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import pytest
import numpy as np
import librosa
from preprocess import preprocess_audio # Adjust the import based on your actual file structure
def test_preprocess_audio():
"""
Test the `preprocess_audio` function to ensure it processes audio correctly.
"""
# Generate a dummy audio signal (sine wave) for testing
sample_rate = 16000
duration = 1.0 # seconds
t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
audio = 0.5 * np.sin(2 * np.pi * 440 * t) # 440 Hz sine wave
# Process the dummy audio
processed_audio = preprocess_audio(audio, sample_rate)
# Define expected properties of the processed audio
# These properties depend on what `preprocess_audio` does
expected_length = len(processed_audio) # Adjust based on preprocessing steps
# Check if the processed audio meets expected properties
assert isinstance(processed_audio, np.ndarray), "Processed audio should be a numpy array"
assert processed_audio.shape[0] == expected_length, "Processed audio length is incorrect"
assert np.all(np.isfinite(processed_audio)), "Processed audio contains non-finite values"
def test_preprocess_audio_edge_cases():
"""
Test edge cases for `preprocess_audio` function.
"""
sample_rate = 16000
empty_audio = np.array([])
# Process the empty audio
processed_audio = preprocess_audio(empty_audio, sample_rate)
# Check if the processed audio is also empty
assert isinstance(processed_audio, np.ndarray), "Processed audio should be a numpy array"
assert processed_audio.size == 0, "Processed audio should be empty for empty input"
# Test with audio of very short duration
short_audio = np.array([0.0]) # Single sample
# Process the short audio
processed_audio = preprocess_audio(short_audio, sample_rate)
# Check if the processed audio has been handled properly
assert isinstance(processed_audio, np.ndarray), "Processed audio should be a numpy array"
assert processed_audio.size > 0, "Processed audio should not be empty for short input"
if __name__ == "__main__":
pytest.main()
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