ParakeetRebeccaRosario/examples/transformer_tts/config.py

# 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=16,  # 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
        fmin=0,  # Hz, min frequency when converting to mel
        fmax=8000,  # Hz, max frequency when converting to mel
        n_mels=80,  # mel bands
        padding_idx=0,  # text embedding's padding index
        mel_start_value=0.5,  # value for starting frame
        mel_end_value=-0.5,  # # value for ending frame
    ))

_C.model = CN(
    dict(
        d_encoder=512,  # embedding & encoder's internal size
        d_decoder=256,  # decoder's internal size
        n_heads=4,  # actually it can differ at each layer
        d_ffn=1024,  # encoder_d_ffn & decoder_d_ffn
        encoder_layers=4,  # number of transformer encoder layer
        decoder_layers=4,  # number of transformer decoder layer
        d_prenet=256,  # decoder prenet's hidden size (n_mels=>d_prenet=>d_decoder)
        d_postnet=256,  # decoder postnet(cnn)'s internal channel
        postnet_layers=5,  # decoder postnet(cnn)'s layer
        postnet_kernel_size=5,  # decoder postnet(cnn)'s kernel size
        max_reduction_factor=10,  # max_reduction factor
        dropout=0.1,  # global droput probability
        stop_loss_scale=8.0,  # scaler for stop _loss
        decoder_prenet_dropout=0.5,  # decoder prenet dropout probability
    ))

_C.training = CN(
    dict(
        lr=1e-4,  # learning rate
        drop_n_heads=[[0, 0], [15000, 1]],
        reduction_factor=[[0, 10], [80000, 4], [200000, 2]],
        plot_interval=1000,  # plot attention and spectrogram
        valid_interval=1000,  # validation
        save_interval=10000,  # 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()