Joel Mathew Thomas
dc81f36d74
- Implemented test case to verify audio classification with YAMNet - Used 'cafe_crowd_talk.wav' as test file, expecting 'Speech' as the output class
28 lines
918 B
Python
28 lines
918 B
Python
import pytest
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import librosa
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from src.preprocessing.normalize import normalize_audio
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from src.preprocessing.trim import trim_audio
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from src.preprocessing.classify import classify_audio
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from src.input.file_reader import read_audio
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def test_normalize_audio():
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file_path = "samples/cafe_crowd_talk.aiff"
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audio, _ = read_audio(file_path)
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normalized_audio = normalize_audio(audio)
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assert normalized_audio.max() <= 1.0
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assert normalized_audio.min() >= -1.0
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def test_trim_audio():
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file_path = "samples/cafe_crowd_talk.aiff"
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audio, sr = read_audio(file_path)
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trimmed_audio = trim_audio(audio, sr)
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assert len(trimmed_audio) <= len(audio)
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def test_classify():
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file_path = "samples/cafe_crowd_talk.wav"
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expected_class = "Speech"
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predicted_class = classify_audio(file_path)
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assert predicted_class == expected_class , f"Expected {expected_class}, but got {predicted_class}" |