.__subclasses__(), lambda t: t.model.default.value +) + + +model_discriminator = make_enum_discriminator("model", ASRModel) + + +class ASRPipelineConfig(BaseModel): + device: TorchDevice = TorchDevice.CPU + preprocessing: PreprocessorConfig = Field( + discriminator=Discriminator(model_discriminator), + ) + inference: InferenceRunnerConfig = Field( + discriminator=Discriminator(model_discriminator), + ) + postprocessing: PostprocessorConfig = Field( + discriminator=Discriminator(model_discriminator), + ) + + @classmethod + def parakeet(cls, device: TorchDevice = TorchDevice.CPU) -> Self: + return cls( + device=device, + preprocessing=ParakeetPreprocessorConfig(), + inference=ParakeetInferenceRunnerConfig(), + postprocessing=ParakeetPostprocessorConfig(), + ) + + @classmethod + def parakeet_trt(cls, device: TorchDevice = TorchDevice.CPU) -> Self: + return cls( + device=device, + preprocessing=ParakeetPreprocessorConfig(), + inference=ParakeetTrtInferenceRunnerConfig(), + postprocessing=ParakeetPostprocessorConfig(), + ) + + @classmethod + def fireredasr2( + cls, *, tmp_dir_fallback: bool = True, device: TorchDevice = TorchDevice.CPU + ) -> Self: + return cls( + device=device, + preprocessing=FireRedASR2PreprocessorConfig(), + inference=FireRedASR2InferenceRunnerConfig( + tmp_dir_fallback=tmp_dir_fallback + ), + postprocessing=FireRedASR2PostprocessorConfig(), + ) + + @classmethod + def faster_whisper(cls, device: TorchDevice = TorchDevice.CPU) -> Self: + return cls( + device=device, + preprocessing=FasterWhisperPreprocessorConfig(), + inference=FasterWhisperInferenceRunnerConfig(), + postprocessing=FasterWhisperPostprocessorConfig(), + ) + + @classmethod + def whisper_trt( + cls, + encoder_path: str, + *, + decoder_path: str, + device: TorchDevice = TorchDevice.CPU, + ) -> Self: + return cls( + device=device, + preprocessing=WhisperTrtPreprocessorConfig(), + inference=WhisperTrtInferenceRunnerConfig( + encoder_path=encoder_path, decoder_path=decoder_path + ), + postprocessing=WhisperTrtPostprocessorConfig(), + ) diff --git a/caul-core/caul_core/constants.py b/caul-core/caul_core/constants.py new file mode 100644 index 00000000..5cef5555 --- /dev/null +++ b/caul-core/caul_core/constants.py @@ -0,0 +1,354 @@ +from enum import StrEnum + + +class VadModel(StrEnum): + SILERO_MODEL = "silero_vad" + PYANNOTE_MODEL = "pyannote/segmentation-3.0" + + +class TorchDevice(StrEnum): + CPU = "cpu" + GPU = "cuda" + MPS = "mps" + + +DEFAULT_SAMPLE_RATE = 16000 +DEFAULT_BATCH_SIZE = 32 +DEFAULT_MAX_FRAMES = 25 * 60 * DEFAULT_SAMPLE_RATE # twenty-five minutes +DEFAULT_LARGE_FILE_THRESHOLD_BYTES = 30 * 1024 * 1024 # 30mb + +# Languages +PARAKEET_TDT_0_6B_V3_LANGUAGES = { + "bg", + "hr", + "cs", + "da", + "nl", + "en", + "et", + "fi", + "fr", + "de", + "el", + "hu", + "it", + "lv", + "lt", + "mt", + "pl", + "pt", + "ro", + "sk", + "sl", + "es", + "sv", + "ru", + "uk", +} +FIREREDASR2_LANGUAGES = {"zh"} +WHISPER_TRT_LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "he": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", + "yue": "cantonese", +} + +# Silero +SILERO_TORCH_HUB_REPO = "snakers4/silero-vad" + +# Parakeet + +PARAKEET_MODEL_REF = "nvidia/parakeet-tdt-0.6b-v3" +PARAKEET_INFERENCE_MAX_DURATION_S = ( + 20 * 60 # actually 24, but we want to give ourselves some room to avoid CUDA OOMs +) +PARAKEET_INFERENCE_MAX_FRAMES = PARAKEET_INFERENCE_MAX_DURATION_S * DEFAULT_SAMPLE_RATE + +# Faster Whisper (SYSTRAN/faster-whisper) + +FASTER_WHISPER_COMPUTE_TYPE_DEFAULT = "float32" +FASTER_WHISPER_WORD_TIMESTAMPS_DEFAULT = True +FASTER_WHISPER_BEAM_SIZE_DEFAULT = 5 +FASTER_WHISPER_BEST_OF_DEFAULT = 5 +FASTER_WHISPER_PATIENCE_DEFAULT = 1 +FASTER_WHISPER_LENGTH_PENALTY_DEFAULT = 1 +FASTER_WHISPER_REPETITION_PENALTY_DEFAULT = 1 +FASTER_WHISPER_NO_REPEAT_NGRAM_SIZE_DEFAULT = 0 +FASTER_WHISPER_LOG_PROB_THRESHOLD_DEFAULT = -1.0 +FASTER_WHISPER_NO_SPEECH_THRESHOLD_DEFAULT = 0.6 +FASTER_WHISPER_COMPRESSION_RATIO_THRESHOLD_DEFAULT = 2.4 +FASTER_WHISPER_CONDITION_ON_PREVIOUS_TEXT_DEFAULT = False +FASTER_WHISPER_PROMPT_RESET_ON_TEMPERATURE_DEFAULT = 0.5 +FASTER_WHISPER_TEMPERATURES_DEFAULT = [0.0] +FASTER_WHISPER_SUPPRESS_BLANK_DEFAULT = True +FASTER_WHISPER_WITHOUT_TIMESTAMPS_DEFAULT = True +FASTER_WHISPER_MAX_INITIAL_TIMESTAMP_DEFAULT = 0.0 +FASTER_WHISPER_PREPEND_PUNCTUATIONS_DEFAULT = "\"'‘’“¿([{—-" +FASTER_WHISPER_APPEND_PUNCTUATIONS_DEFAULT = "\"'’”.。,，!！?？:：””)]、結}" +FASTER_WHISPER_MULTILINGUAL_DEFAULT = False +FASTER_WHISPER_CLIP_TIMESTAMPS_DEFAULT = [] +FASTER_WHISPER_LARGE_V3_TURBO_SUPPRESSED_TOKENS = [ + 1, + 2, + 7, + 8, + 9, + 10, + 14, + 25, + 26, + 27, + 28, + 29, + 31, + 58, + 59, + 60, + 61, + 62, + 63, + 90, + 91, + 92, + 93, + 359, + 503, + 522, + 542, + 873, + 893, + 902, + 918, + 922, + 931, + 1350, + 1853, + 1982, + 2460, + 2627, + 3246, + 3253, + 3268, + 3536, + 3846, + 3961, + 4183, + 4667, + 6585, + 6647, + 7273, + 9061, + 9383, + 10428, + 10929, + 11938, + 12033, + 12331, + 12562, + 13793, + 14157, + 14635, + 15265, + 15618, + 16553, + 16604, + 18362, + 18956, + 20075, + 21675, + 22520, + 26130, + 26161, + 26435, + 28279, + 29464, + 31650, + 32302, + 32470, + 36865, + 42863, + 47425, + 49870, + 50254, + 50258, + 50359, + 50360, + 50361, + 50362, + 50363, +] + +# Parakeet TRT +PARAKEET_TRT_MODEL_REF = "ICIJ/parakeet-v3-trt" + +# FireRedASR2s + +FIREREDASR2_MODEL_HUB_PREFIX = "FireRedTeam/FireRed" +FIREREDASR2_INFERENCE_MAX_DURATION_S = 60 +FIREREDASR2_INFERENCE_MAX_FRAMES = ( + FIREREDASR2_INFERENCE_MAX_DURATION_S * DEFAULT_SAMPLE_RATE +) +FIREREDASR2_USE_HALF_DEFAULT = False +FIREREDASR2_BEAM_SIZE_DEFAULT = 3 +FIREREDASR2_NBEST_DEFAULT = 1 +FIREREDASR2_DECODE_MAX_LEN_DEFAULT = 0 +FIREREDASR2_SOFTMAX_SMOOTHING_DEFAULT = 1.25 +FIREREDASR2_AED_LENGTH_PENALTY_DEFAULT = 0.6 +FIREREDASR2_EOS_PENALTY_DEFAULT = 1.0 +FIREREDASR2_RETURN_TIMESTAMP_DEFAULT = True + +# Whisper TRT + +WHISPER_TRT_N_FFT = 400 +WHISPER_TRT_HOP_LENGTH = 160 +WHISPER_TRT_WORLD_SIZE = 1 # TODO: Explain what this is +WHISPER_TRT_EOT_TOKEN = "<|endoftext|>" +WHISPER_TRT_ML_TOKENIZER_VOCAB_SIZE_THRESHOLD = 51865 +WHISPER_TRT_PROMPT_PREFIX = "<|startoftranscript|><|en|><|transcribe|>" +WHISPER_TRT_MAX_NEW_TOKENS = 3000 +WHISPER_TRT_MAX_MEL_PADDING_LEN = 3000 +WHISPER_TRT_PATTERN_STR = ( + r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" +) +WHISPER_TRT_PAD_TOKEN_ID = 50256 +WHISPER_TRT_MAX_FRAMES = 480000 # 30 secs + +WHISPER_TRT_N_MELS = 128 +WHISPER_TRT_DTYPE = "float16" + +WHISPER_TRT_PREPROCESSOR_CLAMP_MIN = 1e-10 +WHISPER_TRT_PREPROCESSOR_LOG_RANGE_MAX_SHIFT = 8.0 +WHISPER_TRT_PREPROCESSOR_LOG_RANGE_NORMALIZER = 4.0 + +WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR = 2 + +WHISPER_TRT_DECODER_BATCH_SIZE = 8 +WHISPER_TRT_DECODER_BEAM_WIDTH = 1 +WHISPER_TRT_DECODER_NUM_HIDDEN_LAYERS = 2 +WHISPER_TRT_DECODER_HIDDEN_SIZE = 1280 +WHISPER_TRT_DECODER_VOCAB_SIZE = 51866 +WHISPER_TRT_DECODER_NUM_HEADS = 20 +WHISPER_TRT_RETURN_TIMESTAMPS = True +WHISPER_TRT_DECODER_GPT_ATTENTION_PLUGIN = "auto" +WHISPER_TRT_DECODER_PAGED_KV_CACHE = True +WHISPER_TRT_DECODER_HAS_POSITION_EMBEDDING = True +WHISPER_TRT_DECODER_CROSS_ATTENTION = True +WHISPER_TRT_DECODER_HAS_TOKEN_TYPE_EMBEDDING = False +WHISPER_TRT_DECODER_DEBUG_MODE = False +WHISPER_TRT_DECODER_REMOVE_INPUT_PADDING = False +WHISPER_TRT_SPECIAL_TOKENS = [ + "<|endoftext|>", + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in list(WHISPER_TRT_LANGUAGES.keys())], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + *[f"<|{i * 0.02:.2f}|>" for i in range(1501)], +] +WHISPER_TRT_ENCODER_INPUT_FEATURES = "input_features" +WHISPER_TRT_ENCODER_INPUT_LENGTHS = "input_lengths" +WHISPER_TRT_ENCODER_POSITION_IDS = "position_ids" + + +# Silero + +SILERO_VAD_MODEL = "silero_vad" diff --git a/caul-core/caul_core/objects.py b/caul-core/caul_core/objects.py new file mode 100644 index 00000000..9abb5ab8 --- /dev/null +++ b/caul-core/caul_core/objects.py @@ -0,0 +1,288 @@ +import datetime +import math +import traceback +import uuid +from collections.abc import Iterable +from dataclasses import dataclass +from enum import StrEnum, unique +from pathlib import Path +from typing import TYPE_CHECKING, Any, Self + +import langcodes +from icij_common.pydantic_utils import ( + icij_config, + merge_configs, + no_enum_values_config, + safe_copy, +) +from pydantic import BaseModel as _BaseModel +from pydantic import Field, GetCoreSchemaHandler, TypeAdapter +from pydantic_core import core_schema +from pydantic_extra_types.language_code import LanguageAlpha2 + +from .constants import FIREREDASR2_LANGUAGES, PARAKEET_TDT_0_6B_V3_LANGUAGES + +if TYPE_CHECKING: + import torch + from faster_whisper.transcribe import Segment + from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis + + +# Enums + + +class FireRedASR2ModelRef(StrEnum): + ASR2 = "ASR2" + + +class FireRedASR2ModelTag(StrEnum): + AED = "aed" + LLM = "llm" + + +class VadModelRef(StrEnum): + SILERO_MODEL = "snakers4/silero-vad" + PYANNOTE_MODEL = "pyannote/segmentation-3.0" + + +@unique +class FasterWhisperModel(StrEnum): + TINY = "tiny" + BASE = "base" + SMALL = "small" + MEDIUM = "medium" + LARGE = "large" + LARGE_V3 = "large-v3" + DISTIL_LARGE_V3 = "distil-large-v3" + + @property + def to_repo_id(self) -> str: + match self: + case FasterWhisperModel.TINY: + return "Systran/faster-whisper-tiny" + case FasterWhisperModel.BASE: + return "Systran/faster-whisper-base" + case FasterWhisperModel.SMALL: + return "Systran/faster-whisper-small" + case FasterWhisperModel.MEDIUM: + return "Systran/faster-whisper-medium" + case FasterWhisperModel.LARGE: + return "Systran/faster-whisper-large-v3" + case FasterWhisperModel.LARGE_V3: + return "Systran/faster-whisper-large-v3" + case FasterWhisperModel.DISTIL_LARGE_V3: + return "Systran/faster-distil-whisper-large-v3" + case _: + raise NotImplementedError(f"invalid faster whisper model {self}") + + +# Models + + +class BaseModel(_BaseModel): + model_config = merge_configs(icij_config(), no_enum_values_config()) + + +class IETFLanguage(str): + @classmethod + def __get_pydantic_core_schema__( + cls, source: Any, handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + return core_schema.no_info_plain_validator_function(cls.validate) + + @classmethod + def validate(cls, v: object) -> Self: + tag = langcodes.get(str(v)) + if not tag.is_valid(): + raise ValueError(f"Invalid IETF language: {v}") + return cls(v) + + +ASRLanguage = IETFLanguage | LanguageAlpha2 +_LANGUAGE_TYPE_ADAPTER = TypeAdapter(ASRLanguage) + +_VALIDATED_PARAKEET_LANGUAGES = { + _LANGUAGE_TYPE_ADAPTER.validate_python(lang) + for lang in PARAKEET_TDT_0_6B_V3_LANGUAGES +} + +_VALIDATED_FIREREDASR2_LANGUAGES = { + _LANGUAGE_TYPE_ADAPTER.validate_python(lang) for lang in FIREREDASR2_LANGUAGES +} + + +class ASRModel(StrEnum): + PARAKEET = "parakeet" + PARAKEET_TRT = "parakeet_trt" + FASTER_WHISPER = "faster_whisper" + FIREREDASR2_AED = "fireredasr2_aed" + WHISPER_TRT = "whisper_trt" + PREPROCESSING = "preprocessing" + + def supported_languages(self) -> set[ASRLanguage]: + match self: + case ASRModel.PARAKEET | ASRModel.PARAKEET_TRT: + return _VALIDATED_PARAKEET_LANGUAGES + case ASRModel.FASTER_WHISPER | ASRModel.WHISPER_TRT: + return set() + case ASRModel.FIREREDASR2_AED: + return _VALIDATED_FIREREDASR2_LANGUAGES + case ASRModel.PREPROCESSING: + return set() + case _: + msg = f"model {self} should expose supported languages" + raise NotImplementedError(msg) + + +class BatcherType(StrEnum): + CONSTANT_SIZE = "constant_size" + MAX_DURATION = "max_duration" + + +class SegmentIndex(BaseModel): + audio: int | str = 0 + segment: int = 0 + + +class ASRResult(BaseModel): + """Base result class for ASR models""" + + index: SegmentIndex = Field(default_factory=SegmentIndex) + transcription: list[tuple] = Field(default_factory=list) + score: float = 1.0 + + @property + def duration(self) -> float: + if not self.transcription: + return 0.0 + total = sum(end - start for start, end, _ in self.transcription) + return total + + @classmethod + def from_parakeet_hypothesis(cls, hypothesis: "Hypothesis", **extra) -> Self: + """Parse a hypothesis returned by a Parakeet RNN model + + :param hypothesis: Parakeet hypothesis + :return: copy of self + """ + # there's some weird inconsistency here between nemo versions + timestamps = hypothesis.timestamp + transcription = [ + (s["start"], s["end"], s["segment"]) for s in timestamps["segment"] + ] + return cls(transcription=transcription, score=hypothesis.score, **extra) + + @classmethod + def from_fireredasr2_result(cls, result: dict, **extra) -> Self: + """Parse a result dict returned by a FireRedASR2 AED model + + :param result: dict with keys 'text', 'confidence', 'dur_s', 'timestamp' + :return: ASRResult + """ + text = result.get("text") or "" + timestamp = result.get("timestamp") or [] + if timestamp: + start_s = float(timestamp[0][1]) + end_s = float(timestamp[-1][2]) + else: + start_s = 0.0 + end_s = float(result.get("dur_s") or 0.0) + transcription = [(start_s, end_s, text)] if text.strip() else [] + confidence = result.get("confidence") + score = float(confidence) if confidence is not None else -1.0 + return cls(transcription=transcription, score=score, **extra) + + @classmethod + def from_faster_whisper_result(cls, segments: "Iterable[Segment]", **extra) -> Self: + """Parse segments returned by a faster-whisper model + + :param segments: iterable of Segment objects with start, end, text, avg_logprob + :return: ASRResult + """ + transcription = [] + weighted_logprob = 0.0 + total_duration = 0.0 + for segment in segments: + duration = segment.end - segment.start + transcription.append((segment.start, segment.end, segment.text)) + weighted_logprob += segment.avg_logprob * duration + total_duration += duration + score = math.exp(weighted_logprob / total_duration) if total_duration else -1.0 + return cls(transcription=transcription, score=score, **extra) + + def __add__(self, other: Self) -> Self: + if not isinstance(other, ASRResult): + msg = f"expected {ASRResult.__class__.__class__} but found: {type(other)}" + raise TypeError(msg) + + if other.index.audio != self.index.audio: + raise ValueError("can't merge transcriptions from different audios") + + transcription = self.transcription + other.transcription + # We have to weight by total segment len + total_duration = self.duration + other.duration + score = 1.0 + if total_duration: + score = self.score * self.duration + other.score * other.duration + score /= total_duration + return ASRResult(index=self.index, transcription=transcription, score=score) + + +def _utc_now() -> datetime.datetime: + return datetime.datetime.now(datetime.UTC).replace(microsecond=0) + + +def _uuid() -> str: + return uuid.uuid4().hex + + +class AudioMetadata(BaseModel): + index: int | str + audio_format: str | None = None + audio_path: Path | None = None + + +class SegmentMetadata(BaseModel): + uuid: str = Field(default_factory=_uuid) + index: SegmentIndex = Field(default_factory=SegmentIndex) + audio_format: str | None = None + audio_path: Path | None = None + duration_s: float + preprocessed_at: datetime.datetime = Field(default_factory=_utc_now) + + def now(self) -> "SegmentMetadata": + update = {"preprocessed_at": datetime.datetime.now(datetime.UTC)} + return safe_copy(self, update=update) + + +class Error(BaseModel): + title: str + detail: str + metadata: SegmentMetadata | AudioMetadata + + @classmethod + def from_exception( + cls, exception: BaseException, metadata: SegmentMetadata | AudioMetadata + ) -> "Error": + title = exception.__class__.__name__ + trace_lines = traceback.format_exception( + None, value=exception, tb=exception.__traceback__ + ) + detail = f"{exception}\n{''.join(trace_lines)}" + error = Error(title=title, detail=detail, metadata=metadata) + return error + + +class FSProcessedSegment(BaseModel): + metadata: SegmentMetadata + path: Path + + +@dataclass(frozen=True) +class MemoryProcessedSegment: + # Avoid importing torch when importing objects + metadata: SegmentMetadata + tensor: "torch.Tensor" + + +ProcessedAudioSegment = FSProcessedSegment | MemoryProcessedSegment diff 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package and project management. Installation's as simple as +```aiignore +cd caul +uv python install 3.12 +uv sync --dev --extra cpu +``` + +A handler object can be instantiated and run on one or more audio file paths or +directly on NumPy/Torch tensors, returning a list of `ASRHandlerResult` +for each input. `transcriptions` contains a list of +tuples of the form `(start_time, end_time, text_segment)` and `scores` a measure +of confidence in a transcription in the `range(0, -250)`: +```aiignore +>>> from .handler import ASRHandler +>>> handler = ASRHandler(models="parakeet") +>>> handler.startup() +>>> results = handler.transcribe("<...path to some audio file...>") +>>> print(results) +[ASRInferenceHandlerResult(transcription=[(0.0, 1.5, "We're spending too much time here.")], score=-250.0), +ASRInferenceHandlerResult(transcription=[(1.5, 2.9, "Stay a little longer.")], score=-250.0), +ASRInferenceHandlerResult(transcription=[(2.9, 4.0, "He'd kill us if he got the chance.")], score=-250.0)] +``` \ No newline at end of file diff --git a/caul/caul/__init__.py b/caul/caul/__init__.py new file mode 100644 index 00000000..46939f84 --- /dev/null +++ b/caul/caul/__init__.py @@ -0,0 +1,32 @@ +# We import everything top level to trigger component registration +from .tasks import ( + FasterWhisperInferenceRunner, + FasterWhisperPostprocessor, + FasterWhisperPreprocessor, + FireRedASR2InferenceRunner, + FireRedASR2Postprocessor, + FireRedASR2Preprocessor, + ParakeetInferenceRunner, + ParakeetPostprocessor, + ParakeetPreprocessor, + ParakeetTrtInferenceRunner, + WhisperTrtInferenceRunner, + WhisperTrtPostprocessor, + WhisperTrtPreprocessor, +) + +__all__ = [ + "FasterWhisperInferenceRunner", + "FasterWhisperPostprocessor", + "FasterWhisperPreprocessor", + "FireRedASR2InferenceRunner", + "FireRedASR2Postprocessor", + "FireRedASR2Preprocessor", + "ParakeetInferenceRunner", + "ParakeetPostprocessor", + "ParakeetPreprocessor", + "ParakeetTrtInferenceRunner", + "WhisperTrtInferenceRunner", + "WhisperTrtPostprocessor", + "WhisperTrtPreprocessor", +] diff --git a/caul/caul/__main__.py b/caul/caul/__main__.py new file mode 100644 index 00000000..372d863b --- /dev/null +++ b/caul/caul/__main__.py @@ -0,0 +1,9 @@ +from .cli import cli_app + + +def main() -> None: + cli_app() + + +if __name__ == "__main__": + main() diff --git a/caul/caul/cli/__init__.py b/caul/caul/cli/__init__.py new file mode 100644 index 00000000..d09b19a2 --- /dev/null +++ b/caul/caul/cli/__init__.py @@ -0,0 +1,47 @@ +import importlib.metadata +import os +from typing import Annotated + +import typer +from icij_common.logging_utils import setup_loggers + +import caul + +from .models import models_app +from .utils import AsyncTyper + +cli_app = AsyncTyper( + context_settings={"help_option_names": ["-h", "--help"]}, + pretty_exceptions_enable=False, +) +cli_app.add_typer(models_app) + + +def version_callback(value: bool) -> None: # noqa: FBT001 typer callback signature + if value: + package_version = importlib.metadata.version(caul.__name__) + print(package_version) + raise typer.Exit() + + +def pretty_exc_callback(value: bool) -> None: # noqa: FBT001 typer callback signature + if not value: + os.environ["TYPER_STANDARD_TRACEBACK"] = "1" + + +@cli_app.callback() +def main( + version: Annotated[ # noqa: ARG001 handled by its eager typer callback + bool | None, + typer.Option("--version", callback=version_callback, is_eager=True), + ] = None, + *, + pretty_exceptions: Annotated[ # noqa: ARG001 handled by its eager typer callback + bool, + typer.Option( + "--pretty-exceptions", callback=pretty_exc_callback, is_eager=True + ), + ] = False, +) -> None: + """Datashare Python CLI.""" + setup_loggers(["__main__", caul.__name__]) diff --git a/caul/caul/cli/models.py b/caul/caul/cli/models.py new file mode 100644 index 00000000..c3b2c0d4 --- /dev/null +++ b/caul/caul/cli/models.py @@ -0,0 +1,42 @@ +import logging +from pathlib import Path +from typing import Annotated + +import typer +from caul_core import ASRModel + +from caul.model_cache import cache_models + +from .utils import AsyncTyper + +_START_WORKER_HELP = "start a datashare worker" + +_CACHE_MODELS_HELP = "cache ASR models to the provided location" +_CACHE_MODELS_NAMES_HELP = ( + "ASR model for which we should cache models, if not provided cache all models" +) +_CACHE_MODELS_CACHE_DIR_HELP = ( + "cache directory, if not provided defaults to HuggingFace cache directory" +) + +_MODEL = "model" + +models_app = AsyncTyper(name=_MODEL) + +logger = logging.getLogger(__name__) + + +@models_app.async_command(help=_CACHE_MODELS_HELP) +async def cache( + model: Annotated[ASRModel, typer.Argument(help=_CACHE_MODELS_NAMES_HELP)] = None, + cache_dir: Annotated[ + Path | None, typer.Argument(help=_CACHE_MODELS_CACHE_DIR_HELP) + ] = None, +) -> None: + from huggingface_hub.constants import ( + HF_HUB_CACHE, + ) + + if cache_dir is None: + cache_dir = HF_HUB_CACHE + cache_models(model, cache_dir) diff --git a/caul/caul/cli/utils.py b/caul/caul/cli/utils.py new file mode 100644 index 00000000..34ac2e7b --- /dev/null +++ b/caul/caul/cli/utils.py @@ -0,0 +1,25 @@ +import asyncio +import sys +from collections.abc import Callable +from functools import wraps +from typing import Any + +import typer + + +class AsyncTyper(typer.Typer): + def async_command(self, *args, **kwargs) -> Callable[[Callable], Callable]: + def decorator(async_func: Callable) -> Callable: + @wraps(async_func) + def sync_func(*_args, **_kwargs) -> Any: + res = asyncio.run(async_func(*_args, **_kwargs)) + return res + + self.command(*args, **kwargs)(sync_func) + return async_func + + return decorator + + +def eprint(*args, **kwargs) -> None: + print(*args, file=sys.stderr, **kwargs) diff --git a/caul/caul/constants.py b/caul/caul/constants.py new file mode 100644 index 00000000..012433a1 --- /dev/null +++ b/caul/caul/constants.py @@ -0,0 +1,4 @@ +from caul_core import DEFAULT_SAMPLE_RATE + +DEFAULT_BIT_DEPTH = 16 +DEFAULT_BIT_RATE = DEFAULT_SAMPLE_RATE * DEFAULT_BIT_DEPTH diff --git a/caul/caul/exception.py b/caul/caul/exception.py new file mode 100644 index 00000000..c63f393a --- /dev/null +++ b/caul/caul/exception.py @@ -0,0 +1,48 @@ +from pathlib import Path + + +class MissingModelSpecificationException(Exception): + """Raise if referencing a missing model""" + + +class UnsupportedModelException(Exception): + """Raise if an unsupported model type is passed""" + + +class MissingFireRedAsr2OutputDirException(Exception): + """Raise if no file path is available to store FireRedAsr2 inputs at""" + + +class LanguageInputMismatchException(Exception): + """Raise when inputs and input languages to ASRHandler.transcribe don't align""" + + +class MissingTokenizerException(Exception): + """Raise if no tokenizer or tokenizer path provided""" + + +class UnreadableAudio(Exception): + def __init__(self, path: Path): + msg = f"failed to read audio file at {path}" + super().__init__(msg) + + +class TrtEngineLoadError(Exception): + def __init__(self, path: Path | str, trt_version: str): + msg = ( + f"failed to deserialize TensorRT engine at {path} with TensorRT runtime" + f" {trt_version}; the engine was likely built with a different TensorRT" + f" version and must be rebuilt (see TensorRT logs above for details)" + ) + super().__init__(msg) + + +class TrtInputShapeError(Exception): + def __init__(self, tensor_name: str, shape: tuple, profile: tuple): + min_shape, opt_shape, max_shape = (tuple(s) for s in profile) + msg = ( + f"input {tensor_name!r} has shape {tuple(shape)}, outside TRT" + f" engine's optimization profile (min={min_shape}, opt={opt_shape}," + f" max={max_shape})." + ) + super().__init__(msg) diff --git a/caul/caul/filesystem.py b/caul/caul/filesystem.py new file mode 100644 index 00000000..57aeda2f --- /dev/null +++ b/caul/caul/filesystem.py @@ -0,0 +1,4 @@ +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + pass diff --git a/caul/caul/handler.py b/caul/caul/handler.py new file mode 100644 index 00000000..e69de29b diff --git a/caul/caul/model_cache.py b/caul/caul/model_cache.py new file mode 100644 index 00000000..b58cea6c --- /dev/null +++ b/caul/caul/model_cache.py @@ -0,0 +1,108 @@ +import logging +from collections.abc import Callable +from pathlib import Path + +from caul_core import ( + FIREREDASR2_MODEL_HUB_PREFIX, + PARAKEET_MODEL_REF, + PARAKEET_TRT_MODEL_REF, + ASRModel, + FasterWhisperModel, + FireRedASR2ModelRef, + FireRedASR2ModelTag, +) + +from .utils import cache_hf_model, cache_hf_repo + +logger = logging.getLogger(__name__) + +_FASTER_WHISPER_ALLOW_PATTERNS = [ + "config.json", + "preprocessor_config.json", + "model.bin", + "tokenizer.json", + "vocabulary.*", +] + + +def cache_parakeet_models(cache_dir: Path | None = None) -> None: + cache_hf_model( + model_family=ASRModel.PARAKEET, + models=[PARAKEET_MODEL_REF], + model_ext=".nemo", + library_name="nemo", + cache_dir=cache_dir, + ) + + +def cache_parakeet_trt_models(cache_dir: Path | None = None) -> None: + cache_hf_model( + model_family=ASRModel.PARAKEET_TRT, + models=[PARAKEET_TRT_MODEL_REF], + model_ext=".engine", + cache_dir=cache_dir, + ) + + +def cache_faster_whisper_models(cache_dir: Path | None = None) -> None: + from huggingface_hub import ( + get_token, + snapshot_download, + ) + + for model_id in FasterWhisperModel: + logger.info("Caching faster whisper model of size %s", model_id) + kwargs = { + "allow_patterns": _FASTER_WHISPER_ALLOW_PATTERNS, + "token": get_token(), + } + if cache_dir is not None: + kwargs["cache_dir"] = str(cache_dir) + snapshot_download(model_id.to_repo_id, **kwargs) + + +def cache_fireredasr2_models(cache_dir: Path | None = None) -> None: + for tag in FireRedASR2ModelTag: + logger.info("Caching FireredASR tag %s", tag) + repo_id = ( + f"{FIREREDASR2_MODEL_HUB_PREFIX}{FireRedASR2ModelRef.ASR2}-{tag.upper()}" + ) + cache_hf_repo(repo_id, cache_dir=cache_dir) + + +def cache_whisper_trt_models( + cache_dir: Path | None = None, # noqa: ARG001 shared _MODEL_CACHE_FNS signature +) -> None: + # Not available yet + return None + + +def cache_preprocessing_models( + cache_dir: Path | None = None, # noqa: ARG001 shared _MODEL_CACHE_FNS signature +) -> None: + # We only segment by silence for now, no models to cache + return None + + +_MODEL_CACHE_FNS: dict[ASRModel, Callable[[Path | None], None]] = { + ASRModel.PARAKEET: cache_parakeet_models, + ASRModel.PARAKEET_TRT: cache_parakeet_trt_models, + ASRModel.FASTER_WHISPER: cache_faster_whisper_models, + ASRModel.FIREREDASR2_AED: cache_fireredasr2_models, + ASRModel.WHISPER_TRT: cache_whisper_trt_models, + ASRModel.PREPROCESSING: cache_preprocessing_models, +} + + +def cache_models(asr_model: ASRModel | None, cache_dir: Path | None = None) -> None: + if asr_model is None: + logger.info("Caching all models to %s", cache_dir) + models = list(_MODEL_CACHE_FNS) + else: + logger.info("Caching %s model(s) to %s", asr_model, cache_dir) + if asr_model not in _MODEL_CACHE_FNS: + raise ValueError(f"Invalid model {asr_model}") + models = [asr_model] + + for model in models: + _MODEL_CACHE_FNS[model](cache_dir) diff --git a/caul/caul/segmentation/__init__.py b/caul/caul/segmentation/__init__.py new file mode 100644 index 00000000..3d6a7a7a --- /dev/null +++ b/caul/caul/segmentation/__init__.py @@ -0,0 +1,29 @@ +from .methods import ( + segment_by_pyannote_vad, + segment_by_silence, + segment_by_silero_vad, + segment_fixed, +) +from .objects import ( + FixedSegmentationConfig, + PyannoteVoiceSegmentationConfig, + SegmentationConfig, + SegmentationStrategy, + SilenceSegmentationConfig, + SileroVoiceSegmentationConfig, + TensorSegment, +) + +__all__ = [ + "FixedSegmentationConfig", + "PyannoteVoiceSegmentationConfig", + "SegmentationConfig", + "SegmentationStrategy", + "SilenceSegmentationConfig", + "SileroVoiceSegmentationConfig", + "TensorSegment", + "segment_by_pyannote_vad", + "segment_by_silence", + "segment_by_silero_vad", + "segment_fixed", +] diff --git a/caul/caul/segmentation/methods.py b/caul/caul/segmentation/methods.py new file mode 100644 index 00000000..21dddb11 --- /dev/null +++ b/caul/caul/segmentation/methods.py @@ -0,0 +1,273 @@ +import uuid +from collections.abc import Callable +from typing import TYPE_CHECKING, Protocol + +from caul_core import DEFAULT_SAMPLE_RATE, PARAKEET_INFERENCE_MAX_DURATION_S + +from .objects import TensorSegment + +if TYPE_CHECKING: + import pyannote + import torch + + +class SegmentationFunction(Protocol): + def __call__( + self, + audio_tensor: "torch.Tensor", + *args, + sample_rate: int, + max_segment_len_s: float, + **kwargs, + ) -> list[TensorSegment]: ... + + +def _split_range_fixed( + audio_tensor: "torch.Tensor", + *, + seg_start: int, + seg_end: int, + chunk_samples: int, + sample_rate: int, + tensor_id: str, +) -> list[TensorSegment]: + """Split the range [seg_start, seg_end) into fixed-size chunks, with a + remainder chunk at the end if the range is not an exact multiple. + + :param audio_tensor: 1D source tensor + :param seg_start: start sample index (inclusive) + :param seg_end: end sample index (exclusive) + :param chunk_samples: maximum number of samples per chunk + :param sample_rate: sample rate of the audio + :param tensor_id: shared identifier linking all chunks to the source tensor + :return: list of TensorSegment + """ + segments = [] + while seg_start < seg_end: + chunk_end = min(seg_start + chunk_samples, seg_end) + segments.append( + TensorSegment( + tensor=audio_tensor[seg_start:chunk_end], + segment_start=seg_start, + segment_end=chunk_end, + sample_rate=sample_rate, + tensor_id=tensor_id, + ) + ) + seg_start = chunk_end + return segments + + +def segment_fixed( + audio_tensor: "torch.Tensor", + *, + sample_rate: int = DEFAULT_SAMPLE_RATE, + max_segment_len_s: float = PARAKEET_INFERENCE_MAX_DURATION_S, +) -> list[TensorSegment]: + """Split an audio tensor into fixed-length chunks. + + :param audio_tensor: 1D input tensor + :param sample_rate: sample