-
Notifications
You must be signed in to change notification settings - Fork 436
/
utils.py
154 lines (121 loc) · 4.33 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
# -*- coding: utf-8 -*-
# /usr/bin/python2
'''
By kyubyong park. [email protected].
https://www.github.com/kyubyong/dc_tts
'''
from __future__ import print_function, division
from hyperparams import Hyperparams 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
'''
# num = np.random.randn()
# if num < .2:
# y, sr = librosa.load(fpath, sr=hp.sr)
# else:
# if num < .4:
# tempo = 1.1
# elif num < .6:
# tempo = 1.2
# elif num < .8:
# tempo = 0.9
# else:
# tempo = 0.8
# cmd = "ffmpeg -i {} -y ar {} -hide_banner -loglevel panic -ac 1 -filter:a atempo={} -vn temp.wav".format(fpath, hp.sr, tempo)
# os.system(cmd)
# y, sr = librosa.load('temp.wav', sr=hp.sr)
# 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