multi-speaker-tacotron-tens.../audio/__init__.py
2017-10-16 16:41:44 +09:00

168 lines
5.2 KiB
Python

# Code based on https://github.com/keithito/tacotron/blob/master/util/audio.py
import math
import numpy as np
import tensorflow as tf
from scipy import signal
from hparams import hparams
import librosa
import librosa.filters
def load_audio(path, pre_silence_length=0, post_silence_length=0):
audio = librosa.core.load(path, sr=hparams.sample_rate)[0]
if pre_silence_length > 0 or post_silence_length > 0:
audio = np.concatenate([
get_silence(pre_silence_length),
audio,
get_silence(post_silence_length),
])
return audio
def save_audio(audio, path, sample_rate=None):
audio *= 32767 / max(0.01, np.max(np.abs(audio)))
librosa.output.write_wav(path, audio.astype(np.int16),
hparams.sample_rate if sample_rate is None else sample_rate)
print(" [*] Audio saved: {}".format(path))
def resample_audio(audio, target_sample_rate):
return librosa.core.resample(
audio, hparams.sample_rate, target_sample_rate)
def get_duration(audio):
return librosa.core.get_duration(audio, sr=hparams.sample_rate)
def frames_to_hours(n_frames):
return sum((n_frame for n_frame in n_frames)) * \
hparams.frame_shift_ms / (3600 * 1000)
def get_silence(sec):
return np.zeros(hparams.sample_rate * sec)
def spectrogram(y):
D = _stft(_preemphasis(y))
S = _amp_to_db(np.abs(D)) - hparams.ref_level_db
return _normalize(S)
def inv_spectrogram(spectrogram):
S = _db_to_amp(_denormalize(spectrogram) + hparams.ref_level_db) # Convert back to linear
return inv_preemphasis(_griffin_lim(S ** hparams.power)) # Reconstruct phase
def inv_spectrogram_tensorflow(spectrogram):
S = _db_to_amp_tensorflow(_denormalize_tensorflow(spectrogram) + hparams.ref_level_db)
return _griffin_lim_tensorflow(tf.pow(S, hparams.power))
def melspectrogram(y):
D = _stft(_preemphasis(y))
S = _amp_to_db(_linear_to_mel(np.abs(D)))
return _normalize(S)
def inv_melspectrogram(melspectrogram):
S = _mel_to_linear(_db_to_amp(_denormalize(melspectrogram))) # Convert back to linear
return inv_preemphasis(_griffin_lim(S ** hparams.power)) # Reconstruct phase
# Based on https://github.com/librosa/librosa/issues/434
def _griffin_lim(S):
angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
S_complex = np.abs(S).astype(np.complex)
y = _istft(S_complex * angles)
for i in range(hparams.griffin_lim_iters):
angles = np.exp(1j * np.angle(_stft(y)))
y = _istft(S_complex * angles)
return y
def _griffin_lim_tensorflow(S):
with tf.variable_scope('griffinlim'):
S = tf.expand_dims(S, 0)
S_complex = tf.identity(tf.cast(S, dtype=tf.complex64))
y = _istft_tensorflow(S_complex)
for i in range(hparams.griffin_lim_iters):
est = _stft_tensorflow(y)
angles = est / tf.cast(tf.maximum(1e-8, tf.abs(est)), tf.complex64)
y = _istft_tensorflow(S_complex * angles)
return tf.squeeze(y, 0)
def _stft(y):
n_fft, hop_length, win_length = _stft_parameters()
return librosa.stft(y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length)
def _istft(y):
_, hop_length, win_length = _stft_parameters()
return librosa.istft(y, hop_length=hop_length, win_length=win_length)
def _stft_tensorflow(signals):
n_fft, hop_length, win_length = _stft_parameters()
return tf.contrib.signal.stft(signals, win_length, hop_length, n_fft, pad_end=False)
def _istft_tensorflow(stfts):
n_fft, hop_length, win_length = _stft_parameters()
return tf.contrib.signal.inverse_stft(stfts, win_length, hop_length, n_fft)
def _stft_parameters():
n_fft = (hparams.num_freq - 1) * 2
hop_length = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)
win_length = int(hparams.frame_length_ms / 1000 * hparams.sample_rate)
return n_fft, hop_length, win_length
# Conversions:
_mel_basis = None
_inv_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 _mel_to_linear(mel_spectrogram):
global _inv_mel_basis
if _inv_mel_basis is None:
_inv_mel_basis = np.linalg.pinv(_build_mel_basis())
return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram))
def _build_mel_basis():
n_fft = (hparams.num_freq - 1) * 2
return librosa.filters.mel(hparams.sample_rate, n_fft, n_mels=hparams.num_mels)
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 _db_to_amp_tensorflow(x):
return tf.pow(tf.ones(tf.shape(x)) * 10.0, x * 0.05)
def _preemphasis(x):
return signal.lfilter([1, -hparams.preemphasis], [1], x)
def inv_preemphasis(x):
return signal.lfilter([1], [1, -hparams.preemphasis], x)
def _normalize(S):
return np.clip((S - hparams.min_level_db) / -hparams.min_level_db, 0, 1)
def _denormalize(S):
return (np.clip(S, 0, 1) * -hparams.min_level_db) + hparams.min_level_db
def _denormalize_tensorflow(S):
return (tf.clip_by_value(S, 0, 1) * -hparams.min_level_db) + hparams.min_level_db