rate of the audio + :param max_segment_len_s: duration of each segment in seconds + :return: list of TensorSegment + """ + tensor_id = uuid.uuid4().hex + chunk_samples = int(max_segment_len_s * sample_rate) + return _split_range_fixed( + audio_tensor, + seg_start=0, + seg_end=audio_tensor.shape[-1], + chunk_samples=chunk_samples, + sample_rate=sample_rate, + tensor_id=tensor_id, + ) + + +def segment_by_silence( + audio_tensor: "torch.Tensor", + *, + sample_rate: int = DEFAULT_SAMPLE_RATE, + frame_len: int = 2048, + silence_thresh_db: int = 35, + hop_len: int = 512, + kept_silence_len_s: float = 0.15, + min_silence_len_s: float = 0.5, + max_segment_len_s: float = PARAKEET_INFERENCE_MAX_DURATION_S, +) -> list[TensorSegment]: + """Split an audio tensor on silences using librosa, falling back to fixed splits + where merged intervals exceed the maximum segment length. + + :param audio_tensor: 1D input tensor + :param sample_rate: sample rate of the audio + :param frame_len: number of samples per librosa analysis frame + :param silence_thresh_db: dB threshold below which audio is considered silent + :param hop_len: number of samples between analysis frames + :param kept_silence_len_s: seconds of silence to retain at segment boundaries + :param min_silence_len_s: minimum silence duration to split on + :param max_segment_len_s: maximum segment duration in seconds; segments + exceeding this are split at fixed intervals as a fallback + :return: list of TensorSegment + """ + import librosa + + if len(audio_tensor.shape) != 1: + msg = ( + f"expected 1D input tensor, got {audio_tensor} of shape" + f" {len(audio_tensor.shape)}" + ) + raise ValueError(msg) + + tensor_id = uuid.uuid4().hex + + nonsilent_intervals = librosa.effects.split( + audio_tensor.numpy(), + top_db=silence_thresh_db, + frame_length=frame_len, + hop_length=hop_len, + ) + + min_silence_samples = int(min_silence_len_s * sample_rate) + kept_silence_samples = int(kept_silence_len_s * sample_rate) + max_segment_samples = int(max_segment_len_s * sample_rate) + + # Merge intervals separated by silences shorter than the minimum + merged: list[tuple[int, int]] = [] + for start, end in nonsilent_intervals: + if len(merged) == 0: + merged.append((start, end)) + else: + prev_end = merged[-1][1] + if start - prev_end < min_silence_samples: + merged[-1] = (merged[-1][0], end) + else: + merged.append((start, end)) + + total_samples = audio_tensor.shape[-1] + segments = [] + + # Fallback for segments over max_segment_len_secs + for interval_start, interval_end in merged: + seg_start = max(0, interval_start - kept_silence_samples) + seg_end = min(total_samples, interval_end + kept_silence_samples) + segments.extend( + _split_range_fixed( + audio_tensor, + seg_start=seg_start, + seg_end=seg_end, + chunk_samples=max_segment_samples, + sample_rate=sample_rate, + tensor_id=tensor_id, + ) + ) + + return segments + + +def segment_by_silero_vad( + audio_tensor: "torch.Tensor", + vad_model: "torch.nn.Module", + vad_parser_fn: Callable, + *, + sample_rate: int = DEFAULT_SAMPLE_RATE, + threshold: float = 0.5, + min_speech_duration_ms: int = 250, + min_silence_duration_ms: int = 100, + speech_pad_ms: int = 30, + max_segment_len_s: float = PARAKEET_INFERENCE_MAX_DURATION_S, +) -> list[TensorSegment]: + """Split an audio tensor into voiced segments using silero VAD. + + :param audio_tensor: 1D input tensor + :param vad_model: silero VAD model + :param vad_parser_fn: silero VAD segmentation function + :param sample_rate: sample rate of the audio; silero-VAD supports 8000 or 16000 + :param threshold: speech probability threshold [0, 1) + :param min_speech_duration_ms: minimum speech segment duration in ms + :param min_silence_duration_ms: minimum silence duration to split on in ms + :param speech_pad_ms: padding added around each speech segment in ms + :param max_segment_len_s: maximum segment duration in seconds; segments + exceeding this are split at fixed intervals as a fallback + :return: list of TensorSegment + """ + tensor_id = uuid.uuid4().hex + max_segment_samples = int(max_segment_len_s * sample_rate) + + speech_timestamps = vad_parser_fn( + audio_tensor, + vad_model, + sampling_rate=sample_rate, + threshold=threshold, + min_speech_duration_ms=min_speech_duration_ms, + min_silence_duration_ms=min_silence_duration_ms, + speech_pad_ms=speech_pad_ms, + return_seconds=False, + ) + + # Fallback for segments over max_segment_len_secs + segments = [] + for ts in speech_timestamps: + segments.extend( + _split_range_fixed( + audio_tensor, + seg_start=ts["start"], + seg_end=ts["end"], + chunk_samples=max_segment_samples, + sample_rate=sample_rate, + tensor_id=tensor_id, + ) + ) + return segments + + +def segment_by_pyannote_vad( + audio_tensor: "torch.Tensor", + pipeline: "pyannote.audio.Pipeline", + *, + sample_rate: int = DEFAULT_SAMPLE_RATE, + min_speech_duration_ms: int = 100, + min_silence_duration_ms: int = 100, + max_segment_len_s: float = PARAKEET_INFERENCE_MAX_DURATION_S, +) -> list[TensorSegment]: + """Split an audio tensor into voiced segments using pyannote VAD. + + :param audio_tensor: 1D input tensor + :param pipeline: pyannote voice activity detection pipeline + :param sample_rate: sample rate of the audio + :param min_speech_duration_ms: minimum speech segment duration in ms + :param min_silence_duration_ms: minimum silence duration to split on in ms + :param max_segment_len_s: maximum segment duration in seconds; segments + exceeding this are split at fixed intervals as a fallback + :return: list of TensorSegment + """ + from torch import float32 + + tensor_id = uuid.uuid4().hex + max_segment_samples = int(max_segment_len_s * sample_rate) + + # pyannote expects seconds + params = { + "min_duration_on": min_speech_duration_ms / 1000, + "min_duration_off": min_silence_duration_ms / 1000, + } + pipeline.instantiate(params) + + # pyannote expects a 2D tensor (channels x samples) + waveform = audio_tensor.unsqueeze(0).to(float32) + output = pipeline({"waveform": waveform, "sample_rate": sample_rate}) + + # Fallback for segments over max_segment_len_s + segments = [] + for segment in output.get_timeline().support(): + start_sample = int(segment.start * sample_rate) + end_sample = int(segment.end * sample_rate) + segments.extend( + _split_range_fixed( + audio_tensor, + seg_start=start_sample, + seg_end=end_sample, + chunk_samples=max_segment_samples, + sample_rate=sample_rate, + tensor_id=tensor_id, + ) + ) + return segments diff --git a/caul/caul/segmentation/objects.py b/caul/caul/segmentation/objects.py new file mode 100644 index 00000000..b2fd72c7 --- /dev/null +++ b/caul/caul/segmentation/objects.py @@ -0,0 +1,94 @@ +from dataclasses import dataclass +from enum import StrEnum +from typing import TYPE_CHECKING, ClassVar + +from caul_core import DEFAULT_SAMPLE_RATE, PARAKEET_INFERENCE_MAX_DURATION_S +from icij_common.registrable import RegistrableConfig +from pydantic import Field + +if TYPE_CHECKING: + import torch + + +@dataclass(frozen=True) +class TensorSegment: + """An audio tensor segment with position and duration metadata""" + + tensor: "torch.Tensor" + segment_start: int + segment_end: int + # Unique identifier linking segments to source tensor + tensor_id: str + sample_rate: int = DEFAULT_SAMPLE_RATE + + @property + def duration(self) -> float: + """Duration of segment in seconds""" + return self.n_frames / self.sample_rate + + @property + def n_frames(self) -> int: + return self.segment_end - self.segment_start + + +class SegmentationStrategy(StrEnum): + FIXED = "fixed" + SILENCE = "silence" + VOICE_SILERO = "voice_silero" + VOICE_PYANNOTE = "voice_pyannote" + + +class SegmentationConfig(RegistrableConfig): + registry_key: ClassVar[str] = Field(frozen=True, default="strategy") + strategy: ClassVar[SegmentationStrategy] + # TODO: parakeet specific variables shouldn't pop in here, rename or define global + # naming + max_segment_len_s: float = PARAKEET_INFERENCE_MAX_DURATION_S + + +class FixedSegmentationConfig(SegmentationConfig): + strategy: ClassVar[SegmentationStrategy] = Field( + frozen=True, default=SegmentationStrategy.FIXED + ) + + +class SilenceSegmentationConfig(SegmentationConfig): + """Silence segmentation parameters; see librosa for explanation of parameters""" + + strategy: ClassVar[SegmentationStrategy] = Field( + frozen=True, default=SegmentationStrategy.SILENCE + ) + frame_len: int = 2048 + silence_thresh_db: int = 35 + hop_len: int = 512 + kept_silence_len_s: float = 0.15 + min_silence_len_s: float = 0.5 + + +class VoiceSegmentationConfig(SegmentationConfig): + """Generic VAD config""" + + min_speech_duration_ms: int = 250 + min_silence_duration_ms: int = 100 + + +class SileroVoiceSegmentationConfig(VoiceSegmentationConfig): + """Voice segmentation parameters using silero VAD; + see silero for explanation of parameters + """ + + strategy: ClassVar[SegmentationStrategy] = Field( + frozen=True, default=SegmentationStrategy.VOICE_SILERO + ) + threshold: float = 0.5 + speech_pad_ms: int = 30 + + +class PyannoteVoiceSegmentationConfig(VoiceSegmentationConfig): + """Voice segmentation parameters using pyannote VAD; + see pyannote for explanation of parameters + """ + + strategy: ClassVar[SegmentationStrategy] = Field( + frozen=True, default=SegmentationStrategy.VOICE_PYANNOTE + ) diff --git a/caul/caul/segmentation/segmenter.py b/caul/caul/segmentation/segmenter.py new file mode 100644 index 00000000..fd7338c0 --- /dev/null +++ b/caul/caul/segmentation/segmenter.py @@ -0,0 +1,171 @@ +import gc +from collections.abc import Callable +from contextlib import suppress +from functools import partial +from types import TracebackType +from typing import TYPE_CHECKING, Any, Protocol, Self + +from caul_core import SILERO_VAD_MODEL, VadModelRef +from icij_common.registrable import RegistrableFromConfig + +from .methods import ( + segment_by_pyannote_vad, + segment_by_silence, + segment_by_silero_vad, + segment_fixed, +) +from .objects import ( + PyannoteVoiceSegmentationConfig, + SegmentationConfig, + SegmentationStrategy, + TensorSegment, +) + +if TYPE_CHECKING: + import torch + + +def _load_vad_model() -> tuple["torch.nn.Module", Callable]: + """Load silero VAD from torch.hub. + + :return: tuple of silero VAD model with VAD parsing function + """ + import torch + + model, utils = torch.hub.load( + VadModelRef.SILERO_MODEL, SILERO_VAD_MODEL, trust_repo=True + ) + return model, utils[0] + + +class SegmentationFn(Protocol): + def __call__( + self, audio_tensor: "torch.Tensor", **kwargs + ) -> list[TensorSegment]: ... + + +class AudioSegmenter(RegistrableFromConfig): + def __init__(self, config: SegmentationConfig): + self._config = config + args = self._config.model_dump() + args.pop(SegmentationConfig.registry_key.default) + self._args = args + self._segmentation_fn: SegmentationFn | None = None + + def __enter__(self) -> Self: ... + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: ... + + @classmethod + def _from_config(cls, config: SegmentationConfig, **_extras) -> Self: + return cls(config) + + def segment(self, audio_tensor: "torch.Tensor") -> list[TensorSegment]: + return self._segmentation_fn(audio_tensor, **self._args) + + +@AudioSegmenter.register(SegmentationStrategy.FIXED) +class FixedSizeAudioSegmenter(AudioSegmenter): + def __enter__(self) -> Self: + self._segmentation_fn = segment_fixed + return self + + +@AudioSegmenter.register(SegmentationStrategy.SILENCE) +class SilenceAudioSegmenter(AudioSegmenter): + def __enter__(self) -> Self: + self._segmentation_fn = segment_by_silence + return self + + +@AudioSegmenter.register(SegmentationStrategy.VOICE_SILERO) +class SileroVoiceAudioSegmenter(AudioSegmenter): + def __init__(self, config: SegmentationConfig, device: str = "cpu"): + import torch + + super().__init__(config) + self._vad_model: torch.nn.Module | None = None + self._vad_parser_fn: Callable | None = None + self._device = torch.device(device) + + def __enter__(self) -> Self: + self._vad_model, self._vad_parser_fn = self._load_vad_model() + + self._segmentation_fn = partial( + segment_by_silero_vad, + vad_model=self._vad_model, + vad_parser_fn=self._vad_parser_fn, + ) + return self + + def _load_vad_model(self) -> tuple["torch.nn.Module", Callable]: + model, parser_fn = _load_vad_model() + + return model.to(device=self._device), parser_fn + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: + import torch + + self._vad_model = None + self._vad_parser_fn = None + # Depending on the exact accelerator running might fail to empty the cache + # but that's OK + with suppress(RuntimeError): + torch.accelerator.empty_cache() + gc.collect() + + +@AudioSegmenter.register(SegmentationStrategy.VOICE_PYANNOTE) +class PyannoteVoiceAudioSegmenter(AudioSegmenter): + def __init__( + self, + config: PyannoteVoiceSegmentationConfig, + hf_token: str, + device: str = "cpu", + ): + import torch + + super().__init__(config) + self._pipeline: Any | None = None + self._hf_token = hf_token + self._device = torch.device(device) + + def __enter__(self) -> Self: + self._pipeline = self._load_pipeline() + self._segmentation_fn = partial( + segment_by_pyannote_vad, pipeline=self._pipeline + ) + return self + + def _load_pipeline(self) -> Any: + from pyannote.audio import Model + from pyannote.audio.pipelines import ( + VoiceActivityDetection, + ) + + model = Model.from_pretrained(VadModelRef.PYANNOTE_MODEL, token=self._hf_token) + + return VoiceActivityDetection(segmentation=model).to(device=self._device) + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: + import torch + + self._pipeline = None + with suppress(RuntimeError): + torch.accelerator.empty_cache() + gc.collect() diff --git a/caul/caul/task_defaults.py b/caul/caul/task_defaults.py new file mode 100644 index 00000000..b026420c --- /dev/null +++ b/caul/caul/task_defaults.py @@ -0,0 +1,42 @@ +from collections.abc import Iterable +from itertools import groupby + +from caul_core import ASRResult, SegmentIndex + + +def generic_unbatching_fn(batched_results: Iterable[ASRResult]) -> Iterable[ASRResult]: + """Remap unordered and segmented tensors to original inputs for return + + :param batched_results: list of unordered results + :return: list[ParakeetModelHandlerResult] + """ + seen: set[int] = set() + by_audio = groupby(batched_results, key=lambda r: r.index.audio) + for audio_index, group in by_audio: + seg_results = list(group) + if audio_index in seen: + msg = ( + f"expected contiguous segments, already processed segments from " + f"audio of index {audio_index}" + ) + raise ValueError(msg) + # Drop segments with no recognized speech + expected_indices = list(range(len(seg_results))) + segment_indices = [r.index.segment for r in seg_results] + if expected_indices != segment_indices: + msg = ( + f"received audio segment results for audio {audio_index}" + f" out of order: {segment_indices}" + ) + raise ValueError(msg) + seg_results = [r for r in seg_results if r.transcription] + seg_results = sorted(seg_results, key=lambda r: r.transcription[0]) + + seen.add(audio_index) + base = ASRResult( + index=SegmentIndex(audio=audio_index, segment=0), + transcription=[], + score=1.0, + ) + merged_results = sum(seg_results, base) + yield merged_results diff --git a/caul/caul/tasks/__init__.py b/caul/caul/tasks/__init__.py new file mode 100644 index 00000000..02518a36 --- /dev/null +++ b/caul/caul/tasks/__init__.py @@ -0,0 +1,35 @@ +from .inference import ( + FasterWhisperInferenceRunner, + FireRedASR2InferenceRunner, + ParakeetInferenceRunner, + ParakeetTrtInferenceRunner, + WhisperTrtInferenceRunner, +) +from .postprocessing import ( + FasterWhisperPostprocessor, + FireRedASR2Postprocessor, + ParakeetPostprocessor, + WhisperTrtPostprocessor, +) +from .preprocessing import ( + FasterWhisperPreprocessor, + FireRedASR2Preprocessor, + ParakeetPreprocessor, + WhisperTrtPreprocessor, +) + +__all__ = [ + "FasterWhisperInferenceRunner", + "FasterWhisperPostprocessor", + "FasterWhisperPreprocessor", + "FireRedASR2InferenceRunner", + "FireRedASR2Postprocessor", + "FireRedASR2Preprocessor", + "ParakeetInferenceRunner", + "ParakeetPostprocessor", + "ParakeetPreprocessor", + "ParakeetTrtInferenceRunner", + "WhisperTrtInferenceRunner", + "WhisperTrtPostprocessor", + "WhisperTrtPreprocessor", +] diff --git a/caul/caul/tasks/inference/__init__.py b/caul/caul/tasks/inference/__init__.py new file mode 100644 index 00000000..3cdf63df --- /dev/null +++ b/caul/caul/tasks/inference/__init__.py @@ -0,0 +1,20 @@ +try: + from .parakeet import ParakeetInferenceRunner +except ModuleNotFoundError: + ParakeetInferenceRunner = None +try: + from .faster_whisper import FasterWhisperInferenceRunner +except ModuleNotFoundError: + FasterWhisperInferenceRunner = None +try: + from .fireredasr import FireRedASR2InferenceRunner +except ModuleNotFoundError: + FireRedASR2InferenceRunner = None +try: + from .parakeet_trt import ParakeetTrtInferenceRunner +except ModuleNotFoundError: + ParakeetTrtInferenceRunner = None +try: + from .whisper_trt import WhisperTrtInferenceRunner +except ModuleNotFoundError: + WhisperTrtInferenceRunner = None diff --git a/caul/caul/tasks/inference/faster_whisper.py b/caul/caul/tasks/inference/faster_whisper.py new file mode 100644 index 00000000..4e24aba5 --- /dev/null +++ b/caul/caul/tasks/inference/faster_whisper.py @@ -0,0 +1,204 @@ +import logging +from collections.abc import Iterable +from typing import TYPE_CHECKING + +from caul_core import ( + DEFAULT_SAMPLE_RATE, + ASRModel, + ASRResult, + Error, + FasterWhisperInferenceRunnerConfig, + FSProcessedSegment, + InferenceRunner, + MemoryProcessedSegment, + ProcessedAudioSegment, + TorchDevice, +) +from icij_common.registrable import FromConfig + +from ...utils import load_audio + +if TYPE_CHECKING: + from faster_whisper.transcribe import TranscriptionOptions + +logger = logging.getLogger(__name__) + + +def inference_config_to_transcription_options( + inference_config: FasterWhisperInferenceRunnerConfig, +) -> "TranscriptionOptions": + from faster_whisper.transcribe import ( + TranscriptionOptions, + ) + + return TranscriptionOptions( + beam_size=inference_config.beam_size, + best_of=inference_config.best_of, + patience=inference_config.patience, + length_penalty=inference_config.length_penalty, + repetition_penalty=inference_config.repetition_penalty, + no_repeat_ngram_size=inference_config.no_repeat_ngram_size, + log_prob_threshold=inference_config.log_prob_threshold, + no_speech_threshold=inference_config.no_speech_threshold, + compression_ratio_threshold=inference_config.compression_ratio_threshold, + condition_on_previous_text=inference_config.condition_on_previous_text, + prompt_reset_on_temperature=inference_config.prompt_reset_on_temperature, + temperatures=inference_config.temperatures, + initial_prompt=inference_config.initial_prompt, + prefix=inference_config.prefix, + suppress_blank=inference_config.suppress_blank, + suppress_tokens=inference_config.suppress_tokens, + without_timestamps=inference_config.without_timestamps, + max_initial_timestamp=inference_config.max_initial_timestamp, + word_timestamps=inference_config.word_timestamps, + prepend_punctuations=inference_config.prepend_punctuations, + append_punctuations=inference_config.append_punctuations, + multilingual=inference_config.multilingual, + max_new_tokens=inference_config.max_new_tokens, + clip_timestamps=inference_config.clip_timestamps, + hallucination_silence_threshold=inference_config.hallucination_silence_threshold, + hotwords=inference_config.hotwords, + ) + + +@InferenceRunner.register(ASRModel.FASTER_WHISPER) +class FasterWhisperInferenceRunner(InferenceRunner): + """Inference runner for faster-whisper large-v3-turbo. + + Transcribes multilingual audio. Expects 16 kHz mono wav files or tensors. + Requires faster-whisper from https://github.com/SYSTRAN/faster-whisper. + """ + + def __init__( + self, + config: FasterWhisperInferenceRunnerConfig = None, + device: TorchDevice = TorchDevice.CPU, + ): + super().__init__(device=device) + if config is None: + config = FasterWhisperInferenceRunnerConfig() + self._config = config + self._model = None + + @classmethod + def _from_config( + cls, + config: FasterWhisperInferenceRunnerConfig, + device: TorchDevice = TorchDevice.CPU, + **extras, + ) -> FromConfig: + return cls(config=config, device=device, **extras) + + def __enter__(self): + import faster_whisper + + self._model = faster_whisper.BatchedInferencePipeline( + faster_whisper.WhisperModel( + self._config.whisper_model_name, + device=str(self._torch_device), + compute_type=self._config.compute_type, + ) + ) + return self + + def process( + self, + inputs: Iterable[tuple[ProcessedAudioSegment, ...]], + *, + languages: tuple[str] | None = None, + **kwargs, + ) -> Iterable[ASRResult | Error]: + """Transcribe batches of preprocessed audio segments using + faster_whisper.generate_segment_batched. + + :param inputs: batches of PreprocessorOutput (file-backed or tensor) + :param languages: ISO-639-1 code or list of codes + :return: ASRResult per input, in batch order + """ + import numpy as np + from faster_whisper.audio import ( + pad_or_trim, + ) + from faster_whisper.tokenizer import ( + Tokenizer, + ) + from faster_whisper.transcribe import ( + Segment, + ) + + options = inference_config_to_transcription_options(self._config) + + for idx, input_batch in enumerate(inputs): + if len(input_batch) == 0: + continue + + if isinstance(input_batch[0], MemoryProcessedSegment): + tensors = [inp.tensor.detach().cpu().numpy() for inp in input_batch] + elif ( + isinstance(input_batch[0], FSProcessedSegment) + and input_batch[0].path is not None + ): + tensors = [load_audio(inp.path).numpy() for inp in input_batch] + else: + continue + + raw_features = [ + self._model.model.feature_extractor(tensor)[..., :-1] + for tensor in tensors + ] + + del tensors + + features = np.stack([pad_or_trim(f) for f in raw_features]) + segment_metadata = [ + { + "offset": 0.0, + "duration": inp.metadata.duration_s, + "segments": [ + { + "start": 0, + "end": int(inp.metadata.duration_s * DEFAULT_SAMPLE_RATE), + } + ], + } + for inp in input_batch + ] + + if languages is None: + language = self._model.model.detect_language(features=raw_features[0])[ + 0 + ] + else: + language = languages[idx] + + tokenizer = Tokenizer( + self._model.model.hf_tokenizer, + self._model.model.model.is_multilingual, + task="transcribe", + language=language, + ) + + outputs = self._model.forward( + features, tokenizer, segment_metadata, options + ) + + for inp, output in zip(input_batch, outputs, strict=False): + segments = [ + Segment( + id=seg_idx, + seek=out["seek"], + start=out["start"], + end=out["end"], + text=out["text"], + tokens=out["tokens"], + avg_logprob=out["avg_logprob"], + no_speech_prob=out["no_speech_prob"], + compression_ratio=out["compression_ratio"], + words=None, + temperature=options.temperatures[0], + ) + for seg_idx, out in enumerate(output) + ] + yield ASRResult.from_faster_whisper_result( + segments, index=inp.metadata.index + ) diff --git a/caul/caul/tasks/inference/fireredasr.py b/caul/caul/tasks/inference/fireredasr.py new file mode 100644 index 00000000..4e3a67a8 --- /dev/null +++ b/caul/caul/tasks/inference/fireredasr.py @@ -0,0 +1,177 @@ +import logging +from collections.abc import Iterable +from contextlib import nullcontext +from pathlib import Path +from tempfile import TemporaryDirectory +from typing import TYPE_CHECKING + +from caul_core import ( + FIREREDASR2_MODEL_HUB_PREFIX, + ASRModel, + ASRResult, + Error, + FireRedASR2InferenceRunnerConfig, + FireRedASR2ModelRef, + FireRedASR2ModelTag, + InferenceRunner, + ProcessedAudioSegment, + TorchDevice, +) +from icij_common.registrable import FromConfig + +from caul.utils import to_filesystem + +logger = logging.getLogger(__name__) + +if TYPE_CHECKING: + import torch + from fireredasr2s.fireredasr2 import FireRedAsr2, FireRedAsr2Config + + +def fireredasr2_from_pretrained( + model_ref: FireRedASR2ModelRef, + *, + cache_dir: Path | None = None, + model_tag: FireRedASR2ModelTag = FireRedASR2ModelTag.AED, + config: "FireRedAsr2Config | None" = None, +) -> "FireRedAsr2": + """Wrap FireRedAsr2.from_pretrained, downloading the model from HuggingFace Hub + if not already available in cache_dir. + + :param model_ref: One of ASR2, VAD, LID, or Punc + :param cache_dir: Directory to download model to + :param model_tag: For ASR models, can be either AED or LLM + :param config: Configuration options for model + """ + from fireredasr2s.fireredasr2 import ( + FireRedAsr2, + FireRedAsr2Config, + ) + from huggingface_hub import ( + snapshot_download, + ) + from huggingface_hub.constants import ( + HF_HUB_CACHE, + ) + + if cache_dir is None: + cache_dir = HF_HUB_CACHE + + if config is None: + config = FireRedAsr2Config() + + if model_tag is not None: + model_ref = f"{model_ref}-{model_tag}" + + repo_id = FIREREDASR2_MODEL_HUB_PREFIX + model_ref.upper() + + model_dir = snapshot_download(repo_id=repo_id, cache_dir=cache_dir) + return FireRedAsr2.from_pretrained(model_tag, model_dir, config) + + +def inference_config_to_fire_red_asr_model( + inference_config: FireRedASR2InferenceRunnerConfig, + *, + use_gpu: bool = True, + cache_dir: Path | None = None, +) -> "torch.Module": + from fireredasr2s.fireredasr2 import ( + FireRedAsr2Config, + ) + + asr_config = FireRedAsr2Config( + use_gpu=use_gpu, + use_half=inference_config.use_half, + beam_size=inference_config.beam_size, + nbest=inference_config.nbest, + decode_max_len=inference_config.decode_max_len, + softmax_smoothing=inference_config.softmax_smoothing, + aed_length_penalty=inference_config.aed_length_penalty, + eos_penalty=inference_config.eos_penalty, + return_timestamp=inference_config.return_timestamp, + ) + + return fireredasr2_from_pretrained( + model_ref=FireRedASR2ModelRef.ASR2, + model_tag=FireRedASR2ModelTag.AED, + cache_dir=cache_dir, + config=asr_config, + ) + + +@InferenceRunner.register(ASRModel.FIREREDASR2_AED) +class FireRedASR2InferenceRunner(InferenceRunner): + """Inference runner for the FireRedASR2 AED model. + + Transcribes Chinese-language (and multilingual) audio. Expects 16 kHz + mono wav files or tensors (segments up to 60 s). Requires fireredasr2s to + be installed from https://github.com/FireRedTeam/FireRedASR2S. + """ + + def __init__( + self, + config: FireRedASR2InferenceRunnerConfig = None, + device: TorchDevice = TorchDevice.CPU, + ): + super().__init__(device) + if config is None: + config = FireRedASR2InferenceRunnerConfig() + + self._config = config + self._model = None + + @classmethod + def _from_config( + cls, + config: FireRedASR2InferenceRunnerConfig, + device: TorchDevice = TorchDevice.CPU, + **extras, + ) -> FromConfig: + return cls(config=config, device=device, **extras) + + def __enter__(self): + use_gpu = self._device is TorchDevice.GPU + self._model = inference_config_to_fire_red_asr_model( + self._config, use_gpu=use_gpu + ) + return self + + def process( + self, + inputs: Iterable[tuple[ProcessedAudioSegment, ...]], + *, + output_dir: str | Path = None, + **kwargs, + ) -> Iterable[ASRResult | Error]: + """Transcribe batches of preprocessed audio segments. + + :param inputs: batches of PreprocessorOutput (file-backed or tensor) + :param output_dir: directory to save output files (will default to tempdir if + not provided + :return: ASRResult per segment, in batch order + """ + if isinstance(output_dir, str): + output_dir = Path(output_dir) + tmp_dir_ctx = nullcontext() + if output_dir is None and self._config.tmp_dir_fallback: + tmp_dir_ctx = TemporaryDirectory() + output_dir = Path(tmp_dir_ctx.name) + with tmp_dir_ctx: + for input_batch in inputs: + if len(input_batch) == 0: + continue + batch_inputs, batch_paths = zip( + *to_filesystem(input_batch, output_dir), strict=False + ) + batch_paths = [str(p) for p in batch_paths] + batch_inputs = list(batch_inputs) + inp_ids = [i.metadata.uuid for i in batch_inputs] + results = self._model.transcribe(inp_ids, batch_paths) + for inp, result in zip(batch_inputs, results, strict=True): + # FireRedASR2 uses uttid to refer to a unique identifier for each + # item in a batch; technically results should be returned in the + # order given by uttids, but we use a map to the original index + # just to be safe. + yield ASRResult.from_fireredasr2_result( + result, index=inp.metadata.index + ) diff --git a/caul/caul/tasks/inference/parakeet.py b/caul/caul/tasks/inference/parakeet.py new file mode 100644 index 00000000..a20e2d93 --- /dev/null +++ b/caul/caul/tasks/inference/parakeet.py @@ -0,0 +1,126 @@ +import logging +from collections.abc import Iterable +from typing import TYPE_CHECKING + +from caul_core import ( + PARAKEET_MODEL_REF, + ASRModel, + ASRResult, + Error, + InferenceRunner, + ParakeetInferenceRunnerConfig, + ProcessedAudioSegment, + TorchDevice, +) +from icij_common.registrable import FromConfig + +logger = logging.getLogger(__name__) + +if TYPE_CHECKING: + import torch + from nemo.collections.asr.parts.mixins import TranscribeConfig + + +@InferenceRunner.register(ASRModel.PARAKEET) +class ParakeetInferenceRunner(InferenceRunner): + """Inference handler for NVIDIA's Parakeet family of ASR models. + + Supports up to 24 minutes of audio (batched or unbatched) in a single pass. Assumes + that audio inputs (wav files or tensors) are single-channel with a sample rate of + 16000—this last is very important for segmenting. + """ + + def __init__( + self, + model_name: str = PARAKEET_MODEL_REF, + device: TorchDevice = TorchDevice.CPU, + batch_size: int = 4, + ): + super().__init__(device) + self.model_name = model_name + self._model = None + self.__transcribe_config = None + self._batch_size = batch_size + + @classmethod + def _from_config( + cls, config: ParakeetInferenceRunnerConfig, **extras + ) -> FromConfig: + return cls( + model_name=config.model_name, + **extras, + ) + + @property + def _transcribe_config(self) -> "TranscribeConfig": + if self.__transcribe_config is None: + from nemo.collections.asr.parts.mixins import TranscribeConfig + from nemo.collections.asr.parts.mixins.transcription import ( + InternalTranscribeConfig, + ) + + self.