48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita)
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# Licensed under the MIT license.
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#
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# This module is for computing audio features
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import librosa
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import numpy as np
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def transform(Y, dtype=np.float32):
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Y = np.abs(Y)
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n_fft = 2 * (Y.shape[1] - 1)
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sr = 8000
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n_mels = 23
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mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
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Y = np.dot(Y**2, mel_basis.T)
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Y = np.log10(np.maximum(Y, 1e-10))
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mean = np.mean(Y, axis=0)
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Y = Y - mean
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return Y.astype(dtype)
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def subsample(Y, T, subsampling=1):
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Y_ss = Y[::subsampling]
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T_ss = T[::subsampling]
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return Y_ss, T_ss
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def splice(Y, context_size=0):
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Y_pad = np.pad(Y, [(context_size, context_size), (0, 0)], "constant")
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Y_spliced = np.lib.stride_tricks.as_strided(
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np.ascontiguousarray(Y_pad),
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(Y.shape[0], Y.shape[1] * (2 * context_size + 1)),
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(Y.itemsize * Y.shape[1], Y.itemsize),
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writeable=False,
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)
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return Y_spliced
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def stft(data, frame_size=1024, frame_shift=256):
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fft_size = 1 << (frame_size - 1).bit_length()
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if len(data) % frame_shift == 0:
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return librosa.stft(data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift).T[
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:-1
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]
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else:
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return librosa.stft(data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift).T
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