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import librosa
import librosa.filters
import numpy as np
from scipy import signal
from scipy.io import wavfile
import soundfile as sf
def load_wav(path, sr):
return librosa.core.load(path, sr=sr)[0]
def save_wav(wav, path, sr):
wav *= 32767 / max(0.01, np.max(np.abs(wav)))
#proposed by @dsmiller
wavfile.write(path, sr, wav.astype(np.int16))
def save_wavenet_wav(wav, path, sr):
sf.write(path, wav.astype(np.float32), sr)
def preemphasis(wav, k, preemphasize=True):
if preemphasize:
return signal.lfilter([1, -k], [1], wav)
return wav
def inv_preemphasis(wav, k, inv_preemphasize=True):
if inv_preemphasize:
return signal.lfilter([1], [1, -k], wav)
return wav
#From https://github.com/r9y9/wavenet_vocoder/blob/master/audio.py
def start_and_end_indices(quantized, silence_threshold=2):
for start in range(quantized.size):
if abs(quantized[start] - 127) > silence_threshold:
break
for end in range(quantized.size - 1, 1, -1):
if abs(quantized[end] - 127) > silence_threshold:
break
assert abs(quantized[start] - 127) > silence_threshold
assert abs(quantized[end] - 127) > silence_threshold
return start, end
def get_hop_size(hparams):
hop_size = hparams.hop_size
if hop_size is None:
assert hparams.frame_shift_ms is not None
hop_size = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)
return hop_size
def linearspectrogram(wav, hparams):
D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
S = _amp_to_db(np.abs(D), hparams) - hparams.ref_level_db
if hparams.signal_normalization:
return _normalize(S, hparams)
return S
def melspectrogram(wav, hparams):
D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
S = _amp_to_db(_linear_to_mel(np.abs(D), hparams), hparams) - hparams.ref_level_db
if hparams.signal_normalization:
return _normalize(S, hparams)
return S
def inv_linear_spectrogram(linear_spectrogram, hparams):
"""Converts linear spectrogram to waveform using librosa"""
if hparams.signal_normalization:
D = _denormalize(linear_spectrogram, hparams)
else:
D = linear_spectrogram
S = _db_to_amp(D + hparams.ref_level_db) #Convert back to linear
if hparams.use_lws:
processor = _lws_processor(hparams)
D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
y = processor.istft(D).astype(np.float32)
return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize)
else:
return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize)
def inv_mel_spectrogram(mel_spectrogram, hparams):
"""Converts mel spectrogram to waveform using librosa"""
if hparams.signal_normalization:
D = _denormalize(mel_spectrogram, hparams)
else:
D = mel_spectrogram
S = _mel_to_linear(_db_to_amp(D + hparams.ref_level_db), hparams) # Convert back to linear
if hparams.use_lws:
processor = _lws_processor(hparams)
D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
y = processor.istft(D).astype(np.float32)
return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize)
else:
return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize)
def _lws_processor(hparams):
import lws
return lws.lws(hparams.n_fft, get_hop_size(hparams), fftsize=hparams.win_size, mode="speech")
def _griffin_lim(S, hparams):
"""librosa implementation of Griffin-Lim
Based on https://github.com/librosa/librosa/issues/434
"""
angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
S_complex = np.abs(S).astype(np.complex)
y = _istft(S_complex * angles, hparams)
for i in range(hparams.griffin_lim_iters):
angles = np.exp(1j * np.angle(_stft(y, hparams)))
y = _istft(S_complex * angles, hparams)
return y
def _stft(y, hparams):
if hparams.use_lws:
return _lws_processor(hparams).stft(y).T
else:
return librosa.stft(y=y, n_fft=hparams.n_fft, hop_length=get_hop_size(hparams), win_length=hparams.win_size)
def _istft(y, hparams):
return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams.win_size)
##########################################################
#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
def num_frames(length, fsize, fshift):
"""Compute number of time frames of spectrogram
"""
pad = (fsize - fshift)
if length % fshift == 0:
M = (length + pad * 2 - fsize) // fshift + 1
else:
M = (length + pad * 2 - fsize) // fshift + 2
return M
def pad_lr(x, fsize, fshift):
"""Compute left and right padding
"""
M = num_frames(len(x), fsize, fshift)
pad = (fsize - fshift)
T = len(x) + 2 * pad
r = (M - 1) * fshift + fsize - T
return pad, pad + r
##########################################################
#Librosa correct padding
def librosa_pad_lr(x, fsize, fshift):
return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
# Conversions
_mel_basis = None
_inv_mel_basis = None
def _linear_to_mel(spectogram, hparams):
global _mel_basis
if _mel_basis is None:
_mel_basis = _build_mel_basis(hparams)
return np.dot(_mel_basis, spectogram)
def _mel_to_linear(mel_spectrogram, hparams):
global _inv_mel_basis
if _inv_mel_basis is None:
_inv_mel_basis = np.linalg.pinv(_build_mel_basis(hparams))
return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram))
def _build_mel_basis(hparams):
assert hparams.fmax <= hparams.sample_rate // 2
return librosa.filters.mel(hparams.sample_rate, hparams.n_fft, n_mels=hparams.num_mels,
fmin=hparams.fmin, fmax=hparams.fmax)
def _amp_to_db(x, hparams):
min_level = np.exp(hparams.min_level_db / 20 * np.log(10))
return 20 * np.log10(np.maximum(min_level, x))
def _db_to_amp(x):
return np.power(10.0, (x) * 0.05)
def _normalize(S, hparams):
if hparams.allow_clipping_in_normalization:
if hparams.symmetric_mels:
return np.clip((2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value,
-hparams.max_abs_value, hparams.max_abs_value)
else:
return np.clip(hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db)), 0, hparams.max_abs_value)
assert S.max() <= 0 and S.min() - hparams.min_level_db >= 0
if hparams.symmetric_mels:
return (2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value
else:
return hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db))
def _denormalize(D, hparams):
if hparams.allow_clipping_in_normalization:
if hparams.symmetric_mels:
return (((np.clip(D, -hparams.max_abs_value,
hparams.max_abs_value) + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value))
+ hparams.min_level_db)
else:
return ((np.clip(D, 0, hparams.max_abs_value) * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)
if hparams.symmetric_mels:
return (((D + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value)) + hparams.min_level_db)
else:
return ((D * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)
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