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PaddleSpeech/examples/ljspeech/tts0/local/tacotron2/config.py

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from yacs.config import CfgNode as CN
_C = CN()
_C.data = CN(
dict(
batch_size=32, # batch size
valid_size=64, # the first N examples are reserved for validation
sample_rate=22050, # Hz, sample rate
n_fft=1024, # fft frame size
win_length=1024, # window size
hop_length=256, # hop size between ajacent frame
fmax=8000, # Hz, max frequency when converting to mel
fmin=0, # Hz, min frequency when converting to mel
n_mels=80, # mel bands
padding_idx=0, # text embedding's padding index
))
_C.model = CN(
dict(
vocab_size=37, # set this according to the frontend's vocab size
n_tones=None,
reduction_factor=1, # reduction factor
d_encoder=512, # embedding & encoder's internal size
encoder_conv_layers=3, # number of conv layer in tacotron2 encoder
encoder_kernel_size=5, # kernel size of conv layers in tacotron2 encoder
d_prenet=256, # hidden size of decoder prenet
d_attention_rnn=1024, # hidden size of the first rnn layer in tacotron2 decoder
d_decoder_rnn=1024, # hidden size of the second rnn layer in tacotron2 decoder
d_attention=128, # hidden size of decoder location linear layer
attention_filters=32, # number of filter in decoder location conv layer
attention_kernel_size=31, # kernel size of decoder location conv layer
d_postnet=512, # hidden size of decoder postnet
postnet_kernel_size=5, # kernel size of conv layers in postnet
postnet_conv_layers=5, # number of conv layer in decoder postnet
p_encoder_dropout=0.5, # droput probability in encoder
p_prenet_dropout=0.5, # droput probability in decoder prenet
p_attention_dropout=0.1, # droput probability of first rnn layer in decoder
p_decoder_dropout=0.1, # droput probability of second rnn layer in decoder
p_postnet_dropout=0.5, # droput probability in decoder postnet
d_global_condition=None,
use_stop_token=True, # wherther to use binary classifier to predict when to stop
use_guided_attention_loss=False, # whether to use guided attention loss
guided_attention_loss_sigma=0.2 # sigma in guided attention loss
))
_C.training = CN(
dict(
lr=1e-3, # learning rate
weight_decay=1e-6, # the coeff of weight decay
grad_clip_thresh=1.0, # the clip norm of grad clip.
plot_interval=1000, # plot attention and spectrogram
valid_interval=1000, # validation
save_interval=1000, # checkpoint
max_iteration=500000, # max iteration to train
))
def get_cfg_defaults():
"""Get a yacs CfgNode object with default values for my_project."""
# Return a clone so that the defaults will not be altered
# This is for the "local variable" use pattern
return _C.clone()