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utils.py
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utils.py
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from __future__ import print_function, division
from Hyperparameters import Hyperparameters as hp
import numpy as np
# import tensorflow as tf
import librosa
import copy
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as plt
from scipy import signal
import os
def get_spectrograms(fpath):
'''Returns normalized log(melspectrogram) and log(magnitude) from `sound_file`.
Args:
sound_file: A string. The full path of a sound file.
Returns:
mel: A 2d array of shape (T, n_mels) <- Transposed
mag: A 2d array of shape (T, 1+n_fft/2) <- Transposed
'''
# Loading sound file
y, sr = librosa.load(fpath, sr=hp.sr)
# Trimming
y, _ = librosa.effects.trim(y)
# Preemphasis
y = np.append(y[0], y[1:] - hp.preemphasis * y[:-1])
# stft
linear = librosa.stft(y=y,
n_fft=hp.n_fft,
hop_length=hp.hop_length,
win_length=hp.win_length)
# magnitude spectrogram
mag = np.abs(linear) # (1+n_fft//2, T)
# mel spectrogram
mel_basis = librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels) # (n_mels, 1+n_fft//2)
mel = np.dot(mel_basis, mag) # (n_mels, t)
# to decibel
mel = 20 * np.log10(np.maximum(1e-5, mel))
mag = 20 * np.log10(np.maximum(1e-5, mag))
# normalize
mel = np.clip((mel - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1)
mag = np.clip((mag - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1)
# Transpose
mel = mel.T.astype(np.float32) # (T, n_mels)
mag = mag.T.astype(np.float32) # (T, 1+n_fft//2)
return mel, mag
def spectrogram2wav(mag):
'''# Generate wave file from spectrogram'''
# transpose
mag = mag.T
# de-noramlize
mag = (np.clip(mag, 0, 1) * hp.max_db) - hp.max_db + hp.ref_db
# to amplitude
mag = np.power(10.0, mag * 0.05)
# wav reconstruction
wav = griffin_lim(mag)
# de-preemphasis
wav = signal.lfilter([1], [1, -hp.preemphasis], wav)
# trim
wav, _ = librosa.effects.trim(wav)
return wav.astype(np.float32)
def griffin_lim(spectrogram):
'''Applies Griffin-Lim's raw.
'''
X_best = copy.deepcopy(spectrogram)
for i in range(hp.n_iter):
X_t = invert_spectrogram(X_best)
est = librosa.stft(X_t, hp.n_fft, hp.hop_length, win_length=hp.win_length)
phase = est / np.maximum(1e-8, np.abs(est))
X_best = spectrogram * phase
X_t = invert_spectrogram(X_best)
y = np.real(X_t)
return y
def invert_spectrogram(spectrogram):
'''
spectrogram: [f, t]
'''
return librosa.istft(spectrogram, hp.hop_length, win_length=hp.win_length, window="hann")
def plot_alignment(alignment, gs):
"""Plots the alignment
alignments: A list of (numpy) matrix of shape (encoder_steps, decoder_steps)
gs : (int) global step
"""
fig, ax = plt.subplots()
im = ax.imshow(alignment)
# cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im)
plt.title('{} Steps'.format(gs))
plt.savefig('{}/alignment_{}k.png'.format(hp.logdir, gs // 1000), format='png')
def learning_rate_decay(init_lr, global_step, warmup_steps=4000.):
'''Noam scheme from tensor2tensor'''
step = tf.cast(global_step + 1, dtype=tf.float32)
return init_lr * warmup_steps ** 0.5 * tf.minimum(step * warmup_steps ** -1.5, step ** -0.5)
def load_spectrograms(fpath):
fname = os.path.basename(fpath)
mel, mag = get_spectrograms(fpath)
t = mel.shape[0]
num_paddings = hp.r - (t % hp.r) if t % hp.r != 0 else 0 # for reduction
mel = np.pad(mel, [[0, num_paddings], [0, 0]], mode="constant")
mag = np.pad(mag, [[0, num_paddings], [0, 0]], mode="constant")
return fname, mel.reshape((-1, hp.n_mels * hp.r)), mag