__transcribe_config = TranscribeConfig( + use_lhotse=False, + batch_size=self._batch_size, + timestamps=True, + return_hypotheses=True, + # Bug in Nemo's AudioToBPEDataset—by default TranscribeConfig spawns 2 + # DataLoader workers, but AudioToBPEDataset defines a class + # TokenizerWrapper inside __init__, meaning it can't be pickled. + num_workers=0, + _internal=InternalTranscribeConfig(device=self._torch_device), + ) + + return self.__transcribe_config + + def __enter__(self): + import nemo.collections.asr as nemo_asr + + self._model = nemo_asr.models.ASRModel.from_pretrained( + self.model_name, map_location=self._torch_device + ).eval() + return self + + def _transcribe( + self, audio_inputs: Iterable["torch.Tensor"], **kwargs + ) -> ASRResult: + """Transcribe normalized audio tensors + + :param audio_inputs: input tensors + :return: transcription results + """ + return self._model.transcribe( + audio_inputs, + timestamps=True, + override_config=self._transcribe_config, + ) + + def process( + self, inputs: Iterable[tuple[ProcessedAudioSegment, ...]], *args, **kwargs + ) -> Iterable[ASRResult | Error]: + """Transcribe a batch of audio tensors or file names of max duration <= 20 min + + :param inputs: List of np.ndarray or torch.Tensor or str, or singleton of same + types + :return: List of results + """ + for input_batch in inputs: + if not input_batch: + continue + if hasattr(input_batch[0], "tensor"): + audios = [i.tensor.to(self._torch_device) for i in input_batch] + else: + audios = [str(i.path) for i in input_batch] + + hypotheses = self._transcribe(audios) + for idx, hyps in enumerate(hypotheses): + best_hyp = hyps + + if isinstance(best_hyp, (list, tuple)): + best_hyp = hyps[0] + + input_ordering_idx = input_batch[idx].metadata.index + model_result = ASRResult.from_parakeet_hypothesis( + best_hyp, index=input_ordering_idx + ) + yield model_result diff --git a/caul/caul/tasks/inference/parakeet_trt.py b/caul/caul/tasks/inference/parakeet_trt.py new file mode 100644 index 00000000..1d8cb803 --- /dev/null +++ b/caul/caul/tasks/inference/parakeet_trt.py @@ -0,0 +1,207 @@ +import logging +from collections.abc import Iterable +from functools import cache +from pathlib import Path +from typing import TYPE_CHECKING + +from caul_core import ( + ASRModel, + InferenceRunner, + ParakeetTrtInferenceRunnerConfig, + TorchDevice, +) +from icij_common.registrable import FromConfig + +from ...exception import TrtEngineLoadError +from ...trt import import_trt +from ...trt.handler import TrtInferenceHandler +from ...utils import load_audio +from ..inference.parakeet import ParakeetInferenceRunner +from .trt_inference import TrtInferenceMixin + +logger = logging.getLogger(__name__) + +_MIN_ENGINE_SAMPLES = 16_000 + +if TYPE_CHECKING: + from types import TracebackType + + import torch + from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis + from nemo.core.connectors.save_restore_connector import SaveRestoreConnector + + +@cache +def _decoder_joint_connector() -> "SaveRestoreConnector": + from nemo.core.connectors.save_restore_connector import ( + SaveRestoreConnector, + ) + + class DecoderJointConnector(SaveRestoreConnector): + @staticmethod + def _load_state_dict_from_disk( + model_weights: dict, map_location: TorchDevice = "cpu" + ) -> dict: + """Map model weights into virtual address space + + :param model_weights: model weights + :param map_location: device to load weights to + :return: decoder + joint weights + """ + import torch + + weight_pointers = torch.load( + model_weights, + map_location=map_location, + mmap=True, + weights_only=True, + ) + without_encoder = { + k: v + for k, v in weight_pointers.items() + if k.startswith(("decoder.", "joint.")) + } + del weight_pointers # release mmap handles + return without_encoder + + return DecoderJointConnector() + + +@InferenceRunner.register(ASRModel.PARAKEET_TRT) +class ParakeetTrtInferenceRunner(ParakeetInferenceRunner, TrtInferenceMixin): + """Inference handler for NVIDIA parakeet models converted to TRT. Expects only the + encoder to be converted, passing its output to decoder and joint layers using Nemo. + Note that batch_size must match the shape profile used to convert to TRT. + """ + + def __init__( + self, + model_path: Path | str, + engine_path: Path | str, + device: TorchDevice = TorchDevice.CPU, + batch_size: int = 4, + ): + ParakeetInferenceRunner.__init__( + self, + device=device, + batch_size=batch_size, + ) + TrtInferenceMixin.__init__(self) + + self._model_path = str(model_path) + self._engine_path = str(engine_path) + + @classmethod + def _from_config( + cls, config: ParakeetTrtInferenceRunnerConfig, **extras + ) -> FromConfig: + return cls( + model_path=config.model_path, + engine_path=config.engine_path, + **extras, + ) + + def __enter__(self): + trt = import_trt() + + runtime = trt.Runtime(trt.Logger(trt.Logger.ERROR)) + runtime.engine_host_code_allowed = True + with open(self._engine_path, "rb") as f: + self._encoder = runtime.deserialize_cuda_engine(f.read()) + if self._encoder is None: + raise TrtEngineLoadError(self._engine_path, trt.__version__) + + import nemo.collections.asr as nemo_asr + from omegaconf import open_dict + + config = nemo_asr.models.ASRModel.restore_from( + self._model_path, return_config=True + ) + with open_dict(config.decoding): + config.decoding.compute_timestamps = True + + self._decoder = nemo_asr.models.ASRModel.restore_from( + self._model_path, + override_config_path=config, + map_location=self._torch_device, + save_restore_connector=_decoder_joint_connector(), + strict=False, + ).eval() + + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: "TracebackType | None", + ): + return TrtInferenceMixin.__exit__(self, exc_type, exc_val, exc_tb) + + def _transcribe( + self, + audio_inputs: "torch.Tensor | str | Path | Iterable[torch.Tensor | str | Path]", + trt_device: TorchDevice = None, + ) -> "list[Hypothesis] | list[list[Hypothesis]]": + """Transcribe audio tensors + + :param audio_inputs: audio tensors or audio file paths + :return: transcription results + """ + import torch + + # trt only runs on cuda + if trt_device is None: + trt_device = torch.device("cuda") + + if isinstance(audio_inputs, torch.Tensor): + # 1D is a single signal, 2D is a batch of signals + audio_inputs = ( + [audio_inputs] if audio_inputs.dim() == 1 else list(audio_inputs) + ) + elif isinstance(audio_inputs, (str, Path)) or not isinstance( + audio_inputs, Iterable + ): + audio_inputs = [audio_inputs] + + audio_inputs = [ + load_audio(ai) if isinstance(ai, (str, Path)) else ai for ai in audio_inputs + ] + + audio_inputs_len = torch.tensor( + [ai.shape[-1] for ai in audio_inputs], dtype=torch.int64 + ).to(trt_device) + + # pad to len(max(t)), setting dim[0] to batch_size + audio_inputs = torch.nn.utils.rnn.pad_sequence( + audio_inputs, batch_first=True + ).to(trt_device) + # zero-pad short batches up to profile minimum + if audio_inputs.shape[-1] < _MIN_ENGINE_SAMPLES: + audio_inputs = torch.nn.functional.pad( + audio_inputs, (0, _MIN_ENGINE_SAMPLES - audio_inputs.shape[-1]) + ) + + with TrtInferenceHandler(self._encoder) as handler: + enc_out, enc_len = handler.infer( + {"input_signal": audio_inputs, "input_signal_length": audio_inputs_len} + ) + + enc_out = enc_out.to(self._torch_device) + enc_len = enc_len.to(self._torch_device) + + with torch.no_grad(): + hypotheses = self._decoder.decoding.rnnt_decoder_predictions_tensor( + enc_out, enc_len, return_hypotheses=True + ) + + from nemo.collections.asr.parts.utils.timestamp_utils import ( + process_timestamp_outputs, + ) + + # convert frame offsets to seconds, as the model's transcribe() would + return process_timestamp_outputs( + hypotheses, + self._decoder.encoder.subsampling_factor, + self._decoder.cfg.preprocessor.window_stride, + ) diff --git a/caul/caul/tasks/inference/trt_inference.py b/caul/caul/tasks/inference/trt_inference.py new file mode 100644 index 00000000..f335e263 --- /dev/null +++ b/caul/caul/tasks/inference/trt_inference.py @@ -0,0 +1,29 @@ +import gc +from typing import TYPE_CHECKING + +from caul_core import TorchDevice + +if TYPE_CHECKING: + from types import TracebackType + + +class TrtInferenceMixin: + def __init__(self): + self._encoder = None + self._decoder = None + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: "TracebackType | None", + ): + import torch + + self._encoder = None + self._decoder = None + if hasattr(self, "_device") and self._device == torch.device(TorchDevice.GPU): + torch.cuda.empty_cache() + gc.collect() + + return False diff --git a/caul/caul/tasks/inference/whisper_trt.py b/caul/caul/tasks/inference/whisper_trt.py new file mode 100644 index 00000000..9ed921bd --- /dev/null +++ b/caul/caul/tasks/inference/whisper_trt.py @@ -0,0 +1,422 @@ +import base64 +import math +from collections import OrderedDict +from collections.abc import Callable, Iterable +from pathlib import Path +from typing import TYPE_CHECKING + +from caul_core import ( + WHISPER_TRT_ENCODER_INPUT_FEATURES, + WHISPER_TRT_ENCODER_INPUT_LENGTHS, + WHISPER_TRT_ENCODER_POSITION_IDS, + WHISPER_TRT_EOT_TOKEN, + WHISPER_TRT_MAX_MEL_PADDING_LEN, + WHISPER_TRT_MAX_NEW_TOKENS, + WHISPER_TRT_PAD_TOKEN_ID, + WHISPER_TRT_PATTERN_STR, + WHISPER_TRT_PROMPT_PREFIX, + WHISPER_TRT_SPECIAL_TOKENS, + WHISPER_TRT_WORLD_SIZE, + ASRModel, + ASRResult, + Error, + InferenceRunner, + ProcessedAudioSegment, + TorchDevice, + TrtLlmDecoderConfig, + TrtLlmEncoderConfig, + WhisperTrtInferenceRunnerConfig, +) +from icij_common.registrable import FromConfig + +from ...exception import MissingTokenizerException +from .trt_inference import TrtInferenceMixin + +if TYPE_CHECKING: + import tiktoken + import torch + + try: + from tensorrt_llm.runtime import GenerationSession + from tensorrt_llm.runtime.session import Session + except ModuleNotFoundError: + Session = None + GenerationSession = None + + +def _encoder_factory(encoder_path: Path | str) -> "Callable[[], Session]": + def _load_encoder() -> "Session": + from tensorrt_llm.runtime.session import ( + Session, + ) + + with open(encoder_path, "rb") as f: + return Session.from_serialized_engine(f.read()) + + return _load_encoder + + +def _decoder_factory( + decoder_path: Path | str, + decoder_config: TrtLlmDecoderConfig | None = None, +) -> "Callable[[torch.cuda.Stream | None], GenerationSession]": + if decoder_config is None: + decoder_config = TrtLlmDecoderConfig() + + def _load_decoder(stream: "torch.cuda.Stream | None" = None) -> "GenerationSession": + from tensorrt_llm import ( + Mapping, + mpi_rank, + ) + from tensorrt_llm.runtime import ( + GenerationSession, + ) + + with open(decoder_path, "rb") as f: + decoder_engine = f.read() + + runtime_rank = mpi_rank() + runtime_mapping = Mapping(WHISPER_TRT_WORLD_SIZE, runtime_rank) + + return GenerationSession( + decoder_config.to_model_config(), + decoder_engine, + runtime_mapping, + stream=stream, + debug_mode=decoder_config.debug_mode, + ) + + return _load_decoder + + +def _get_tiktoken_tokenizer(vocab_path: Path) -> "tiktoken.Encoding": + import tiktoken + + with open(vocab_path) as f: + ranks = { + base64.b64decode(token): int(rank) + for token, rank in (line.split() for line in f if line) + } + + n_vocab = len(ranks) + + return tiktoken.Encoding( + name=vocab_path.name, + explicit_n_vocab=n_vocab, + pat_str=WHISPER_TRT_PATTERN_STR, + mergeable_ranks=ranks, + special_tokens=WHISPER_TRT_SPECIAL_TOKENS, + ) + + +def _remove_tensor_padding( + input_tensor: "torch.Tensor", + input_tensor_lens: "torch.Tensor" = None, + pad_value: int = None, +) -> "torch.Tensor": + import torch + + if pad_value and input_tensor_lens is None: + # Create a mask for all non-pad tokens + mask = input_tensor != pad_value + + # Apply the mask to input_tensor to remove pad tokens + output_tensor = input_tensor[mask].view(1, -1) + + else: + # Initialize a list to collect valid sequences + valid_sequences = [] + + for i in range(input_tensor.shape[0]): + valid_len = input_tensor_lens[i] + valid_sequences.append(input_tensor[i, :valid_len]) + + # Concatenate all valid sequences along the batch dimension + output_tensor = torch.cat(valid_sequences, dim=0) + return output_tensor + + +@InferenceRunner.register(ASRModel.WHISPER_TRT) +class WhisperTrtInferenceRunner(InferenceRunner, TrtInferenceMixin): + def __init__( + self, + encoder_factory: Callable[[], "Session"], + decoder_factory: Callable[["torch.cuda.Stream | None"], "GenerationSession"], + *, + tokenizer: "tiktoken.Encoding" = None, + tokenizer_vocab_path: Path | str = None, + encoder_config: TrtLlmEncoderConfig = None, + decoder_config: TrtLlmDecoderConfig = None, + max_new_tokens: int = WHISPER_TRT_MAX_NEW_TOKENS, + prompt_prefix: str = WHISPER_TRT_PROMPT_PREFIX, + return_timestamps: bool = True, + max_mel_padding_len: int = WHISPER_TRT_MAX_MEL_PADDING_LEN, + device: TorchDevice = TorchDevice.CPU, + ): + if tokenizer is None and tokenizer_vocab_path is None: + raise MissingTokenizerException( + "Either tokenizer or tokenizer_path must be specified." + ) + + import torch + + InferenceRunner.__init__(self, device=device) + TrtInferenceMixin.__init__(self) + + if encoder_config is None: + encoder_config = TrtLlmEncoderConfig() + + if decoder_config is None: + decoder_config = TrtLlmDecoderConfig() + + self._tokenizer_vocab_path = ( + Path(tokenizer_vocab_path) + if isinstance(tokenizer_vocab_path, str) + else tokenizer_vocab_path + ) + self._encoder_factory = encoder_factory + self._decoder_factory = decoder_factory + self._encoder_config = encoder_config + self._decoder_config = decoder_config + self._max_new_tokens = max_new_tokens + self._return_timestamps = return_timestamps + self._max_mel_padding_len = max_mel_padding_len + + # tokenizer + self._tokenizer = ( + tokenizer + if tokenizer is not None + else _get_tiktoken_tokenizer(tokenizer_vocab_path) + ) + + self._prompt_ids = torch.tensor( + self._tokenizer.encode( + prompt_prefix, allowed_special=self._tokenizer.special_tokens_set + ) + ) + self._eot_token_id = self._tokenizer.encode( + WHISPER_TRT_EOT_TOKEN, allowed_special=self._tokenizer.special_tokens_set + )[0] + + @classmethod + def _from_config( + cls, + config: WhisperTrtInferenceRunnerConfig, + **extras, + ) -> FromConfig: + return cls( + encoder_factory=_encoder_factory(config.encoder_path), + decoder_factory=_decoder_factory( + config.decoder_path, config.decoder_config + ), + encoder_config=config.encoder_config, + decoder_config=config.decoder_config, + prompt_prefix=config.prompt_prefix, + return_timestamps=config.return_timestamps, + max_mel_padding_len=config.max_mel_padding_len, + **extras, + ) + + def __enter__(self, stream: "torch.cuda.stream | None" = None): + self._encoder = self._encoder_factory() + self._decoder = self._decoder_factory(stream) + return self + + def process( + self, inputs: Iterable[tuple[ProcessedAudioSegment, ...]], *args, **kwargs + ) -> Iterable[ASRResult | Error]: + import torch + + for input_batch in inputs: + if not input_batch or not hasattr(input_batch[0], "tensor"): + continue + + audio_inputs = torch.stack( + [ + torch.nn.functional.pad( + preprocessed_input.tensor, + ( + 0, + self._max_mel_padding_len + - preprocessed_input.tensor.shape[-1], + ), + ) + for preprocessed_input in input_batch + ] + ).to(self._torch_device) + + # capture original (unpadded) time lengths before padding + audio_inputs_lens = torch.tensor( + [t.shape[-1] for t in audio_inputs], dtype=torch.int32 + ).to(self._torch_device) + + # ravel tensors for GPU + audio_inputs = audio_inputs.contiguous() + batch_size, seq_len = audio_inputs.shape[0], audio_inputs.shape[-1] + + # generate position ids + position_ids = ( + torch.arange( + math.ceil(seq_len / self._encoder_config.downsampling_factor), + dtype=torch.int32, + device=self._torch_device, + ) + .expand(batch_size, -1) + .contiguous() + ) + + encoder_output, encoder_output_lens = self._run_encoder( + audio_inputs, audio_inputs_lens, position_ids + ) + + decoder_output = self._run_decoder(encoder_output, encoder_output_lens) + + for decoder_out_idx, decoder_out in enumerate( + decoder_output.numpy().tolist() + ): + transcription = self._decode_model_output(decoder_out[0]) + + input_ordering_idx = input_batch[decoder_out_idx].metadata.index + yield ASRResult(transcription=transcription, index=input_ordering_idx) + + def _run_encoder( + self, + audio_inputs: "torch.Tensor", + audio_inputs_lens: "torch.Tensor", + position_ids: "torch.Tensor", + stream: "torch.cuda.Stream | None" = None, + ) -> tuple["torch.Tensor", "torch.Tensor"]: + import torch + from tensorrt_llm._utils import ( + torch_dtype_to_trt, + trt_dtype_to_torch, + ) + from tensorrt_llm.runtime.session import ( + TensorInfo, + ) + + encoder_input_list = [ + TensorInfo( + WHISPER_TRT_ENCODER_INPUT_FEATURES, + torch_dtype_to_trt(audio_inputs.dtype), + audio_inputs.shape, + ), + TensorInfo( + WHISPER_TRT_ENCODER_INPUT_LENGTHS, + torch_dtype_to_trt(audio_inputs_lens.dtype), + audio_inputs_lens.shape, + ), + TensorInfo( + WHISPER_TRT_ENCODER_POSITION_IDS, + torch_dtype_to_trt(position_ids.dtype), + position_ids.shape, + ), + ] + + encoder_output_shapes = self._encoder.infer_shapes(encoder_input_list) + + encoder_outputs = { + t.name: torch.empty( + tuple(t.shape), + dtype=trt_dtype_to_torch(t.dtype), + device=self._torch_device, + ) + for t in encoder_output_shapes + } + + if stream is None: + stream = torch.cuda.current_stream() + + encoder_inputs = OrderedDict() + encoder_inputs[WHISPER_TRT_ENCODER_INPUT_FEATURES] = audio_inputs + encoder_inputs[WHISPER_TRT_ENCODER_INPUT_LENGTHS] = audio_inputs_lens + encoder_inputs[WHISPER_TRT_ENCODER_POSITION_IDS] = position_ids + + self._encoder.run( + inputs=encoder_inputs, outputs=encoder_outputs, stream=stream.cuda_stream + ) + + stream.synchronize() + + encoder_output = encoder_outputs["encoder_output"] + encoder_output_lens = ( + audio_inputs_lens // self._encoder_config.downsampling_factor + ) + + return encoder_output, encoder_output_lens + + def _run_decoder( + self, decoder_inputs: "torch.Tensor", decoder_inputs_lens: "torch.Tensor" + ) -> "torch.Tensor": + import torch + from tensorrt_llm.runtime import ( + SamplingConfig, + ) + + decoder_inputs_max_len = decoder_inputs_lens.max().item() + prompt_ids_max_len = self._prompt_ids.shape[-1] + batch_size = decoder_inputs_lens.shape[0] + prompt_inputs = ( + self._prompt_ids.unsqueeze(0).repeat(batch_size, 1).type(torch.int32).cuda() + ) + prompt_inputs_lens = torch.tensor([prompt_ids_max_len] * batch_size).cuda() + cross_attention_mask = ( + torch.ones( + [ + batch_size, + prompt_ids_max_len + self._max_new_tokens, + int(decoder_inputs_max_len), + ] + ) + .int() + .cuda() + if self._decoder_config.cross_attention + else None + ) + + if self._decoder_config.remove_input_padding: + prompt_inputs = _remove_tensor_padding( + prompt_inputs, pad_value=WHISPER_TRT_PAD_TOKEN_ID + ) + if decoder_inputs.dim() == 3: + decoder_inputs_lens = decoder_inputs.new_full( + (decoder_inputs.shape[0],), + decoder_inputs.shape[1], + dtype=torch.int32, + ) + + decoder_inputs = _remove_tensor_padding( + decoder_inputs, decoder_inputs_lens + ) + + sampling_config = SamplingConfig( + end_id=self._eot_token_id, + pad_id=self._eot_token_id, + num_beams=self._decoder_config.beam_width, + ) + + self._decoder.setup( + decoder_inputs_lens.size(0), + prompt_ids_max_len, + self._max_new_tokens, + beam_width=self._decoder_config.beam_width, + encoder_max_input_length=decoder_inputs_max_len, + ) + + decoder_output = self._decoder.decode( + prompt_inputs, + prompt_inputs_lens, + sampling_config, + encoder_output=decoder_inputs, + encoder_input_lengths=decoder_inputs_lens, + cross_attention_mask=cross_attention_mask, + ) + + # get the list of int from output_ids tensor + return decoder_output.cpu() + + def _decode_model_output(self, decoder_output: list) -> list[tuple]: + # TODO: By default, Whisper TRT doesn't return timestamps. Need to implement a + # custom alignment method + text = self._tokenizer.decode(decoder_output).strip() + return [(0.0, 0.0, text)] if text else [] diff --git a/caul/caul/tasks/postprocessing/__init__.py b/caul/caul/tasks/postprocessing/__init__.py new file mode 100644 index 00000000..e5210b08 --- /dev/null +++ b/caul/caul/tasks/postprocessing/__init__.py @@ -0,0 +1,16 @@ +try: + from .parakeet import ParakeetPostprocessor +except ModuleNotFoundError: + ParakeetPostprocessor = None +try: + from .fireredasr import FireRedASR2Postprocessor +except ModuleNotFoundError: + FireRedASR2Postprocessor = None +try: + from .faster_whisper import FasterWhisperPostprocessor +except ModuleNotFoundError: + FasterWhisperPostprocessor = None +try: + from .whisper_trt import WhisperTrtPostprocessor +except ModuleNotFoundError: + WhisperTrtPostprocessor = None diff --git a/caul/caul/tasks/postprocessing/asr_postprocessor.py b/caul/caul/tasks/postprocessing/asr_postprocessor.py new file mode 100644 index 00000000..8b578056 --- /dev/null +++ b/caul/caul/tasks/postprocessing/asr_postprocessor.py @@ -0,0 +1,27 @@ +from collections.abc import Callable, Iterable + +from caul_core import ASRResult, BasePreprocessorConfig, Postprocessor +from icij_common.registrable import FromConfig + +from .default import generic_unbatching_fn + + +class PostprocessorMixin(Postprocessor): + """Postprocessing logic""" + + def __init__(self, unbatching_fn: Callable = generic_unbatching_fn): + self._unbatching_fn = unbatching_fn + + @classmethod + def _from_config(cls, config: BasePreprocessorConfig, **extras) -> FromConfig: + return cls(**extras) + + def process( + self, inputs: Iterable[ASRResult], *args, **kwargs + ) -> Iterable[ASRResult]: + """Unbatch batched inference results + + :param inputs: List of model results + :return: list of model results in input ordering + """ + yield from self._unbatching_fn(inputs) diff --git a/caul/caul/tasks/postprocessing/default.py b/caul/caul/tasks/postprocessing/default.py new file mode 100644 index 00000000..a65ab859 --- /dev/null +++ b/caul/caul/tasks/postprocessing/default.py @@ -0,0 +1,42 @@ +from collections.abc import Iterable +from itertools import groupby + +from caul_core import ASRResult, SegmentIndex + + +def generic_unbatching_fn(batched_results: Iterable[ASRResult]) -> Iterable[ASRResult]: + """Remap unordered and segmented tensors to original inputs for return + + :param batched_results: list of unordered results + :return: list[ParakeetModelHandlerResult] + """ + seen: set[int] = set() + by_audio = groupby(batched_results, key=lambda r: r.index.audio) + for audio_index, audio_seg_results in by_audio: + seg_results = list(audio_seg_results) + if audio_index in seen: + msg = ( + f"expected contiguous segments, already processed segments from " + f"audio of index {audio_index}" + ) + raise ValueError(msg) + # Drop segments with no recognized speech + expected_indices = list(range(len(seg_results))) + segment_indices = [r.index.segment for r in seg_results] + if expected_indices != segment_indices: + msg = ( + f"received audio segment results for audio {audio_index}" + f" out of order: {segment_indices}" + ) + raise ValueError(msg) + seg_results = [r for r in seg_results if r.transcription] + seg_results = sorted(seg_results, key=lambda r: r.transcription[0]) + + seen.add(audio_index) + base = ASRResult( + index=SegmentIndex(audio=audio_index, segment=0), + transcription=[], + score=1.0, + ) + merged_results = sum(seg_results, base) + yield merged_results diff --git a/caul/caul/tasks/postprocessing/faster_whisper.py b/caul/caul/tasks/postprocessing/faster_whisper.py new file mode 100644 index 00000000..120c9457 --- /dev/null +++ b/caul/caul/tasks/postprocessing/faster_whisper.py @@ -0,0 +1,7 @@ +from caul_core import ASRModel, Postprocessor + +from .asr_postprocessor import PostprocessorMixin + + +@Postprocessor.register(ASRModel.FASTER_WHISPER) +class FasterWhisperPostprocessor(PostprocessorMixin): ... diff --git a/caul/caul/tasks/postprocessing/fireredasr.py b/caul/caul/tasks/postprocessing/fireredasr.py new file mode 100644 index 00000000..f8f0f077 --- /dev/null +++ b/caul/caul/tasks/postprocessing/fireredasr.py @@ -0,0 +1,7 @@ +from caul_core import ASRModel, Postprocessor + +from .asr_postprocessor import PostprocessorMixin + + +@Postprocessor.register(ASRModel.FIREREDASR2_AED) +class FireRedASR2Postprocessor(PostprocessorMixin): ... diff --git a/caul/caul/tasks/postprocessing/parakeet.py b/caul/caul/tasks/postprocessing/parakeet.py new file mode 100644 index 00000000..02b123a3 --- /dev/null +++ b/caul/caul/tasks/postprocessing/parakeet.py @@ -0,0 +1,7 @@ +from caul_core import ASRModel, Postprocessor + +from .asr_postprocessor import PostprocessorMixin + + +@Postprocessor.register(ASRModel.PARAKEET) +class ParakeetPostprocessor(PostprocessorMixin): ... diff --git a/caul/caul/tasks/postprocessing/whisper_trt.py b/caul/caul/tasks/postprocessing/whisper_trt.py new file mode 100644 index 00000000..b8ba9e03 --- /dev/null +++ b/caul/caul/tasks/postprocessing/whisper_trt.py @@ -0,0 +1,7 @@ +from caul_core import ASRModel, Postprocessor + +from .asr_postprocessor import PostprocessorMixin + + +@Postprocessor.register(ASRModel.WHISPER_TRT) +class WhisperTrtPostprocessor(PostprocessorMixin): ... diff --git a/caul/caul/tasks/preprocessing/__init__.py b/caul/caul/tasks/preprocessing/__init__.py new file mode 100644 index 00000000..e1f92454 --- /dev/null +++ b/caul/caul/tasks/preprocessing/__init__.py @@ -0,0 +1,18 @@ +try: + from .parakeet import ParakeetPreprocessor +except ModuleNotFoundError: + ParakeetPreprocessor = None +try: + from .fireredasr import FireRedASR2Preprocessor +except ModuleNotFoundError: + FireRedASR2Preprocessor = None +try: + from .faster_whisper import ( + FasterWhisperPreprocessor, + ) +except ModuleNotFoundError: + FasterWhisperPreprocessor = None +try: + from .whisper_trt import WhisperTrtPreprocessor +except ModuleNotFoundError: + WhisperTrtPreprocessor = None diff --git a/caul/caul/tasks/preprocessing/asr_preprocessor.py b/caul/caul/tasks/preprocessing/asr_preprocessor.py new file mode 100644 index 00000000..926ba90d --- /dev/null +++ b/caul/caul/tasks/preprocessing/asr_preprocessor.py @@ -0,0 +1,328 @@ +import logging +import uuid +from collections.abc import Iterable +from functools import partial +from hashlib import sha256 +from itertools import repeat +from pathlib import Path +from typing import TYPE_CHECKING, Self, final + +from caul_core import ( + DEFAULT_BATCH_SIZE, + DEFAULT_LARGE_FILE_THRESHOLD_BYTES, + DEFAULT_MAX_FRAMES, + DEFAULT_SAMPLE_RATE, + ASRInput, + Audio, + AudioMetadata, + BaseBatcherConfig, + BasePreprocessorConfig, + ConstantSizeBatcherConfig, + Error, + FSProcessedSegment, + MemoryProcessedSegment, + Preprocessor, + ProcessedAudioSegment, + SampleRate, + SegmentIndex, + SegmentMetadata, +) +from torch import Tensor + +from caul.segmentation.methods import SegmentationFunction + +from ...exception import UnreadableAudio +from ...segmentation import segment_by_silence +from ...utils import audio_decoder, load_audio, save_tensor +from .batcher import Batcher + +if TYPE_CHECKING: + import numpy as np + import torch + +_NoneType = type(None) + +logger = logging.getLogger(__name__) + + +class ASRPreprocessorMixin(Preprocessor): + """Preprocessing logic for ASR model inputs""" + + def __init__( + self, + *, + batcher: BaseBatcherConfig | None = None, + max_frames: int = DEFAULT_MAX_FRAMES, + sample_rate: int = DEFAULT_SAMPLE_RATE, + large_file_threshold_bytes: int = DEFAULT_LARGE_FILE_THRESHOLD_BYTES, + segmentation_fn: SegmentationFunction = segment_by_silence, + reported_errors: tuple[type[Exception]] | None = None, + ): + super().