import os 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 ) 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=8, initial_learning_rate=1e-4, nepochs=300000, ### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs num_workers=20, checkpoint_interval=3000, eval_interval=3000, writer_interval=300, 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, ) # Default hyperparameters hparamsdebug = 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 ) 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. ) 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)