102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
from glob import glob
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import os
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def get_image_list(data_root, split):
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filelist = []
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with open('filelists/{}.txt'.format(split)) as f:
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for line in f:
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line = line.strip()
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if ' ' in line: line = line.split()[0]
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filelist.append(os.path.join(data_root, line))
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return filelist
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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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hparams = 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=96,
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fps=25,
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batch_size=16,
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initial_learning_rate=1e-4,
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nepochs=200000000000000000, ### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs
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num_workers=16,
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checkpoint_interval=3000,
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eval_interval=3000,
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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=64,
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syncnet_lr=1e-4,
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syncnet_eval_interval=10000,
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syncnet_checkpoint_interval=10000,
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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 hparams_debug_string():
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values = hparams.values()
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hp = [" %s: %s" % (name, values[name]) for name in sorted(values) if name != "sentences"]
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return "Hyperparameters:\n" + "\n".join(hp)
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