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93 lines
2.8 KiB
93 lines
2.8 KiB
import tensorflow as tf
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from text import symbols
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def create_hparams(hparams_string=None, verbose=False):
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"""Create model hyperparameters. Parse nondefault from given string."""
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hparams = tf.contrib.training.HParams(
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################################
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# Experiment Parameters #
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################################
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epochs=500,
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iters_per_checkpoint=500,
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seed=1234,
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dynamic_loss_scaling=True,
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fp16_run=False,
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distributed_run=False,
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dist_backend="nccl",
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dist_url="file://distributed.dpt",
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cudnn_enabled=True,
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cudnn_benchmark=False,
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################################
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# Data Parameters #
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################################
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load_mel_from_disk=False,
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training_files='filelists/ljs_audio_text_train_filelist.txt',
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validation_files='filelists/ljs_audio_text_val_filelist.txt',
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text_cleaners=['english_cleaners'],
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sort_by_length=False,
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################################
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# Audio Parameters #
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################################
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max_wav_value=32768.0,
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sampling_rate=22050,
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filter_length=1024,
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hop_length=256,
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win_length=1024,
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n_mel_channels=80,
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mel_fmin=0.0,
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mel_fmax=None, # if None, half the sampling rate
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################################
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# Model Parameters #
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################################
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n_symbols=len(symbols),
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symbols_embedding_dim=512,
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# Encoder parameters
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encoder_kernel_size=5,
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encoder_n_convolutions=3,
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encoder_embedding_dim=512,
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# Decoder parameters
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n_frames_per_step=1,
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decoder_rnn_dim=1024,
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prenet_dim=256,
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max_decoder_steps=1000,
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gate_threshold=0.6,
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# Attention parameters
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attention_rnn_dim=1024,
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attention_dim=128,
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# Location Layer parameters
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attention_location_n_filters=32,
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attention_location_kernel_size=31,
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# Mel-post processing network parameters
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postnet_embedding_dim=512,
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postnet_kernel_size=5,
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postnet_n_convolutions=5,
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################################
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# Optimization Hyperparameters #
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################################
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used_saved_learning_rate=False,
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learning_rate=1e-3,
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weight_decay=1e-6,
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grad_clip_thresh=1,
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batch_size=48,
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mask_padding=False # set model's padded outputs to padded values
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)
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if hparams_string:
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tf.logging.info('Parsing command line hparams: %s', hparams_string)
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hparams.parse(hparams_string)
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if verbose:
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tf.logging.info('Final parsed hparams: %s', hparams.values())
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return hparams
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