77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
import numpy as np
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from pytest import mark
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@mark.non_docker
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@mark.speech
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class SpeechTest:
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def test_dummy_3(self):
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assert True
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def test_get_transcribe(self, processing_speech_mp4, processing_speech_wav):
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actual_totaltime = 87.978685
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len_words_count = 57
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res_mp4 = processing_speech_mp4.get_transcribe().to_dataframe()
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audio_duration_mp4 = res_mp4["nlp_totalTime"].item()
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transcribed_text_mp4 = res_mp4["nlp_transcribe"].item()
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res_wav = processing_speech_wav.get_transcribe().to_dataframe()
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audio_duration_wav = res_wav["nlp_totalTime"].item()
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transcribed_text_wav = res_wav["nlp_transcribe"].item()
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# test if duration is matched
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assert np.isclose(audio_duration_mp4, actual_totaltime, rtol=0.1, atol=1e-8)
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assert np.isclose(audio_duration_wav, actual_totaltime, rtol=0.1, atol=1e-8)
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# test if there is transcribed text or not
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assert type(transcribed_text_mp4) == str
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assert type(transcribed_text_wav) == str
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# test the length of the text
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assert np.isclose(
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len(transcribed_text_mp4.split(" ")), len_words_count, rtol=0.5, atol=1e-8
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)
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assert np.isclose(
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len(transcribed_text_wav.split(" ")), len_words_count, rtol=0.5, atol=1e-8
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)
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def test_get_speech_features(self, processing_speech_mp4, processing_speech_wav):
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# actual = [
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# 1.0,
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# 2.0,
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# 2.0,
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# 1.0,
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# 1.0,
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# 6.0,
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# 6.0,
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# 11.0,
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# 11.0,
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# 5.0,
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# 5.0,
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# 15.0,
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# 15.0,
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# -0.8256,
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# 0.08860759493670886,
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# 38.873052120437336,
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# 87.97868480725624,
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# ]
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res_mp4 = (
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processing_speech_mp4.get_speech_features()
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.to_dataframe()
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.drop(columns="dbm_master_url")
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)
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res_wav = (
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processing_speech_wav.get_speech_features()
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.to_dataframe()
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.drop(columns="dbm_master_url")
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)
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desired_mp4 = np.array((res_mp4.iloc[0]))
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desired_wav = np.array((res_wav.iloc[0]))
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# check if there is any zero value or not
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for v1, v2 in zip(desired_mp4, desired_wav):
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assert bool(v1)
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assert bool(v2)
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