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import numpy as np
from numpy import linalg as LA
import librosa
from scipy.io import wavfile
import soundfile as sf
import librosa.filters
def load_wav(wav_path, raw_sr, target_sr=16000, win_size=800, hop_size=200):
audio = librosa.core.load(wav_path, sr=raw_sr)[0]
if raw_sr != target_sr:
audio = librosa.core.resample(audio,
raw_sr,
target_sr,
res_type='kaiser_best')
target_length = (audio.size // hop_size +
win_size // hop_size) * hop_size
pad_len = (target_length - audio.size) // 2
if audio.size % 2 == 0:
audio = np.pad(audio, (pad_len, pad_len), mode='reflect')
else:
audio = np.pad(audio, (pad_len, pad_len + 1), mode='reflect')
return audio
def save_wav(wav, path, sample_rate, norm=False):
if norm:
wav *= 32767 / max(0.01, np.max(np.abs(wav)))
wavfile.write(path, sample_rate, wav.astype(np.int16))
else:
sf.write(path, wav, sample_rate)
_mel_basis = None
_inv_mel_basis = None
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.acoustic_dim,
fmin=hparams.fmin,
fmax=hparams.fmax)
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 _stft(y, hparams):
return librosa.stft(y=y,
n_fft=hparams.n_fft,
hop_length=hparams.hop_size,
win_length=hparams.win_size)
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 _normalize(S, hparams):
return hparams.max_abs_value * np.clip(((S - hparams.min_db) /
(-hparams.min_db)), 0, 1)
def _db_to_amp(x):
return np.power(10.0, (x) * 0.05)
def _stft(y, hparams):
return librosa.stft(y=y,
n_fft=hparams.n_fft,
hop_length=hparams.hop_size,
win_length=hparams.win_size)
def _istft(y, hparams):
return librosa.istft(y,
hop_length=hparams.hop_size,
win_length=hparams.win_size)
def melspectrogram(wav, hparams):
D = _stft(wav, hparams)
S = _amp_to_db(_linear_to_mel(np.abs(D), hparams),
hparams) - hparams.ref_level_db
return _normalize(S, hparams)
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