hparams.py

"""Hyper parameters."""
__author__ = 'Erdene-Ochir Tuguldur'


class HParams:
    """Hyper parameters"""

    disable_progress_bar = False  # set True if you don't want the progress bar in the console

    logdir = "logdir"  # log dir where the checkpoints and tensorboard files are saved

    # audio.py options, these values are from https://github.com/Kyubyong/dc_tts/blob/master/hyperparams.py
    reduction_rate = 4  # melspectrogram reduction rate, don't change because SSRN is using this rate
    n_fft = 2048 # fft points (samples)
    n_mels = 80  # Number of Mel banks to generate
    power = 1.5  # Exponent for amplifying the predicted magnitude
    n_iter = 50  # Number of inversion iterations
    preemphasis = .97
    max_db = 100
    ref_db = 20
    sr = 22050  # Sampling rate
    frame_shift = 0.0125  # seconds
    frame_length = 0.05  # seconds
    hop_length = int(sr * frame_shift)  # samples. =276.
    win_length = int(sr * frame_length)  # samples. =1102.
    max_N = 180  # Maximum number of characters.
    max_T = 210  # Maximum number of mel frames.

    e = 128  # embedding dimension
    d = 256  # Text2Mel hidden unit dimension
    c = 512+128  # SSRN hidden unit dimension

    dropout_rate = 0.05  # dropout

    # Text2Mel network options
    text2mel_lr = 0.005  # learning rate
    text2mel_max_iteration = 300000  # max train step
    text2mel_weight_init = 'none'  # 'kaiming', 'xavier' or 'none'
    text2mel_normalization = 'layer'  # 'layer', 'weight' or 'none'
    text2mel_basic_block = 'gated_conv'  # 'highway', 'gated_conv' or 'residual'

    # SSRN network options
    ssrn_lr = 0.0005  # learning rate
    ssrn_max_iteration = 150000  # max train step
    ssrn_weight_init = 'kaiming'  # 'kaiming', 'xavier' or 'none'
    ssrn_normalization = 'weight'  # 'layer', 'weight' or 'none'
    ssrn_basic_block = 'residual'  # 'highway', 'gated_conv' or 'residual'