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utils.py
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utils.py
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# -*- 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