hparams.py

# NOTE: If you want full control for model architecture. please take a look
# at the code and change whatever you want. Some hyper parameters are hardcoded.


class Map(dict):
	"""
    Example:
    m = Map({'first_name': 'Eduardo'}, last_name='Pool', age=24, sports=['Soccer'])

    Credits to epool:
    https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary
    """

	def __init__(self, *args, **kwargs):
		super(Map, self).__init__(*args, **kwargs)
		for arg in args:
			if isinstance(arg, dict):
				for k, v in arg.items():
					self[k] = v

		if kwargs:
			for k, v in kwargs.iteritems():
				self[k] = v

	def __getattr__(self, attr):
		return self.get(attr)

	def __setattr__(self, key, value):
		self.__setitem__(key, value)

	def __setitem__(self, key, value):
		super(Map, self).__setitem__(key, value)
		self.__dict__.update({key: value})

	def __delattr__(self, item):
		self.__delitem__(item)

	def __delitem__(self, key):
		super(Map, self).__delitem__(key)
		del self.__dict__[key]


# Default hyperparameters:
hparams = Map({
	'name': "wavenet_vocoder",

	# Convenient model builder
	'builder': "wavenet",

	# Input type:
	# 1. raw [-1, 1]
	# 2. mulaw [-1, 1]
	# 3. mulaw-quantize [0, mu]
	# If input_type is raw or mulaw, network assumes scalar input and
	# discretized mixture of logistic distributions output, otherwise one-hot
	# input and softmax output are assumed.
	# **NOTE**: if you change the one of the two parameters below, you need to
	# re-run preprocessing before training.
	'input_type': "raw",
	'quantize_channels': 65536,  # 65536 or 256

	# Audio:
	'sample_rate': 16000,
	# this is only valid for mulaw is True
	'silence_threshold': 2,
	'num_mels': 80,
	'fmin': 125,
	'fmax': 7600,
	'fft_size': 1024,
	# shift can be specified by either hop_size or frame_shift_ms
	'hop_size': 256,
	'frame_shift_ms': None,
	'min_level_db': -100,
	'ref_level_db': 20,
	# whether to rescale waveform or not.
	# Let x is an input waveform, rescaled waveform y is given by:
	# y = x / np.abs(x).max() * rescaling_max
	'rescaling': True,
	'rescaling_max': 0.999,
	# mel-spectrogram is normalized to [0, 1] for each utterance and clipping may
	# happen depends on min_level_db and ref_level_db, causing clipping noise.
	# If False, assertion is added to ensure no clipping happens.o0
	'allow_clipping_in_normalization': True,

	# Mixture of logistic distributions:
	'log_scale_min': float(-32.23619130191664),

	# Model:
	# This should equal to `quantize_channels` if mu-law quantize enabled
	# otherwise num_mixture * 3 (pi, mean, log_scale)
	'out_channels': 10 * 3,
	'layers': 24,
	'stacks': 4,
	'residual_channels': 512,
	'gate_channels': 512,  # split into 2 gropus internally for gated activation
	'skip_out_channels': 256,
	'dropout': 1 - 0.95,
	'kernel_size': 3,
	# If True, apply weight normalization as same as DeepVoice3
	'weight_normalization': True,
	# Use legacy code or not. Default is True since we already provided a model
	# based on the legacy code that can generate high-quality audio.
	# Ref: https://github.com/r9y9/wavenet_vocoder/pull/73
	'legacy': True,

	# Local conditioning (set negative value to disable))
	'cin_channels': 80,
	# If True, use transposed convolutions to upsample conditional features,
	# otherwise repeat features to adjust time resolution
	'upsample_conditional_features': True,
	# should np.prod(upsample_scales) == hop_size
	'upsample_scales': [4, 4, 4, 4],
	# Freq axis kernel size for upsampling network
	'freq_axis_kernel_size': 3,

	# Global conditioning (set negative value to disable)
	# currently limited for speaker embedding
	# this should only be enabled for multi-speaker dataset
	'gin_channels': -1,  # i.e., speaker embedding dim
	'n_speakers': -1,

	# Data loader
	'pin_memory': True,
	'num_workers': 2,

	# train/test
	# test size can be specified as portion or num samples
	'test_size': 0.0441,  # 50 for CMU ARCTIC single speaker
	'test_num_samples': None,
	'random_state': 1234,

	# Loss

	# Training:
	'batch_size': 2,
	'adam_beta1': 0.9,
	'adam_beta2': 0.999,
	'adam_eps': 1e-8,
	'amsgrad': False,
	'initial_learning_rate': 1e-3,
	# see lrschedule.py for available lr_schedule
	'lr_schedule': "noam_learning_rate_decay",
	'lr_schedule_kwargs': {},  # {"anneal_rate": 0.5, "anneal_interval": 50000},
	'nepochs': 2000,
	'weight_decay': 0.0,
	'clip_thresh': -1,
	# max time steps can either be specified as sec or steps
	# if both are None, then full audio samples are used in a batch
	'max_time_sec': None,
	'max_time_steps': 8000,
	# Hold moving averaged parameters and use them for evaluation
	'exponential_moving_average': True,
	# averaged = decay * averaged + (1 - decay) * x
	'ema_decay': 0.9999,

	# Save
	# per-step intervals
	'checkpoint_interval': 10000,
	'train_eval_interval': 10000,
	# per-epoch interval
	'test_eval_epoch_interval': 5,
	'save_optimizer_state': True,

	# Eval:
})


def hparams_debug_string():
	values = hparams.values()
	hp = ['  %s: %s' % (name, values[name]) for name in sorted(values)]
	return 'Hyperparameters:\n' + '\n'.join(hp)