Just a quick test for LPCNet inference

audio.py

import librosa
import librosa.filters
import numpy as np
import tensorflow as tf
from scipy import signal
from scipy.io import wavfile

def save_wavenet_wav(wav, path, sr, inv_preemphasize, k):
	# wav = inv_preemphasis(wav, k, inv_preemphasize)
	wav *= 32767 / max(0.01, np.max(np.abs(wav)))
	wavfile.write(path, sr, wav.astype(np.int16))
 
 
def save_wav(wav, path, sr):
    wav *= 32767 / max(0.01, np.max(np.abs(wav))) 
    #proposed by @dsmiller
    wavfile.write(path, sr, wav.astype(np.int16))

#From https://github.com/r9y9/wavenet_vocoder/blob/master/audio.py
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 trim_silence(wav, hparams):
    '''Trim leading and trailing silence

    Useful for M-AILABS dataset if we choose to trim the extra 0.5 silence at beginning and end.
    '''
    #Thanks @begeekmyfriend and @lautjy for pointing out the params contradiction. These params are separate and tunable per dataset.
    return librosa.effects.trim(wav, top_db= hparams.trim_top_db, frame_length=hparams.trim_fft_size, hop_length=hparams.trim_hop_size)[0]

def get_hop_size(hparams):
    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

def linearspectrogram(wav, hparams):
    D = _stft(wav, hparams)
    S = _amp_to_db(np.abs(D), hparams) - hparams.ref_level_db

    if hparams.signal_normalization:
        return _normalize(S, hparams)
    return S

def melspectrogram(wav, hparams):
    D = _stft(wav, hparams)
    S = _amp_to_db(_linear_to_mel(np.abs(D), hparams), hparams) - hparams.ref_level_db

    if hparams.signal_normalization:
        return _normalize(S, hparams)
    return S

def inv_linear_spectrogram(linear_spectrogram, hparams):
    '''Converts linear spectrogram to waveform using librosa'''
    if hparams.signal_normalization:
        D = _denormalize(linear_spectrogram, hparams)
    else:
        D = linear_spectrogram

    S = _db_to_amp(D + hparams.ref_level_db) #Convert back to linear

    if hparams.use_lws:
        processor = _lws_processor(hparams)
        D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
        y = processor.istft(D).astype(np.float32)
        return y
    else:
        return _griffin_lim(S ** hparams.power, hparams)
    

def inv_mel_spectrogram(mel_spectrogram, hparams):
    '''Converts mel spectrogram to waveform using librosa'''
    if hparams.signal_normalization:
        D = _denormalize(mel_spectrogram, hparams)
    else:
        D = mel_spectrogram

    S = _mel_to_linear(_db_to_amp(D + hparams.ref_level_db), hparams)  # Convert back to linear

    if hparams.use_lws:
        processor = _lws_processor(hparams)
        D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
        y = processor.istft(D).astype(np.float32)
        return y
    else:
        return _griffin_lim(S ** hparams.power, hparams)

def _lws_processor(hparams):
    import lws
    return lws.lws(hparams.n_fft, get_hop_size(hparams), fftsize=hparams.win_size, mode="speech")

def _griffin_lim(S, hparams):
    '''librosa implementation of Griffin-Lim
    Based on https://github.com/librosa/librosa/issues/434
    '''
    angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
    S_complex = np.abs(S).astype(np.complex)
    y = _istft(S_complex * angles, hparams)
    for i in range(hparams.griffin_lim_iters):
        angles = np.exp(1j * np.angle(_stft(y, hparams)))
        y = _istft(S_complex * angles, hparams)
    return y

def _stft(y, hparams):
    if hparams.use_lws:
        return _lws_processor(hparams).stft(y).T
    else:
        return librosa.stft(y=y, n_fft=hparams.n_fft, hop_length=get_hop_size(hparams), win_length=hparams.win_size)

def _istft(y, hparams):
    return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams.win_size)

def num_frames(length, fsize, fshift):
    """Compute number of time frames of spectrogram
    """
    pad = (fsize - fshift)
    if length % fshift == 0:
        M = (length + pad * 2 - fsize) // fshift + 1
    else:
        M = (length + pad * 2 - fsize) // fshift + 2
    return M


def pad_lr(x, fsize, fshift):
    """Compute left and right padding
    """
    M = num_frames(len(x), fsize, fshift)
    pad = (fsize - fshift)
    T = len(x) + 2 * pad
    r = (M - 1) * fshift + fsize - T
    return pad, pad + r


# Conversions
_mel_basis = None
_inv_mel_basis = None

def _linear_to_mel(spectogram, hparams):
    global _mel_basis
    if _mel_basis is None:
        _mel_basis = _build_mel_basis(hparams)
    return np.dot(_mel_basis, spectogram)

def _mel_to_linear(mel_spectrogram, hparams):
    global _inv_mel_basis
    if _inv_mel_basis is None:
        _inv_mel_basis = np.linalg.pinv(_build_mel_basis(hparams))
    return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram))

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

def _amp_to_db(x, hparams):
    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, hparams):
    if hparams.allow_clipping_in_normalization:
        if hparams.symmetric_mels:
            return np.clip((2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value,
             -hparams.max_abs_value, hparams.max_abs_value)
        else:
            return np.clip(hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db)), 0, hparams.max_abs_value)

    assert S.max() <= 0 and S.min() - hparams.min_level_db >= 0
    if hparams.symmetric_mels:
        return (2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value
    else:
        return hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db))

def _denormalize(D, hparams):
    if hparams.allow_clipping_in_normalization:
        if hparams.symmetric_mels:
            return (((np.clip(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)
        else:
            return ((np.clip(D, 0, hparams.max_abs_value) * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)

    if hparams.symmetric_mels:
        return (((D + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value)) + hparams.min_level_db)
    else:
        return ((D * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)