hparams.py

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


hparams = HParams(
	num_mels=80,
	rescale=True,
	rescaling_max=0.9,
	use_lws=False,
	n_fft=800,
	hop_size=200,
	win_size=800,
	sample_rate=16000,
	frame_shift_ms=None,
	power = 1.5,
	griffin_lim_iters = 60,
	signal_normalization=True,
	allow_clipping_in_normalization=True,
	symmetric_mels=True,
	max_abs_value=4.,
	preemphasize=True,
	preemphasis=0.97,
	
	# Limits
	min_level_db=-100,
	ref_level_db=20,
	fmin=55,
	fmax=7600,

	# Training hyperparameters
	img_size=128,
 	# img_size=512,
	# img_size=256,
    # img_size=96,
	fps=25,
	# batch_size = 2,
 	# batch_size = 3,
  	batch_size = 24,
	initial_learning_rate=1e-4,
    nepochs = 2000000000000000000,
	disc_initial_learning_rate=5e-4,

	eval_interval=3000,
	checkpoint_interval=3000,
	
	l1_wt = 10.,
	mem_wt=0.2,
	vv_wt = 0.2,
	av_wt=0.2,
	disc_wt=0.2,

	# num_workers=16,
	num_workers=1,
	m_slot = 96,
	min = 0,
	max = 0.7,

	syncnet_wt=0.03, # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence. 
	# for pretraining SyncNet
	# syncnet_batch_size=256,
 	syncnet_batch_size=64,
  	save_optimizer_state=True,
	syncnet_lr=1e-4,
	# syncnet_lr=1e-3,
	syncnet_eval_interval=10000,
	syncnet_checkpoint_interval=10000,
	# syncnet_eval_interval=5000,
	# syncnet_checkpoint_interval=5000,
)


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)