Marko Henning
51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
import pytest
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from scraibe import Transcriber
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import torch
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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TEST_WAVEFORM = "Hello World"
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"""
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@pytest.mark.parametrize("audio_file, expected_transcription",[("path_to_test_audiofile", "test_transcription")] )
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@patch("scraibe.Transcriber.load_model")
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def test_transcriber(mock_load_model, audio_file, expected_transcription):
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Args:
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mock_load_model (_type_): _description_
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audio_file (_type_): _description_
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expected_transcription (_type_): _description_
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mock_model = mock_load_model.return_value
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mock_model.transcribe.return_value ={"text": expected_transcription}
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transcriber = Transcriber.load_model(model="medium")
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transcription_result = transcriber.transcribe(audio=audio_file)
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assert transcription_result == expected_transcription """
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@pytest.fixture
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def transcriber_instance():
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return Transcriber.load_model('medium')
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def test_transcriber_initialization(transcriber_instance):
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assert isinstance(transcriber_instance, Transcriber)
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def test_get_whisper_kwargs():
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kwargs = {"arg1": 1, "arg3": 3}
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valid_kwargs = Transcriber._get_whisper_kwargs(**kwargs)
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assert not valid_kwargs == {"arg1": 1, "arg3": 3}
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def test_transcribe(transcriber_instance):
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model = transcriber_instance
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# mocker.patch.object(transcriber_instance.model, 'transcribe', return_value={'Hello, World !'} )
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transcript = model.transcribe('test/audio_test_2.mp4')
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assert isinstance(transcript, str)
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