Marko Henning
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import pytest
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from scraibe import Scraibe, Diariser, Transcriber, Transcript
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import os
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@pytest.fixture
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def create_scraibe_instance():
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if "HF_TOKEN" in os.environ:
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return Scraibe(use_auth_token=os.environ["HF_TOKEN"])
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else:
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return Scraibe()
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def test_scraibe_init(create_scraibe_instance):
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model = create_scraibe_instance
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assert isinstance(model.transcriber, Transcriber)
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assert isinstance(model.diariser, Diariser)
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def test_scraibe_autotranscribe(create_scraibe_instance):
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model = create_scraibe_instance
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transcript = model.autotranscribe('test/audio_test_2.mp4')
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assert isinstance(transcript, Transcript)
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def test_scraibe_diarization(create_scraibe_instance):
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model = create_scraibe_instance
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diarisation_result = model.diarization('test/audio_test_2.mp4')
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assert isinstance(diarisation_result, dict)
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def test_scraibe_transcribe(create_scraibe_instance):
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model = create_scraibe_instance
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transcription_result = model.transcribe('test/audio_test_2.mp4')
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assert isinstance(transcription_result, str)
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""" def test_remove_audio_file(create_scraibe_instance):
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model = create_scraibe_instance
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with pytest.raises(ValueError):
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model.remove_audio_file("non_existing_audio_file")
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model.remove_audio_file("audio_test_2.mp4")
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assert not os.path.exists("audio_test_2.mp4") """
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""" def test_get_audio_file(create_scraibe_instance):
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model = create_scraibe_instance
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audio_file = os.path.exist("audio_test_2.mp4")
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assert isinstance(audio_file, AudioProcessor)
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assert isinstance(audio_file.waveform, torch.Tensor)
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assert isinstance(audio_file.sr, torch.Tensor) """
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