157 lines
3.6 KiB
Python
157 lines
3.6 KiB
Python
import tensorflow as tf
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SCALE_FACTOR = 1
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def f(num):
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return num // SCALE_FACTOR
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basic_params = {
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# Comma-separated list of cleaners to run on text prior to training and eval. For non-English
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# text, you may want to use "basic_cleaners" or "transliteration_cleaners" See TRAINING_DATA.md.
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'cleaners': 'korean_cleaners',
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}
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basic_params.update({
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# Audio
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'num_mels': 80,
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'num_freq': 1025,
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'sample_rate': 20000,
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'frame_length_ms': 50,
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'frame_shift_ms': 12.5,
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'preemphasis': 0.97,
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'min_level_db': -100,
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'ref_level_db': 20,
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})
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if True:
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basic_params.update({
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'sample_rate': 24000,
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})
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basic_params.update({
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# Model
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'model_type': 'single', # [single, simple, deepvoice]
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'speaker_embedding_size': f(16),
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'embedding_size': f(256),
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'dropout_prob': 0.5,
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# Encoder
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'enc_prenet_sizes': [f(256), f(128)],
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'enc_bank_size': 16,
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'enc_bank_channel_size': f(128),
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'enc_maxpool_width': 2,
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'enc_highway_depth': 4,
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'enc_rnn_size': f(128),
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'enc_proj_sizes': [f(128), f(128)],
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'enc_proj_width': 3,
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# Attention
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'attention_type': 'bah_mon', # ntm2-5
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'attention_size': f(256),
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'attention_state_size': f(256),
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# Decoder recurrent network
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'dec_layer_num': 2,
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'dec_rnn_size': f(256),
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# Decoder
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'dec_prenet_sizes': [f(256), f(128)],
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'post_bank_size': 8,
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'post_bank_channel_size': f(256),
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'post_maxpool_width': 2,
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'post_highway_depth': 4,
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'post_rnn_size': f(128),
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'post_proj_sizes': [f(256), 80], # num_mels=80
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'post_proj_width': 3,
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'reduction_factor': 4,
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})
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if False: # Deep Voice 2
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basic_params.update({
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'dropout_prob': 0.8,
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'attention_size': f(512),
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'dec_prenet_sizes': [f(256), f(128), f(64)],
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'post_bank_channel_size': f(512),
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'post_rnn_size': f(256),
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'reduction_factor': 4,
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})
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elif True: # Deep Voice 2
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basic_params.update({
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'dropout_prob': 0.8,
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#'attention_size': f(512),
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#'dec_prenet_sizes': [f(256), f(128)],
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#'post_bank_channel_size': f(512),
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'post_rnn_size': f(256),
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'reduction_factor': 4,
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})
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elif False: # Single Speaker
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basic_params.update({
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'dropout_prob': 0.5,
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'attention_size': f(128),
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'post_bank_channel_size': f(128),
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#'post_rnn_size': f(128),
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'reduction_factor': 4,
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})
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elif False: # Single Speaker with generalization
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basic_params.update({
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'dropout_prob': 0.8,
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'attention_size': f(256),
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'dec_prenet_sizes': [f(256), f(128), f(64)],
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'post_bank_channel_size': f(128),
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'post_rnn_size': f(128),
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'reduction_factor': 4,
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})
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basic_params.update({
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# Training
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'batch_size': 16,
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'adam_beta1': 0.9,
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'adam_beta2': 0.999,
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'use_fixed_test_inputs': False,
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'initial_learning_rate': 0.002,
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'decay_learning_rate_mode': 0,
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'initial_data_greedy': True,
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'initial_phase_step': 8000,
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'main_data_greedy_factor': 0,
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'main_data': [''],
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'prioritize_loss': False,
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'recognition_loss_coeff': 0.2,
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'ignore_recognition_level': 1, # 0: use all, 1: ignore only unmatched_alignment, 2: fully ignore recognition
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# Eval
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'min_tokens': 50,
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'min_iters': 30,
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'max_iters': 200,
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'skip_inadequate': False,
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'griffin_lim_iters': 60,
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'power': 1.5, # Power to raise magnitudes to prior to Griffin-Lim
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})
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# Default hyperparameters:
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hparams = tf.contrib.training.HParams(**basic_params)
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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)]
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return 'Hyperparameters:\n' + '\n'.join(hp)
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