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