89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
import tensorflow as tf
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from text import symbols
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class hparams:
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def __init__(self) -> None:
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super().__init__()
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################################
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# Experiment Parameters #
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################################
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epochs = 3 #500
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iters_per_checkpoint = 1000
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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 = "tcp://localhost:54321"
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cudnn_enabled = True
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cudnn_benchmark = True
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ignore_layers = ['embedding.weight']
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################################
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# Data Parameters #
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################################
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load_mel_from_disk = False #实际上是区别用 numpy读wav ，还是用scipy读wav
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training_files = 'filelists/zh_audio_text_train_filelist.txt'
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validation_files = 'filelists/zh_audio_text_val_filelist.txt'
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text_cleaners = ['english_cleaners']
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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 #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 = 8000.0
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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 # currently only 1 is supported
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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 = 0.5
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p_attention_dropout = 0.1
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p_decoder_dropout = 0.1
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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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use_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 = 1.0
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batch_size = 2 #64
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mask_padding = True # set model's padded outputs to padded values
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def create_hparams(hparams_string=None, verbose=False):
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return hparams
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