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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.
"""FastSpeech Config object."""
import collections
from tensorflow_tts.configs import BaseConfig
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.jsut import JSUT_SYMBOLS as jsut_symbols
SelfAttentionParams = collections.namedtuple(
"SelfAttentionParams",
[
"n_speakers",
"hidden_size",
"num_hidden_layers",
"num_attention_heads",
"attention_head_size",
"intermediate_size",
"intermediate_kernel_size",
"hidden_act",
"output_attentions",
"output_hidden_states",
"initializer_range",
"hidden_dropout_prob",
"attention_probs_dropout_prob",
"layer_norm_eps",
"max_position_embeddings",
],
)
class FastSpeechConfig(BaseConfig):
"""Initialize FastSpeech Config."""
def __init__(
self,
dataset="ljspeech",
vocab_size=len(lj_symbols),
n_speakers=1,
encoder_hidden_size=384,
encoder_num_hidden_layers=4,
encoder_num_attention_heads=2,
encoder_attention_head_size=192,
encoder_intermediate_size=1024,
encoder_intermediate_kernel_size=3,
encoder_hidden_act="mish",
decoder_hidden_size=384,
decoder_num_hidden_layers=4,
decoder_num_attention_heads=2,
decoder_attention_head_size=192,
decoder_intermediate_size=1024,
decoder_intermediate_kernel_size=3,
decoder_hidden_act="mish",
output_attentions=True,
output_hidden_states=True,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
max_position_embeddings=2048,
num_duration_conv_layers=2,
duration_predictor_filters=256,
duration_predictor_kernel_sizes=3,
num_mels=80,
duration_predictor_dropout_probs=0.1,
n_conv_postnet=5,
postnet_conv_filters=512,
postnet_conv_kernel_sizes=5,
postnet_dropout_rate=0.1,
**kwargs
):
"""Init parameters for Fastspeech model."""
# encoder params
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 == "jsut":
self.vocab_size = len(jsut_symbols)
else:
raise ValueError("No such dataset: {}".format(dataset))
self.initializer_range = initializer_range
self.max_position_embeddings = max_position_embeddings
self.n_speakers = n_speakers
self.layer_norm_eps = layer_norm_eps
# encoder params
self.encoder_self_attention_params = SelfAttentionParams(
n_speakers=n_speakers,
hidden_size=encoder_hidden_size,
num_hidden_layers=encoder_num_hidden_layers,
num_attention_heads=encoder_num_attention_heads,
attention_head_size=encoder_attention_head_size,
hidden_act=encoder_hidden_act,
intermediate_size=encoder_intermediate_size,
intermediate_kernel_size=encoder_intermediate_kernel_size,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
initializer_range=initializer_range,
hidden_dropout_prob=hidden_dropout_prob,
attention_probs_dropout_prob=attention_probs_dropout_prob,
layer_norm_eps=layer_norm_eps,
max_position_embeddings=max_position_embeddings,
)
# decoder params
self.decoder_self_attention_params = SelfAttentionParams(
n_speakers=n_speakers,
hidden_size=decoder_hidden_size,
num_hidden_layers=decoder_num_hidden_layers,
num_attention_heads=decoder_num_attention_heads,
attention_head_size=decoder_attention_head_size,
hidden_act=decoder_hidden_act,
intermediate_size=decoder_intermediate_size,
intermediate_kernel_size=decoder_intermediate_kernel_size,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
initializer_range=initializer_range,
hidden_dropout_prob=hidden_dropout_prob,
attention_probs_dropout_prob=attention_probs_dropout_prob,
layer_norm_eps=layer_norm_eps,
max_position_embeddings=max_position_embeddings,
)
self.duration_predictor_dropout_probs = duration_predictor_dropout_probs
self.num_duration_conv_layers = num_duration_conv_layers
self.duration_predictor_filters = duration_predictor_filters
self.duration_predictor_kernel_sizes = duration_predictor_kernel_sizes
self.num_mels = num_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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