audio.py

# from https://github.com/keithito/tacotron/blob/master/util/audio.py

import librosa
import librosa.filters
import math
import numpy as np
from scipy import signal
from hparams import hparams
from scipy.io import wavfile


n_fft = hparams.fft_size * 2
hop_length = hparams.hop_length
win_length = hparams.win_length

def load_wav(path):
    return librosa.core.load(path, sr=hparams.sample_rate)[0]


def save_wav(wav, path):
    wav *= hparams.audio_max_value / max(0.01, np.max(np.abs(wav)))
    wavfile.write(path, hparams.sample_rate, wav.astype(np.int16))


def trim(quantized):
    start, end = start_and_end_indices(quantized, hparams.silence_threshold)
    return quantized[start:end]


def adjust_time_resolution(quantized, mel):
    """Adjust time resolution by repeating features

    Args:
        quantized (ndarray): (T,)
        mel (ndarray): (N, D)

    Returns:
        tuple: Tuple of (T,) and (T, D)
    """
    assert len(quantized.shape) == 1
    assert len(mel.shape) == 2

    upsample_factor = quantized.size // mel.shape[0]
    mel = np.repeat(mel, upsample_factor, axis=0)
    n_pad = quantized.size - mel.shape[0]
    if n_pad != 0:
        assert n_pad > 0
        mel = np.pad(mel, [(0, n_pad), (0, 0)], mode="constant", constant_values=0)

    # trim
    start, end = start_and_end_indices(quantized, hparams.silence_threshold)

    return quantized[start:end], mel[start:end, :]
adjast_time_resolution = adjust_time_resolution  # 'adjust' is correct spelling, this is for compatibility


def start_and_end_indices(quantized, silence_threshold=2):
    for start in range(quantized.size):
        if abs(quantized[start] - 127) > silence_threshold:
            break
    for end in range(quantized.size - 1, 1, -1):
        if abs(quantized[end] - 127) > silence_threshold:
            break

    assert abs(quantized[start] - 127) > silence_threshold
    assert abs(quantized[end] - 127) > silence_threshold

    return start, end

def _stft(y):
  return librosa.stft(y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=True)

def melspectrogram(y):
    D = _stft(y)
    S = _amp_to_db(_linear_to_mel(np.abs(D))) - hparams.ref_level_db
    if not hparams.allow_clipping_in_normalization:
        assert S.max() <= 0 and S.min() - hparams.min_level_db >= 0
    return _normalize(S)

def spectrogram(y):
    D = _stft(y)
    S = _amp_to_db(np.abs(D)) - hparams.ref_level_db
    return _normalize(S)




def get_hop_size():
    hop_size = hparams.hop_size
    if hop_size is None:
        assert hparams.frame_shift_ms is not None
        hop_size = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)
    return hop_size

# Conversions:

_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 _build_mel_basis():
    assert hparams.fmax <= hparams.sample_rate // 2
    return librosa.filters.mel(hparams.sample_rate, n_fft,
                               fmin=hparams.fmin, fmax=hparams.fmax,
                               n_mels=hparams.num_mels)


def _amp_to_db(x):
    min_level = np.exp(hparams.min_level_db / 20 * np.log(10))
    return 20 * np.log10(np.maximum(min_level, x))


def _db_to_amp(x):
    return np.power(10.0, x * 0.05)


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