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https://github.com/deepset-ai/haystack.git
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5ac3dffbef
* Split root conftest into multiple ones and remove unused fixtures * Remove some constants and make them fixtures * Remove unnecessary fixture scoping * Fix failing whisper tests * Fix image_file_paths fixture
60 lines
2.7 KiB
Python
60 lines
2.7 KiB
Python
import os
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import pytest
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from haystack import Pipeline
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from haystack.nodes.audio import WhisperTranscriber
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from haystack.utils.import_utils import is_whisper_available
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@pytest.mark.skipif(os.environ.get("OPENAI_API_KEY", "") == "", reason="OpenAI API key not found")
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@pytest.mark.integration
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def test_whisper_api_transcribe(samples_path):
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w = WhisperTranscriber(api_key=os.environ.get("OPENAI_API_KEY"))
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audio_object_transcript, audio_path_transcript = transcribe_test_helper(w, samples_path=samples_path)
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assert "segments" not in audio_object_transcript and "segments" not in audio_path_transcript
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@pytest.mark.skip("Fails on CI cause it fills up memory")
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@pytest.mark.integration
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@pytest.mark.skipif(not is_whisper_available(), reason="Whisper is not installed")
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def test_whisper_local_transcribe(samples_path):
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w = WhisperTranscriber()
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audio_object_transcript, audio_path_transcript = transcribe_test_helper(w, samples_path=samples_path, language="en")
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assert "segments" not in audio_object_transcript and "segments" not in audio_path_transcript
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@pytest.mark.skip("Fails on CI cause it fills up memory")
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@pytest.mark.integration
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@pytest.mark.skipif(not is_whisper_available(), reason="Whisper is not installed")
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def test_whisper_local_transcribe_with_params(samples_path):
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w = WhisperTranscriber()
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audio_object, audio_path = transcribe_test_helper(w, samples_path=samples_path, language="en", return_segments=True)
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assert len(audio_object["segments"]) == 1 and len(audio_path["segments"]) == 1
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def transcribe_test_helper(whisper, samples_path, **kwargs):
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# this file is 1 second long and contains the word "answer"
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file_path = str(samples_path / "audio" / "answer.wav")
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# using audio object
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with open(file_path, mode="rb") as audio_file:
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audio_object_transcript = whisper.transcribe(audio_file=audio_file, **kwargs)
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assert "answer" in audio_object_transcript["text"].lower()
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# using path to audio file
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audio_path_transcript = whisper.transcribe(audio_file=file_path, **kwargs)
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assert "answer" in audio_path_transcript["text"].lower()
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return audio_object_transcript, audio_path_transcript
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@pytest.mark.skipif(os.environ.get("OPENAI_API_KEY", "") == "", reason="OpenAI API key not found")
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@pytest.mark.integration
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def test_whisper_pipeline(samples_path):
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w = WhisperTranscriber(api_key=os.environ.get("OPENAI_API_KEY"))
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pipeline = Pipeline()
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pipeline.add_node(component=w, name="whisper", inputs=["File"])
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res = pipeline.run(file_paths=[str(samples_path / "audio" / "answer.wav")])
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assert res["documents"] and len(res["documents"]) == 1
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assert "answer" in res["documents"][0].content.lower()
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