__init__() + if batcher is None: + batcher = ConstantSizeBatcherConfig(batch_size=DEFAULT_BATCH_SIZE) + self._batcher_factory = partial(Batcher.from_config, config=batcher) + self._max_frames = max_frames + self._sample_rate = sample_rate + self._large_file_threshold_bytes = large_file_threshold_bytes + self._segmentation_fn = segmentation_fn + if reported_errors is None: + reported_errors = (UnreadableAudio,) + self._reported_errors = reported_errors + + @classmethod + def _from_config(cls, config: BasePreprocessorConfig, **extras) -> Self: + # TODO: configure segmentation fn + return cls( + max_frames=config.max_frames, + batcher=config.batcher, + sample_rate=config.sample_rate, + large_file_threshold_bytes=config.large_file_threshold_bytes, + ) + + def process( + self, + inputs: ASRInput, + sample_rates: SampleRate | None = None, + output_dir: Path | None = None, + **kwargs, + ) -> Iterable[tuple[ProcessedAudioSegment, ...] | Error]: + """Segment and batch audio inputs + + :param inputs: List of np.ndarray or torch.Tensor or str, or singleton of same + types + :param sample_rates: sample rate(s) of audio inputs + :param output_dir: optional directory to write preprocessed wav segments + :return: batches of indexed preprocessed audio tensors + (input_idx, preprocessed_input) + """ + processed_segments = self.preprocess_inputs( + inputs, sample_rates, output_dir=output_dir + ) + batcher = self._batcher_factory(items=processed_segments) + # Yield result first, we need to iterate on all processed segments to batch + # only successful segments and collect failures. Order is not maintained + # between successful results and errors which is fine as long order is + # maintained for results which processed downstream + yield from batcher.batch() + + @final + def preprocess_inputs( + self, + inputs: ASRInput, + input_sample_rates: SampleRate | None = None, + output_dir: str | Path | None = None, + ) -> Iterable[ProcessedAudioSegment | Error]: + """Convert audio inputs to normalized and segmented torch.Tensor. + + Accepts audio inputs as a list of file paths, np.ndarray, or torch.Tensor, + converting to torch.Tensor, normalizing, segmenting inputs longer than seg_max + and batching segments + + :param inputs: List of np.ndarray or torch.Tensor or str, or a singleton of same + types + :param input_sample_rates: sample rate(s) of audio inputs + :param output_dir: if provided, save segments as wav files here + :return: List of processed inputs + """ + if output_dir is not None and not isinstance(output_dir, Path): + output_dir = Path(output_dir) + + if isinstance(input_sample_rates, (int, _NoneType)): + input_sample_rates = repeat(input_sample_rates) + inputs_and_sample_rates = zip(inputs, input_sample_rates, strict=False) + else: + inputs_and_sample_rates = zip(inputs, input_sample_rates, strict=True) + seg_meta = None + for audio_idx, (raw_input, input_sample_rate) in enumerate( + inputs_and_sample_rates + ): + audio_input, audio_meta = self._audio_meta_from_input(audio_idx, raw_input) + try: + audio, sample_rate = self._load_audio(audio_input, input_sample_rate) + segs = self._segment_audio(audio, sample_rate) + for seg_position, (raw_audio_seg, seg_duration) in enumerate(segs): + seg_idx = SegmentIndex(audio=audio_meta.index, segment=seg_position) + seg_meta = SegmentMetadata( + index=seg_idx, + audio_format=audio_meta.audio_format, + audio_path=audio_meta.audio_path, + duration_s=seg_duration, + ) + audio_seg = self._preprocess_segment(raw_audio_seg) + if output_dir is not None: + seg_path = _persist_seg( + audio_seg, + seg_idx.segment, + audio_meta.audio_path, + output_dir=output_dir, + ).relative_to(output_dir) + res = FSProcessedSegment(metadata=seg_meta.now(), path=seg_path) + else: + res = MemoryProcessedSegment( + metadata=seg_meta.now(), tensor=audio_seg + ) + yield res + # Catch expected audio processing errors, let the other stop the processing + # (implem bug or unexpected errors which should be dealt with) + except self._reported_errors as cause: + meta = seg_meta or audio_meta + msg = f"error while preprocessing segment {seg_meta}. Skipping !" + logger.exception(msg) + error = Error.from_exception(cause, meta) + yield error + + def _audio_meta_from_input( + self, audio_idx: int, audio_input: ASRInput + ) -> tuple[Audio, AudioMetadata]: + audio_id = audio_idx + if isinstance(audio_input, tuple): + audio_id, audio_input = audio_input + audio_format, audio_path = None, None + if isinstance(audio_input, str): + audio_input = Path(audio_input) + if isinstance(audio_input, Path): + audio_path = audio_input + audio_format = audio_input.suffix.removeprefix(".") or None + audio_meta = AudioMetadata( + index=audio_id, audio_format=audio_format, audio_path=audio_path + ) + return audio_input, audio_meta + + def _segment_audio( + self, audio_chunks: Iterable[Tensor], sample_rate: int + ) -> Iterable[tuple[Tensor, float]]: + for chunk in audio_chunks: + n_frames = chunk.shape[-1] + if n_frames <= self._max_frames: + yield (chunk, n_frames / sample_rate) + continue + max_seg_len_s = self._max_frames / sample_rate + for s in self._segmentation_fn( + chunk, sample_rate=sample_rate, max_segment_len_s=max_seg_len_s + ): + yield (s.tensor, s.duration) + + def _load_audio( + self, audio_input: "np.ndarray | torch.Tensor | Path", sample_rate: int | None + ) -> tuple[Iterable[Tensor], int]: + import numpy as np + + if isinstance(audio_input, (Path, str)): + audio_path = Path(audio_input) + sample_rate = self._sample_rate + audio_chunks = self._load_file_as_chunks(audio_path, sample_rate) + else: + if isinstance(audio_input, np.ndarray): + import torch + + audio_input = torch.Tensor(audio_input) + if sample_rate is None: + sample_rate = self._sample_rate + audio_input = self._normalize(audio_input, sample_rate) + elif len(audio_input.shape) > 1: + audio_input = audio_input.squeeze(0) + audio_chunks = iter([audio_input]) + return audio_chunks, sample_rate + + def _preprocess_segment(self, audio_tensor: "torch.Tensor") -> "torch.Tensor": + """Stub for subclasses that have specific audio preprocessing logic""" + return audio_tensor + + def _load_file_as_chunks( + self, path: Path, sample_rate: int + ) -> "Iterable[torch.Tensor]": + """Return a lazy iterator of normalized 1D audio chunks at self._sample_rate. + + Reads file metadata first; falls back to a single eager load for small files. + + :param path: path to audio file + :return: Iterable of tensor segments + """ + try: + native_meta = audio_decoder(path).metadata + except ValueError as e: + raise UnreadableAudio(path) from e + num_frames = int(native_meta.duration_seconds * native_meta.sample_rate) + estimated_bytes = num_frames * native_meta.num_channels * 4 # float32 + + try: + if estimated_bytes > self._large_file_threshold_bytes: + yield from self._iter_audio_chunks(path, native_meta.duration_seconds) + else: + yield load_audio(path, sample_rate=sample_rate, num_channels=1) + except Exception as e: + raise UnreadableAudio(path) from e + + def _iter_audio_chunks( + self, path: Path, total_duration_s: float + ) -> "Iterable[torch.Tensor]": + """Lazily decode a large audio file in max-frame-sized windows. + + :param path: path to audio file + :param total_duration_s: total duration of audio file in seconds + :return: Iterable of tensor segments + """ + chunk_duration_s = self._max_frames / self._sample_rate + decoder = audio_decoder(path, self._sample_rate, num_channels=1) + start = 0.0 + while start < total_duration_s: + end = min(start + chunk_duration_s, total_duration_s) + samples = decoder.get_samples_played_in_range( + start_seconds=start, stop_seconds=end + ) + yield samples.data.squeeze() + start = end + + def _normalize( + self, audio_tensor: "torch.Tensor", sample_rate: int + ) -> "torch.Tensor": + """Normalize audio_tensor (single channel, sample rate = 16000) + + :param audio_tensor: input tensor + :param sample_rate: input sample rate + :return: normalized 1D tensor at self._sample_rate + """ + if sample_rate != self._sample_rate: + audio_tensor = _resample_audio( + audio_tensor, self._sample_rate, target_rate=sample_rate + ) + + # Stereo dims (channels, aud_length); need mono (aud_length) + if len(audio_tensor.shape) > 1: + audio_tensor = audio_tensor.squeeze(0) + + return audio_tensor + + +def _persist_seg( + audio_seg: Tensor, index: int, audio_path: Path | None, *, output_dir: Path +) -> Path: + original_file = ( + _displayable_prefix(audio_path) if audio_path is not None else uuid.uuid4().hex + ) + seg_name = f"{original_file}-{index}.wav" + seg_path = output_dir / seg_name + save_tensor(audio_seg, seg_path) + return seg_path + + +def _resample_audio( + audio: "torch.Tensor", sample_rate: int, *, target_rate: int +) -> "torch.Tensor": + from torchcodec.encoders import AudioEncoder + + encoder = AudioEncoder(samples=audio, sample_rate=sample_rate) + + return encoder.to_tensor(format="wav", num_channels=1, sample_rate=target_rate) + + +def _displayable_prefix( + path: Path, component_size_limit: int = 10, *, deterministic: bool = False +) -> str: + displayable_file_name = path.name[:component_size_limit].replace(".", "__") + if deterministic: + uid = sha256(str(path).encode()).hexdigest()[:20] + else: + uid = uuid.uuid4().hex[:20] + return f"{displayable_file_name}-{uid}" diff --git a/caul/caul/tasks/preprocessing/batcher.py b/caul/caul/tasks/preprocessing/batcher.py new file mode 100644 index 00000000..d99ca175 --- /dev/null +++ b/caul/caul/tasks/preprocessing/batcher.py @@ -0,0 +1,72 @@ +from collections.abc import Iterable +from typing import cast + +from caul_core import ( + Batcher, + BatcherType, + ConstantSizeBatcherConfig, + Error, + MaxDurationBatcherConfig, + ProcessedAudioSegment, +) + + +@Batcher.register(BatcherType.CONSTANT_SIZE) +class ConstantSizeBatcher(Batcher): + def __init__( + self, + items: Iterable[ProcessedAudioSegment | Error], + config: ConstantSizeBatcherConfig | None = None, + ): + if config is None: + config = ConstantSizeBatcherConfig() + super().__init__(items, config) + + def batch(self) -> Iterable[tuple[ProcessedAudioSegment, ...] | Error]: + batch: list[ProcessedAudioSegment] = [] + for item in self._items: + if isinstance(item, Error): + yield item + continue + item = cast(ProcessedAudioSegment, item) + batch.append(item) + if len(batch) >= self._config.batch_size: + yield tuple(batch) + batch = [] + if batch: + yield tuple(batch) + + +@Batcher.register(BatcherType.MAX_DURATION) +class MaxDurationBatcher(Batcher): + def __init__( + self, + items: Iterable[ProcessedAudioSegment], + config: MaxDurationBatcherConfig | None = None, + ): + if config is None: + config = MaxDurationBatcherConfig() + super().__init__(items, config) + + def batch(self) -> Iterable[tuple[ProcessedAudioSegment, ...] | Error]: + current_batch: list[ProcessedAudioSegment] = [] + current_batch_duration_s = 0.0 + + for item in self._items: + if isinstance(item, Error): + yield item + continue + input_duration_s = item.metadata.duration_s + if current_batch and ( + current_batch_duration_s + input_duration_s + > self._config.max_duration_s + or 0 < self._config.batch_size <= len(current_batch) + ): + yield tuple(current_batch) + current_batch = [] + current_batch_duration_s = 0.0 + current_batch.append(item) + current_batch_duration_s += input_duration_s + + if current_batch: + yield tuple(current_batch) diff --git a/caul/caul/tasks/preprocessing/faster_whisper.py b/caul/caul/tasks/preprocessing/faster_whisper.py new file mode 100644 index 00000000..0e127122 --- /dev/null +++ b/caul/caul/tasks/preprocessing/faster_whisper.py @@ -0,0 +1,7 @@ +from caul_core import ASRModel, Preprocessor + +from .asr_preprocessor import ASRPreprocessorMixin + + +@Preprocessor.register(ASRModel.FASTER_WHISPER) +class FasterWhisperPreprocessor(ASRPreprocessorMixin): ... diff --git a/caul/caul/tasks/preprocessing/fireredasr.py b/caul/caul/tasks/preprocessing/fireredasr.py new file mode 100644 index 00000000..c5bde2eb --- /dev/null +++ b/caul/caul/tasks/preprocessing/fireredasr.py @@ -0,0 +1,27 @@ +from caul_core import ( + DEFAULT_LARGE_FILE_THRESHOLD_BYTES, + DEFAULT_SAMPLE_RATE, + FIREREDASR2_INFERENCE_MAX_FRAMES, + ASRModel, + BaseBatcherConfig, + Preprocessor, +) + +from .asr_preprocessor import ASRPreprocessorMixin + + +@Preprocessor.register(ASRModel.FIREREDASR2_AED) +class FireRedASR2Preprocessor(ASRPreprocessorMixin): + def __init__( + self, + max_frames: int = FIREREDASR2_INFERENCE_MAX_FRAMES, + batcher: BaseBatcherConfig | None = None, + sample_rate: int = DEFAULT_SAMPLE_RATE, + large_file_threshold_bytes: int = DEFAULT_LARGE_FILE_THRESHOLD_BYTES, + ): + super().__init__( + max_frames=max_frames, + batcher=batcher, + sample_rate=sample_rate, + large_file_threshold_bytes=large_file_threshold_bytes, + ) diff --git a/caul/caul/tasks/preprocessing/parakeet.py b/caul/caul/tasks/preprocessing/parakeet.py new file mode 100644 index 00000000..bbcd1833 --- /dev/null +++ b/caul/caul/tasks/preprocessing/parakeet.py @@ -0,0 +1,42 @@ +from typing import Self + +from caul_core import ( + DEFAULT_LARGE_FILE_THRESHOLD_BYTES, + DEFAULT_SAMPLE_RATE, + PARAKEET_INFERENCE_MAX_DURATION_S, + PARAKEET_INFERENCE_MAX_FRAMES, + ASRModel, + BaseBatcherConfig, + ParakeetPreprocessorConfig, + Preprocessor, +) + +from .asr_preprocessor import ASRPreprocessorMixin + + +@Preprocessor.register(ASRModel.PARAKEET) +class ParakeetPreprocessor(ASRPreprocessorMixin): + def __init__( + self, + max_frames: int = PARAKEET_INFERENCE_MAX_FRAMES, + sample_rate: int = DEFAULT_SAMPLE_RATE, + large_file_threshold_bytes: int = DEFAULT_LARGE_FILE_THRESHOLD_BYTES, + max_duration_s: float = PARAKEET_INFERENCE_MAX_DURATION_S, + batcher: BaseBatcherConfig | None = None, + ): + super().__init__( + max_frames=max_frames, + batcher=batcher, + sample_rate=sample_rate, + large_file_threshold_bytes=large_file_threshold_bytes, + ) + self._max_duration_s = max_duration_s + + @classmethod + def _from_config(cls, config: ParakeetPreprocessorConfig, **extras) -> Self: + return cls( + max_frames=config.max_frames, + batcher=config.batcher, + sample_rate=config.sample_rate, + large_file_threshold_bytes=config.large_file_threshold_bytes, + ) diff --git a/caul/caul/tasks/preprocessing/whisper_trt.py b/caul/caul/tasks/preprocessing/whisper_trt.py new file mode 100644 index 00000000..d9a25342 --- /dev/null +++ b/caul/caul/tasks/preprocessing/whisper_trt.py @@ -0,0 +1,123 @@ +from collections.abc import Callable +from pathlib import Path +from types import TracebackType +from typing import TYPE_CHECKING, Self + +from caul_core import ( + WHISPER_TRT_HOP_LENGTH, + WHISPER_TRT_MAX_FRAMES, + WHISPER_TRT_N_FFT, + WHISPER_TRT_PREPROCESSOR_CLAMP_MIN, + WHISPER_TRT_PREPROCESSOR_LOG_RANGE_MAX_SHIFT, + WHISPER_TRT_PREPROCESSOR_LOG_RANGE_NORMALIZER, + ASRModel, + BaseBatcherConfig, + Preprocessor, + WhisperTrtPreprocessorConfig, +) + +from ...utils import load_mel_filters +from .asr_preprocessor import ASRPreprocessorMixin + +if TYPE_CHECKING: + import torch + + +def _mel_filters_factory( + n_mels: int, mel_filters_dir: Path | str, device: "str | torch.Device" = "cpu" +) -> Callable[[], "torch.Tensor"]: + def _load_mel_filters() -> "torch.Tensor": + return load_mel_filters(n_mels, mel_filters_dir, device) + + return _load_mel_filters + + +@Preprocessor.register(ASRModel.WHISPER_TRT) +class WhisperTrtPreprocessor(ASRPreprocessorMixin): + def __init__( + self, + batcher: BaseBatcherConfig | None = None, + n_mels: int = 80, + mel_filters_factory: Callable[[], "torch.Tensor"] = None, + dtype: "str | None" = None, + max_frames: int = WHISPER_TRT_MAX_FRAMES, + ) -> None: + import torch + + super().__init__(batcher=batcher, max_frames=max_frames) + self._n_mels = n_mels + self._mel_filters_factory = mel_filters_factory() + self._mel_filters = None + + self._dtype = getattr(torch, dtype if dtype is not None else "float16") + + @classmethod + def _from_config( + cls, + config: WhisperTrtPreprocessorConfig, + **extras, + ) -> Self: + return cls( + n_mels=config.n_mels, + mel_filters_factory=_mel_filters_factory( + config.n_mels, config.mel_filters_dir + ), + dtype=config.dtype, + batcher=config.batcher, + max_frames=config.max_frames, + ) + + def __enter__(self) -> Self: + self._mel_filters = ( + self._mel_filters_factory() + if self._mel_filters_factory is not None + else None + ) + + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_val: BaseException | None, + exc_tb: "TracebackType | None", + ): + self._mel_filters = None + + def _preprocess_segment(self, audio_tensor: "torch.Tensor") -> "torch.Tensor": + """Map input tensor to log mel spectrogram, needed by Whisper TRT + + :audio_tensor: input audio tensor + :returns: log mel spectrogram + """ + import torch + + window = torch.hann_window(WHISPER_TRT_N_FFT) + stft = torch.stft( + audio_tensor, + WHISPER_TRT_N_FFT, + WHISPER_TRT_HOP_LENGTH, + window=window, + return_complex=True, + ) + magnitudes = stft[..., :-1].abs() ** 2 + + mel_spec = ( + self._mel_filters @ magnitudes + if self._mel_filters is not None + else magnitudes + ) + + audio_tensor = torch.clamp( + mel_spec, min=WHISPER_TRT_PREPROCESSOR_CLAMP_MIN + ).log10() + audio_tensor = torch.maximum( + audio_tensor, + audio_tensor.max() - WHISPER_TRT_PREPROCESSOR_LOG_RANGE_MAX_SHIFT, + ) + audio_tensor = ( + audio_tensor + WHISPER_TRT_PREPROCESSOR_LOG_RANGE_NORMALIZER + ) / WHISPER_TRT_PREPROCESSOR_LOG_RANGE_NORMALIZER + audio_tensor = audio_tensor.unsqueeze(0) + + return audio_tensor.type(self._dtype) diff --git a/caul/caul/trt/__init__.py b/caul/caul/trt/__init__.py new file mode 100644 index 00000000..68650f47 --- /dev/null +++ b/caul/caul/trt/__init__.py @@ -0,0 +1,11 @@ +from types import ModuleType + + +def import_trt() -> ModuleType: + """Import TensorRT, preferring the lean runtime over the full package.""" + try: + import tensorrt_lean as trt + except ImportError: + import tensorrt as trt + + return trt diff --git a/caul/caul/trt/handler.py b/caul/caul/trt/handler.py new file mode 100644 index 00000000..4139ae92 --- /dev/null +++ b/caul/caul/trt/handler.py @@ -0,0 +1,81 @@ +from functools import cache +from typing import TYPE_CHECKING + +from caul.exception import TrtInputShapeError +from caul.trt import import_trt + +if TYPE_CHECKING: + import tensorrt as trt + import torch + + +@cache +def _dtypes_map() -> "dict[trt.DataType, torch.dtype]": + trt = import_trt() + import torch + + return { + trt.float16: torch.float16, + trt.float32: torch.float32, + trt.int32: torch.int32, + trt.int64: torch.int64, + trt.bool: torch.bool, + } + + +class TrtInferenceHandler: + """Context wrapper for inference with TRT engines""" + + def __init__(self, engine: "trt.ICudaEngine"): + self._engine = engine + self._context = None + + def __enter__(self): + self._context = self._engine.create_execution_context() + return self + + def __exit__(self, *args): + del self._context + self._context = None + + def infer(self, inputs: dict) -> tuple: + trt = import_trt() + import torch + + trt_to_torch_dtypes = _dtypes_map() + + device = torch.device("cuda") + + for tensor_name, tensor in inputs.items(): + in_dtype = trt_to_torch_dtypes[self._engine.get_tensor_dtype(tensor_name)] + inputs[tensor_name] = tensor.contiguous().to(device=device, dtype=in_dtype) + shape = tuple(inputs[tensor_name].shape) + # TRT rejects shapes outside an optimization profile by returning False, + # leaving dynamic output dims unresolved + if not self._context.set_input_shape(tensor_name, shape): + raise TrtInputShapeError( + tensor_name, + shape, + self._engine.get_tensor_profile_shape( + tensor_name, self._context.active_optimization_profile + ), + ) + + outputs = {} + for i in range(self._engine.num_io_tensors): + out_name = self._engine.get_tensor_name(i) + if self._engine.get_tensor_mode(out_name) == trt.TensorIOMode.OUTPUT: + out_dtype = trt_to_torch_dtypes[self._engine.get_tensor_dtype(out_name)] + out_shape = tuple(self._context.get_tensor_shape(out_name)) + outputs[out_name] = torch.empty( + out_shape, dtype=out_dtype, device=device + ) + + for io_name, tensor in {**inputs, **outputs}.items(): + self._context.set_tensor_address(io_name, tensor.data_ptr()) + + stream = torch.cuda.current_stream() + self._context.execute_async_v3(stream.cuda_stream) + stream.synchronize() + + return tuple(outputs.values()) diff --git a/caul/caul/trt/llm_handler.py b/caul/caul/trt/llm_handler.py new file mode 100644 index 00000000..09f1b104 --- /dev/null +++ b/caul/caul/trt/llm_handler.py @@ -0,0 +1 @@ +class TrtLlmInferenceHandler: ... diff --git a/caul/caul/utils.py b/caul/caul/utils.py new file mode 100644 index 00000000..53e80156 --- /dev/null +++ b/caul/caul/utils.py @@ -0,0 +1,208 @@ +import logging +from collections.abc import Iterable +from functools import cache +from pathlib import Path, PurePosixPath +from typing import TYPE_CHECKING + +from caul_core import ( + DEFAULT_SAMPLE_RATE, + FSProcessedSegment, + MemoryProcessedSegment, + TorchDevice, +) + +from .constants import DEFAULT_BIT_RATE + +logger = logging.getLogger(__name__) + +if TYPE_CHECKING: + import torch + from torchcodec.decoders import AudioDecoder + + +def save_tensor(audio: "torch.Tensor", path: Path) -> None: + """Filesystem routine for audio tensor; defaults to wav + + :param audio: input tensor + :return: string file uri + """ + # TODO: Change paths to run_id + tensor uuid + pagination + # Allow for remote paths + from torchcodec.encoders import AudioEncoder + + # Channel required as first dim + audio = audio.unsqueeze(0) + encoder = AudioEncoder(samples=audio, sample_rate=DEFAULT_SAMPLE_RATE) + encoder.to_file( + path, sample_rate=DEFAULT_SAMPLE_RATE, num_channels=1, bit_rate=DEFAULT_BIT_RATE + ) + + +def audio_decoder( + path: str | Path, sample_rate: int | None = None, *, num_channels: int | None = None +) -> "AudioDecoder": + """Open an audio file for decoding; None keeps the file's native value + + :param path: audio file path + :param sample_rate: output sample rate + :param num_channels: output channels + :return: audio decoder + """ + from torchcodec.decoders import ( + AudioDecoder, + ) + + return AudioDecoder(path, sample_rate=sample_rate, num_channels=num_channels) + + +def load_audio( + path: str | Path, sample_rate: int = DEFAULT_SAMPLE_RATE, *, num_channels: int = 1 +) -> "torch.Tensor": + """Load an audio file as a tensor, resampled to sample_rate + + :param path: audio file path + :param sample_rate: output sample rate + :param num_channels: output channels; mono returns a 1D tensor + :return: audio tensor + """ + decoder = audio_decoder(path, sample_rate, num_channels=num_channels) + # Drop channel dim for mono + return decoder.get_all_samples().data.squeeze(0) + + +def device_to_torch(device: TorchDevice) -> "torch.device": + import torch + + match device: + case TorchDevice.GPU: + return torch.device("cuda") + case TorchDevice.CPU: + return torch.device("cpu") + case TorchDevice.MPS: + return torch.device("mps") + case _: + raise ValueError(f"unknown device for torch: {device}") + + +def fuzzy_match(key: str, candidates: set[str]) -> set[str]: + if key in candidates: + return {key} + fuzzy_matches = {k for k in candidates if key in k or k in key} + return fuzzy_matches + + +def to_filesystem( + input_batch: Iterable[FSProcessedSegment | MemoryProcessedSegment], + output_dir: Path | None, +) -> Iterable[tuple[FSProcessedSegment | MemoryProcessedSegment, Path]]: + for inp in input_batch: + inp_id = inp.metadata.uuid + + match inp: + case FSProcessedSegment(): + wav_path = inp.path + case MemoryProcessedSegment(): + if output_dir is None: + msg = ( + f"output_dir was not provided for " + f"{MemoryProcessedSegment.__name__} input" + ) + raise ValueError(msg) + index = inp.metadata.index + filename = f"{index.audio}-{index.segment}-{inp_id}.wav" + wav_path = output_dir / filename + save_tensor(inp.tensor, wav_path) + case _: + raise TypeError(f"unexpected segment type: {inp}") + yield inp, wav_path + + +def cache_hf_model_file( + repo_id: str, + *, + filename: str, + library_name: str | None = None, + library_version: str | None = None, + cache_dir: Path | None = None, +) -> None: + from huggingface_hub import ( + get_token, + hf_hub_download, + ) + + hf_hub_download( + repo_id=repo_id, + filename=filename, + cache_dir=cache_dir, + library_name=library_name, + library_version=library_version, + force_download=False, + token=get_token(), + ) + + +def cache_hf_repo( + repo_id: str, + *, + library_name: str | None = None, + library_version: str | None = None, + cache_dir: Path | None = None, +) -> None: + from huggingface_hub import ( + get_token, + snapshot_download, + ) + + snapshot_download( + repo_id=repo_id, + cache_dir=cache_dir, + library_name=library_name, + library_version=library_version, + force_download=False, + token=get_token(), + ) + + +def cache_hf_model( + model_family: str, + models: Iterable[str], + model_ext: str, + library_name: str | None = None, + cache_dir: Path | None = None, +) -> None: + from huggingface_hub.constants import ( + HF_HUB_CACHE, + ) + + if cache_dir is not None and str(cache_dir) != HF_HUB_CACHE: + msg = f"{model_family} models must be loaded from {HF_HUB_CACHE}, " + raise ValueError(msg) + + for m in models: + logger.info("caching %s model %s", model_family, m) + filename = PurePosixPath(m).name + model_ext + cache_hf_model_file( + repo_id=m, filename=filename, library_name=library_name, cache_dir=cache_dir + ) + + +@cache +def load_mel_filters( + n_mels: int, + mel_filters_path: Path | str = None, + device: "str | torch.Device" = "cpu", +) -> "torch.Tensor | None": + """Load the mel filterbank matrix for projecting an stft into a mel spectrogram + + :param n_mels: number of mel filterbank matrix + :param mel_filters_dir: directory where mel filterbank matrix is saved + :param device: cpu or cuda + """ + import numpy as np + import torch + + if mel_filters_path is None or n_mels not in {80, 128}: + return None + + with np.load(mel_filters_path) as f: + return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) diff --git a/caul/pyproject.toml b/caul/pyproject.toml new file mode 100644 index 00000000..105e8cc1 --- /dev/null +++ b/caul/pyproject.toml @@ -0,0 +1,168 @@ +[project] +name = "caul" +version = "0.13.13" +description = "Python implementation of an ASR service" +authors = [ + {name = "Lion Summerbell", email = "lsummerbell@icij.org"} +] +requires-python = ">=3.12.0, <3.13.0" +dependencies = [ + "icij-common~=0.8.1", + "kaldialign~=0.9", + "langcodes~=3.5", + "librosa>=0.10.1,<0.12", + "pydantic-extra-types[pycountry]~=2.11", + "typer~=0.24", + "huggingface-hub~=1.11", + "caul-core~=0.6.6", + "tiktoken~=0.13", +] + +[project.scripts] +caul = "caul.__main__:main" + +[project.optional-dependencies] +preprocessing = [ + "torchcodec>=0.10.0,<0.11", # pulls in torch automatically so we've made it optional for a base install +] +nemo = [ + "nemo-toolkit[asr]~=3.0", + "torch~=2.10", +] +pyannote = [ + "pyannote-audio~=4.0", + "torchaudio~=2.10", +] +fireredasr2 = [ + "fireredasr2s", + "kaldi-native-fbank~=1.22", + "torchaudio~=2.10", +] +faster-whisper = [ + "faster_whisper~=1.2" +] +parakeet-trt = [ + # TensorRT only builds on linux or windown + "tensorrt-lean==11.2.1.2; platform_system == 'Linux' or platform_system == 'Windows'", +] +whisper-trt = [ + # TensorRT only builds on linux or windown + "tensorrt_llm~=1.2; platform_system == 'Linux' or platform_system == 'Windows'", +] +cpu = [ + # Pins torch packages to the pytorch CPU index for local dev/CI, scoped to this extra so + # downstream path dependents (e.g. datashare-asr-worker) can pick their own torch index + "torch~=2.10", + "torchaudio~=2.10", + "torchcodec>=0.10.0,<0.11", +] + +[tool.uv] +package = true +override-dependencies = [ + # TODO: needed by tensorrt-llm which needs transformers < 5, remove when bumping to tensorrt-llm 1.3.0 + "transformers~=5.5.4", +] +conflicts = [ + [ + { extra = "whisper-trt" }, + { extra = "pyannote" }, + ], + [ + { extra = "whisper-trt" }, + { extra = "nemo" }, + ], + [ + { extra = "whisper-trt" }, + { extra = "fireredasr2" }, + ], +] +# Static metadata for TensorRT packages to avoid running their stub setup.py +# during cross-platform lockfile generation (stubs fail on macOS). +# See: https://github.com/astral-sh/uv/issues/13942 +# +# We provide metadata for both cu12 and cu13 variants since: +# - Older torch-tensorrt versions depend directly on tensorrt-cu12 +# - Newer torch-tensorrt versions use the tensorrt metapackage which depends on cu13 +# +# The tensorrt metapackage metadata assumes cu13 (for tensorrt >=10.13). + +[[tool.uv.dependency-metadata]] +name = "tensorrt" +requires-dist = ["tensorrt-cu13"] + +# CUDA 12 variant packages (for older torch-tensorrt direct dependencies) +[[tool.uv.dependency-metadata]] +name = "tensorrt-cu12" +requires-dist = ["tensorrt-cu12-bindings", "tensorrt-cu12-libs"] + +[[tool.uv.dependency-metadata]] +name = "tensorrt-cu12-bindings" + +[[tool.uv.dependency-metadata]] +name = "tensorrt-cu12-libs" +requires-dist = ["nvidia-cuda-runtime-cu12"] + +# CUDA 13 variant packages (for tensorrt >=10.13) +[[tool.uv.dependency-metadata]] +name = "tensorrt-cu13" +requires-dist = ["tensorrt-cu13-bindings", "tensorrt-cu13-libs"] + +[[tool.uv.dependency-metadata]] +name = "tensorrt-cu13-bindings" + +[[tool.uv.dependency-metadata]] +name = "tensorrt-cu13-libs" +requires-dist = ["nvidia-cuda-runtime-cu13"] + +# Common CUDA runtime dependency +[[tool.uv.dependency-metadata]] +name = "nvidia-cuda-runtime-cu12" + +[[tool.uv.dependency-metadata]] +name = "nvidia-cuda-runtime" + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[tool.uv.sources] +torch = [ + { index = "pytorch-cpu", extra = "cpu" } +] +torchaudio = [ + { index = "pytorch-cpu", extra = "cpu" } +] +torchcodec = [ + { index = "pytorch-cpu", extra = "cpu" } +] +fireredasr2s = [ + { git = "https://github.com/FireRedTeam/FireRedASR2S.git" } +] +caul-core = { path = "../caul-core", editable = true } + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["caul"] + +[dependency-groups] +dev = [ + "pre-commit>=4.5.1", + "pytest>=9.0.2", + "nest-asyncio>=1.6.0", + "pytest-asyncio>=1.3.0", + "ruff==0.16.1", +] + +[tool.pytest.ini_options] +markers = [ + "e2e: end-to-end tests requiring live models and infrastructure", + "no_ci: test which are too slow to run in the CI", + "integration: integration tests that download and run real ASR models (slow)", +] +asyncio_mode = "auto" +asyncio_default_fixture_loop_scope = "session" diff --git a/caul/tests/__init__.py b/caul/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/caul/tests/conftest.py b/caul/tests/conftest.py new file mode 100644 index 00000000..fb8f0d61 --- /dev/null +++ b/caul/tests/conftest.py @@ -0,0 +1,7 @@ +import nest_asyncio +import pytest + + +@pytest.fixture +def typer_asyncio_patch() -> None: + nest_asyncio.apply() diff --git a/caul/tests/integration/__init__.py b/caul/tests/integration/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/caul/tests/integration/test_asr_handler.py b/caul/tests/integration/test_asr_handler.py new file mode 100644 index 00000000..e69de29b diff --git a/caul/tests/resources/ar_test.wav b/caul/tests/resources/ar_test.wav new file mode 100644 index 00000000..8c249329 Binary files /dev/null and b/caul/tests/resources/ar_test.wav differ diff --git a/caul/tests/resources/asr_test.mp4 b/caul/tests/resources/asr_test.mp4 new file mode 100644 index 00000000..a1f75b06 Binary files /dev/null and b/caul/tests/resources/asr_test.mp4 differ diff --git a/caul/tests/resources/asr_test.wav b/caul/tests/resources/asr_test.wav new file mode 100644 index 00000000..2259c520 Binary files /dev/null and b/caul/tests/resources/asr_test.wav differ diff --git a/caul/tests/resources/ru_test.wav b/caul/tests/resources/ru_test.wav new file mode 100644 index 00000000..a0a0b99d Binary files /dev/null and b/caul/tests/resources/ru_test.wav differ diff --git a/caul/tests/resources/zh_test.wav b/caul/tests/resources/zh_test.wav new file mode 100644 index 00000000..af161f6d Binary files /dev/null and b/caul/tests/resources/zh_test.wav differ diff --git a/caul/tests/unit/__init__.py b/caul/tests/unit/__init__.py new file mode 100644 index 00000000..836d3e86 --- /dev/null +++ b/caul/tests/unit/__init__.py @@ -0,0 +1,3 @@ +from pathlib import Path + +TEST_RESOURCES_PATH = Path(__file__).parents[1] / "resources" diff --git a/caul/tests/unit/cli/test_models.py b/caul/tests/unit/cli/test_models.py new file mode 100644 index 00000000..cec3e480 --- /dev/null +++ b/caul/tests/unit/cli/test_models.py @@ -0,0 +1,33 @@ +import logging +from pathlib import Path + +import pytest +from _pytest.logging import LogCaptureFixture +from _pytest.monkeypatch import MonkeyPatch +from caul.cli import cli_app, models +from caul_core import ASRModel +from typer.testing import CliRunner + +logger = logging.getLogger(__name__) + + +def _mock_cache_models(model: ASRModel | None, cache_dir: Path) -> None: + logger.info("%s models were cached %s", model, cache_dir) + + +@pytest.mark.asyncio +@pytest.mark.usefixtures("typer_asyncio_patch") +async def test_cache_models( + monkeypatch: MonkeyPatch, caplog: LogCaptureFixture +) -> None: + # Given + runner = CliRunner(catch_exceptions=False) + monkeypatch.setattr(models, "cache_models", _mock_cache_models) + # When + cmd = ["model", "cache", "parakeet"] + with caplog.at_level(logging.INFO, logger=logger.name): + result = runner.invoke(cli_app, cmd, catch_exceptions=False) + # Then + assert result.exit_code == 0 + expected = "parakeet models were cached" + assert expected in caplog.text diff --git a/caul/tests/unit/constant.py b/caul/tests/unit/constant.py new file mode 100644 index 00000000..8e421e01 --- /dev/null +++ b/caul/tests/unit/constant.py @@ -0,0 +1,17 @@ +from pathlib import Path + +TEST_PATH = Path(__file__).parent.parent +TEST_RESOURCES_PATH = TEST_PATH / "resources" +TEST_WAV_PATH = TEST_RESOURCES_PATH / "asr_test.wav" +TEST_MP4_PATH = TEST_RESOURCES_PATH / "asr_test.mp4" + +PARAKEET_TEST_SEGMENT_START = 0.08 + +PARAKEET_TEST_SEGMENT_END = 1.26 + +TEST_WAV_TRANSCRIPTION = "To embrace the chaos that they fought in this battle." +TEST_MP4_TRANSCRIPTION = "This is a test." + +PARAKEET_TEST_CONFIDENCE = -248 + +TEST_TIMESTAMPS = [{"start": 0, "end": 8000}] diff --git a/caul/tests/unit/mock.py b/caul/tests/unit/mock.py new file mode 100644 index 00000000..ad516a25 --- /dev/null +++ b/caul/tests/unit/mock.py @@ -0,0 +1,78 @@ +from typing import ClassVar + +import numpy as np +import torch +from caul.tasks import ParakeetInferenceRunner +from caul_core import InferenceRunner, ParakeetInferenceRunnerConfig, TorchDevice +from nemo.collections.asr.parts.mixins import TranscribeConfig +from nemo.collections.asr.parts.utils import Hypothesis +from pydantic import Field + +from tests.unit.constant import ( + PARAKEET_TEST_CONFIDENCE, + PARAKEET_TEST_SEGMENT_END, + PARAKEET_TEST_SEGMENT_START, + TEST_WAV_TRANSCRIPTION, +) + + +class MockNvidiaASRInferenceHandlerResult: + def __init__(self): + self.timestep = { + "segment": [ + { + "segment": TEST_WAV_TRANSCRIPTION, + "start": PARAKEET_TEST_SEGMENT_START, + "end": PARAKEET_TEST_SEGMENT_END, + } + ] + } + self.text = TEST_WAV_TRANSCRIPTION + self.score = PARAKEET_TEST_CONFIDENCE + + +def mock_result() -> Hypothesis: + timestamp = { + "segment": [ + { + "segment": TEST_WAV_TRANSCRIPTION, + "start": PARAKEET_TEST_SEGMENT_START, + "end": PARAKEET_TEST_SEGMENT_END, + } + ] + } + text = TEST_WAV_TRANSCRIPTION + score = PARAKEET_TEST_CONFIDENCE + return Hypothesis(timestamp=timestamp, text=text, score=score, y_sequence=[]) + + +class MockParakeetModel: + # Mirrors the nemo ASRModel.transcribe signature + def transcribe( + self, + audio: list[np.ndarray | torch.Tensor | str] # noqa: ARG002 + | np.ndarray + | torch.Tensor + | str, + timestamps: bool, # noqa: ARG002, FBT001 + override_config: TranscribeConfig | None = None, # noqa: ARG002 + ) -> tuple[list[Hypothesis]]: + return ([mock_result()],) + + +class MockNvidiaASRInferenceRunnerConfig(ParakeetInferenceRunnerConfig): + model: ClassVar[str] = Field(frozen=True, default="mock_nvidia") + + +@InferenceRunner.register("mock_nvidia") +class MockNvidiaASRInferenceRunner(ParakeetInferenceRunner): + def __init__( + self, + model_name: str, + device: TorchDevice | torch.device = TorchDevice.CPU, + batch_size: int = 4, + ): + super().