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hparams.py
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from text import symbols
class Hparams:
def __init__(self):
################################
# Experiment Parameters #
################################
self.epochs = 500
self.iters_per_checkpoint = 1000
self.iters_per_validation = 1000
self.seed = 1234
self.dynamic_loss_scaling = True
self.fp16_run = False
self.distributed_run = False
self.cudnn_enabled = True
self.cudnn_benchmark = False
self.ignore_layers = ["embedding.weight"]
################################
# Data Parameters #
################################
self.training_files = "DATASET/train.csv.txt"
self.validation_files = "DATASET/val.csv.txt"
self.text_cleaners = ["basic_cleaners"]
self.symbols_lang = "en" # en: English characters; py: Chinese Pinyin symbols
################################
# Model Parameters #
################################
self.tacotron_version = "2" # 1: Tacotron; 2: Tacotron-2
self.tacotron_config = "tacotron2.json"
self.num_symbols = len(symbols(self.symbols_lang))
self.symbols_embed_dim = 512
self.mel_dim = 80
self.r = 3
self.max_decoder_steps = 1000
self.stop_threshold = 0.5
################################
# 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 = 32
self.mask_padding = True # set model's padded outputs to padded values
def __str__(self):
return "\n".join(
["Hyper Parameters:"]
+ ["{}:{}".format(key, getattr(self, key, None)) for key in self.__dict__]
)
def create_hparams():
"""Create model hyperparameters. Parse nondefault from object args."""
return Hparams()