DeepLearningExamples/CUDA-Optimized/FastSpeech/tacotron2/hparams.py

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"""https://github.com/NVIDIA/tacotron2"""

from fastspeech.text_norm 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()