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import math
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import numpy as np
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import librosa
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from utils import hparams as hp
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from scipy.signal import lfilter
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def label_2_float(x, bits):
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return 2 * x / (2**bits - 1.) - 1.
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def float_2_label(x, bits):
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assert abs(x).max() <= 1.0
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x = (x + 1.) * (2**bits - 1) / 2
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return x.clip(0, 2**bits - 1)
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def load_wav(path):
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return librosa.load(path, sr=hp.sample_rate)[0]
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def save_wav(x, path):
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librosa.output.write_wav(path, x.astype(np.float32), sr=hp.sample_rate)
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def split_signal(x):
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unsigned = x + 2**15
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coarse = unsigned // 256
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fine = unsigned % 256
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return coarse, fine
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def combine_signal(coarse, fine):
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return coarse * 256 + fine - 2**15
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def encode_16bits(x):
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return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16)
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def linear_to_mel(spectrogram):
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return librosa.feature.melspectrogram(
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S=spectrogram, sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin)
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'''
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def build_mel_basis():
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return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin)
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'''
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def normalize(S):
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return np.clip((S - hp.min_level_db) / -hp.min_level_db, 0, 1)
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def denormalize(S):
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return (np.clip(S, 0, 1) * -hp.min_level_db) + hp.min_level_db
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def amp_to_db(x):
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return 20 * np.log10(np.maximum(1e-5, x))
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def db_to_amp(x):
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return np.power(10.0, x * 0.05)
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def spectrogram(y):
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D = stft(y)
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S = amp_to_db(np.abs(D)) - hp.ref_level_db
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return normalize(S)
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def melspectrogram(y):
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D = stft(y)
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S = amp_to_db(linear_to_mel(np.abs(D)))
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return normalize(S)
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def stft(y):
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return librosa.stft(
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y=y,
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n_fft=hp.n_fft, hop_length=hp.hop_length, win_length=hp.win_length)
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def pre_emphasis(x):
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return lfilter([1, -hp.preemphasis], [1], x)
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def de_emphasis(x):
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return lfilter([1], [1, -hp.preemphasis], x)
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def encode_mu_law(x, mu):
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mu = mu - 1
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fx = np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu)
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return np.floor((fx + 1) / 2 * mu + 0.5)
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def decode_mu_law(y, mu, from_labels=True):
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if from_labels: y = label_2_float(y, math.log2(mu))
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mu = mu - 1
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x = np.sign(y) / mu * ((1 + mu) ** np.abs(y) - 1)
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return x
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def reconstruct_waveform(mel, n_iter=32):
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"""Uses Griffin-Lim phase reconstruction to convert from a normalized
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mel spectrogram back into a waveform."""
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denormalized = denormalize(mel)
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amp_mel = db_to_amp(denormalized)
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S = librosa.feature.inverse.mel_to_stft(
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amp_mel, power=1, sr=hp.sample_rate,
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n_fft=hp.n_fft, fmin=hp.fmin)
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wav = librosa.core.griffinlim(
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S, n_iter=n_iter,
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hop_length=hp.hop_length, win_length=hp.win_length)
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return wav
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