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audio_hjk2.py
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# 问题：
# fmin和fmax到底有什么用，目前mfcc的fmin=0,fmax=none;mel的fmin=30,fmax=7600;spec又没有限制。所以怎么办？
# 以后只用power谱了，统一起来，都用stft之后先算平方，然后转换log后乘以10，但是其实不懂区别，哪一个更好？
# Griffinlim超参数临时使用1.2和80，区别在哪里？
# 取log的时候，浮点数（power值）统一加上了1e-5
# min_db没有详细统计，直接用的-80
# 户建坤-hujk17为了理解长河10ms版本cbhg-ppg代码进行了一次梳理，抄写的。2020-10-14-16-13
import librosa
import numpy as np
from scipy.io import wavfile
from scipy import signal
from scipy.fftpack import dct
import matplotlib.pyplot as plt
# 超参数个数：16
hparams = {
'sample_rate': 16000,
'preemphasis': 0.97,
'n_fft': 400,
'hop_length': 160,
'win_length': 400,
'num_mels': 80,
'n_mfcc': 13,
'window': 'hann',
'fmin': 30.,
'fmax': 7600.,
'ref_db': 20,
'min_db': -80.0,
'griffin_lim_power': 1.5,
'griffin_lim_iterations': 60,
'silence_db': -28.0,
'center': True, # 不知道为什么提取ppg要是True
}
_mel_basis = None
_inv_mel_basis = None
# 超参数个数：1
def load_wav(wav_f, sr = hparams['sample_rate']):
wav_arr, _ = librosa.load(wav_f, sr=sr)
return wav_arr
# 超参数个数：1
def write_wav(write_path, wav_arr, sr = hparams['sample_rate']):
wav_arr *= 32767 / max(0.01, np.max(np.abs(wav_arr)))
wavfile.write(write_path, sr, wav_arr.astype(np.int16))
return
# 超参数个数：1
def split_wav(wav_arr, top_db = -hparams['silence_db']):
intervals = librosa.effects.split(wav_arr, top_db=top_db)
return intervals
# 超参数个数：12
def wav2unnormalized_mfcc(wav_arr, sr=hparams['sample_rate'], preemphasis=hparams['preemphasis'],
n_fft=hparams['n_fft'], hop_len=hparams['hop_length'],
win_len=hparams['win_length'], num_mels=hparams['num_mels'],
n_mfcc=hparams['n_mfcc'], window=hparams['window'],fmin=0.0,
fmax=None, ref_db=hparams['ref_db'],
center=hparams['center']):
emph_wav_arr = _preempahsis(wav_arr, pre_param=preemphasis)
power_spec = _power_spec(emph_wav_arr, n_fft=n_fft, hop_len=hop_len, win_len=win_len, window=window, center=center)
power_mel = _power_spec2power_mel(power_spec, sr=sr, n_fft=n_fft, num_mels=num_mels, fmin=fmin, fmax=fmax)
db_mel = _power2db(power_mel, ref_db=ref_db)
# 没有进行norm
mfcc = dct(x=db_mel.T, axis=0, type=2, norm='ortho')[:n_mfcc]
deltas = librosa.feature.delta(mfcc)
delta_deltas = librosa.feature.delta(mfcc, order=2)
mfcc_feature = np.concatenate((mfcc, deltas, delta_deltas), axis=0)
return mfcc_feature.T
# 超参数个数：12
def wav2normalized_db_mel(wav_arr, sr=hparams['sample_rate'], preemphasis=hparams['preemphasis'],
n_fft=hparams['n_fft'], hop_len=hparams['hop_length'],
win_len=hparams['win_length'], num_mels=hparams['num_mels'],
window=hparams['window'],fmin=hparams['fmin'],
fmax=hparams['fmax'], ref_db=hparams['ref_db'], min_db=hparams['min_db'],
center=hparams['center']):
emph_wav_arr = _preempahsis(wav_arr, pre_param=preemphasis)
power_spec = _power_spec(emph_wav_arr, n_fft=n_fft, hop_len=hop_len, win_len=win_len, window=window, center=center) # (time, n_fft/2+1)
power_mel = _power_spec2power_mel(power_spec, sr=sr, n_fft=n_fft, num_mels=num_mels, fmin=fmin, fmax=fmax)
db_mel = _power2db(power_mel, ref_db=ref_db)
normalized_db_mel = _db_normalize(db_mel, min_db=min_db)
return normalized_db_mel
# 超参数个数：9
def wav2normalized_db_spec(wav_arr, sr=hparams['sample_rate'], preemphasis=hparams['preemphasis'],
n_fft=hparams['n_fft'], hop_len=hparams['hop_length'],
win_len=hparams['win_length'],
window=hparams['window'], ref_db=hparams['ref_db'], min_db=hparams['min_db'],
center=hparams['center']):
emph_wav_arr = _preempahsis(wav_arr, pre_param=preemphasis)
