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| import librosa | |
| import librosa.filters | |
| import numpy as np | |
| # import tensorflow as tf | |
| from scipy import signal | |
| from scipy.io import wavfile | |
| # from hparams import hparams as hp | |
| class HParams: | |
| def __init__(self, **kwargs): | |
| self.data = {} | |
| for key, value in kwargs.items(): | |
| self.data[key] = value | |
| def __getattr__(self, key): | |
| if key not in self.data: | |
| raise AttributeError("'HParams' object has no attribute %s" % key) | |
| return self.data[key] | |
| def set_hparam(self, key, value): | |
| self.data[key] = value | |
| # Default hyperparameters | |
| hp = HParams( | |
| num_mels=80, # Number of mel-spectrogram channels and local conditioning dimensionality | |
| # network | |
| rescale=True, # Whether to rescale audio prior to preprocessing | |
| rescaling_max=0.9, # Rescaling value | |
| # Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction | |
| # It"s preferred to set True to use with https://github.com/r9y9/wavenet_vocoder | |
| # Does not work if n_ffit is not multiple of hop_size!! | |
| use_lws=False, | |
| n_fft=800, # Extra window size is filled with 0 paddings to match this parameter | |
| hop_size=200, # For 16000Hz, 200 = 12.5 ms (0.0125 * sample_rate) | |
| win_size=800, # For 16000Hz, 800 = 50 ms (If None, win_size = n_fft) (0.05 * sample_rate) | |
| sample_rate=16000, # 16000Hz (corresponding to librispeech) (sox --i <filename>) | |
| frame_shift_ms=None, # Can replace hop_size parameter. (Recommended: 12.5) | |
| # Mel and Linear spectrograms normalization/scaling and clipping | |
| signal_normalization=True, | |
| # Whether to normalize mel spectrograms to some predefined range (following below parameters) | |
| allow_clipping_in_normalization=True, # Only relevant if mel_normalization = True | |
| symmetric_mels=True, | |
| # Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2, | |
| # faster and cleaner convergence) | |
| max_abs_value=4., | |
| # max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not | |
| # be too big to avoid gradient explosion, | |
| # not too small for fast convergence) | |
| # Contribution by @begeekmyfriend | |
| # Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude | |
| # levels. Also allows for better G&L phase reconstruction) | |
| preemphasize=True, # whether to apply filter | |
| preemphasis=0.97, # filter coefficient. | |
| # Limits | |
| min_level_db=-100, | |
| ref_level_db=20, | |
| fmin=55, | |
| # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To | |
| # test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) | |
| fmax=7600, # To be increased/reduced depending on data. | |
| ###################### Our training parameters ################################# | |
| img_size=96, | |
| fps=25, | |
| batch_size=16, | |
| initial_learning_rate=1e-4, | |
| nepochs=200000000000000000, ### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs | |
| num_workers=16, | |
| checkpoint_interval=3000, | |
| eval_interval=3000, | |
| save_optimizer_state=True, | |
| syncnet_wt=0.0, # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence. | |
| syncnet_batch_size=64, | |
| syncnet_lr=1e-4, | |
| syncnet_eval_interval=10000, | |
| syncnet_checkpoint_interval=10000, | |
| disc_wt=0.07, | |
| disc_initial_learning_rate=1e-4, | |
| ) | |
| def load_wav(path, sr): | |
| return librosa.core.load(path, sr=sr)[0] | |
| 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)) | |
| def save_wavenet_wav(wav, path, sr): | |
| librosa.output.write_wav(path, wav, sr=sr) | |
| def preemphasis(wav, k, preemphasize=True): | |
| if preemphasize: | |
| return signal.lfilter([1, -k], [1], wav) | |
| return wav | |
| def inv_preemphasis(wav, k, inv_preemphasize=True): | |
| if inv_preemphasize: | |
| return signal.lfilter([1], [1, -k], wav) | |
| return wav | |
| def get_hop_size(): | |
| hop_size = hp.hop_size | |
| if hop_size is None: | |
| assert hp.frame_shift_ms is not None | |
| hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate) | |
| return hop_size | |
| def linearspectrogram(wav): | |
| D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) | |
| S = _amp_to_db(np.abs(D)) - hp.ref_level_db | |
| if hp.signal_normalization: | |
| return _normalize(S) | |
| return S | |
| def melspectrogram(wav): | |
| D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) | |
| S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db | |
| if hp.signal_normalization: | |
| return _normalize(S) | |
| return S | |
| def _lws_processor(): | |
| import lws | |
| return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech") | |
| def _stft(y): | |
| if hp.use_lws: | |
| return _lws_processor(hp).stft(y).T | |
| else: | |
| return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size) | |
| ########################################################## | |
| #Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) | |
| 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 | |
| ########################################################## | |
| #Librosa correct padding | |
| def librosa_pad_lr(x, fsize, fshift): | |
| return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] | |
| # Conversions | |
| _mel_basis = None | |
| def _linear_to_mel(spectogram): | |
| global _mel_basis | |
| if _mel_basis is None: | |
| _mel_basis = _build_mel_basis() | |
| return np.dot(_mel_basis, spectogram) | |
| def _build_mel_basis(): | |
| assert hp.fmax <= hp.sample_rate // 2 | |
| # return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels, | |
| # fmin=hp.fmin, fmax=hp.fmax) | |
| return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft) | |
| def _amp_to_db(x): | |
| min_level = np.exp(hp.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): | |
| if hp.allow_clipping_in_normalization: | |
| if hp.symmetric_mels: | |
| return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value, | |
| -hp.max_abs_value, hp.max_abs_value) | |
| else: | |
| return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value) | |
| assert S.max() <= 0 and S.min() - hp.min_level_db >= 0 | |
| if hp.symmetric_mels: | |
| return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value | |
| else: | |
| return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)) | |
| def _denormalize(D): | |
| if hp.allow_clipping_in_normalization: | |
| if hp.symmetric_mels: | |
| return (((np.clip(D, -hp.max_abs_value, | |
| hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) | |
| + hp.min_level_db) | |
| else: | |
| return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) | |
| if hp.symmetric_mels: | |
| return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db) | |
| else: | |
| return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) | |