__init__(model_name, device, batch_size) + + def __enter__(self): + self._model = MockParakeetModel() diff --git a/caul/tests/unit/test_asr_preprocessor.py b/caul/tests/unit/test_asr_preprocessor.py new file mode 100644 index 00000000..df120b14 --- /dev/null +++ b/caul/tests/unit/test_asr_preprocessor.py @@ -0,0 +1,162 @@ +import inspect +import math +import sys +from pathlib import Path +from typing import Never + +import pytest +import torch +import torchcodec.decoders +from caul.tasks.preprocessing.asr_preprocessor import ASRPreprocessorMixin +from caul.utils import load_audio +from caul_core import DEFAULT_SAMPLE_RATE, Error +from torchcodec.decoders import AudioDecoder +from torchcodec.encoders import AudioEncoder + +from tests.unit.constant import TEST_WAV_PATH + + +@pytest.fixture +def long_wav(tmp_path: Path) -> Path: + """3-second silent wav at 16 kHz, long enough for several 1-second chunks""" + audio = torch.zeros(1, DEFAULT_SAMPLE_RATE * 3) + path = tmp_path / "long.wav" + AudioEncoder(audio, sample_rate=DEFAULT_SAMPLE_RATE).to_file(path) + return path + + +def _chunking_preprocessor( + max_frames: int = DEFAULT_SAMPLE_RATE, +) -> ASRPreprocessorMixin: + """Preprocessor with threshold=1 so every file triggers the chunked path""" + return ASRPreprocessorMixin(large_file_threshold_bytes=1, max_frames=max_frames) + + +def test_load_stereo_24bit_audio() -> None: + tensor = load_audio(TEST_WAV_PATH) + assert len(tensor.shape) == 1 + + +class TestASRPreprocessorChunking: + def test__below_threshold_loads_eagerly(self, long_wav: Path) -> None: + """Files under the byte threshold produce exactly one chunk""" + preprocessor = ASRPreprocessorMixin(large_file_threshold_bytes=sys.maxsize) + chunks = list( + preprocessor._load_file_as_chunks(long_wav, DEFAULT_SAMPLE_RATE), + ) + assert len(chunks) == 1 + + def test__above_threshold_produces_multiple_chunks(self, long_wav: Path) -> None: + """Files over the byte threshold are split into several time-window chunks""" + chunks = list( + _chunking_preprocessor()._load_file_as_chunks(long_wav, DEFAULT_SAMPLE_RATE) + ) + assert len(chunks) > 1 + + def test__chunk_count_matches_expected(self, long_wav: Path) -> None: + """Number of chunks equals ceil(file_duration / chunk_duration)""" + preprocessor = _chunking_preprocessor() + duration_s = AudioDecoder(long_wav).metadata.duration_seconds + chunk_duration_s = DEFAULT_SAMPLE_RATE / DEFAULT_SAMPLE_RATE + expected = math.ceil(duration_s / chunk_duration_s) + + chunks = list(preprocessor._load_file_as_chunks(long_wav, DEFAULT_SAMPLE_RATE)) + assert len(chunks) == expected + + def test__each_chunk_at_most_max_frames(self, long_wav: Path) -> None: + """No chunk contains more than max_frames samples""" + chunks = list( + _chunking_preprocessor()._load_file_as_chunks(long_wav, DEFAULT_SAMPLE_RATE) + ) + for chunk in chunks: + # +1 tolerance for floating-point rounding in the decoder's resampler + assert chunk.shape[-1] <= DEFAULT_SAMPLE_RATE + 1 + + def test__chunks_are_1d_tensors(self, long_wav: Path) -> None: + """Chunks are 1D (mono, squeezed) tensors at the target sample rate""" + chunks = list( + _chunking_preprocessor()._load_file_as_chunks(long_wav, DEFAULT_SAMPLE_RATE) + ) + for chunk in chunks: + assert len(chunk.shape) == 1 + + def test__load_file_as_chunks_is_lazy(self, long_wav: Path) -> None: + """_load_file_as_chunks must return a generator, not a materialized list""" + result = _chunking_preprocessor()._load_file_as_chunks( + long_wav, DEFAULT_SAMPLE_RATE + ) + assert inspect.isgenerator(result) + + def test__chunking_triggered_by_byte_threshold(self, long_wav: Path) -> None: + """Threshold comparison uses byte size derived from file metadata""" + meta = AudioDecoder(long_wav).metadata + estimated_bytes = ( + int(meta.duration_seconds * meta.sample_rate) * meta.num_channels * 4 + ) + + eager = ASRPreprocessorMixin( + large_file_threshold_bytes=estimated_bytes + 1, + max_frames=DEFAULT_SAMPLE_RATE, + ) + chunked = ASRPreprocessorMixin( + large_file_threshold_bytes=estimated_bytes - 1, + max_frames=DEFAULT_SAMPLE_RATE, + ) + + assert len(list(eager._load_file_as_chunks(long_wav, DEFAULT_SAMPLE_RATE))) == 1 + assert ( + len(list(chunked._load_file_as_chunks(long_wav, DEFAULT_SAMPLE_RATE))) > 1 + ) + + def test__seg_i_contiguous_across_chunks( + self, long_wav: Path, tmp_path: Path + ) -> None: + """Segment output file indices are sequential even across chunk boundaries""" + preprocessor = _chunking_preprocessor() + output_dir = tmp_path / "segments" + output_dir.mkdir() + results = list( + preprocessor.preprocess_inputs([long_wav], output_dir=output_dir) + ) + indices = sorted(int(f.stem.rsplit("-", 1)[-1]) for f in output_dir.iterdir()) + assert indices == list(range(len(results))) + + def test__all_segments_share_index(self, long_wav: Path) -> None: + """All segments produced from one file have the same index""" + results = list(_chunking_preprocessor().preprocess_inputs([long_wav])) + assert all(r.metadata.index.audio == 0 for r in results) + + def test__two_large_files_have_distinct_orderings(self, long_wav: Path) -> None: + """Two chunked files produce segments with index 0 and 1""" + results = list(_chunking_preprocessor().preprocess_inputs([long_wav, long_wav])) + assert {r.metadata.index.audio for r in results} == {0, 1} + + def test__should_index_by_provided_id( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + """A ValueError during decoding (as, for instance, when there is no audio stream + is kept as an error record to preserve input ordering + """ + + def _raise(*_args, **_kwargs) -> Never: + raise ValueError( + "The best audio stream is unknown and there is no specified stream." + ) + + monkeypatch.setattr(torchcodec.decoders, "AudioDecoder", _raise) + + results = list( + ASRPreprocessorMixin().preprocess_inputs(["no_audio_stream.mp4"]) + ) + + assert len(results) == 1 + assert isinstance(results[0], Error) + error = results[0] + assert error.title == "UnreadableAudio" + + def test__decode_failure_yields_error_record_instead_of_raising( + self, long_wav: Path + ) -> None: + inputs = [("some_id", long_wav)] + results = list(_chunking_preprocessor().preprocess_inputs(inputs)) + assert all(r.metadata.index.audio == "some_id" for r in results) diff --git a/caul/tests/unit/test_asr_processors.py b/caul/tests/unit/test_asr_processors.py new file mode 100644 index 00000000..c6016f4a --- /dev/null +++ b/caul/tests/unit/test_asr_processors.py @@ -0,0 +1,188 @@ +from pathlib import Path + +import pytest +import torch +from caul.tasks.postprocessing.asr_postprocessor import PostprocessorMixin +from caul.tasks.preprocessing.asr_preprocessor import ASRPreprocessorMixin +from caul_core import FIREREDASR2_INFERENCE_MAX_FRAMES, ASRResult, SegmentIndex + + +class TestASRPreprocessor: + def setup_method(self) -> None: + self._preprocessor = ASRPreprocessorMixin() + + def test__short_audio_single_segment(self) -> None: + """Audio shorter than 60 seconds should produce exactly one segment""" + audio = [torch.zeros(FIREREDASR2_INFERENCE_MAX_FRAMES * 10)] # 10 s + + result = list(self._preprocessor.preprocess_inputs(audio)) + + assert len(result) == 1 + assert result[0].metadata.index == SegmentIndex(audio=0, segment=0) + + def test__long_audio_gets_segmented(self) -> None: + """Audio longer than 60 seconds must be split into multiple segments""" + # 70 s of silence — segment_by_silence will fall back to fixed splits + audio = [torch.zeros(FIREREDASR2_INFERENCE_MAX_FRAMES * 70)] + + result = list(self._preprocessor.preprocess_inputs(audio)) + + assert len(result) > 1 + for i, seg in enumerate(result): + assert seg.metadata.index == SegmentIndex(audio=0, segment=i) + + def test__multiple_inputs_ordering(self) -> None: + """Index must match the original list index""" + audio = [ + torch.zeros(FIREREDASR2_INFERENCE_MAX_FRAMES * 5), + torch.zeros(FIREREDASR2_INFERENCE_MAX_FRAMES * 3), + ] + + result = list(self._preprocessor.preprocess_inputs(audio)) + + orderings = [r.metadata.index for r in result] + assert orderings == [ + SegmentIndex(audio=0, segment=0), + SegmentIndex(audio=1, segment=0), + ] + + def test__write_wavs_to_fs(self, tmpdir: Path) -> None: + """When output_dir is provided, wav files are written to disk""" + output_dir = Path(tmpdir) + audio = [torch.zeros(FIREREDASR2_INFERENCE_MAX_FRAMES * 2)] + + result = list( + self._preprocessor.preprocess_inputs(audio, output_dir=output_dir) + ) + + assert len(result) == 1 + saved = output_dir / result[0].path + assert saved.exists() + + +class TestASRPostprocessor: + def setup_method(self) -> None: + self._postprocessor = PostprocessorMixin() + + def test__merges_segments(self) -> None: + """Segments belonging to the same index are merged in time order""" + results = [ + ASRResult( + index=SegmentIndex(audio=0, segment=0), + transcription=[(0.0, 1.0, "你好")], + score=0.9, + ), + ASRResult( + index=SegmentIndex(audio=0, segment=1), + transcription=[(1.0, 2.0, "世界")], + score=0.8, + ), + ] + + merged = list(self._postprocessor.process(results)) + + assert len(merged) == 1 + assert merged[0].index == SegmentIndex(audio=0, segment=0) + assert merged[0].transcription == [(0.0, 1.0, "你好"), (1.0, 2.0, "世界")] + + def test__multiple_inputs(self) -> None: + """Each unique index yields exactly one merged result""" + results = [ + ASRResult( + index=SegmentIndex(audio=0, segment=0), + transcription=[(0.0, 1.0, "你好")], + score=0.9, + ), + ASRResult( + index=SegmentIndex(audio=1, segment=0), + transcription=[(0.0, 2.0, "世界")], + score=0.8, + ), + ] + + merged = list(self._postprocessor.process(results)) + + assert len(merged) == 2 + expected_idx = [ + SegmentIndex(audio=0, segment=0), + SegmentIndex(audio=1, segment=0), + ] + assert [r.index for r in merged] == expected_idx + + def test__drops_empty_transcription_segments(self) -> None: + """Segments with empty transcriptions must be dropped before merging""" + results = [ + ASRResult( + index=SegmentIndex(audio=0, segment=0), transcription=[], score=1.0 + ), # silent segment + ASRResult( + index=SegmentIndex(audio=0, segment=1), + transcription=[(0.5, 1.5, "你好")], + score=0.9, + ), + ] + + merged = list(self._postprocessor.process(results)) + + assert len(merged) == 1 + assert merged[0].transcription == [(0.5, 1.5, "你好")] + + def test__raises_for_already_processed_audio(self) -> None: + """Non-contiguous ordering (interleaved groups) should raise ValueError""" + results = [ + ASRResult( + index=SegmentIndex(audio=0, segment=0), + transcription=[(0.0, 1.0, "a")], + score=1.0, + ), + ASRResult( + index=SegmentIndex(audio=1, segment=0), + transcription=[(0.0, 1.0, "b")], + score=1.0, + ), + ASRResult( + index=SegmentIndex(audio=0, segment=1), + transcription=[(1.0, 2.0, "a again")], + score=1.0, + ), + ] + + expected = "expected contiguous segments, already processed segments from audio" + with pytest.raises(ValueError, match=expected): + list(self._postprocessor.process(results)) + + def test__raises_for_unordered_segments_audio(self) -> None: + """Non-contiguous ordering (interleaved groups) should raise ValueError""" + results = [ + ASRResult( + index=SegmentIndex(audio=0, segment=1), + transcription=[(0.0, 1.0, "b")], + score=1.0, + ), + ASRResult( + index=SegmentIndex(audio=0, segment=0), + transcription=[(0.0, 1.0, "a")], + score=1.0, + ), + ] + + with pytest.raises( + ValueError, match="received audio segment results for audio 0 out of order" + ): + list(self._postprocessor.process(results)) + + def test__inputs_all_silent(self) -> None: + """An input whose segments are silent should yield an empty transcription""" + results = [ + ASRResult( + index=SegmentIndex(audio=0, segment=0), transcription=[], score=1.0 + ), + ASRResult( + index=SegmentIndex(audio=0, segment=1), transcription=[], score=1.0 + ), + ] + + merged = list(self._postprocessor.process(results)) + + assert len(merged) == 1 + assert merged[0].transcription == [] diff --git a/caul/tests/unit/test_config.py b/caul/tests/unit/test_config.py new file mode 100644 index 00000000..b2e525df --- /dev/null +++ b/caul/tests/unit/test_config.py @@ -0,0 +1,107 @@ +import pytest +from caul.tasks import ( + FasterWhisperInferenceRunner, + FireRedASR2InferenceRunner, + FireRedASR2Postprocessor, + FireRedASR2Preprocessor, + ParakeetInferenceRunner, + ParakeetPostprocessor, + ParakeetPreprocessor, +) +from caul_core import ( + ASRPipeline, + ASRPipelineConfig, + ParakeetInferenceRunnerConfig, + ParakeetPostprocessorConfig, + ParakeetPreprocessorConfig, +) + +_PARAKEET = """ +{ + "preprocessing": { + "model": "parakeet" + }, + "inference": { + "model": "parakeet" + }, + "postprocessing": { + "model": "parakeet" + } +} +""" + +_FIREREDASR2 = """ +{ + "preprocessing": { + "model": "fireredasr2_aed" + }, + "inference": { + "model": "fireredasr2_aed" + }, + "postprocessing": { + "model": "fireredasr2_aed" + } +} +""" + +_PARAKEET_WITH_WHISPER_INFERENCE = """ +{ + "preprocessing": { + "model": "parakeet" + }, + "inference": { + "model": "faster_whisper" + }, + "postprocessing": { + "model": "parakeet" + } +} +""" + + +@pytest.mark.parametrize( + ("pipeline_config_json", "expected_types"), + [ + ( + _PARAKEET, + [ParakeetPreprocessor, ParakeetInferenceRunner, ParakeetPostprocessor], + ), + ( + _PARAKEET_WITH_WHISPER_INFERENCE, + [ParakeetPreprocessor, FasterWhisperInferenceRunner, ParakeetPostprocessor], + ), + ( + _FIREREDASR2, + [ + FireRedASR2Preprocessor, + FireRedASR2InferenceRunner, + FireRedASR2Postprocessor, + ], + ), + ], +) +def test_asr_pipeline_from_config( + pipeline_config_json: str, expected_types: list[type] +) -> None: + # Given + pipeline_config = ASRPipelineConfig.model_validate_json(pipeline_config_json) + # When + pipeline = ASRPipeline.from_config(pipeline_config) + + # Then + for component, expected_type in zip(pipeline.tasks, expected_types, strict=True): + assert isinstance(component, expected_type) + + +def test_config_serde() -> None: + # Given + config = ASRPipelineConfig( + preprocessing=ParakeetPreprocessorConfig(), + inference=ParakeetInferenceRunnerConfig(), + postprocessing=ParakeetPostprocessorConfig(), + ) + # When + ser = config.model_dump_json(indent=2) + deser = ASRPipelineConfig.model_validate_json(ser) + # Then + assert deser == config diff --git a/caul/tests/unit/test_faster_whisper.py b/caul/tests/unit/test_faster_whisper.py new file mode 100644 index 00000000..33482f6c --- /dev/null +++ b/caul/tests/unit/test_faster_whisper.py @@ -0,0 +1,252 @@ +import math +from collections import namedtuple +from pathlib import Path + +import pytest +import torch +from caul.model_cache import cache_faster_whisper_models +from caul.tasks.inference.faster_whisper import FasterWhisperInferenceRunner +from caul_core import ( + DEFAULT_SAMPLE_RATE, + ASRResult, + FasterWhisperInferenceRunnerConfig, + FSProcessedSegment, + MemoryProcessedSegment, + SegmentIndex, + SegmentMetadata, +) +from faster_whisper import BatchedInferencePipeline +from faster_whisper.tokenizer import Tokenizer +from faster_whisper.transcribe import TranscriptionOptions +from huggingface_hub.constants import HF_HUB_CACHE +from numpy import ndarray +from torchcodec.encoders import AudioEncoder + +EN_TEXT_A = "hello" +EN_TEXT_B = "world" + +SEG_START_A, SEG_END_A, LOG_PROB_A = 0.0, 2.0, -0.2 +SEG_START_B, SEG_END_B, LOG_PROB_B = 2.0, 5.0, -0.3 + + +def _mock_raw_segment( + start: float, + end: float, + text: str, + avg_logprob: float, + *, + seek: int | None = None, + tokens: list[int] | None = None, + no_speech_prob: float = 0.5, + compression_ratio: float = 0.5, +) -> dict: + if tokens is None: + tokens = [] + return { + "start": start, + "end": end, + "seek": seek, + "text": text, + "avg_logprob": avg_logprob, + "tokens": tokens, + "no_speech_prob": no_speech_prob, + "compression_ratio": compression_ratio, + } + + +MOCK_RAW_SEGMENTS = [ + [_mock_raw_segment(SEG_START_A, SEG_END_A, EN_TEXT_A, LOG_PROB_A)], + [_mock_raw_segment(SEG_START_B, SEG_END_B, EN_TEXT_B, LOG_PROB_B)], +] + + +_MockSegment = namedtuple("_MockSegment", ["start", "end", "text", "avg_logprob"]) + +MOCK_SEGMENTS = [ + _MockSegment(s[0]["start"], s[0]["end"], s[0]["text"], s[0]["avg_logprob"]) + for s in MOCK_RAW_SEGMENTS +] + +_MOCK_OUTPUT_SEGMENTS = [ + _mock_raw_segment(SEG_START_A, SEG_END_A, EN_TEXT_A, LOG_PROB_A), + _mock_raw_segment(SEG_START_B, SEG_END_B, EN_TEXT_B, LOG_PROB_B), +] + + +class MockWhisperModelModel: + def __init__(self): + self.is_multilingual = True + + +class MockHfTokenizer: + def token_to_id(self, token: str) -> str: # noqa: ARG002 + return "task" + + +class _MockWhisperModel: + def __init__(self): + self.hf_tokenizer = MockHfTokenizer() + self.model = MockWhisperModelModel() + + # Mirrors the faster_whisper WhisperModel API + def feature_extractor( + self, + waveform: ndarray, + padding: int = 160, # noqa: ARG002 + chunk_length: int | None = None, # noqa: ARG002 + ) -> ndarray: + return waveform + + def detect_language(self, features: ndarray) -> tuple[str, float]: # noqa: ARG002 + return "en", 1.0 + + +class _MockBatchedInferencePipeline: + """Returns the same two-segment result for every input in the batch""" + + def __init__(self): + self.model = _MockWhisperModel() + + # Mirrors the faster_whisper BatchedInferencePipeline.forward signature + def forward( + self, + features: ndarray, # noqa: ARG002 + tokenizer: Tokenizer, # noqa: ARG002 + chunks_metadata: list[dict], + options: TranscriptionOptions, # noqa: ARG002 + ) -> list[list[dict]]: + return [_MOCK_OUTPUT_SEGMENTS for _ in range(len(chunks_metadata))] + + +class MockFasterWhisperInferenceRunner(FasterWhisperInferenceRunner): + def __enter__(self): + self._model = _MockBatchedInferencePipeline() + return self + + +def _fs_backed( + root: Path, + name: str = "audio.wav", + index: SegmentIndex | None = None, + duration_s: float = 2.0, +) -> FSProcessedSegment: + if index is None: + index = SegmentIndex() + path = root / name + audio = torch.zeros(1, int(duration_s * 16000)) + AudioEncoder(audio, sample_rate=DEFAULT_SAMPLE_RATE).to_file(path) + return FSProcessedSegment( + path=path, metadata=SegmentMetadata(duration_s=duration_s, index=index) + ) + + +def _memory_backed( + index: SegmentIndex | None = None, duration_s: float = 2.0 +) -> MemoryProcessedSegment: + if index is None: + index = SegmentIndex() + return MemoryProcessedSegment( + metadata=SegmentMetadata(duration_s=duration_s, index=index), + tensor=torch.zeros(int(duration_s * 16000)), + ) + + +class TestASRResultFromFasterWhisper: + def test__multiple_segments_build_transcription(self) -> None: + result = ASRResult.from_faster_whisper_result( + MOCK_SEGMENTS, index=SegmentIndex() + ) + + assert result.index == SegmentIndex() + assert result.transcription == [ + (SEG_START_A, SEG_END_A, EN_TEXT_A), + (SEG_START_B, SEG_END_B, EN_TEXT_B), + ] + + def test__score_is_duration_weighted_avg_logprob(self) -> None: + result = ASRResult.from_faster_whisper_result( + MOCK_SEGMENTS, index=SegmentIndex() + ) + + dur_a = SEG_END_A - SEG_START_A + dur_b = SEG_END_B - SEG_START_B + expected = math.exp((LOG_PROB_A * dur_a + LOG_PROB_B * dur_b) / (dur_a + dur_b)) + assert result.score == pytest.approx(expected, abs=1e-9) + + def test__single_segment(self) -> None: + seg = _MockSegment(1.0, 4.0, "only", -0.1) + result = ASRResult.from_faster_whisper_result( + [seg], index=SegmentIndex(audio=2) + ) + + assert result.transcription == [(1.0, 4.0, "only")] + assert result.score == pytest.approx(math.exp(-0.1), abs=1e-9) + + def test__empty_segments_give_empty_transcription_and_unit_score(self) -> None: + result = ASRResult.from_faster_whisper_result([], index=SegmentIndex()) + + assert result.transcription == [] + assert result.score == -1.0 + + +class TestFasterWhisperInferenceRunner: + def setup_method(self) -> None: + self._runner = MockFasterWhisperInferenceRunner() + + def test__yields_one_result_per_input_in_batch(self, tmp_path: Path) -> None: + inputs = [ + _fs_backed(tmp_path, "a.wav", index=SegmentIndex(audio=0)), + _fs_backed(tmp_path, "b.wav", index=SegmentIndex(audio=1)), + ] + with self._runner: + results = list(self._runner.process([inputs])) + + assert len(results) == 2 + assert all(isinstance(r, ASRResult) for r in results) + + def test__preserves_index(self, tmp_path: Path) -> None: + inputs = [ + _fs_backed(tmp_path, f"{i}.wav", index=SegmentIndex(audio=i)) + for i in range(3) + ] + with self._runner: + results = list(self._runner.process([inputs])) + expected = [SegmentIndex(audio=i) for i in range(3)] + assert [r.index for r in results] == expected + + def test__results_contain_transcription(self, tmp_path: Path) -> None: + inp = _fs_backed(tmp_path, index=SegmentIndex(audio=0)) + with self._runner: + results = list(self._runner.process([[inp]])) + + assert results[0].transcription == [ + (SEG_START_A, SEG_END_A, EN_TEXT_A), + (SEG_START_B, SEG_END_B, EN_TEXT_B), + ] + + def test__skips_empty_batches(self) -> None: + with self._runner: + results = list(self._runner.process([[], []])) + assert results == [] + + def test__multiple_batches_each_yield_results(self, tmp_path: Path) -> None: + batch_a = [_fs_backed(tmp_path, "a.wav", index=SegmentIndex(audio=0))] + batch_b = [_fs_backed(tmp_path, "b.wav", index=SegmentIndex(audio=1))] + with self._runner: + results = list(self._runner.process([batch_a, batch_b])) + + assert len(results) == 2 + + @pytest.mark.no_ci + def test_inference_runner_should_cache_to_dir_and_load_from_it(self) -> None: + # Given + # Let's use the local cache to avoid downloading for ages + cache_dir = HF_HUB_CACHE + # When + cache_faster_whisper_models(cache_dir) + runner = FasterWhisperInferenceRunner.from_config( + FasterWhisperInferenceRunnerConfig() + ) + with runner: + # Then + assert isinstance(runner._model, BatchedInferencePipeline) diff --git a/caul/tests/unit/test_fireredasr.py b/caul/tests/unit/test_fireredasr.py new file mode 100644 index 00000000..43819dae --- /dev/null +++ b/caul/tests/unit/test_fireredasr.py @@ -0,0 +1,145 @@ +from pathlib import Path + +import pytest +import torch +from caul.model_cache import cache_fireredasr2_models +from caul.tasks import FireRedASR2InferenceRunner, FireRedASR2Preprocessor +from caul_core import ( + FIREREDASR2_INFERENCE_MAX_FRAMES, + ASRResult, + ConstantSizeBatcherConfig, + FireRedASR2InferenceRunnerConfig, + SegmentIndex, +) +from fireredasr2s.fireredasr2 import FireRedAsr2 +from huggingface_hub.constants import HF_HUB_CACHE + +ZH_TEXT = "你好世界" +ZH_CONFIDENCE = 0.97 +ZH_TIMESTAMP = [("你", 0.1, 0.4), ("好", 0.4, 0.7), ("世", 0.7, 1.0), ("界", 1.0, 1.3)] + + +def _make_result(uttid: str, text: str = ZH_TEXT, dur_s: float = 2.0) -> dict: + return { + "uttid": uttid, + "text": text, + "confidence": ZH_CONFIDENCE, + "dur_s": dur_s, + "rtf": "0.05", + "wav": "dummy.wav", + "timestamp": ZH_TIMESTAMP, + } + + +class MockFireRedAsr2: + # Mirrors the FireRedAsr2.transcribe signature + def transcribe( + self, + batch_uttid: list[str], + batch_wav_path: list[str], # noqa: ARG002 + ) -> list[dict]: + return [_make_result(uid) for uid in batch_uttid] + + +class MockFireRedASR2InferenceRunner(FireRedASR2InferenceRunner): + def __enter__(self): + self._model = MockFireRedAsr2() + return self + + +# ASRResult.from_fireredasr2_result + + +class TestASRResultFromFireRedASR2: + def test__result_with_timestamps(self) -> None: + result = _make_result("utt0") + asr = ASRResult.from_fireredasr2_result(result, index=SegmentIndex(audio=0)) + + assert asr.index == SegmentIndex(audio=0) + assert len(asr.transcription) == 1 + start, end, text = asr.transcription[0] + assert start == pytest.approx(ZH_TIMESTAMP[0][1]) + assert end == pytest.approx(ZH_TIMESTAMP[-1][2]) + assert text == ZH_TEXT + assert asr.score == pytest.approx(ZH_CONFIDENCE, abs=1e-4) + + def test__result_without_timestamps(self) -> None: + result = {"uttid": "utt0", "text": ZH_TEXT, "confidence": 0.9, "dur_s": 3.5} + asr = ASRResult.from_fireredasr2_result(result, index=SegmentIndex(audio=1)) + + assert len(asr.transcription) == 1 + start, end, text = asr.transcription[0] + assert start == pytest.approx(0.0) + assert end == pytest.approx(3.5) + assert text == ZH_TEXT + + def test__result_empty_text(self) -> None: + result = {"uttid": "utt0", "text": "", "confidence": 0.5, "dur_s": 1.0} + asr = ASRResult.from_fireredasr2_result(result, index=SegmentIndex(audio=0)) + assert asr.transcription == [] + + def test__result_whitespace_only_text(self) -> None: + result = {"uttid": "utt0", "text": " ", "confidence": 0.5, "dur_s": 1.0} + asr = ASRResult.from_fireredasr2_result(result, index=SegmentIndex(audio=0)) + assert asr.transcription == [] + + +# Inference + + +class TestFireRedASR2InferenceRunner: + def setup_method(self) -> None: + mock_config = FireRedASR2InferenceRunnerConfig() + + self._inference_runner = MockFireRedASR2InferenceRunner(config=mock_config) + + def test__yields_asr_results(self, tmp_path: Path) -> None: + """Inference runner should yield one ASRResult per segment in the batch""" + preprocessor = FireRedASR2Preprocessor( + batcher=ConstantSizeBatcherConfig(batch_size=4) + ) + # Use audio shorter than max so each input produces exactly one segment + audio = [ + torch.zeros(FIREREDASR2_INFERENCE_MAX_FRAMES // 2), + torch.zeros(FIREREDASR2_INFERENCE_MAX_FRAMES // 2), + ] + + batches = list(preprocessor.process(audio, output_dir=tmp_path)) + + with self._inference_runner: + results = list(self._inference_runner.process(batches)) + + assert len(results) == 2 + assert all(isinstance(r, ASRResult) for r in results) + assert [r.index.audio for r in results] == [0, 1] + + def test__skips_empty_batches(self) -> None: + """Empty batches should not cause errors and should be silently skipped""" + with self._inference_runner: + results = list(self._inference_runner.process([[], []])) + assert results == [] + + @pytest.mark.no_ci + def test__cache_to_dir_and_load_from_it(self) -> None: + # Given + # Let's use the local cache to avoid downloading for ages + cache_dir = HF_HUB_CACHE + # When + cache_fireredasr2_models(cache_dir) + runner = FireRedASR2InferenceRunner.from_config( + FireRedASR2InferenceRunnerConfig() + ) + with runner: + # Then + assert isinstance(runner._model, FireRedAsr2) + + @pytest.mark.no_ci + def test__load_asr_without_cache(self) -> None: + # When + runner = FireRedASR2InferenceRunner.from_config( + FireRedASR2InferenceRunnerConfig() + ) + # When + with runner: + # Then + assert isinstance(runner._model, FireRedAsr2) diff --git a/caul/tests/unit/test_objects.py b/caul/tests/unit/test_objects.py new file mode 100644 index 00000000..f46000a3 --- /dev/null +++ b/caul/tests/unit/test_objects.py @@ -0,0 +1,10 @@ +import pytest +from caul_core import ASRModel + + +def test_all_asr_models_expose_languages() -> None: + for model in ASRModel: + try: + model.supported_languages() + except NotImplementedError: + pytest.fail(f"{model} does not expose supported languages") diff --git a/caul/tests/unit/test_parakeet.py b/caul/tests/unit/test_parakeet.py new file mode 100644 index 00000000..e0a29b46 --- /dev/null +++ b/caul/tests/unit/test_parakeet.py @@ -0,0 +1,185 @@ +from pathlib import Path + +import pytest +import torch +from caul.model_cache import cache_parakeet_models +from caul.tasks import ( + ParakeetInferenceRunner, + ParakeetPostprocessor, + ParakeetPreprocessor, +) +from caul.tasks.preprocessing.batcher import MaxDurationBatcher +from caul_core import ( + DEFAULT_SAMPLE_RATE, + ASRPipeline, + ASRResult, + ConstantSizeBatcherConfig, + MaxDurationBatcherConfig, + ParakeetInferenceRunnerConfig, + ParakeetPreprocessorConfig, + Preprocessor, + SegmentIndex, + TorchDevice, +) +from huggingface_hub.constants import HF_HUB_CACHE + + +def test__parakeet_preprocess_inputs_to_fs(tmpdir: Path) -> None: + # Given + output_dir = Path(tmpdir) + preprocessor = ParakeetPreprocessor() + audio = [torch.zeros([1])] + # When + result = list(preprocessor.preprocess_inputs(audio, output_dir=output_dir)) + # Then + assert len(result) == 1 + result = result[0] + save_path = output_dir / result.path + assert save_path.exists() + + +def test__parakeet_preprocessor_from_config_uses_configured_batcher() -> None: + # Given + config = ParakeetPreprocessorConfig(batcher=ConstantSizeBatcherConfig(batch_size=8)) + preprocessor = Preprocessor.from_config(config) + audio = [torch.zeros([DEFAULT_SAMPLE_RATE]) for _ in range(20)] + # When + batches = list(preprocessor.process(audio)) + # Then + assert [len(b) for b in batches] == [8, 8, 4] + + +def test__max_duration_batcher_caps_batch_size() -> None: + # Given + preprocessor = ParakeetPreprocessor() + audio = [torch.zeros([DEFAULT_SAMPLE_RATE]) for _ in range(5)] + config = MaxDurationBatcherConfig(batch_size=2) + # When + batches = list( + MaxDurationBatcher(preprocessor.preprocess_inputs(audio), config).batch() + ) + # Then + assert [len(b) for b in batches] == [2, 2, 1] + + +def test__parakeet_batching_unbatching() -> None: + """Test audio segmentation with batching (max length 20 minutes) and unbatching""" + preprocessor = ParakeetPreprocessor() + samples_per_min = DEFAULT_SAMPLE_RATE * 60 + + audio = [torch.zeros([samples_per_min * i]) for i in [12, 11, 5, 4, 7, 10, 30]] + + result = MaxDurationBatcher(preprocessor.preprocess_inputs(audio)).batch() + + expected = [ + [(SegmentIndex(audio=0, segment=0), 12.0)], + [ + (SegmentIndex(audio=1, segment=0), 11.0), + (SegmentIndex(audio=2, segment=0), 5.0), + (SegmentIndex(audio=3, segment=0), 4.0), + ], + [ + (SegmentIndex(audio=4, segment=0), 7.0), + (SegmentIndex(audio=5, segment=0), 10.0), + ], + [(SegmentIndex(audio=6, segment=0), 20.0)], + [(SegmentIndex(audio=6, segment=1), 10.0)], + ] + batches = [ + [(r.metadata.index, r.tensor.shape[-1] / samples_per_min) for r in re] + for re in result + ] + assert batches == expected + + +def test__parakeet_unbatching_should_raise_for_unordered_inputs() -> None: + # Given + postprocessor = ParakeetPostprocessor() + results = [ + ASRResult( + index=SegmentIndex(audio=2), transcription=[(1, 2, "two one")], score=2.1 + ), + ASRResult( + index=SegmentIndex(audio=0), transcription=[(0, 1, "zero")], score=0.0 + ), + ASRResult( + index=SegmentIndex(audio=2), transcription=[(2, 3, "two two")], score=2.2 + ), + ASRResult( + index=SegmentIndex(audio=1), transcription=[(0, 1, "one")], score=1.0 + ), + ] + + # When/Then + expected_msg = "expected contiguous segments" + with pytest.raises(ValueError, match=expected_msg): + list(postprocessor.process(results)) + + +def test__parakeet_unbatching() -> None: + """Test parakeet unbatching including reassembling segmented tensors""" + postprocessor = ParakeetPostprocessor() + + results = [ + ASRResult( + index=SegmentIndex(audio=1), transcription=[(0, 1, "one")], score=1.0 + ), + ASRResult( + index=SegmentIndex(audio=0), transcription=[(0, 1, "zero")], score=0.0 + ), + ASRResult( + index=SegmentIndex(audio=2, segment=0), + transcription=[(2, 3, "two two")], + score=2.2, + ), + ASRResult( + index=SegmentIndex(audio=2, segment=1), + transcription=[(1, 2, "two one")], + score=2.1, + ), + ] + + postprocessed_result = list(postprocessor.process(results)) + + expected = [ + results[0], + results[1], + ASRResult( + index=SegmentIndex(audio=2), + transcription=[(1, 2, "two one"), (2, 3, "two two")], + score=2.15, + ), + ] + expected = [r.model_dump() for r in expected] + postprocessed_result = [r.model_dump() for r in postprocessed_result] + scores = [r.pop("score") for r in postprocessed_result] + expected_scores = [expected.pop("score") for expected in expected] + assert postprocessed_result == expected + assert scores == pytest.approx(expected_scores, abs=1e-6) + + +def test__parakeet_device_setting() -> None: + """Test parakeet device setting""" + # Given + pipeline = ASRPipeline.parakeet(TorchDevice.CPU) + assert pipeline.tasks[1].device == TorchDevice.CPU + + # When + pipeline.device = TorchDevice.MPS + + # Then + assert pipeline.device == TorchDevice.MPS + assert pipeline.tasks[1].device == TorchDevice.MPS + + +@pytest.mark.no_ci +def test_parakeet_should_cache_to_dir_and_load_from_it() -> None: + # Given + # Let's use the local cache to avoid downloading for ages + cache_dir = HF_HUB_CACHE + # When + cache_parakeet_models(cache_dir) + runner = ParakeetInferenceRunner.from_config(ParakeetInferenceRunnerConfig()) + with runner: + # Then + assert isinstance(runner._model, torch.nn.Module) diff --git a/caul/tests/unit/test_parakeet_trt.py b/caul/tests/unit/test_parakeet_trt.py new file mode 100644 index 00000000..e72e9e08 --- /dev/null +++ b/caul/tests/unit/test_parakeet_trt.py @@ -0,0 +1,229 @@ +from unittest.mock import MagicMock, mock_open, patch + +import pytest +import torch +from caul.exception import TrtEngineLoadError +from caul.tasks.inference.parakeet_trt import ParakeetTrtInferenceRunner +from caul_core import PARAKEET_MODEL_REF +from omegaconf import OmegaConf + +_ENGINE_PATH = "/fake/encoder.trt" +_INFERENCE_HANDLER_PATH = "caul.tasks.inference.parakeet_trt.TrtInferenceHandler" +_PROCESS_TIMESTAMPS_PATH = ( + "nemo.collections.asr.parts.utils.timestamp_utils.process_timestamp_outputs" +) +_BATCH_SIZE = 2 +_SIGNAL_LEN = 16000 +_AUDIO_INPUT = torch.zeros(_BATCH_SIZE, _SIGNAL_LEN) +_ENC_OUT = torch.zeros((_BATCH_SIZE, 50, 256), dtype=torch.float32) +_ENC_OUT_LEN = torch.full((_BATCH_SIZE,), 50, dtype=torch.int32) + + +def _mock_inference_runner() -> ParakeetTrtInferenceRunner: + runner = ParakeetTrtInferenceRunner(PARAKEET_MODEL_REF, _ENGINE_PATH) + runner._encoder = MagicMock() + runner._decoder = MagicMock() + return runner + + +def _mock_trt_handler(enc_out: torch.Tensor, enc_len: torch.Tensor) -> MagicMock: + instance = MagicMock() + instance.infer.return_value = (enc_out, enc_len) + instance.__enter__ = MagicMock(return_value=instance) + instance.__exit__ = MagicMock(return_value=False) + return MagicMock(return_value=instance) + + +class TestParakeetTrtInferenceRunner: + def test__builds_length_tensor_from_audio_shape(self) -> None: + mock_inference_runner = _mock_inference_runner() + mock_trt_handler = _mock_trt_handler(_ENC_OUT, _ENC_OUT_LEN) + + with ( + patch(_INFERENCE_HANDLER_PATH, mock_trt_handler), + patch(_PROCESS_TIMESTAMPS_PATH), + ): + mock_inference_runner._transcribe( + _AUDIO_INPUT, trt_device=torch.device("cpu") + ) + + call_args = mock_trt_handler.return_value.infer.call_args[0][0] + input_signal_length = call_args["input_signal_length"] + assert input_signal_length.shape == (_BATCH_SIZE,) + assert input_signal_length.tolist() == [_SIGNAL_LEN] * _BATCH_SIZE + + def test__encoder_outputs_forwarded_to_decoder(self) -> None: + mock_inference_runner = _mock_inference_runner() + mock_trt_handler = _mock_trt_handler(_ENC_OUT, _ENC_OUT_LEN) + + with ( + patch(_INFERENCE_HANDLER_PATH, mock_trt_handler), + patch(_PROCESS_TIMESTAMPS_PATH), + ): + mock_inference_runner._transcribe( + _AUDIO_INPUT, trt_device=torch.device("cpu") + ) + + enc_out_arg, enc_len_arg = ( + mock_inference_runner._decoder.decoding.rnnt_decoder_predictions_tensor.call_args[ + 0 + ] + ) + assert torch.equal(enc_out_arg, _ENC_OUT) + assert torch.equal(enc_len_arg, _ENC_OUT_LEN) + + def test__returns_decoder_predictions(self) -> None: + mock_inference_runner = _mock_inference_runner() + mock_trt_handler = _mock_trt_handler(_ENC_OUT, _ENC_OUT_LEN) + expected = [MagicMock(), MagicMock()] + decoding = mock_inference_runner._decoder.decoding + decoding.rnnt_decoder_predictions_tensor.return_value = expected + + with ( + patch(_INFERENCE_HANDLER_PATH, mock_trt_handler), + patch(_PROCESS_TIMESTAMPS_PATH, side_effect=lambda hyps, *_: hyps), + ): + result = mock_inference_runner._transcribe( + _AUDIO_INPUT, trt_device=torch.device("cpu") + ) + + assert result is expected + + def test__converts_decoder_timestamps_to_seconds(self) -> None: + mock_inference_runner = _mock_inference_runner() + mock_trt_handler = _mock_trt_handler(_ENC_OUT, _ENC_OUT_LEN) + decoder = mock_inference_runner._decoder + decoder.encoder.subsampling_factor = 8 + decoder.cfg.preprocessor.window_stride = 0.01 + hypotheses = [MagicMock(), MagicMock()] + decoder.decoding.rnnt_decoder_predictions_tensor.return_value = hypotheses + processed = [MagicMock(), MagicMock()] + + with ( + patch(_INFERENCE_HANDLER_PATH, mock_trt_handler), + patch(_PROCESS_TIMESTAMPS_PATH, return_value=processed) as mock_process, + ): + result = mock_inference_runner._transcribe( + _AUDIO_INPUT, trt_device=torch.device("cpu") + ) + + mock_process.assert_called_once_with(hypotheses, 8, 0.01) + assert result is processed + + def test__requests_hypotheses_with_timestamps_from_decoder(self) -> None: + mock_inference_runner = _mock_inference_runner() + mock_trt_handler = _mock_trt_handler(_ENC_OUT, _ENC_OUT_LEN) + + with ( + patch(_INFERENCE_HANDLER_PATH, mock_trt_handler), + patch(_PROCESS_TIMESTAMPS_PATH), + ): + mock_inference_runner._transcribe( + _AUDIO_INPUT, trt_device=torch.device("cpu") + ) + + decoding = mock_inference_runner._decoder.decoding + call_kwargs = decoding.rnnt_decoder_predictions_tensor.call_args.kwargs + assert call_kwargs.get("return_hypotheses") is True + + def test__builds_length_tensor_from_original_audio_not_padded_audio(self) -> None: + mock_inference_runner = _mock_inference_runner() + mock_trt_handler = _mock_trt_handler(_ENC_OUT, _ENC_OUT_LEN) + short_audio = torch.zeros(_SIGNAL_LEN // 2) + long_audio = torch.zeros(_SIGNAL_LEN) + + with ( + patch(_INFERENCE_HANDLER_PATH, mock_trt_handler), + patch(_PROCESS_TIMESTAMPS_PATH), + ): + mock_inference_runner._transcribe( + [short_audio, long_audio], trt_device=torch.device("cpu") + ) + + call_args = mock_trt_handler.return_value.infer.call_args[0][0] + input_signal = call_args["input_signal"] + input_signal_length = call_args["input_signal_length"] + + assert input_signal.shape == (2, _SIGNAL_LEN) + assert input_signal_length.shape == (2,) + assert input_signal_length.tolist() == [_SIGNAL_LEN // 2, _SIGNAL_LEN] + + def test__loads_path_inputs_as_tensors(self) -> None: + mock_inference_runner = _mock_inference_runner() + mock_trt_handler = _mock_trt_handler(_ENC_OUT, _ENC_OUT_LEN) + loaded = [torch.zeros(_SIGNAL_LEN), torch.zeros(_SIGNAL_LEN // 2)] + + with ( + patch(_INFERENCE_HANDLER_PATH, mock_trt_handler), + patch(_PROCESS_TIMESTAMPS_PATH), + patch( + "caul.tasks.inference.parakeet_trt.load_audio", side_effect=loaded + ) as mock_load, + ): + mock_inference_runner._transcribe( + ["/fake/a.wav", "/fake/b.wav"], trt_device=torch.device("cpu") + ) + + assert [c.args[0] for c in mock_load.call_args_list] == [ + "/fake/a.wav", + "/fake/b.wav", + ] + call_args = mock_trt_handler.return_value.infer.call_args[0][0] + assert call_args["input_signal"].shape == (2, _SIGNAL_LEN) + assert call_args["input_signal_length"].tolist() == [ + _SIGNAL_LEN, + _SIGNAL_LEN // 2, + ] + + def test__raises_when_engine_fails_to_deserialize(self) -> None: + runner = ParakeetTrtInferenceRunner(PARAKEET_MODEL_REF, _ENGINE_PATH) + mock_trt = MagicMock(__version__="11.2.1.2") + mock_trt.Runtime.return_value.deserialize_cuda_engine.return_value = None + + with ( + patch( + "caul.tasks.inference.parakeet_trt.import_trt", return_value=mock_trt + ), + patch("builtins.open", mock_open(read_data=b"engine")), + pytest.raises(TrtEngineLoadError, match=r"encoder\.trt.*11\.2\.1\.2"), + ): + runner.__enter__() + + assert runner._decoder is None + + def test__restores_decoder_with_compute_timestamps(self) -> None: + runner = ParakeetTrtInferenceRunner(PARAKEET_MODEL_REF, _ENGINE_PATH) + config = OmegaConf.create({"decoding": {"strategy": "greedy_batch"}}) + OmegaConf.set_struct(config, value=True) + mock_restore = MagicMock(side_effect=[config, MagicMock()]) + + with ( + patch("caul.tasks.inference.parakeet_trt.import_trt"), + patch("builtins.open", mock_open(read_data=b"engine")), + patch("nemo.collections.asr.models.ASRModel.restore_from", mock_restore), + ): + runner.__enter__() + + config_call, model_call = mock_restore.call_args_list + assert config_call.kwargs["return_config"] is True + override = model_call.kwargs["override_config_path"] + assert override.decoding.compute_timestamps is True + + def test__pads_short_batches_to_engine_minimum(self) -> None: + mock_inference_runner = _mock_inference_runner() + mock_trt_handler = _mock_trt_handler(_ENC_OUT, _ENC_OUT_LEN) + short_audio = [torch.ones(1600), torch.ones(8000)] + + with ( + patch(_INFERENCE_HANDLER_PATH, mock_trt_handler), + patch(_PROCESS_TIMESTAMPS_PATH), + ): + mock_inference_runner._transcribe( + short_audio, trt_device=torch.device("cpu") + ) + + call_args = mock_trt_handler.return_value.infer.call_args[0][0] + input_signal = call_args["input_signal"] + assert input_signal.shape == (2, 16000) + assert input_signal[:, 8000:].abs().sum() == 0 + assert call_args["input_signal_length"].tolist() == [1600, 8000] diff --git a/caul/tests/unit/test_pipeline.py b/caul/tests/unit/test_pipeline.py new file mode 100644 index 00000000..170da96b --- /dev/null +++ b/caul/tests/unit/test_pipeline.py @@ -0,0 +1,22 @@ +from collections.abc import Callable + +import pytest +from caul_core import ASRPipeline, ASRPipelineConfig + + +@pytest.mark.parametrize( + "model", + ["parakeet", "fireredasr2"], +) +def test_asr_pipeline_config(model: str) -> None: + config = getattr(ASRPipelineConfig, model)() + assert isinstance(config, ASRPipelineConfig) + + +@pytest.mark.parametrize( + "pipeline", + [ASRPipeline.parakeet, ASRPipeline.fireredasr2], +) +def test_should_initialize_asr_pipeline(pipeline: Callable[[], ASRPipeline]) -> None: + instance = pipeline() + assert isinstance(instance, ASRPipeline) diff --git a/caul/tests/unit/test_segmenter.py b/caul/tests/unit/test_segmenter.py new file mode 100644 index 00000000..0d4183d6 --- /dev/null +++ b/caul/tests/unit/test_segmenter.py @@ -0,0 +1,477 @@ +from collections.abc import Callable +from unittest.mock import MagicMock + +import pytest +import torch +from caul.segmentation import ( + FixedSegmentationConfig, + PyannoteVoiceSegmentationConfig, + SegmentationConfig, + SilenceSegmentationConfig, + SileroVoiceSegmentationConfig, + TensorSegment, + segment_by_pyannote_vad, + segment_by_silence, + segment_by_silero_vad, + segment_fixed, +) +from caul.segmentation.segmenter import ( + AudioSegmenter, + PyannoteVoiceAudioSegmenter, + SileroVoiceAudioSegmenter, +) +from caul_core import DEFAULT_SAMPLE_RATE + +from tests.unit.constant import TEST_TIMESTAMPS + + +def make_silent_tensor( + duration_secs: float, sample_rate: int = DEFAULT_SAMPLE_RATE +) -> "torch.Tensor": + """Zero tensor treated as silence by librosa.effects.split""" + return torch.zeros(int(duration_secs * sample_rate)) + + +def make_noisy_tensor( + duration_secs: float, sample_rate: int = DEFAULT_SAMPLE_RATE +) -> "torch.Tensor": + """Noisy tensor with uniform random noise""" + return torch.rand(int(duration_secs * sample_rate)) + + +def make_voiced_tensor( + voice_durations_secs: list[float], + silence_between_secs: float = 1.5, + sample_rate: int = DEFAULT_SAMPLE_RATE, +) -> "torch.Tensor": + """Build a tensor alternating noise and silent regions""" + chunks = [] + for i, duration in enumerate(voice_durations_secs): + chunks.append(torch.rand(int(duration * sample_rate))) + if i < len(voice_durations_secs) - 1: + chunks.append(torch.zeros(int(silence_between_secs * sample_rate))) + return torch.cat(chunks) + + +def _make_silero_mock(speech_timestamps: list[dict]) -> tuple[MagicMock, MagicMock]: + vad_model = MagicMock() + vad_parser_fn = MagicMock(return_value=speech_timestamps) + return vad_model, vad_parser_fn + + +def _make_pyannote_mock(speech_intervals_s: list[tuple[float, float]]) -> MagicMock: + """Create a mock pyannote pipeline returning the given speech intervals (in s)""" + + class _Seg: + def __init__(self, start: float, end: float): + self.start = start + self.end = end + + segs = [_Seg(s, e) for s, e in speech_intervals_s] + timeline = MagicMock() + timeline.support.return_value = segs + annotation = MagicMock() + annotation.get_timeline.return_value = timeline + pipeline = MagicMock() + pipeline.return_value = annotation + return pipeline + + +# TensorSegment + + +class TestTensorSegment: + def test__duration_computed_correctly(self) -> None: + + seg = TensorSegment( + tensor=torch.zeros(16000), + segment_start=0, + segment_end=16000, + sample_rate=16000, + tensor_id="abc", + ) + assert seg.duration == 1 + + def test__lower_sample_rate(self) -> None: + + seg = TensorSegment( + tensor=torch.zeros(8000), + segment_start=0, + segment_end=8000, + sample_rate=8000, + tensor_id="abc", + ) + assert seg.duration == 1 + + +# segment_fixed + + +class TestSegmentFixed: + def test__single_chunk_shorter_than_segment_duration(self) -> None: + tensor = make_silent_tensor(5.0) + segments = segment_fixed(tensor, max_segment_len_s=10.0) + assert len(segments) == 1 + assert segments[0].segment_start == 0 + assert segments[0].segment_end == tensor.shape[-1] + + def test__exact_multiple_produces_correct_num_segments(self) -> None: + tensor = make_silent_tensor(10.0) + segments = segment_fixed(tensor, max_segment_len_s=5.0) + assert len(segments) == 2 + + def test__inexact_includes_remainder(self) -> None: + tensor = make_silent_tensor(11.0) + segments = segment_fixed(tensor, max_segment_len_s=5.0) + assert len(segments) == 3 + + def test__segments_are_contiguous_and_non_overlapping(self) -> None: + tensor = make_silent_tensor(13.0) + segments = segment_fixed(tensor, max_segment_len_s=5.0) + for i in range(1, len(segments)): + assert segments[i].segment_start == segments[i - 1].segment_end + + def test__tensor_content_matches_slice(self) -> None: + tensor = torch.arange(float(DEFAULT_SAMPLE_RATE * 3)) + segments = segment_fixed(tensor, max_segment_len_s=1.0) + for seg in segments: + assert torch.equal(seg.tensor, tensor[seg.segment_start : seg.segment_end]) + + def test__all_segments_share_tensor_id(self) -> None: + segments = segment_fixed(make_silent_tensor(6.0), max_segment_len_s=2.0) + tensor_ids = {s.tensor_id for s in segments} + assert len(tensor_ids) == 1 + + def test__separate_calls_produce_different_tensor_ids(self) -> None: + tensor = make_silent_tensor(3.0) + segments_a = segment_fixed(tensor, max_segment_len_s=1.0) + segments_b = segment_fixed(tensor, max_segment_len_s=1.0) + assert segments_a[0].tensor_id != segments_b[0].tensor_id + + +# segment_by_silence + + +class TestSegmentBySilence: + def test__all_silent_returns_single_segment(self) -> None: + """librosa.effects.split treats an all-zero tensor as one interval spanning + the full tensor rather than as pure silence, so we get one segment back + """ + tensor = make_silent_tensor(2.0) + segments = segment_by_silence(tensor) + assert len(segments) == 1 + assert segments[0].segment_start == 0 + assert segments[0].segment_end == tensor.shape[-1] + + def test__segments_do_not_exceed_max_length(self) -> None: + tensor = make_noisy_tensor(5.0) + max_secs = 2.0 + segments = segment_by_silence(tensor, max_segment_len_s=max_secs) + for seg in segments: + assert seg.duration <= max_secs + + def test__oversized_interval_splits_correctly(self) -> None: + tensor = make_noisy_tensor(6.0) + segments = segment_by_silence(tensor, max_segment_len_s=2.0) + assert len(segments) >= 3 + + def test__all_segments_share_tensor_id(self) -> None: + tensor = make_noisy_tensor(6.0) + segments = segment_by_silence(tensor, max_segment_len_s=2.0) + tensor_ids = {s.tensor_id for s in segments} + assert len(tensor_ids) == 1 + + def test__separate_calls_produce_different_tensor_ids(self) -> None: + tensor = make_noisy_tensor(2.0) + segments_a = segment_by_silence(tensor) + segments_b = segment_by_silence(tensor) + assert segments_a[0].tensor_id != segments_b[0].tensor_id + + def test__splits_voiced_tensor_into_one_segment_per_voice(self) -> None: + voice_durations = [1.0, 1.5, 0.8] + tensor = make_voiced_tensor(voice_durations) + segments = segment_by_silence(tensor) + assert len(segments) == len(voice_durations) + + def test__segment_durations_approximate_voice_regions(self) -> None: + voice_durations = [1.0, 1.5, 0.8] + silence_secs = 1.0 + tensor = make_voiced_tensor(voice_durations, silence_between_secs=silence_secs) + segments = segment_by_silence(tensor) + for seg, expected_secs in zip(segments, voice_durations, strict=True): + assert seg.duration == pytest.approx(expected_secs, abs=silence_secs) + + +class TestSegmentBySileroVad: + def test__returns_tensor_segment_per_timestamp(self) -> None: + timestamps = [{"start": 0, "end": 8000}, {"start": 16000, "end": 24000}] + model, vad_parser_fn = _make_silero_mock(timestamps) + segments = segment_by_silero_vad( + make_silent_tensor(2.0), vad_model=model, vad_parser_fn=vad_parser_fn + ) + + assert len(segments) == 2 + assert segments[0].segment_start == 0 + assert segments[0].segment_end == 8000 + assert segments[1].segment_start == 16000 + assert segments[1].segment_end == 24000 + + def test__no_speech_returns_empty(self) -> None: + model, vad_parser_fn = _make_silero_mock([]) + segments = segment_by_silero_vad( + make_silent_tensor(1.0), vad_model=model, vad_parser_fn=vad_parser_fn + ) + assert segments == [] + + def test__config_params_forwarded_to_model(self) -> None: + timestamps = [{"start": 0, "end": 4000}] + model, vad_parser_fn = _make_silero_mock(timestamps) + segment_by_silero_vad( + make_silent_tensor(1.0), + vad_model=model, + vad_parser_fn=vad_parser_fn, + sample_rate=8000, + threshold=0.7, + min_speech_duration_ms=500, + min_silence_duration_ms=200, + speech_pad_ms=50, + ) + call_kwargs = vad_parser_fn.call_args.kwargs + assert call_kwargs["sampling_rate"] == 8000 + assert call_kwargs["threshold"] == 0.7 + assert call_kwargs["min_speech_duration_ms"] == 500 + assert call_kwargs["min_silence_duration_ms"] == 200 + assert call_kwargs["speech_pad_ms"] == 50 + + def test__oversized_segment_is_split(self) -> None: + # One 4-second timestamp with a 2-second cap should produce 2 segments + timestamps = [{"start": 0, "end": DEFAULT_SAMPLE_RATE * 4}] + model, vad_parser_fn = _make_silero_mock(timestamps) + segments = segment_by_silero_vad( + make_silent_tensor(4.0), + vad_model=model, + vad_parser_fn=vad_parser_fn, + max_segment_len_s=2.0, + ) + assert len(segments) == 2 + assert segments[0].segment_end == DEFAULT_SAMPLE_RATE * 2 + assert segments[1].segment_start == DEFAULT_SAMPLE_RATE * 2 + + def test__segments_do_not_exceed_max_length(self) -> None: + timestamps = [{"start": 0, "end": DEFAULT_SAMPLE_RATE * 10}] + model, vad_parser_fn = _make_silero_mock(timestamps) + segments = segment_by_silero_vad( + make_silent_tensor(10.0), + vad_model=model, + vad_parser_fn=vad_parser_fn, + max_segment_len_s=3.0, + ) + for seg in segments: + assert seg.duration <= 3.0 + + def test__all_segments_share_tensor_id(self) -> None: + timestamps = [{"start": 0, "end": 8000}, {"start": 16000, "end": 24000}] + model, vad_parser_fn = _make_silero_mock(timestamps) + segments = segment_by_silero_vad( + make_silent_tensor(2.0), vad_model=model, vad_parser_fn=vad_parser_fn + ) + tensor_ids = {s.tensor_id for s in segments} + assert len(tensor_ids) == 1 + + def test__separate_calls_produce_different_tensor_ids(self) -> None: + timestamps = [{"start": 0, "end": 8000}] + model, vad_parser_fn = _make_silero_mock(timestamps) + tensor = make_silent_tensor(1.0) + segments_a = segment_by_silero_vad( + tensor, vad_model=model, vad_parser_fn=vad_parser_fn + ) + segments_b = segment_by_silero_vad( + tensor, vad_model=model, vad_parser_fn=vad_parser_fn + ) + assert segments_a[0].tensor_id != segments_b[0].tensor_id + + def test__segments_match_voiced_regions(self) -> None: + """Build a tensor with three voiced regions and derive sample-accurate + timestamps + """ + voice_durations = [1.0, 1.5, 0.8] + silence_secs = 0.6 + tensor = make_voiced_tensor(voice_durations, silence_between_secs=silence_secs) + + silence_samples = int(silence_secs * DEFAULT_SAMPLE_RATE) + timestamps = [] + cursor = 0 + for duration in voice_durations: + voice_samples = int(duration * DEFAULT_SAMPLE_RATE) + timestamps.append({"start": cursor, "end": cursor + voice_samples}) + cursor += voice_samples + silence_samples + + model, vad_parser_fn = _make_silero_mock(timestamps) + segments = segment_by_silero_vad( + tensor, vad_model=model, vad_parser_fn=vad_parser_fn + ) + + assert len(segments) == len(voice_durations) + for seg, ts in zip(segments, timestamps, strict=True): + assert seg.segment_start == ts["start"] + assert seg.segment_end == ts["end"] + assert torch.equal(seg.tensor, tensor[ts["start"] : ts["end"]]) + + +class TestSegmentByPyannoteVad: + def test__returns_tensor_segment_per_speech_interval(self) -> None: + intervals_s = [(0.0, 0.5), (1.0, 1.5)] + pipeline = _make_pyannote_mock(intervals_s) + tensor = make_silent_tensor(2.0) + segments = segment_by_pyannote_vad(tensor, pipeline=pipeline) + + assert len(segments) == 2 + assert segments[0].segment_start == 0 + assert segments[0].segment_end == int(0.5 * DEFAULT_SAMPLE_RATE) + assert segments[1].segment_start == int(1.0 * DEFAULT_SAMPLE_RATE) + assert segments[1].segment_end == int(1.5 * DEFAULT_SAMPLE_RATE) + + def test__no_speech_returns_empty(self) -> None: + pipeline = _make_pyannote_mock([]) + segments = segment_by_pyannote_vad(make_silent_tensor(1.0), pipeline=pipeline) + assert segments == [] + + def test__pipeline_instantiate_called_with_correct_params(self) -> None: + pipeline = _make_pyannote_mock([(0.0, 0.5)]) + segment_by_pyannote_vad( + make_silent_tensor(1.0), + pipeline=pipeline, + min_speech_duration_ms=200, + min_silence_duration_ms=150, + ) + call_args = pipeline.instantiate.call_args[0][0] + assert call_args["min_duration_on"] == pytest.approx(0.2) + assert call_args["min_duration_off"] == pytest.approx(0.15) + + def test__oversized_segment_is_split(self) -> None: + # One 4-second interval with a 2-second cap should produce 2 segments + pipeline = _make_pyannote_mock([(0.0, 4.0)]) + tensor = make_silent_tensor(4.0) + segments = segment_by_pyannote_vad( + tensor, pipeline=pipeline, max_segment_len_s=2.0 + ) + assert len(segments) == 2 + assert segments[0].segment_end == DEFAULT_SAMPLE_RATE * 2 + assert segments[1].segment_start == DEFAULT_SAMPLE_RATE * 2 + + def test__segments_do_not_exceed_max_length(self) -> None: + pipeline = _make_pyannote_mock([(0.0, 10.0)]) + segments = segment_by_pyannote_vad( + make_silent_tensor(10.0), pipeline=pipeline, max_segment_len_s=3.0 + ) + for seg in segments: + assert seg.duration <= 3.0 + + def test__all_segments_share_tensor_id(self) -> None: + pipeline = _make_pyannote_mock([(0.0, 0.5), (1.0, 1.5)]) + segments = segment_by_pyannote_vad(make_silent_tensor(2.0), pipeline=pipeline) + tensor_ids = {s.tensor_id for s in segments} + assert len(tensor_ids) == 1 + + def test__separate_calls_produce_different_tensor_ids(self) -> None: + tensor = make_silent_tensor(1.0) + segments_a = segment_by_pyannote_vad( + tensor, pipeline=_make_pyannote_mock([(0.0, 0.5)]) + ) + segments_b = segment_by_pyannote_vad( + tensor, pipeline=_make_pyannote_mock([(0.0, 0.5)]) + ) + assert segments_a[0].tensor_id != segments_b[0].tensor_id + + def test__tensor_content_matches_slice(self) -> None: + intervals_s = [(0.0, 0.5), (1.0, 1.5)] + pipeline = _make_pyannote_mock(intervals_s) + tensor = torch.arange(float(DEFAULT_SAMPLE_RATE * 2)) + segments = segment_by_pyannote_vad(tensor, pipeline=pipeline) + + for seg, (start_s, end_s) in zip(segments, intervals_s, strict=True): + start_sample = int(start_s * DEFAULT_SAMPLE_RATE) + end_sample = int(end_s * DEFAULT_SAMPLE_RATE) + assert torch.equal(seg.tensor, tensor[start_sample:end_sample]) + + +@AudioSegmenter.register("mock_silero") +class _MockSileroVADSegmenter(SileroVoiceAudioSegmenter): + def __init__( + self, config: SegmentationConfig, timestamps: list[dict] = TEST_TIMESTAMPS + ) -> None: + super().__init__(config) + self._timestamps = timestamps + + def _load_vad_model(self) -> tuple["torch.nn.Module", Callable]: + return _make_silero_mock(self._timestamps) + + +@AudioSegmenter.register("mock_pyannote") +class _MockPyannoteSegmenter(PyannoteVoiceAudioSegmenter): + def __init__( + self, + config: PyannoteVoiceSegmentationConfig, + intervals_s: list[tuple[float, float]] | None = None, + ) -> None: + super().