Create hparams.py

hparams.py

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+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)