docling/tests/test_asr_pipeline.py

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feat: Support audio input (#1763) * scaffolding in place Signed-off-by: Peter Staar <taa@zurich.ibm.com> * doing scaffolding for audio pipeline Signed-off-by: Peter Staar <taa@zurich.ibm.com> * WIP: got first transcription working Signed-off-by: Peter Staar <taa@zurich.ibm.com> * all working, time to start cleaning up Signed-off-by: Peter Staar <taa@zurich.ibm.com> * first working ASR pipeline Signed-off-by: Peter Staar <taa@zurich.ibm.com> * added openai-whisper as a first transcription model Signed-off-by: Peter Staar <taa@zurich.ibm.com> * updating with asr_options Signed-off-by: Peter Staar <taa@zurich.ibm.com> * finalised the first working ASR pipeline with Whisper Signed-off-by: Peter Staar <taa@zurich.ibm.com> * use whisper from the latest git commit Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * Update docling/datamodel/pipeline_options.py Co-authored-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> Signed-off-by: Peter W. J. Staar <91719829+PeterStaar-IBM@users.noreply.github.com> * Update docling/datamodel/pipeline_options.py Co-authored-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> Signed-off-by: Peter W. J. Staar <91719829+PeterStaar-IBM@users.noreply.github.com> * updated comment Signed-off-by: Peter Staar <taa@zurich.ibm.com> * AudioBackend -> DummyBackend Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * file rename Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Rename to NoOpBackend, add test for ASR pipeline Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Support every format in NoOpBackend Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Add missing audio file and test Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Install ffmpeg system dependency for ASR test Signed-off-by: Christoph Auer <cau@zurich.ibm.com> --------- Signed-off-by: Peter Staar <taa@zurich.ibm.com> Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> Signed-off-by: Peter W. J. Staar <91719829+PeterStaar-IBM@users.noreply.github.com> Signed-off-by: Christoph Auer <cau@zurich.ibm.com> Co-authored-by: Michele Dolfi <dol@zurich.ibm.com> Co-authored-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> Co-authored-by: Christoph Auer <cau@zurich.ibm.com>
2025-06-23 14:47:26 +02:00
from pathlib import Path
import pytest
from docling.datamodel import asr_model_specs
from docling.datamodel.base_models import ConversionStatus, InputFormat
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import AsrPipelineOptions
from docling.document_converter import AudioFormatOption, DocumentConverter
from docling.pipeline.asr_pipeline import AsrPipeline
@pytest.fixture
def test_audio_path():
return Path("./tests/data/audio/sample_10s.mp3")
def get_asr_converter():
"""Create a DocumentConverter configured for ASR with whisper_turbo model."""
pipeline_options = AsrPipelineOptions()
pipeline_options.asr_options = asr_model_specs.WHISPER_TINY
converter = DocumentConverter(
format_options={
InputFormat.AUDIO: AudioFormatOption(
pipeline_cls=AsrPipeline,
pipeline_options=pipeline_options,
)
}
)
return converter
def test_asr_pipeline_conversion(test_audio_path):
"""Test ASR pipeline conversion using whisper_turbo model on sample_10s.mp3."""
# Check if the test audio file exists
assert test_audio_path.exists(), f"Test audio file not found: {test_audio_path}"
converter = get_asr_converter()
# Convert the audio file
doc_result: ConversionResult = converter.convert(test_audio_path)
# Verify conversion was successful
assert doc_result.status == ConversionStatus.SUCCESS, (
f"Conversion failed with status: {doc_result.status}"
)
# Verify we have a document
assert doc_result.document is not None, "No document was created"
# Verify we have text content (transcribed audio)
texts = doc_result.document.texts
assert len(texts) > 0, "No text content found in transcribed audio"
# Print transcribed text for verification (optional, for debugging)
print(f"Transcribed text from {test_audio_path.name}:")
for i, text_item in enumerate(texts):
print(f" {i + 1}: {text_item.text}")