__init__(config, hf_token="") + self._intervals_s = intervals_s or [] + + def _load_pipeline(self) -> MagicMock: + return _make_pyannote_mock(self._intervals_s) + + +class TestAudioSegmenter: + def test__dispatches_to_segment_fixed(self) -> None: + segmenter = AudioSegmenter.from_config( + FixedSegmentationConfig(max_segment_len_s=2.0) + ) + with segmenter: + segments = segmenter.segment(make_silent_tensor(5.0)) + + assert len(segments) == 3 + + def test__dispatches_to_segment_by_silence(self) -> None: + segmenter = AudioSegmenter.from_config(SilenceSegmentationConfig()) + tensor = make_voiced_tensor([1.0, 1.0]) + with segmenter: + segments = segmenter.segment(tensor) + assert len(segments) == 2 + + def test__dispatches_to_segment_by_silero_vad(self) -> None: + voice_durations = [1.0, 1.5, 0.8] + silence_secs = 0.6 + tensor = make_voiced_tensor(voice_durations, silence_between_secs=silence_secs) + + silence_samples = int(silence_secs * DEFAULT_SAMPLE_RATE) + timestamps = [] + cursor = 0 + for duration in voice_durations: + voice_samples = int(duration * DEFAULT_SAMPLE_RATE) + timestamps.append({"start": cursor, "end": cursor + voice_samples}) + cursor += voice_samples + silence_samples + + segmenter = _MockSileroVADSegmenter(SileroVoiceSegmentationConfig(), timestamps) + with segmenter: + segments = segmenter.segment(tensor) + + assert len(segments) == 3 + + def test__dispatches_to_segment_by_pyannote_vad(self) -> None: + intervals_s = [(0.0, 1.0), (2.0, 3.5), (4.0, 4.8)] + tensor = make_silent_tensor(5.0) + segmenter = _MockPyannoteSegmenter( + PyannoteVoiceSegmentationConfig(), intervals_s + ) + with segmenter: + segments = segmenter.segment(tensor) + assert len(segments) == 3 + + def test__context_manager_clears_vad_model_on_exit(self) -> None: + segmenter = _MockSileroVADSegmenter(SileroVoiceSegmentationConfig()) + with segmenter: + segmenter.segment(make_silent_tensor(1.0)) + assert segmenter._vad_model is not None + assert segmenter._vad_parser_fn is not None + assert segmenter._vad_model is None + assert segmenter._vad_parser_fn is None diff --git a/caul/tests/unit/test_trt_handler.py b/caul/tests/unit/test_trt_handler.py new file mode 100644 index 00000000..ae571f5b --- /dev/null +++ b/caul/tests/unit/test_trt_handler.py @@ -0,0 +1,57 @@ +from unittest.mock import MagicMock, patch + +import pytest +import torch +from caul.exception import TrtInputShapeError +from caul.trt.handler import TrtInferenceHandler + +_IMPORT_TRT_PATH = "caul.trt.handler.import_trt" +_DTYPES_MAP_PATH = "caul.trt.handler._dtypes_map" + + +def _mock_tensor(shape: tuple) -> MagicMock: + tensor = MagicMock() + tensor.contiguous.return_value.to.return_value.shape = shape + return tensor + + +def test_infer__raises_when_input_shape_outside_profile() -> None: + engine = MagicMock() + engine.get_tensor_profile_shape.return_value = ( + (1, 16000), + (4, 160000), + (4, 320000), + ) + handler = TrtInferenceHandler(engine) + handler._context = MagicMock() + handler._context.set_input_shape.return_value = False + + with ( + patch(_IMPORT_TRT_PATH), + patch(_DTYPES_MAP_PATH), + pytest.raises(TrtInputShapeError, match=r"\(4, 480000\).*max=\(4, 320000\)"), + ): + handler.infer({"input_signal": _mock_tensor((4, 480000))}) + + handler._context.execute_async_v3.assert_not_called() + + +def test_infer__casts_inputs_to_engine_dtype() -> None: + engine = MagicMock() + trt_int64 = object() + engine.get_tensor_dtype.return_value = trt_int64 + engine.get_tensor_profile_shape.return_value = ((1,), (4,), (8,)) + handler = TrtInferenceHandler(engine) + handler._context = MagicMock() + # stop right after the input loop + handler._context.set_input_shape.return_value = False + lengths = _mock_tensor((4,)) + + with ( + patch(_IMPORT_TRT_PATH), + patch(_DTYPES_MAP_PATH, return_value={trt_int64: torch.int64}), + pytest.raises(TrtInputShapeError), + ): + handler.infer({"input_signal_length": lengths}) + + assert lengths.contiguous.return_value.to.call_args.kwargs["dtype"] == torch.int64 diff --git a/caul/tests/unit/test_utils.py b/caul/tests/unit/test_utils.py new file mode 100644 index 00000000..0e72c42d --- /dev/null +++ b/caul/tests/unit/test_utils.py @@ -0,0 +1,79 @@ +from pathlib import Path +from unittest.mock import call, patch + +import pytest +import torch +from caul.utils import cache_hf_model, to_filesystem +from caul_core import MemoryProcessedSegment, SegmentMetadata + +_HF_HUB_DOWNLOAD_PATH = "huggingface_hub.hf_hub_download" +_GET_TOKEN_PATH = "huggingface_hub.get_token" +_HF_HUB_CACHE_PATH = "huggingface_hub.constants.HF_HUB_CACHE" + + +class TestCacheHfModel: + def test__forwards_args_through_cache_hf_model_file_to_hf_hub_download( + self, + ) -> None: + cache_dir = Path("/fake/cache") + models = ["org/model-a", "org/model-b"] + token = "fake-token" + + with ( + patch(_HF_HUB_CACHE_PATH, str(cache_dir)), + patch(_GET_TOKEN_PATH, return_value=token), + patch(_HF_HUB_DOWNLOAD_PATH) as mock_hf_hub_download, + ): + cache_hf_model( + model_family="parakeet", + models=models, + model_ext=".engine", + library_name="nemo", + cache_dir=cache_dir, + ) + + assert mock_hf_hub_download.call_args_list == [ + call( + repo_id="org/model-a", + filename="model-a.engine", + cache_dir=cache_dir, + library_name="nemo", + library_version=None, + force_download=False, + token=token, + ), + call( + repo_id="org/model-b", + filename="model-b.engine", + cache_dir=cache_dir, + library_name="nemo", + library_version=None, + force_download=False, + token=token, + ), + ] + + +def test_to_filesystem_should_raise_for_missing_output_dir() -> None: + # Given + pp_out = MemoryProcessedSegment( + metadata=SegmentMetadata(duration_s=1), tensor=torch.zeros([1]) + ) + batches = [pp_out] + + # When/Then + expected = "output_dir was not provided for MemoryProcessedSegment input" + with pytest.raises(ValueError, match=expected): + list(to_filesystem(batches, output_dir=None)) + + +def test_to_filesystem_should_handle_in_memory_tensor(tmpdir: Path) -> None: + # Given + pp_out = MemoryProcessedSegment( + metadata=SegmentMetadata(duration_s=1), tensor=torch.zeros([1]) + ) + batches = [pp_out] + + # When/Then + out = list(to_filesystem(batches, output_dir=tmpdir)) + assert isinstance(out, list) diff --git a/caul/tests/unit/test_whisper_trt.py b/caul/tests/unit/test_whisper_trt.py new file mode 100644 index 00000000..c69c1261 --- /dev/null +++ b/caul/tests/unit/test_whisper_trt.py @@ -0,0 +1,362 @@ +import contextlib +import math +import sys +from collections.abc import Callable +from unittest.mock import MagicMock, patch + +import torch + +for _mod in [ + "tensorrt_llm", + "tensorrt_llm.runtime", + "tensorrt_llm.runtime.session", + "tensorrt_llm._utils", + "tensorrt_llm.llmapi", + "tensorrt_llm.llmapi.kv_cache_type", + "tiktoken", +]: + sys.modules.setdefault(_mod, MagicMock()) + +sys.modules["tensorrt_llm._utils"].trt_dtype_to_torch = lambda _: torch.float32 +sys.modules["tensorrt_llm._utils"].torch_dtype_to_trt = MagicMock() + +# These imports must run after the tensorrt_llm stubs above are installed +from caul.tasks.inference.whisper_trt import WhisperTrtInferenceRunner # noqa: E402 +from caul.tasks.preprocessing.whisper_trt import WhisperTrtPreprocessor # noqa: E402 +from caul_core import ( # noqa: E402 + DEFAULT_SAMPLE_RATE, + WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR, + WHISPER_TRT_HOP_LENGTH, + WHISPER_TRT_MAX_MEL_PADDING_LEN, + WHISPER_TRT_N_FFT, + WHISPER_TRT_N_MELS, + MemoryProcessedSegment, + SegmentIndex, + SegmentMetadata, +) +from tensorrt_llm.runtime import SamplingConfig # noqa: E402 - mocked above + +_BATCH_SIZE = 2 +_PROMPT_IDS = [50258, 50259, 50360, 50363] # fake prompt token ids +_EOT_ID = 50257 +_MAX_NEW_TOKENS = 8 +_TEST_1D_TENSOR_A = 60 +_TEST_1D_TENSOR_B = 50 +_TEST_SIGNAL_LEN = 512 +_ENC_LEN_VAL = 25 + +_MEL_FILTER_PATH = "caul.tasks.preprocessing.whisper_trt.load_mel_filters" + +_STREAM_MOCK = MagicMock(cuda_stream=0) + + +def _mock_mel_filters_factory() -> Callable[[], torch.Tensor]: + def _load_mel_filters() -> torch.Tensor: + return torch.ones( + WHISPER_TRT_N_MELS, WHISPER_TRT_N_FFT // 2 + 1, dtype=torch.float32 + ) + + return _load_mel_filters + + +def _make_tokenizer_mock() -> MagicMock: + tokenizer = MagicMock() + tokenizer.encode.side_effect = lambda text, **_kw: ( + _PROMPT_IDS if "startoftranscript" in text else [_EOT_ID] + ) + tokenizer.decode.return_value = "return value" + tokenizer.special_tokens_set = set() + return tokenizer + + +def _make_runner( + encoder_factory: Callable[[], MagicMock] | None = None, + decoder_factory: Callable[[torch.cuda.Stream | None], MagicMock] | None = None, + max_new_tokens: int = _MAX_NEW_TOKENS, +) -> WhisperTrtInferenceRunner: + if encoder_factory is None: + encoder_factory = MagicMock() + + if decoder_factory is None: + decoder_factory = MagicMock() + + runner = WhisperTrtInferenceRunner( + encoder_factory=encoder_factory, + decoder_factory=decoder_factory, + tokenizer=_make_tokenizer_mock(), + max_new_tokens=max_new_tokens, + ) + runner._encoder = MagicMock() + runner._decoder = MagicMock() + runner._device = torch.device("cpu") + return runner + + +def _make_batch( + batch_size: int = _BATCH_SIZE, t: int = 100 +) -> list[MemoryProcessedSegment]: + return [ + MemoryProcessedSegment( + metadata=SegmentMetadata(index=SegmentIndex(audio=i), duration_s=1.0), + tensor=torch.zeros(1, WHISPER_TRT_N_MELS, t), + ) + for i in range(batch_size) + ] + + +class TestWhisperTrtPreprocessor: + @property + def _preprocessor(self) -> WhisperTrtPreprocessor: + return WhisperTrtPreprocessor( + n_mels=WHISPER_TRT_N_MELS, + mel_filters_factory=_mock_mel_filters_factory, + dtype="float32", + ) + + def test__output_shape_has_correct_mel_dim(self) -> None: + preprocessor = self._preprocessor + with preprocessor: + out = preprocessor._preprocess_segment(torch.zeros(DEFAULT_SAMPLE_RATE)) + assert out.ndim == 3 + assert out.shape[1] == WHISPER_TRT_N_MELS + + def test__time_dim_consistent_with_stft_frame_count(self) -> None: + """T must equal the number of STFT frames minus the last dropped frame.""" + audio = torch.zeros(DEFAULT_SAMPLE_RATE) + stft = torch.stft( + audio, + WHISPER_TRT_N_FFT, + WHISPER_TRT_HOP_LENGTH, + window=torch.hann_window(WHISPER_TRT_N_FFT), + return_complex=True, + ) + expected_t = stft.shape[-1] - 1 + out = self._preprocessor._preprocess_segment(audio) + assert out.shape[2] == expected_t + + def test__normalized_values_in_bounded_range(self) -> None: + """After (log10 + 4) / 4, values for real audio stay within bounded range""" + out = self._preprocessor._preprocess_segment(torch.randn(DEFAULT_SAMPLE_RATE)) + assert out.min() >= -2.0 + assert out.max() <= 3.0 + + +class TestInferenceRunnerSetup: + def test__sets_encoder_and_decoder_in__init__(self) -> None: + encoder_factory = MagicMock() + decoder_factory = MagicMock() + + runner = _make_runner( + encoder_factory=encoder_factory, decoder_factory=decoder_factory + ) + + assert runner._encoder_factory is encoder_factory + assert runner._decoder_factory is decoder_factory + + +class TestInferenceRunnerRunEncoder: + @staticmethod + def _make_inputs() -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + audio = torch.zeros(_BATCH_SIZE, WHISPER_TRT_N_MELS, _TEST_1D_TENSOR_A) + lens = torch.tensor([_TEST_1D_TENSOR_A, _TEST_1D_TENSOR_B], dtype=torch.int32) + positions = ( + torch.arange( + math.ceil(_TEST_1D_TENSOR_A / WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR), + dtype=torch.int32, + ) + .expand(_BATCH_SIZE, -1) + .contiguous() + ) + return audio, lens, positions + + def test__encoder_run_receives_all_three_input_keys(self) -> None: + runner = _make_runner() + audio, lens, positions = self._make_inputs() + runner._encoder.infer_shapes.return_value = [] + # Only the encoder inputs matter here, whatever happens downstream of run() + with contextlib.suppress(Exception): + runner._run_encoder(audio, lens, positions, stream=_STREAM_MOCK) + + inputs = runner._encoder.run.call_args[1]["inputs"] + assert set(inputs.keys()) == {"input_features", "input_lengths", "position_ids"} + + def test__output_lengths_equal_input_divided_by_downsampling_factor( + self, + ) -> None: + runner = _make_runner() + audio, lens, positions = self._make_inputs() + fake_encoder_output = MagicMock() + fake_encoder_output.name = "encoder_output" + fake_encoder_output.shape = ( + audio.shape[0], + audio.shape[-1] // WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR, + 256, + ) + runner._encoder.infer_shapes.return_value = [fake_encoder_output] + + _, output_lens = runner._run_encoder( + audio, lens, positions, stream=_STREAM_MOCK + ) + + assert torch.equal(output_lens, lens // WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR) + + def test__audio_inputs_len_are_padded_to_max_mel_padding_len(self) -> None: + runner = _make_runner() + t_a, t_b = _TEST_1D_TENSOR_A, _TEST_1D_TENSOR_B + inputs_a = torch.zeros(1, WHISPER_TRT_N_MELS, t_a) + inputs_b = torch.zeros(1, WHISPER_TRT_N_MELS, t_b) + batch = [ + MemoryProcessedSegment( + metadata=SegmentMetadata(index=SegmentIndex(audio=0), duration_s=1.0), + tensor=inputs_a, + ), + MemoryProcessedSegment( + metadata=SegmentMetadata(index=SegmentIndex(audio=1), duration_s=1.0), + tensor=inputs_b, + ), + ] + + enc_out = torch.zeros( + _BATCH_SIZE, + t_a // WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR, + _TEST_SIGNAL_LEN, + ) + enc_len = torch.tensor( + [ + t_a // WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR, + t_b // WHISPER_TRT_ENCODER_DOWNSAMPLING_FACTOR, + ], + dtype=torch.int32, + ) + runner._run_encoder = MagicMock(return_value=(enc_out, enc_len)) + runner._run_decoder = MagicMock( + return_value=torch.zeros(_BATCH_SIZE, 1, _MAX_NEW_TOKENS, dtype=torch.int32) + ) + list(runner.process([batch])) + + actual_lens = runner._run_encoder.call_args[0][1] + assert actual_lens.tolist() == [ + WHISPER_TRT_MAX_MEL_PADDING_LEN, + WHISPER_TRT_MAX_MEL_PADDING_LEN, + ] + + +class TestInferenceRunnerRunDecoder: + def _run_decoder( + self, + runner: WhisperTrtInferenceRunner, + enc_out: torch.Tensor, + enc_len: torch.Tensor, + ) -> torch.Tensor: + runner._decoder.decode.return_value = torch.zeros( + enc_len.shape[0], WHISPER_TRT_N_MELS, _MAX_NEW_TOKENS, dtype=torch.int32 + ) + with ( + patch.object(torch.Tensor, "cuda", lambda self: self), + ): + return runner._run_decoder(enc_out, enc_len) + + @staticmethod + def _encoder() -> tuple[torch.Tensor, torch.Tensor]: + enc_out = torch.zeros(_BATCH_SIZE, _ENC_LEN_VAL, _TEST_SIGNAL_LEN) + enc_len = torch.tensor([_ENC_LEN_VAL] * _BATCH_SIZE, dtype=torch.int32) + return enc_out, enc_len + + def test__setup_called_with_correct_args(self) -> None: + runner = _make_runner(max_new_tokens=_MAX_NEW_TOKENS) + self._run_decoder(runner, *self._encoder()) + args = runner._decoder.setup.call_args[0] + assert args[0] == _BATCH_SIZE + assert args[2] == _MAX_NEW_TOKENS + + def test__prompt_inputs_shape_is_batch_size_x_prompt_len(self) -> None: + runner = _make_runner() + self._run_decoder(runner, *self._encoder()) + + prompt_inputs = runner._decoder.decode.call_args[0][0] + prompt_len = len(_PROMPT_IDS) + assert prompt_inputs.shape == (_BATCH_SIZE, prompt_len) + + def test__cross_attention_mask_third_dim_is_encoder_length_only(self) -> None: + runner = _make_runner() + self._run_decoder(runner, *self._encoder()) + + mask = runner._decoder.decode.call_args[1]["cross_attention_mask"] + prompt_len = len(_PROMPT_IDS) + expected = (_BATCH_SIZE, prompt_len + _MAX_NEW_TOKENS, _ENC_LEN_VAL) + assert mask.shape == expected + + def test__encoder_output_forwarded_to_decode(self) -> None: + runner = _make_runner() + enc_out, enc_len = self._encoder() + self._run_decoder(runner, enc_out, enc_len) + + kw = runner._decoder.decode.call_args[1] + assert torch.equal(kw["encoder_output"], enc_out) + assert torch.equal(kw["encoder_input_lengths"], enc_len) + + def test__sampling_config_uses_eot_as_end_id(self) -> None: + runner = _make_runner() + self._run_decoder(runner, *self._encoder()) + + # SamplingConfig is mocked; verify it was called with the right end_id + SamplingConfig.assert_called_with( + end_id=_EOT_ID, + pad_id=_EOT_ID, + num_beams=runner._decoder_config.beam_width, + ) + + +class TestInferenceRunnerProcess: + @staticmethod + def _inference_runner() -> WhisperTrtInferenceRunner: + runner = _make_runner() + enc_out = torch.zeros(_BATCH_SIZE, _TEST_1D_TENSOR_B, _TEST_SIGNAL_LEN) + enc_len = torch.tensor([_TEST_1D_TENSOR_B] * _BATCH_SIZE, dtype=torch.int32) + runner._run_encoder = MagicMock(return_value=(enc_out, enc_len)) + runner._run_decoder = MagicMock( + return_value=torch.zeros(_BATCH_SIZE, 1, _MAX_NEW_TOKENS, dtype=torch.int32) + ) + return runner + + def test__empty_batch_size_yields_no_results(self) -> None: + results = list(self._inference_runner().process([[]])) + assert results == [] + + def test__yields_one_result_per_batch_item(self) -> None: + results = list(self._inference_runner().process([_make_batch(_BATCH_SIZE)])) + assert len(results) == _BATCH_SIZE + + def test__index_preserved_in_results(self) -> None: + results = list(self._inference_runner().process([_make_batch(_BATCH_SIZE)])) + assert [r.index.audio for r in results] == list(range(_BATCH_SIZE)) + + def test__transcription_entries_are_start_end_text_tuples(self) -> None: + results = list(self._inference_runner().process([_make_batch(_BATCH_SIZE)])) + for result in results: + for entry in result.transcription: + assert isinstance(entry, tuple) + assert len(entry) == 3 + assert isinstance(entry[2], str) + + def test__run_encoder_called_once_per_batch(self) -> None: + runner = self._inference_runner() + list(runner.process([_make_batch(_BATCH_SIZE)])) + runner._run_encoder.assert_called_once() + + def test__run_decoder_receives_encoder_output(self) -> None: + runner = self._inference_runner() + list(runner.process([_make_batch(_BATCH_SIZE)])) + enc_out_arg = runner._run_decoder.call_args[0][0] + assert enc_out_arg.shape[0] == _BATCH_SIZE + + def test__multiple_batches_each_produce_results(self) -> None: + runner = _make_runner() + enc_out = torch.zeros(1, _TEST_1D_TENSOR_B, _TEST_SIGNAL_LEN) + enc_len = torch.tensor([_TEST_1D_TENSOR_B], dtype=torch.int32) + runner._run_encoder = MagicMock(return_value=(enc_out, enc_len)) + runner._run_decoder = MagicMock( + return_value=torch.zeros(1, 1, _MAX_NEW_TOKENS, dtype=torch.int32) + ) + results = 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b/qa/ruff.toml index adc7d02f..e7f93e2f 100644 --- a/qa/ruff.toml +++ b/qa/ruff.toml @@ -55,4 +55,12 @@ ignore = [ "RET504", # unnecessary-assign ] [lint.flake8-tidy-imports] -ban-relative-imports = "parents" \ No newline at end of file +ban-relative-imports = "parents" + +[lint.per-file-ignores] +# caul lazily imports its optional heavy dependencies (torch, nemo, tensorrt, ...) so that a base +# install works without them +"**/caul/caul/**" = ["PLC0415"] +"**/caul-core/caul_core/**" = ["PLC0415"] +# Task implementations override base class interfaces and don't use every argument +"**/caul/caul/tasks/**" = ["ARG002", "ARG003"] diff --git a/scripts/lock-dist.sh b/scripts/lock-dist.sh index 33543ffd..634eb39e 100755 --- a/scripts/lock-dist.sh +++ b/scripts/lock-dist.sh @@ -8,7 +8,9 @@ else fi shift 1 +# Local monorepo packages are resolved from PyPI in the dist lock +no_sources=(--no-sources-package datashare-python --no-sources-package caul --no-sources-package caul-core) # uv bug -n flag to discard cache takes 2 times to work -uv lock "$@" -n --no-sources-package datashare-python || uv lock "$@" -n --no-sources-package datashare-python +uv lock "$@" -n "${no_sources[@]}" || uv lock "$@" -n "${no_sources[@]}" cp uv.lock uv.dist.lock uv lock \ No newline at end of file diff --git a/workers/asr-worker/pyproject.toml b/workers/asr-worker/pyproject.toml index d62129e0..e02b60e7 100644 --- a/workers/asr-worker/pyproject.toml +++ b/workers/asr-worker/pyproject.toml @@ -8,7 +8,7 @@ authors = [ { name = "Lion Summerbell", email = "lsummerbell@icij.org" } ] readme = "README.md" -requires-python = ">=3.11.0, <3.13" +requires-python = ">=3.12.0, <3.13" dependencies = [ "datashare-python~=0.12.3", "caul-core==0.6.6", @@ -93,6 +93,8 @@ torchcodec = [ { index = "pytorch-cpu", extra = "cpu" }, ] datashare-python = { path = "../../datashare-python", editable = true } +caul = { path = "../../caul", editable = true } +caul-core = { path = "../../caul-core", editable = true } [tool.uv] diff --git a/workers/asr-worker/uv.dist.lock b/workers/asr-worker/uv.dist.lock index 020fd93f..7c1297c9 100644 --- a/workers/asr-worker/uv.dist.lock +++ b/workers/asr-worker/uv.dist.lock @@ -1,11 +1,9 @@ version = 1 revision = 3 -requires-python = ">=3.11.0, <3.13" +requires-python = "==3.12.*" resolution-markers = [ - "python_full_version >= '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux'", - "python_full_version < '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux'", + "platform_machine == 'arm64' and sys_platform == 'darwin'", + "platform_machine == 'x86_64' and sys_platform == 'linux'", ] supported-markers = [ "platform_machine == 'arm64' and sys_platform == 'darwin'", @@ -16,9 +14,6 @@ conflicts = [[ { package = "datashare-asr-worker", extra = 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== 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "cuda-bindings", marker = "sys_platform == 'linux'" }, + { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform == 'linux'" }, + { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform == 'linux'" }, + { name = "torchcodec" }, ] inference = [ - { name = "caul", extra = ["fireredasr2", "nemo", "parakeet-trt"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 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'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, + { name = "caul" }, ] [package.dev-dependencies] dev = [ - { name = "black", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "nest-asyncio", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "pre-commit", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 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== 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "typing-extensions", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, + { name = "black" }, + { name = "nest-asyncio" }, + { name = "pre-commit" }, + { name = "psutil" }, + { name = "pylint" }, + { name = "pytest" }, + { name = "pytest-asyncio" }, + { name = "pytest-timeout" }, + { name = "redis", extra = ["hiredis"] }, + { name = "ruff" }, + { name = "typing-extensions" }, ] [package.metadata] @@ -562,19 +526,19 @@ name = "datashare-python" version = "0.12.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "aiofile", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "aiohttp", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 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{ name = "hatchling", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "icij-common", extra = ["elasticsearch"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 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"lru-dict" }, + { name = "pydantic-extra-types", extra = ["pycountry"] }, + { name = "python-json-logger" }, + { name = "pyyaml" }, + { name = "temporalio" }, + { name = "tomlkit" }, + { name = "typer" }, ] sdist = { url = "https://files.pythonhosted.org/packages/0d/8d/e4048e682f855dd0b606f7a79931a87c7e2045f35d7de78099c8a0840295/datashare_python-0.12.3.tar.gz", hash = "sha256:4156aa1bb71b1ccfc89d7eb298f2f9a215b65dd4107be3cca84568ceb6416fd9", size = 332174, upload-time = "2026-10-08T16:03:45.838Z" } wheels = [ @@ -622,8 +586,8 @@ name = "elasticsearch" version = "7.17.13" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "certifi", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 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'extra-20-datashare-asr-worker-cpu') or (extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, + { name = "torchaudio", version = "2.10.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu') or (extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, + { name = "torchaudio", version = "2.10.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu') or (extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, + { name = "torchcodec" }, ] gpu = [ - { name = "cuda-bindings", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torchcodec", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "cuda-bindings", marker = "sys_platform == 'linux'" }, + { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform == 'linux'" }, + { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform == 'linux'" }, + { name = "torchcodec" }, ] inference = [ - { name = "caul", extra = ["fireredasr2", "nemo", "parakeet-trt"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "fireredasr2s", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, + { name = "caul", extra = ["fireredasr2", "nemo", "parakeet-trt"] }, + { name = "fireredasr2s" }, ] preprocessing = [ - { name = "caul", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, + { name = "caul" }, ] [package.dev-dependencies] dev = [ - { name = "black", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "nest-asyncio", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "pre-commit", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "psutil", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "pylint", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "pytest", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "pytest-asyncio", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "pytest-timeout", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "redis", extra = ["hiredis"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "ruff", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "typing-extensions", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, + { name = "black" }, + { name = "nest-asyncio" }, + { name = "pre-commit" }, + { name = "psutil" }, + { name = "pylint" }, + { name = "pytest" }, + { name = "pytest-asyncio" }, + { name = "pytest-timeout" }, + { name = "redis", extra = ["hiredis"] }, + { name = "ruff" }, + { name = "typing-extensions" }, ] [package.metadata] requires-dist = [ - { name = "caul", marker = "extra == 'preprocessing'", specifier = "==0.13.13" }, - { name = "caul", extras = ["fireredasr2", "nemo", "parakeet-trt"], marker = "extra == 'inference'", specifier = "==0.13.13" }, - { name = "caul-core", specifier = "==0.6.6" }, + { name = "caul", marker = "extra == 'preprocessing'", editable = "../../caul" }, + { name = "caul", extras = ["fireredasr2", "nemo", "parakeet-trt"], marker = "extra == 'inference'", editable = "../../caul" }, + { name = "caul-core", editable = "../../caul-core" }, { name = "cuda-bindings", marker = "sys_platform == 'linux' and extra == 'gpu'", specifier = "==12.9.4" }, { name = "datashare-python", editable = "../../datashare-python" }, { name = "fireredasr2s", marker = "extra == 'inference'" }, @@ -562,19 +562,19 @@ name = "datashare-python" version = "0.12.3" source = { editable = "../../datashare-python" } dependencies = [ - { name = "aiofile", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "aiohttp", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "alive-progress", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "hatchling", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "icij-common", extra = ["elasticsearch"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "langcodes", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "lru-dict", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "pydantic-extra-types", extra = ["pycountry"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "python-json-logger", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "pyyaml", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "temporalio", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "tomlkit", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, - { name = "typer", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu')" }, + { name = "aiofile" }, + { name = "aiohttp" }, + { name = "alive-progress" }, + { name = "hatchling" }, + { name = "icij-common", extra = ["elasticsearch"] }, + { name = "langcodes" }, + { name = "lru-dict" }, + { name = "pydantic-extra-types", extra = ["pycountry"] }, + { name = "python-json-logger" }, + { name = "pyyaml" }, + { name = "temporalio" }, + { name = "tomlkit" }, + { name = "typer" }, ] [package.metadata] @@ -650,8 +650,8 @@ name = "elasticsearch" version = "7.17.13" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "certifi", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 'extra-20-datashare-asr-worker-gpu') or (sys_platform == 'linux' and extra == 'extra-20-datashare-asr-worker-cpu' and extra == 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name = "propcache" }, + { name = "yarl" }, ] sdist = { url = "https://files.pythonhosted.org/packages/58/d9/22ce5786ac0c1653ae8b6c23bded02c1686d11f0dbb45b31ce128e0df985/aiohttp-3.14.3.tar.gz", hash = "sha256:9491196535a88924a60afd5b5f434b5b203b6cc616250878dbdb223a8f7844bc", size = 7971213, upload-time = "2026-07-23T01:57:27.037Z" } wheels = [ @@ -133,7 +130,7 @@ name = "aiosignal" version = "1.4.