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# -*- 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 = .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
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