multi-speaker-tacotron-tensorflow/audio/__init__.py

# 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