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