| from glob import glob | |
| import os | |
| def get_image_list(data_root, split): | |
| filelist = [] | |
| with open('filelists/{}.txt'.format(split)) as f: | |
| for line in f: | |
| line = line.strip() | |
| if ' ' in line: line = line.split()[0] | |
| filelist.append(os.path.join(data_root, line)) | |
| return filelist | |
| 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 | |
| hparams = 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 hparams_debug_string(): | |
| values = hparams.values() | |
| hp = [" %s: %s" % (name, values[name]) for name in sorted(values) if name != "sentences"] | |
| return "Hyperparameters:\n" + "\n".join(hp) | |