power_spec = _power_spec(emph_wav_arr, n_fft=n_fft, hop_len=hop_len, win_len=win_len, window=window, center=center) # (time, n_fft/2+1)
# power_mel = _power_spec2power_mel(power_spec, sr=sr, n_fft=n_fft, num_mels=num_mels, fmin=fmin, fmax=fmax)
db_spec = _power2db(power_spec, ref_db=ref_db)
normalized_db_spec = _db_normalize(db_spec, min_db=min_db)
return normalized_db_spec
# inv操作
# 超参数个数：14
def normalized_db_mel2wav(normalized_db_mel, sr=hparams['sample_rate'], preemphasis=hparams['preemphasis'],
n_fft=hparams['n_fft'], hop_len=hparams['hop_length'],
win_len=hparams['win_length'], num_mels=hparams['num_mels'],
window=hparams['window'], fmin=hparams['fmin'],
fmax=hparams['fmax'],
ref_db=hparams['ref_db'], min_db=hparams['min_db'],
center=hparams['center'], griffin_lim_power=hparams['griffin_lim_power'],
griffin_lim_iterations=hparams['griffin_lim_iterations']):
db_mel = _db_denormalize(normalized_db_mel, min_db=min_db)
power_mel = _db2power(db_mel, ref_db=ref_db)
power_spec = _power_mel2power_spec(power_mel, sr=sr, n_fft=n_fft, num_mels=num_mels, fmin=fmin, fmax=fmax) #矩阵求逆猜出来的spec
magnitude_spec = power_spec ** 0.5 # (time, n_fft/2+1)
# print('-----1:', magnitude_spec.shape)
# magnitude_spec_t = magnitude_spec.T
griffinlim_powered_magnitude_spec = magnitude_spec ** griffin_lim_power # (time, n_fft/2+1)
# print('-----2:', griffinlim_powered_magnitude_spec.shape)
# 送入griffinlim的是正常的 (time, n_fft/2+1)
emph_wav_arr = _griffin_lim(griffinlim_powered_magnitude_spec, gl_iterations=griffin_lim_iterations,
n_fft=n_fft, hop_len=hop_len, win_len=win_len, window=window, center=center)
wav_arr = _deemphasis(emph_wav_arr, pre_param=preemphasis)
return wav_arr
# inv操作
# 超参数个数：11
def normalized_db_spec2wav(normalized_db_spec, sr=hparams['sample_rate'], preemphasis=hparams['preemphasis'],
n_fft=hparams['n_fft'], hop_len=hparams['hop_length'],
win_len=hparams['win_length'],
window=hparams['window'], ref_db=hparams['ref_db'], min_db=hparams['min_db'],
center=hparams['center'], griffin_lim_power=hparams['griffin_lim_power'],
griffin_lim_iterations=hparams['griffin_lim_iterations']):
db_spec = _db_denormalize(normalized_db_spec, min_db=min_db)
power_spec = _db2power(db_spec, ref_db=ref_db) # (time, n_fft/2+1)
magnitude_spec = power_spec ** 0.5 # (time, n_fft/2+1)
# magnitude_spec_t = magnitude_spec.T #(n_fft/2+1, time)
griffinlim_powered_magnitude_spec = magnitude_spec ** griffin_lim_power
emph_wav_arr = _griffin_lim(griffinlim_powered_magnitude_spec, gl_iterations=griffin_lim_iterations,
n_fft=n_fft, hop_len=hop_len, win_len=win_len, window=window, center=center)
wav_arr = _deemphasis(emph_wav_arr, pre_param=preemphasis)
return wav_arr
# 超参数个数：1
def _preempahsis(wav_arr, pre_param):
return signal.lfilter([1, -pre_param], [1], wav_arr)
# 超参数个数：1
def _deemphasis(wav_arr, pre_param):
return signal.lfilter([1], [1, -pre_param], wav_arr)
# 超参数个数：5
# 注意center的参数
# return shape: [n_freqs, time]
def _stft(wav_arr, n_fft, hop_len, win_len, window, center):
return librosa.core.stft(wav_arr, n_fft=n_fft, hop_length=hop_len,
win_length=win_len, window=window, center=center)
# 超参数个数：3
# stft_matrix shape [n_freqs, time]，复数
def _istft(stft_matrix, hop_len, win_len, window):
return librosa.core.istft(stft_matrix, hop_length=hop_len,
win_length=win_len, window=window)
# 超参数个数：5
# 注意center的参数
# 以后只用power谱了，统一起来，都用stft之后先算平方，然后转换log后乘以10，但是其实不懂区别，哪一个更好？
# return shape: [time, n_freqs]
def _power_spec(wav_arr, n_fft, hop_len, win_len, window, center):
s = _stft(wav_arr, n_fft=n_fft, hop_len=hop_len, win_len=win_len, window=window, center=center).T
power = np.abs(s) ** 2
return power
