mirror of https://github.com/coqui-ai/TTS.git
157 lines
7.4 KiB
Python
157 lines
7.4 KiB
Python
# coding: utf-8
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import torch
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from torch import nn
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from TTS.layers.gst_layers import GST
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from TTS.layers.tacotron import Decoder, Encoder, PostCBHG
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from TTS.models.tacotron_abstract import TacotronAbstract
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class Tacotron(TacotronAbstract):
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def __init__(self,
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num_chars,
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num_speakers,
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r=5,
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postnet_output_dim=1025,
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decoder_output_dim=80,
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attn_type='original',
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attn_win=False,
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attn_norm="sigmoid",
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prenet_type="original",
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prenet_dropout=True,
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forward_attn=False,
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trans_agent=False,
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forward_attn_mask=False,
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location_attn=True,
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attn_K=5,
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separate_stopnet=True,
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bidirectional_decoder=False,
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double_decoder_consistency=False,
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ddc_r=None,
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gst=False,
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memory_size=5):
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super(Tacotron,
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self).__init__(num_chars, num_speakers, r, postnet_output_dim,
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decoder_output_dim, attn_type, attn_win,
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attn_norm, prenet_type, prenet_dropout,
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forward_attn, trans_agent, forward_attn_mask,
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location_attn, attn_K, separate_stopnet,
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bidirectional_decoder, double_decoder_consistency,
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ddc_r, gst)
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decoder_in_features = 512 if num_speakers > 1 else 256
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encoder_in_features = 512 if num_speakers > 1 else 256
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speaker_embedding_dim = 256
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proj_speaker_dim = 80 if num_speakers > 1 else 0
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# base model layers
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self.embedding = nn.Embedding(num_chars, 256, padding_idx=0)
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self.embedding.weight.data.normal_(0, 0.3)
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self.encoder = Encoder(encoder_in_features)
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self.decoder = Decoder(decoder_in_features, decoder_output_dim, r, memory_size, attn_type, attn_win,
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attn_norm, prenet_type, prenet_dropout,
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forward_attn, trans_agent, forward_attn_mask,
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location_attn, attn_K, separate_stopnet,
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proj_speaker_dim)
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self.postnet = PostCBHG(decoder_output_dim)
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self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2,
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postnet_output_dim)
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# speaker embedding layers
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if num_speakers > 1:
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self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
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self.speaker_embedding.weight.data.normal_(0, 0.3)
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self.speaker_project_mel = nn.Sequential(
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nn.Linear(speaker_embedding_dim, proj_speaker_dim), nn.Tanh())
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self.speaker_embeddings = None
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self.speaker_embeddings_projected = None
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# global style token layers
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if self.gst:
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gst_embedding_dim = 256
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self.gst_layer = GST(num_mel=80,
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num_heads=4,
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num_style_tokens=10,
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embedding_dim=gst_embedding_dim)
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# backward pass decoder
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if self.bidirectional_decoder:
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self._init_backward_decoder()
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# setup DDC
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if self.double_decoder_consistency:
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self._init_coarse_decoder()
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def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None):
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"""
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Shapes:
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- characters: B x T_in
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- text_lengths: B
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- mel_specs: B x T_out x D
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- speaker_ids: B x 1
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"""
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self._init_states()
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input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
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# B x T_in x embed_dim
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inputs = self.embedding(characters)
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# B x speaker_embed_dim
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if speaker_ids is not None:
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self.compute_speaker_embedding(speaker_ids)
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if self.num_speakers > 1:
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# B x T_in x embed_dim + speaker_embed_dim
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inputs = self._concat_speaker_embedding(inputs,
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self.speaker_embeddings)
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# B x T_in x encoder_in_features
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encoder_outputs = self.encoder(inputs)
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# sequence masking
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encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
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# global style token
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if self.gst:
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# B x gst_dim
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encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
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if self.num_speakers > 1:
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encoder_outputs = self._concat_speaker_embedding(
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encoder_outputs, self.speaker_embeddings)
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# decoder_outputs: B x decoder_in_features x T_out
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# alignments: B x T_in x encoder_in_features
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# stop_tokens: B x T_in
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decoder_outputs, alignments, stop_tokens = self.decoder(
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encoder_outputs, mel_specs, input_mask,
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self.speaker_embeddings_projected)
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# sequence masking
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if output_mask is not None:
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decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
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# B x T_out x decoder_in_features
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postnet_outputs = self.postnet(decoder_outputs)
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# sequence masking
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if output_mask is not None:
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postnet_outputs = postnet_outputs * output_mask.unsqueeze(2).expand_as(postnet_outputs)
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# B x T_out x posnet_dim
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postnet_outputs = self.last_linear(postnet_outputs)
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# B x T_out x decoder_in_features
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decoder_outputs = decoder_outputs.transpose(1, 2).contiguous()
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if self.bidirectional_decoder:
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decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
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return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
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if self.double_decoder_consistency:
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decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
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return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
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return decoder_outputs, postnet_outputs, alignments, stop_tokens
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@torch.no_grad()
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def inference(self, characters, speaker_ids=None, style_mel=None):
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inputs = self.embedding(characters)
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self._init_states()
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if speaker_ids is not None:
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self.compute_speaker_embedding(speaker_ids)
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if self.num_speakers > 1:
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inputs = self._concat_speaker_embedding(inputs,
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self.speaker_embeddings)
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encoder_outputs = self.encoder(inputs)
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if self.gst and style_mel is not None:
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encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
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if self.num_speakers > 1:
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encoder_outputs = self._concat_speaker_embedding(
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encoder_outputs, self.speaker_embeddings)
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decoder_outputs, alignments, stop_tokens = self.decoder.inference(
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encoder_outputs, self.speaker_embeddings_projected)
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postnet_outputs = self.postnet(decoder_outputs)
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postnet_outputs = self.last_linear(postnet_outputs)
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decoder_outputs = decoder_outputs.transpose(1, 2)
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return decoder_outputs, postnet_outputs, alignments, stop_tokens
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