# -*- coding: utf-8 -*-
# /usr/bin/python2
"""
By kyubyong park. kbpark.linguist@gmail.com. 
https://www.github.com/kyubyong/dc_tts
"""


class Hyperparams:
    """Hyper parameters"""

    # pipeline
    prepro = (
        True  # if True, run `python prepro.py` first before running `python train.py`.
    )

    # signal processing
    sr = 22050  # Sampling rate.
    n_fft = 2048  # fft points (samples)
    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.
    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 = 0.97
    max_db = 100
    ref_db = 20

    # Model
    r = 4  # Reduction factor. Do not change this.
    dropout_rate = 0.05
    e = 128  # == embedding
    d = 256  # == hidden units of Text2Mel
    c = 512  # == hidden units of SSRN
    attention_win_size = 3

    # data
    data = "/data/private/voice/LJSpeech-1.0"
    # data = "/data/private/voice/kate"
    test_data = "harvard_sentences.txt"
    vocab = "PE abcdefghijklmnopqrstuvwxyz'.?"  # P: Padding, E: EOS.
    max_N = 180  # Maximum number of characters.
    max_T = 210  # Maximum number of mel frames.

    # training scheme
    lr = 0.001  # Initial learning rate.
    logdir = "logdir/LJ01"
    sampledir = "samples"
    B = 32  # batch size
    num_iterations = 2000000