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "frozenlist", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 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"nvidia-cublas-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cudart = [ - { name = "nvidia-cuda-runtime-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-runtime-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cufft = [ - { name = "nvidia-cufft-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cufft-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cufile = [ - { name = "nvidia-cufile-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cufile-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cupti = [ - { name = "nvidia-cuda-cupti-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-cupti-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] curand = [ - { name = "nvidia-curand-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-curand-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cusolver = [ - { name = "nvidia-cusolver-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cusolver-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cusparse = [ - { name = "nvidia-cusparse-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cusparse-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvjitlink = [ - { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvjitlink-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvrtc = [ - { name = "nvidia-cuda-nvrtc-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-nvrtc-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvtx = [ - { name = "nvidia-nvtx-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvtx-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] [[package]] @@ -600,43 +597,43 @@ wheels = [ [package.optional-dependencies] cublas = [ - { name = "nvidia-cublas", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-cuda-nvrtc", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cublas", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-nvrtc", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cudart = [ - { name = "nvidia-cuda-runtime", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-runtime", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cufft = [ - { name = "nvidia-cufft", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-nvjitlink", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cufft", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvjitlink", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cufile = [ - { name = "nvidia-cufile", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cufile", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cupti = [ - { name = "nvidia-cuda-cupti", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-cupti", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] curand = [ - { name = "nvidia-curand", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-curand", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cusolver = [ - { name = "nvidia-cublas", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-cusolver", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-cusparse", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-nvjitlink", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cublas", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cusolver", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cusparse", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvjitlink", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cusparse = [ - { name = "nvidia-cusparse", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-nvjitlink", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cusparse", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvjitlink", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvjitlink = [ - { name = "nvidia-nvjitlink", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvjitlink", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvrtc = [ - { name = "nvidia-cuda-nvrtc", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-nvrtc", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvtx = [ - { name = "nvidia-nvtx", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvtx", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] [[package]] @@ -663,50 +660,50 @@ wheels = [ name = "datashare-extract-worker" source = { editable = "." } dependencies = [ - { name = "datashare-python", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "extract-core", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "datashare-python" }, + { name = "extract-core" }, ] [package.optional-dependencies] base = [ - { name = "extract-python", extra = ["docling", "marker"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-base') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-24-datashare-extract-worker-base') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra != 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "opencv-python-headless", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "extract-python", extra = ["docling", "marker"], marker = "extra == 'extra-24-datashare-extract-worker-base' or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "opencv-python-headless" }, ] cpu = [ - { name = "torch", version = "2.11.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "torch", version = "2.11.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "torchvision", version = "0.26.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "torchvision", version = "0.26.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torch", version = "2.11.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torch", version = "2.11.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torchvision", version = "0.26.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torchvision", version = "0.26.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] docling-gpu = [ - { name = "extract-python", extra = ["docling-gpu"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "extract-python", extra = ["docling-gpu"] }, ] flash-attn = [ - { name = "flash-attn", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "flash-attn" }, ] gpu = [ - { name = "cuda-bindings", version = "12.9.4", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "torch", version = "2.11.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "torchvision", version = "0.26.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "cuda-bindings", version = "12.9.4", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'linux' or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torch", version = "2.11.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform == 'linux' or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torchvision", version = "0.26.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform == 'linux' or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] mineru = [ - { name = "extract-python", extra = ["mineru"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'darwin' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'darwin' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "opencv-python-headless", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "extract-python", extra = ["mineru"], marker = "(extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "opencv-python-headless" }, ] [package.dev-dependencies] dev = [ - { name = "black", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "extract-python", extra = ["docling"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "nest-asyncio", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or 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'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "scikit-image", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 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sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "tesserocr", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "pandas" }, + { name = "tesserocr" }, ] models-onnxruntime = [ - { name = "onnxruntime", marker = "sys_platform == 'darwin' or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "onnxruntime-gpu", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "onnxruntime", marker = "sys_platform != 'linux' or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "onnxruntime-gpu", marker = "sys_platform == 'linux' or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, ] models-vlm-inline = [ - { name = "accelerate", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "einops", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 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sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "qwen-vl-utils", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or 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name = "typer" }, + { name = "websockets" }, ] [[package]] @@ -988,31 +985,31 @@ name = "docvortex" version = "0.5.8" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "beautifulsoup4", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "click", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = 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'x86_64' and sys_platform == 'linux'", + "python_full_version < '3.14' and sys_platform == 'darwin'", "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'", ] sdist = { url = "https://files.pythonhosted.org/packages/8e/5f/bd69653fbfb76cf8604468d3b4ec4c403197144c7bfe0e6a5fc9e02a07cb/regex-2024.11.6.tar.gz", hash = "sha256:7ab159b063c52a0333c884e4679f8d7a85112ee3078fe3d9004b2dd875585519", size = 399494, upload-time = "2024-11-06T20:12:31.635Z" } @@ -3572,8 +3569,8 @@ version = "2026.9.29" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'darwin'", - "python_full_version < '3.14' and sys_platform == 'darwin'", "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'", + "python_full_version < '3.14' and sys_platform == 'darwin'", "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'", ] sdist = { url = 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'extra-24-datashare-extract-worker-gpu')" }, + { name = "rl-accel" }, ] [[package]] @@ -3618,10 +3615,10 @@ name = "requests" version = "2.34.2" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "certifi", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "charset-normalizer", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or 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'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "pillow", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torch", version = "2.11.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 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'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "idna" }, + { name = "multidict" }, + { name = "propcache" }, ] sdist = { url = "https://files.pythonhosted.org/packages/75/16/e8be8e2fb175bbf41a0680381a319f1199fae256588241a2ac8677eafb49/yarl-1.25.1.tar.gz", hash = "sha256:03dd38de09bc213e9a8b29761eec33ee1d5318dac0e49d8af36e4d27830e23a7", size = 246245, upload-time = "2026-09-15T19:35:02.264Z" } wheels = [ @@ -4733,8 +4730,8 @@ name = "ziamath" version = "0.13" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "latex2mathml", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { 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resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'darwin'", - "python_full_version < '3.14' and sys_platform == 'darwin'", "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'", + "python_full_version < '3.14' and sys_platform == 'darwin'", "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'", ] supported-markers = [ @@ -19,9 +19,6 @@ conflicts = [[ { package = "datashare-extract-worker", extra = "mineru" }, ]] -[options] -prerelease-mode = "if-necessary" - [manifest] overrides = [{ name = "pillow", specifier = "==11.3.0" }] excludes = [ @@ -43,17 +40,17 @@ name = "accelerate" version = "1.15.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "huggingface-hub", version = "0.36.2", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-base') or 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'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvjitlink-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvrtc = [ - { name = "nvidia-cuda-nvrtc-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-nvrtc-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvtx = [ - { name = "nvidia-nvtx-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvtx-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] [[package]] @@ -600,43 +597,43 @@ wheels = [ [package.optional-dependencies] cublas = [ - { name = "nvidia-cublas", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-cuda-nvrtc", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cublas", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-nvrtc", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cudart = [ - { name = "nvidia-cuda-runtime", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-runtime", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cufft = [ - { name = "nvidia-cufft", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-nvjitlink", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cufft", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvjitlink", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cufile = [ - { name = "nvidia-cufile", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cufile", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cupti = [ - { name = "nvidia-cuda-cupti", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-cupti", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] curand = [ - { name = "nvidia-curand", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-curand", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cusolver = [ - { name = "nvidia-cublas", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-cusolver", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-cusparse", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-nvjitlink", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cublas", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cusolver", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cusparse", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvjitlink", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] cusparse = [ - { name = "nvidia-cusparse", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "nvidia-nvjitlink", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cusparse", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvjitlink", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvjitlink = [ - { name = "nvidia-nvjitlink", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvjitlink", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvrtc = [ - { name = "nvidia-cuda-nvrtc", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-cuda-nvrtc", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] nvtx = [ - { name = "nvidia-nvtx", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "nvidia-nvtx", marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra != 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] [[package]] @@ -663,50 +660,50 @@ wheels = [ name = "datashare-extract-worker" source = { editable = "." } dependencies = [ - { name = "datashare-python", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "extract-core", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "datashare-python" }, + { name = "extract-core" }, ] [package.optional-dependencies] base = [ - { name = "extract-python", extra = ["docling", "marker"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-base') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-24-datashare-extract-worker-base') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform == 'darwin' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra != 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "opencv-python-headless", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "extract-python", extra = ["docling", "marker"], marker = "extra == 'extra-24-datashare-extract-worker-base' or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "opencv-python-headless" }, ] cpu = [ - { name = "torch", version = "2.11.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "torch", version = "2.11.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "torchvision", version = "0.26.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "torchvision", version = "0.26.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torch", version = "2.11.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torch", version = "2.11.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torchvision", version = "0.26.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-base' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torchvision", version = "0.26.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-cpu') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] docling-gpu = [ - { name = "extract-python", extra = ["docling-gpu"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "extract-python", extra = ["docling-gpu"] }, ] flash-attn = [ - { name = "flash-attn", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "flash-attn" }, ] gpu = [ - { name = "cuda-bindings", version = "12.9.4", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "torch", version = "2.11.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, - { name = "torchvision", version = "0.26.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "cuda-bindings", version = "12.9.4", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'linux' or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torch", version = "2.11.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform == 'linux' or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "torchvision", version = "0.26.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform == 'linux' or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru')" }, ] mineru = [ - { name = "extract-python", extra = ["mineru"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'darwin' and extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform == 'darwin' and extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "opencv-python-headless", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "extract-python", extra = ["mineru"], marker = "(extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (extra != 'extra-24-datashare-extract-worker-gpu' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra != 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-mineru')" }, + { name = "opencv-python-headless" }, ] [package.dev-dependencies] dev = [ - { name = "black", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "extract-python", extra = ["docling"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "nest-asyncio", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "pre-commit", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "psutil", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "pylint", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "pytest", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "pytest-asyncio", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "pytest-timeout", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "ruff", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "typing-extensions", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "black" }, + { name = "extract-python", extra = ["docling"] }, + { name = "nest-asyncio" }, + { name = "pre-commit" }, + { name = "psutil" }, + { name = "pylint" }, + { name = "pytest" }, + { name = "pytest-asyncio" }, + { name = "pytest-timeout" }, + { name = "ruff" }, + { name = "typing-extensions" }, ] [package.metadata] @@ -747,19 +744,19 @@ name = "datashare-python" version = "0.12.3" source = { editable = "../../datashare-python" } dependencies = [ - { name = "aiofile", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "aiohttp", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "alive-progress", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "hatchling", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "icij-common", extra = ["elasticsearch"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "langcodes", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "lru-dict", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "pydantic-extra-types", extra = ["pycountry"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "python-json-logger", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "pyyaml", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 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'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "transformers", version = "5.18.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-24-datashare-extract-worker-mineru' or extra != 'extra-24-datashare-extract-worker-base' or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "tree-sitter" }, + { name = "tree-sitter-c" }, + { name = "tree-sitter-javascript" }, + { name = "tree-sitter-python" }, + { name = "tree-sitter-typescript" }, ] [[package]] @@ -885,21 +882,21 @@ name = "docling-ibm-models" version = "4.0.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "huggingface-hub", version = "0.36.2", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-base') or (platform_machine != 'x86_64' 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'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "docling-core" }, + { name = "pillow" }, + { name = "pydantic" }, ] sdist = { url = "https://files.pythonhosted.org/packages/53/37/d2b2378cda687cb61b0d86ba11d59ad6e1c6cc32711fdf2e71c783a2cbff/docling_parse-7.22.1.tar.gz", hash = "sha256:bf7da4fa6bac5078b4d43e40c8e988073568829de13c3df25317a4dfda35c03c", size = 7041478, upload-time = "2026-09-28T16:02:31.725Z" } wheels = [ @@ -928,15 +925,15 @@ name = "docling-slim" version = "2.134.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "certifi", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or 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name = "tqdm" }, ] sdist = { url = "https://files.pythonhosted.org/packages/7b/75/ee898b4b7a310c187d0d7ad89002b000efeead0af177f9e50f71554b194b/docling_slim-2.134.0.tar.gz", hash = "sha256:63f12c79bdd0bbe205f244f97f0a8640c590ea46de4d913d23e963ec57e531dd", size = 795345, upload-time = "2026-10-06T04:57:33.257Z" } wheels = [ @@ -945,70 +942,70 @@ wheels = [ [package.optional-dependencies] feat-ocr-easyocr = [ - { name = "easyocr", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 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'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "ocrmac", marker = "sys_platform != 'linux' or (extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, ] feat-ocr-rapidocr = [ - { name = "rapidocr", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "rapidocr" }, ] feat-ocr-tesserocr = [ - { name = "pandas", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "tesserocr", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and 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'x86_64' and sys_platform == 'linux'", + "python_full_version < '3.14' and sys_platform == 'darwin'", "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'", ] sdist = { url = "https://files.pythonhosted.org/packages/8e/5f/bd69653fbfb76cf8604468d3b4ec4c403197144c7bfe0e6a5fc9e02a07cb/regex-2024.11.6.tar.gz", hash = "sha256:7ab159b063c52a0333c884e4679f8d7a85112ee3078fe3d9004b2dd875585519", size = 399494, upload-time = "2024-11-06T20:12:31.635Z" } @@ -3600,8 +3597,8 @@ version = "2026.9.29" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'darwin'", - "python_full_version < '3.14' and sys_platform == 'darwin'", "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'", + "python_full_version < '3.14' and sys_platform == 'darwin'", "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'", ] sdist = { url = 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"https://download-r2.pytorch.org/whl/cpu/torch-2.11.0%2Bcpu-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:45025d7752dbc6b4c784c03afaee9c5f19730ce084b2e43fc9a2fe1677d9ff86", upload-time = "2026-04-28T00:07:02Z" }, @@ -4252,20 +4249,20 @@ resolution-markers = [ "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'", ] dependencies = [ - { name = "cuda-bindings", version = "12.9.4", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-24-datashare-extract-worker-gpu') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 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extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "typing-extensions" }, ] sdist = { url = "https://files.pythonhosted.org/packages/a3/26/b09b8010994eccc3c09092e6b34058f36a460eea2d4c3e8b910c695975a0/typing_inspection-0.4.4.tar.gz", hash = "sha256:547274fa6b0a561ccf549cc9524b999a578e737d015d8709d021f9d0d13bea47", size = 76928, upload-time = "2026-08-12T12:37:25.997Z" } wheels = [ @@ -4617,8 +4614,8 @@ name = "uvicorn" version = "0.54.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "click", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "h11", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "click" }, + { name = "h11" }, ] sdist = { url = "https://files.pythonhosted.org/packages/da/34/30e9280707135d2cfc589dfff3cb796bd07a3aeb1a3e415ba09dd89d7bb4/uvicorn-0.54.0.tar.gz", hash = "sha256:a2e33cbfaa0306f8e6b0c13e0cb89d7d7a2da3e62b90c66e18c33d9807b28620", size = 112283, upload-time = "2026-09-25T06:52:37.601Z" } wheels = [ @@ -4630,11 +4627,11 @@ name = "virtualenv" version = "21.14.2" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "distlib", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "filelock", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 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sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "distlib" }, + { name = "filelock" }, + { name = "packaging" }, + { name = "platformdirs" }, + { name = "python-discovery" }, ] sdist = { url = "https://files.pythonhosted.org/packages/67/57/630a01cf5ab58f33b9c7dc8a7f13464cb5740b5227f8a08cab9d798bd532/virtualenv-21.14.2.tar.gz", hash = "sha256:571930928b11e43db690073ad8228162eca8a3f8fd3a87acdeae07df7dd57068", size = 5463798, upload-time = "2026-10-01T06:22:46.965Z" } wheels = [ @@ -4646,7 +4643,7 @@ name = "watchfiles" version = "1.3.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "anyio", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "anyio" }, ] sdist = { url = "https://files.pythonhosted.org/packages/b3/68/e6aa0b77d217b31f8f486ec0cdfe5e00e6e38dc0be657e7d85819b9faf0a/watchfiles-1.3.0.tar.gz", hash = "sha256:99aee4a07847c06820765fd7b1b49ceac4f3f711ccb7d104655a33231de1c207", size = 108119, upload-time = "2026-09-21T09:08:55.664Z" } wheels = [ @@ -4723,9 +4720,9 @@ name = "yarl" version = "1.25.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "idna", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "multidict", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "propcache", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-base' and extra == 'extra-24-datashare-extract-worker-mineru') or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "idna" }, + { name = "multidict" }, + { name = "propcache" }, ] sdist = { url = "https://files.pythonhosted.org/packages/75/16/e8be8e2fb175bbf41a0680381a319f1199fae256588241a2ac8677eafb49/yarl-1.25.1.tar.gz", hash = "sha256:03dd38de09bc213e9a8b29761eec33ee1d5318dac0e49d8af36e4d27830e23a7", size = 246245, upload-time = "2026-09-15T19:35:02.264Z" } wheels = [ @@ -4761,8 +4758,8 @@ name = "ziamath" version = "0.13" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "latex2mathml", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, - { name = "ziafont", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-24-datashare-extract-worker-cpu' and extra == 'extra-24-datashare-extract-worker-gpu')" }, + { name = "latex2mathml" }, + { name = "ziafont" }, ] sdist = { url = "https://files.pythonhosted.org/packages/88/15/d4f6231e568bb49f8340685efe3020077b1f93f2bb4b4e36471367438b91/ziamath-0.13.tar.gz", hash = "sha256:4f56e9d0b6489f01bcf19c05d68e6da84ed6601d2e1262a62d3a9a7e7c33a171", size = 848924, upload-time = "2026-02-06T17:47:15.055Z" } wheels = [ diff --git a/workers/translation-worker/uv.dist.lock b/workers/translation-worker/uv.dist.lock index db389c01..fa124d15 100644 --- a/workers/translation-worker/uv.dist.lock +++ b/workers/translation-worker/uv.dist.lock @@ -409,37 +409,37 @@ wheels = [ [package.optional-dependencies] cublas = [ - { name = "nvidia-cublas", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cublas" }, ] cudart = [ - { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cuda-runtime" }, ] cufft = [ - { name = "nvidia-cufft", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cufft" }, ] cufile = [ - { name = "nvidia-cufile", marker = "sys_platform == 'linux'" }, + { name = "nvidia-cufile" }, ] cupti = [ - { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cuda-cupti" }, ] curand = [ - { name = "nvidia-curand", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-curand" }, ] cusolver = [ - { name = "nvidia-cusolver", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cusolver" }, ] cusparse = [ - { name = "nvidia-cusparse", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cusparse" }, ] nvjitlink = [ - { name = "nvidia-nvjitlink", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-nvjitlink" }, ] nvrtc = [ - { name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cuda-nvrtc" }, ] nvtx = [ - { name = "nvidia-nvtx", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-nvtx" }, ] [[package]] diff --git a/workers/translation-worker/uv.lock b/workers/translation-worker/uv.lock index e3be82b9..cce75a2a 100644 --- a/workers/translation-worker/uv.lock +++ b/workers/translation-worker/uv.lock @@ -409,37 +409,37 @@ wheels = [ [package.optional-dependencies] cublas = [ - { name = "nvidia-cublas", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cublas" }, ] cudart = [ - { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cuda-runtime" }, ] cufft = [ - { name = "nvidia-cufft", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cufft" }, ] cufile = [ - { name = "nvidia-cufile", marker = "sys_platform == 'linux'" }, + { name = "nvidia-cufile" }, ] cupti = [ - { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cuda-cupti" }, ] curand = [ - { name = "nvidia-curand", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-curand" }, ] cusolver = [ - { name = "nvidia-cusolver", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cusolver" }, ] cusparse = [ - { name = "nvidia-cusparse", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cusparse" }, ] nvjitlink = [ - { name = "nvidia-nvjitlink", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-nvjitlink" }, ] nvrtc = [ - { name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-cuda-nvrtc" }, ] nvtx = [ - { name = "nvidia-nvtx", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "nvidia-nvtx" }, ] [[package]]