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# -*- coding: utf-8 -*-
# Copyright 2020 Minh Nguyen (@dathudeptrai)
#
# 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.
"""Tacotron-2 Config object."""
from tensorflow_tts.configs import BaseConfig
from tensorflow_tts.processor.jsut import JSUT_SYMBOLS
from tensorflow_tts.processor.ljspeech import LJSPEECH_SYMBOLS as lj_symbols
from tensorflow_tts.processor.kss import KSS_SYMBOLS as kss_symbols
from tensorflow_tts.processor.baker import BAKER_SYMBOLS as bk_symbols
from tensorflow_tts.processor.libritts import LIBRITTS_SYMBOLS as lbri_symbols
from tensorflow_tts.processor.ljspeechu import LJSPEECH_U_SYMBOLS as lju_symbols
from tensorflow_tts.processor.synpaflex import SYNPAFLEX_SYMBOLS as synpaflex_symbols
from tensorflow_tts.processor.jsut import JSUT_SYMBOLS as jsut_symbols
class Tacotron2Config(BaseConfig):
"""Initialize Tacotron-2 Config."""
def __init__(
self,
dataset="ljspeech",
vocab_size=len(lj_symbols),
embedding_hidden_size=512,
initializer_range=0.02,
layer_norm_eps=1e-6,
embedding_dropout_prob=0.1,
n_speakers=5,
n_conv_encoder=3,
encoder_conv_filters=512,
encoder_conv_kernel_sizes=5,
encoder_conv_activation="mish",
encoder_conv_dropout_rate=0.5,
encoder_lstm_units=256,
reduction_factor=5,
n_prenet_layers=2,
prenet_units=256,
prenet_activation="mish",
prenet_dropout_rate=0.5,
n_lstm_decoder=1,
decoder_lstm_units=1024,
attention_type="lsa",
attention_dim=128,
attention_filters=32,
attention_kernel=31,
n_mels=80,
n_conv_postnet=5,
postnet_conv_filters=512,
postnet_conv_kernel_sizes=5,
postnet_dropout_rate=0.1,
):
"""Init parameters for Tacotron-2 model."""
if dataset == "ljspeech":
self.vocab_size = vocab_size
elif dataset == "kss":
self.vocab_size = len(kss_symbols)
elif dataset == "baker":
self.vocab_size = len(bk_symbols)
elif dataset == "libritts":
self.vocab_size = len(lbri_symbols)
elif dataset == "ljspeechu":
self.vocab_size = len(lju_symbols)
elif dataset == "synpaflex":
self.vocab_size = len(synpaflex_symbols)
elif dataset == "jsut":
self.vocab_size = len(jsut_symbols)
else:
raise ValueError("No such dataset: {}".format(dataset))
self.embedding_hidden_size = embedding_hidden_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.embedding_dropout_prob = embedding_dropout_prob
self.n_speakers = n_speakers
self.n_conv_encoder = n_conv_encoder
self.encoder_conv_filters = encoder_conv_filters
self.encoder_conv_kernel_sizes = encoder_conv_kernel_sizes
self.encoder_conv_activation = encoder_conv_activation
self.encoder_conv_dropout_rate = encoder_conv_dropout_rate
self.encoder_lstm_units = encoder_lstm_units
# decoder param
self.reduction_factor = reduction_factor
self.n_prenet_layers = n_prenet_layers
self.prenet_units = prenet_units
self.prenet_activation = prenet_activation
self.prenet_dropout_rate = prenet_dropout_rate
self.n_lstm_decoder = n_lstm_decoder
self.decoder_lstm_units = decoder_lstm_units
self.attention_type = attention_type
self.attention_dim = attention_dim
self.attention_filters = attention_filters
self.attention_kernel = attention_kernel
self.n_mels = n_mels
# postnet
self.n_conv_postnet = n_conv_postnet
self.postnet_conv_filters = postnet_conv_filters
self.postnet_conv_kernel_sizes = postnet_conv_kernel_sizes
self.postnet_dropout_rate = postnet_dropout_rate
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