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import math
import numpy as np
import librosa
import vocoder.hparams as hp
from scipy.signal import lfilter
import soundfile as sf
def label_2_float(x, bits) :
return 2 * x / (2**bits - 1.) - 1.
def float_2_label(x, bits) :
assert abs(x).max() <= 1.0
x = (x + 1.) * (2**bits - 1) / 2
return x.clip(0, 2**bits - 1)
def load_wav(path) :
return librosa.load(str(path), sr=hp.sample_rate)[0]
def save_wav(x, path) :
sf.write(path, x.astype(np.float32), hp.sample_rate)
def split_signal(x) :
unsigned = x + 2**15
coarse = unsigned // 256
fine = unsigned % 256
return coarse, fine
def combine_signal(coarse, fine) :
return coarse * 256 + fine - 2**15
def encode_16bits(x) :
return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16)
mel_basis = None
def linear_to_mel(spectrogram):
global mel_basis
if mel_basis is None:
mel_basis = build_mel_basis()
return np.dot(mel_basis, spectrogram)
def build_mel_basis():
return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin)
def normalize(S):
return np.clip((S - hp.min_level_db) / -hp.min_level_db, 0, 1)
def denormalize(S):
return (np.clip(S, 0, 1) * -hp.min_level_db) + hp.min_level_db
def amp_to_db(x):
return 20 * np.log10(np.maximum(1e-5, x))
def db_to_amp(x):
return np.power(10.0, x * 0.05)
def spectrogram(y):
D = stft(y)
S = amp_to_db(np.abs(D)) - hp.ref_level_db
return normalize(S)
def melspectrogram(y):
D = stft(y)
S = amp_to_db(linear_to_mel(np.abs(D)))
return normalize(S)
def stft(y):
return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=hp.hop_length, win_length=hp.win_length)
def pre_emphasis(x):
return lfilter([1, -hp.preemphasis], [1], x)
def de_emphasis(x):
return lfilter([1], [1, -hp.preemphasis], x)
def encode_mu_law(x, mu) :
mu = mu - 1
fx = np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu)
return np.floor((fx + 1) / 2 * mu + 0.5)
def decode_mu_law(y, mu, from_labels=True) :
if from_labels:
y = label_2_float(y, math.log2(mu))
mu = mu - 1
x = np.sign(y) / mu * ((1 + mu) ** np.abs(y) - 1)
return x
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