238 lines
7.8 KiB
Python
238 lines
7.8 KiB
Python
import librosa
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import librosa.filters
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import numpy as np
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from scipy import signal
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from scipy.io import wavfile
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import lws
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class HParams:
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def __init__(self, **kwargs):
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self.data = {}
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for key, value in kwargs.items():
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self.data[key] = value
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def __getattr__(self, key):
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if key not in self.data:
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raise AttributeError("'HParams' object has no attribute %s" % key)
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return self.data[key]
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def set_hparam(self, key, value):
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self.data[key] = value
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# Default hyperparameters
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hp = HParams(
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num_mels=80, # Number of mel-spectrogram channels and local conditioning dimensionality
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# network
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rescale=True, # Whether to rescale audio prior to preprocessing
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rescaling_max=0.9, # Rescaling value
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# Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction
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# It"s preferred to set True to use with https://github.com/r9y9/wavenet_vocoder
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# Does not work if n_ffit is not multiple of hop_size!!
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use_lws=False,
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n_fft=800, # Extra window size is filled with 0 paddings to match this parameter
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hop_size=200, # For 16000Hz, 200 = 12.5 ms (0.0125 * sample_rate)
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win_size=800, # For 16000Hz, 800 = 50 ms (If None, win_size = n_fft) (0.05 * sample_rate)
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sample_rate=16000, # 16000Hz (corresponding to librispeech) (sox --i <filename>)
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frame_shift_ms=None, # Can replace hop_size parameter. (Recommended: 12.5)
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# Mel and Linear spectrograms normalization/scaling and clipping
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signal_normalization=True,
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# Whether to normalize mel spectrograms to some predefined range (following below parameters)
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allow_clipping_in_normalization=True, # Only relevant if mel_normalization = True
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symmetric_mels=True,
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# Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2,
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# faster and cleaner convergence)
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max_abs_value=4.,
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# max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not
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# be too big to avoid gradient explosion,
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# not too small for fast convergence)
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# Contribution by @begeekmyfriend
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# Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude
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# levels. Also allows for better G&L phase reconstruction)
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preemphasize=True, # whether to apply filter
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preemphasis=0.97, # filter coefficient.
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# Limits
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min_level_db=-100,
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ref_level_db=20,
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fmin=55,
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# Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To
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# test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
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fmax=7600, # To be increased/reduced depending on data.
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###################### Our training parameters #################################
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img_size=288,
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fps=25,
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batch_size=8,
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initial_learning_rate=1e-4,
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nepochs=200000000000000000,
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### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs
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num_workers=4,
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checkpoint_interval=6000,
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eval_interval=6000,
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save_optimizer_state=True,
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syncnet_wt=0.0, # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence.
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syncnet_batch_size=128,
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syncnet_lr=1e-4,
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syncnet_eval_interval=4500,
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syncnet_checkpoint_interval=4500,
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disc_wt=0.07,
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disc_initial_learning_rate=1e-4,
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)
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def load_wav(path, sr):
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return librosa.core.load(path, sr=sr)[0]
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def save_wav(wav, path, sr):
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wav *= 32767 / max(0.01, np.max(np.abs(wav)))
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# proposed by @dsmiller
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wavfile.write(path, sr, wav.astype(np.int16))
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def save_wavenet_wav(wav, path, sr):
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librosa.output.write_wav(path, wav, sr=sr)
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def preemphasis(wav, k, preemphasize=True):
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if preemphasize:
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return signal.lfilter([1, -k], [1], wav)
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return wav
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def inv_preemphasis(wav, k, inv_preemphasize=True):
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if inv_preemphasize:
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return signal.lfilter([1], [1, -k], wav)
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return wav
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def get_hop_size():
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hop_size = hp.hop_size
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if hop_size is None:
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assert hp.frame_shift_ms is not None
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hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate)
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return hop_size
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def linearspectrogram(wav):
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D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
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S = _amp_to_db(np.abs(D)) - hp.ref_level_db
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if hp.signal_normalization:
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return _normalize(S)
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return S
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def melspectrogram(wav):
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D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
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S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db
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if hp.signal_normalization:
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return _normalize(S)
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return S
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def _lws_processor():
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return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech")
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def _stft(y):
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if hp.use_lws:
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return _lws_processor(hp).stft(y).T
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else:
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return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size)
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##########################################################
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# Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
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def num_frames(length, fsize, fshift):
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"""Compute number of time frames of spectrogram
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"""
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pad = (fsize - fshift)
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if length % fshift == 0:
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M = (length + pad * 2 - fsize) // fshift + 1
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else:
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M = (length + pad * 2 - fsize) // fshift + 2
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return M
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def pad_lr(x, fsize, fshift):
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"""Compute left and right padding
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"""
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M = num_frames(len(x), fsize, fshift)
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pad = (fsize - fshift)
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T = len(x) + 2 * pad
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r = (M - 1) * fshift + fsize - T
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return pad, pad + r
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##########################################################
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# Librosa correct padding
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def librosa_pad_lr(x, fsize, fshift):
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return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
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# Conversions
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_mel_basis = None
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def _linear_to_mel(spectogram):
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global _mel_basis
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if _mel_basis is None:
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_mel_basis = _build_mel_basis()
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return np.dot(_mel_basis, spectogram)
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def _build_mel_basis():
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assert hp.fmax <= hp.sample_rate // 2
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return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels,
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fmin=hp.fmin, fmax=hp.fmax)
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def _amp_to_db(x):
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min_level = np.exp(hp.min_level_db / 20 * np.log(10))
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return 20 * np.log10(np.maximum(min_level, x))
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def _db_to_amp(x):
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return np.power(10.0, (x) * 0.05)
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def _normalize(S):
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if hp.allow_clipping_in_normalization:
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if hp.symmetric_mels:
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return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value,
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-hp.max_abs_value, hp.max_abs_value)
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else:
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return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value)
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assert S.max() <= 0 and S.min() - hp.min_level_db >= 0
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if hp.symmetric_mels:
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return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value
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else:
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return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db))
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def _denormalize(D):
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if hp.allow_clipping_in_normalization:
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if hp.symmetric_mels:
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return (((np.clip(D, -hp.max_abs_value,
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hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value))
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+ hp.min_level_db)
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else:
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return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
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if hp.symmetric_mels:
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return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db)
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else:
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return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
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