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- from text import symbols
-
-
- class Hparams:
- """ hyper parameters """
-
- def __init__(self):
- ################################
- # Experiment Parameters #
- ################################
- self.epochs = 500
- self.iters_per_checkpoint = 1000
- self.seed = 1234
- self.dynamic_loss_scaling = True
- self.fp16_run = False
- self.distributed_run = False
- self.dist_backend = "nccl"
- self.dist_url = "tcp://localhost:54321"
- self.cudnn_enabled = True
- self.cudnn_benchmark = False
- self.ignore_layers = ['embedding.weight']
-
- ################################
- # Data Parameters #
- ################################
- self.load_mel_from_disk = False
- self.training_files = 'filelists/ljs_audio_text_train_filelist.txt'
- self.validation_files = 'filelists/ljs_audio_text_val_filelist.txt'
- self.text_cleaners = ['english_cleaners']
-
- ################################
- # Audio Parameters #
- ################################
- self.max_wav_value = 32768.0
- self.sampling_rate = 22050
- self.filter_length = 1024
- self.hop_length = 256
- self.win_length = 1024
- self.n_mel_channels = 80
- self.mel_fmin = 0.0
- self.mel_fmax = 8000.0
-
- ################################
- # Model Parameters #
- ################################
- self.n_symbols = len(symbols)
- self.symbols_embedding_dim = 512
-
- # Encoder parameters
- self.encoder_kernel_size = 5
- self.encoder_n_convolutions = 3
- self.encoder_embedding_dim = 512
-
- # Decoder parameters
- self.n_frames_per_step = 1 # currently only 1 is supported
- self.decoder_rnn_dim = 1024
- self.prenet_dim = 256
- self.max_decoder_steps = 1000
- self.gate_threshold = 0.5
- self.p_attention_dropout = 0.1
- self.p_decoder_dropout = 0.1
-
- # Attention parameters
- self.attention_rnn_dim = 1024
- self.attention_dim = 128
-
- # Location Layer parameters
- self.attention_location_n_filters = 32
- self.attention_location_kernel_size = 31
-
- # Mel-post processing network parameters
- self.postnet_embedding_dim = 512
- self.postnet_kernel_size = 5
- self.postnet_n_convolutions = 5
-
- ################################
- # Optimization Hyperparameters #
- ################################
- self.use_saved_learning_rate = False
- self.learning_rate = 1e-3
- self.weight_decay = 1e-6
- self.grad_clip_thresh = 1.0
- self.batch_size = 64
- self.mask_padding = True # set model's padded outputs to padded values
-
- def return_self(self):
- return self
-
-
- def create_hparams():
- hparams = Hparams()
- return hparams.return_self()
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