# encoding: utf-8
import tensorflow as tf
from hparams import hparams
"""
TensorFlow implementation of Griffin-lim Algorithm for voice reconstruction
"""
# TF
def spectrogram2wav(spectrogram, n_iter=hparams.griffin_lim_iters, n_fft=(hparams.num_freq - 1) * 2,
win_length=int(hparams.frame_length_ms / 1000 * hparams.sample_rate),
hop_length=int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)):
'''Converts spectrogram into a waveform using Griffin-lim's raw.
'''
def invert_spectrogram(spectrogram):
'''
spectrogram: [t, f]
'''
spectrogram = tf.expand_dims(spectrogram, 0)
inversed = tf.contrib.signal.inverse_stft(spectrogram, win_length, hop_length, n_fft)
squeezed = tf.squeeze(inversed, 0)
return squeezed
spectrogram = tf.transpose(spectrogram)
spectrogram = tf.cast(spectrogram, dtype=tf.complex64) # [t, f]
X_best = tf.identity(spectrogram)
for i in range(n_iter):
X_t = invert_spectrogram(X_best)
est = tf.contrib.signal.stft(X_t, win_length, hop_length, n_fft, pad_end=False) # (1, T, n_fft/2+1)
phase = est / tf.cast(tf.maximum(1e-8, tf.abs(est)), tf.complex64) # [t, f]
X_best = spectrogram * phase # [t, t]
X_t = invert_spectrogram(X_best)
y = tf.real(X_t)
return y
def inv_spectrogram(spectrogram):
S = _db_to_amp(_denormalize(spectrogram) + hparams.ref_level_db) # Convert back to linear
return _inv_preemphasis(spectrogram2wav(S ** hparams.power)) # Reconstruct phase
def _denormalize(D):
return (((tf.clip_by_value(D, -hparams.max_abs_value,
hparams.max_abs_value) + hparams.max_abs_value) * -hparams.min_level_db / (
2 * hparams.max_abs_value)) + hparams.min_level_db)
def _db_to_amp(x):
return tf.pow(tf.ones(tf.shape(x)) * 10.0, x * 0.05)
def _inv_preemphasis(x):
N = tf.shape(x)[0]
i = tf.constant(0)
W = tf.zeros(shape=tf.shape(x), dtype=tf.float32)
def condition(i, y):
return tf.less(i, N)
def body(i, y):
tmp = tf.slice(x, [0], [i + 1])
tmp = tf.concat([tf.zeros([N - i - 1]), tmp], -1)
y = hparams.preemphasis * y + tmp
i = tf.add(i, 1)
return [i, y]
final = tf.while_loop(condition, body, [i, W])
y = final[1]
return y
