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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)
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