# 超参数个数：5
# input shape: [time, n_freqs]
# return shape: [time, n_mels]
def _power_spec2power_mel(power_spec, sr, n_fft, num_mels, fmin, fmax):
power_spec_t = power_spec.T
global _mel_basis
_mel_basis = (librosa.filters.mel(sr, n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) if _mel_basis is None else _mel_basis) # [n_mels, 1+n_fft/2]
power_mel_t = np.dot(_mel_basis, power_spec_t) # [n_mels, time]
power_mel = power_mel_t.T
return power_mel
# inv操作
# 超参数个数：5
# input shape: [time, n_mels]
# return shape: [time, n_freqs]
def _power_mel2power_spec(power_mel, sr, n_fft, num_mels, fmin, fmax):
power_mel_t = power_mel.T
global _mel_basis, _inv_mel_basis
_mel_basis = (librosa.filters.mel(sr, n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) if _mel_basis is None else _mel_basis) # [n_mels, 1+n_fft/2]
_inv_mel_basis = (np.linalg.pinv(_mel_basis) if _inv_mel_basis is None else _inv_mel_basis)
power_spec_t = np.dot(_inv_mel_basis, power_mel_t)
power_spec_t = np.maximum(1e-10, power_spec_t)
power_spec = power_spec_t.T
return power_spec
# 超参数个数：1
# returned value: (10. * log10(power_spec) - ref_db)
def _power2db(power_spec, ref_db, tol=1e-5):
return 10. * np.log10(power_spec + tol) - ref_db
# inv操作
# 超参数个数：1
def _db2power(power_db, ref_db):
return np.power(10.0, 0.1 * (power_db + ref_db))
# 超参数个数：1
# return: db normalized to [0., 1.]
def _db_normalize(db, min_db):
return np.clip((db - min_db) / -min_db, 0., 1.)
# inv操作
# 超参数个数：1
def _db_denormalize(normalized_db, min_db):
return np.clip(normalized_db, 0., 1.) * -min_db + min_db
# 超参数个数：6
# input: magnitude spectrogram of shape [time, n_freqs]
# return: waveform array
def _griffin_lim(magnitude_spec, gl_iterations, n_fft, hop_len, win_len, window, center):
# # 在这里进行gl的power，输入的是正常的magnitude_spec
# magnitude_spec = magnitude_spec ** gl_power
mag = magnitude_spec.T # transpose to [n_freqs, time]
# print('-----3:', magnitude_spec.shape)
# print('-----4:', mag.shape)
angles = np.exp(2j * np.pi * np.random.rand(*mag.shape))
complex_mag = np.abs(mag).astype(np.complex)
stft_0 = complex_mag * angles
y = _istft(stft_0, hop_len = hop_len, win_len = win_len, window = window)
for _i in range(gl_iterations):
angles = np.exp(1j * np.angle(_stft(y, n_fft=n_fft, hop_len=hop_len, win_len=win_len, window=window, center=center)))
y = _istft(complex_mag * angles, hop_len = hop_len, win_len = win_len, window = window)
return y
def _wav2unnormalized_mfcc_test(wav_path, mfcc_path):
wav_arr = load_wav(wav_path)
mfcc = wav2unnormalized_mfcc(wav_arr)
mfcc_label = np.load(mfcc_path)
print(mfcc.min(), mfcc_label.min())
print(mfcc.max(), mfcc_label.max())
print(mfcc.mean(), mfcc_label.mean())
print(np.abs(mfcc - mfcc_label))
print(np.mean(np.abs(mfcc - mfcc_label)))
plt.figure()
plt.subplot(211)
plt.imshow(mfcc.T, origin='lower')
# plt.colorbar()
plt.subplot(212)
plt.imshow(mfcc_label.T, origin='lower')
# plt.colorbar()
plt.tight_layout()
plt.show()
return
def _wav2normalized_db_mel_test(wav_path, wav_rec_path):
wav_arr = load_wav(wav_path)
spec = wav2normalized_db_spec(wav_arr)
wav_arr_rec = normalized_db_spec2wav(spec)
write_wav(wav_rec_path, wav_arr_rec)
def _wav2normalized_db_spec_test(wav_path, wav_rec_path):
wav_arr = load_wav(wav_path)
mel = wav2normalized_db_mel(wav_arr)
wav_arr_rec = normalized_db_mel2wav(mel)
write_wav(wav_rec_path, wav_arr_rec)
if __name__ == '__main__':
_wav2unnormalized_mfcc_test('test.wav', 'test_mfcc.npy')
_wav2normalized_db_mel_test('test.wav', 'test_mel_rec.wav')
_wav2normalized_db_spec_test('test.wav', 'test_spec_rec.wav')