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Upgrade to Python 3.13 (#11588)

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This commit is contained in:
Christian Clauss
2024-09-30 23:01:15 +02:00
committed by GitHub
parent a7bfa22455
commit 0177ae1cd5
35 changed files with 135 additions and 131 deletions
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13
machine_learning/mfcc.py
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@ -162,9 +162,9 @@ def normalize(audio: np.ndarray) -> np.ndarray:
Examples:
>>> audio = np.array([1, 2, 3, 4, 5])
>>> normalized_audio = normalize(audio)
>>> np.max(normalized_audio)
>>> float(np.max(normalized_audio))
1.0
>>> np.min(normalized_audio)
>>> float(np.min(normalized_audio))
0.2
"""
# Divide the entire audio signal by the maximum absolute value
@ -229,7 +229,8 @@ def calculate_fft(audio_windowed: np.ndarray, ftt_size: int = 1024) -> np.ndarra
Examples:
>>> audio_windowed = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
>>> audio_fft = calculate_fft(audio_windowed, ftt_size=4)
>>> np.allclose(audio_fft[0], np.array([6.0+0.j, -1.5+0.8660254j, -1.5-0.8660254j]))
>>> bool(np.allclose(audio_fft[0], np.array([6.0+0.j, -1.5+0.8660254j,
... -1.5-0.8660254j])))
True
"""
# Transpose the audio data to have time in rows and channels in columns
@ -281,7 +282,7 @@ def freq_to_mel(freq: float) -> float:
The frequency in mel scale.
Examples:
>>> round(freq_to_mel(1000), 2)
>>> float(round(freq_to_mel(1000), 2))
999.99
"""
# Use the formula to convert frequency to the mel scale
@ -321,7 +322,7 @@ def mel_spaced_filterbank(
Mel-spaced filter bank.
Examples:
>>> round(mel_spaced_filterbank(8000, 10, 1024)[0][1], 10)
>>> float(round(mel_spaced_filterbank(8000, 10, 1024)[0][1], 10))
0.0004603981
"""
freq_min = 0
@ -438,7 +439,7 @@ def discrete_cosine_transform(dct_filter_num: int, filter_num: int) -> np.ndarra
The DCT basis matrix.
Examples:
>>> round(discrete_cosine_transform(3, 5)[0][0], 5)
>>> float(round(discrete_cosine_transform(3, 5)[0][0], 5))
0.44721
"""
basis = np.empty((dct_filter_num, filter_num))