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92 lines
3.3 KiB
Python
Vendored
92 lines
3.3 KiB
Python
Vendored
# %% [markdown]
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# Minimal ASR pipeline example: transcribe an audio file to Markdown text.
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#
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# What this example does
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# - Configures the ASR pipeline with a default model spec and converts one audio file.
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# - Prints the recognized speech segments in Markdown with timestamps.
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#
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# Prerequisites
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# - Install Docling with ASR extras and any audio dependencies (ffmpeg, etc.).
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# - Ensure your environment can download or access the configured ASR model.
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# - Some formats require ffmpeg codecs; install ffmpeg and ensure it's on PATH.
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#
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# How to run
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# - From the repository root, run: `python docs/examples/minimal_asr_pipeline.py`.
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# - The script prints the transcription to stdout.
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#
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# Customizing the model
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# - The script automatically selects the best model for your hardware (MLX Whisper for Apple Silicon, native Whisper otherwise).
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# - Edit `get_asr_converter()` to manually override `pipeline_options.asr_options` with any model from `asr_model_specs`.
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# - Keep `InputFormat.AUDIO` and `AsrPipeline` unchanged for a minimal setup.
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#
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# Input audio
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# - Defaults to `tests/data/audio/sample_10s.mp3`. Update `audio_path` to your own file if needed.
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# %%
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from pathlib import Path
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from docling_core.types.doc import DoclingDocument
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from docling.datamodel import asr_model_specs
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from docling.datamodel.base_models import ConversionStatus, InputFormat
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from docling.datamodel.document import ConversionResult
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from docling.datamodel.pipeline_options import AsrPipelineOptions
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from docling.document_converter import AudioFormatOption, DocumentConverter
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from docling.pipeline.asr_pipeline import AsrPipeline
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def get_asr_converter():
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"""Create a DocumentConverter configured for ASR with automatic model selection.
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Uses `asr_model_specs.WHISPER_TURBO` which automatically selects the best
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implementation for your hardware:
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- MLX Whisper Turbo for Apple Silicon (M1/M2/M3) with mlx-whisper installed
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- Native Whisper Turbo as fallback
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You can swap in another model spec from `docling.datamodel.asr_model_specs`
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to experiment with different model sizes.
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"""
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pipeline_options = AsrPipelineOptions()
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pipeline_options.asr_options = asr_model_specs.WHISPER_TURBO
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converter = DocumentConverter(
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format_options={
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InputFormat.AUDIO: AudioFormatOption(
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pipeline_cls=AsrPipeline,
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pipeline_options=pipeline_options,
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)
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}
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)
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return converter
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def asr_pipeline_conversion(audio_path: Path) -> DoclingDocument:
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"""Run the ASR pipeline and return a `DoclingDocument` transcript."""
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# Check if the test audio file exists
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assert audio_path.exists(), f"Test audio file not found: {audio_path}"
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converter = get_asr_converter()
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# Convert the audio file
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result: ConversionResult = converter.convert(audio_path)
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# Verify conversion was successful
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assert result.status == ConversionStatus.SUCCESS, (
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f"Conversion failed with status: {result.status}"
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)
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return result.document
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if __name__ == "__main__":
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audio_path = Path("tests/data/audio/sample_10s.mp3")
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doc = asr_pipeline_conversion(audio_path=audio_path)
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print(doc.export_to_markdown())
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# Expected output:
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#
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# [time: 0.0-4.0] Shakespeare on Scenery by Oscar Wilde
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#
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# [time: 5.28-9.96] This is a LibriVox recording. All LibriVox recordings are in the public domain.
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