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added demo
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# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# MIT License for more details.
from model.utils import fix_len_compatibility
# data parameters
train_filelist_path = "../../id/train.txt"
valid_filelist_path = "../../id/valid.txt"
# test_filelist_path = 'resources/filelists/ljspeech/test.txt'
cmudict_path = "resources/cmu_dictionary_id"
add_blank = True
n_feats = 80
n_spks = 247 # for Libri-TTS filelist and 1 for LJSpeech
spk_emb_dim = 64
n_feats = 80
n_fft = 1024
sample_rate = 22050
hop_length = 256
win_length = 1024
f_min = 0
f_max = 8000
# encoder parameters
n_enc_channels = 192
filter_channels = 768
filter_channels_dp = 256
n_enc_layers = 6
enc_kernel = 3
enc_dropout = 0.1
n_heads = 2
window_size = 4
# decoder parameters
dec_dim = 64
beta_min = 0.05
beta_max = 20.0
pe_scale = 1000 # 1 for `grad-tts-old.pt` checkpoint
# training parameters
log_dir = "logs/grad-tts-bookbot-ft-weildan"
test_size = 4
n_epochs = 24000
batch_size = 8
learning_rate = 1e-4
seed = 37
save_every = 1000
out_size = fix_len_compatibility(2 * 22050 // 256)
num_workers = 6
checkpoint = "checkpts/grad-tts-libri-tts.pt"