mirror of https://github.com/coqui-ai/TTS.git
171 lines
8.4 KiB
Python
171 lines
8.4 KiB
Python
from math import sqrt
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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.tacotron2 import Decoder, Encoder, Postnet
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from TTS.models.tacotron_abstract import TacotronAbstract
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# TODO: match function arguments with tacotron
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class Tacotron2(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,
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postnet_output_dim=80,
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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="softmax",
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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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super(Tacotron2,
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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 512
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encoder_in_features = 512 if num_speakers > 1 else 512
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proj_speaker_dim = 80 if num_speakers > 1 else 0
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# base layers
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self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
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std = sqrt(2.0 / (num_chars + 512))
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val = sqrt(3.0) * std # uniform bounds for std
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self.embedding.weight.data.uniform_(-val, val)
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if num_speakers > 1:
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self.speaker_embedding = nn.Embedding(num_speakers, 512)
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self.speaker_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, self.decoder_output_dim, r, 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, proj_speaker_dim)
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self.postnet = Postnet(self.postnet_output_dim)
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# global style token layers
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if self.gst:
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gst_embedding_dim = encoder_in_features
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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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@staticmethod
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def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
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mel_outputs = mel_outputs.transpose(1, 2)
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mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2)
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return mel_outputs, mel_outputs_postnet, alignments
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def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None):
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self._init_states()
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# compute mask for padding
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# B x T_in_max (boolean)
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input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
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# B x D_embed x T_in_max
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embedded_inputs = self.embedding(text).transpose(1, 2)
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# B x T_in_max x D_en
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encoder_outputs = self.encoder(embedded_inputs, text_lengths)
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# adding speaker embeddding to encoder output
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# TODO: multi-speaker
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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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encoder_outputs = self._add_speaker_embedding(encoder_outputs,
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self.speaker_embeddings)
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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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# B x mel_dim x T_out -- B x T_out//r x T_in -- B x T_out//r
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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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# sequence masking
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if mel_lengths 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 mel_dim x T_out
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postnet_outputs = self.postnet(decoder_outputs)
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postnet_outputs = decoder_outputs + postnet_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(1).expand_as(postnet_outputs)
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# B x T_out x mel_dim -- B x T_out x mel_dim -- B x T_out//r x T_in
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decoder_outputs, postnet_outputs, alignments = self.shape_outputs(
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decoder_outputs, postnet_outputs, alignments)
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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, text, speaker_ids=None):
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embedded_inputs = self.embedding(text).transpose(1, 2)
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encoder_outputs = self.encoder.inference(embedded_inputs)
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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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encoder_outputs = self._add_speaker_embedding(encoder_outputs,
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self.speaker_embeddings)
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decoder_outputs, alignments, stop_tokens = self.decoder.inference(
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encoder_outputs)
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postnet_outputs = self.postnet(decoder_outputs)
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postnet_outputs = decoder_outputs + postnet_outputs
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decoder_outputs, postnet_outputs, alignments = self.shape_outputs(
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decoder_outputs, postnet_outputs, alignments)
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return decoder_outputs, postnet_outputs, alignments, stop_tokens
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def inference_truncated(self, text, speaker_ids=None):
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"""
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Preserve model states for continuous inference
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"""
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embedded_inputs = self.embedding(text).transpose(1, 2)
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encoder_outputs = self.encoder.inference_truncated(embedded_inputs)
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encoder_outputs = self._add_speaker_embedding(encoder_outputs,
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speaker_ids)
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mel_outputs, alignments, stop_tokens = self.decoder.inference_truncated(
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encoder_outputs)
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mel_outputs_postnet = self.postnet(mel_outputs)
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mel_outputs_postnet = mel_outputs + mel_outputs_postnet
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mel_outputs, mel_outputs_postnet, alignments = self.shape_outputs(
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mel_outputs, mel_outputs_postnet, alignments)
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return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
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def _speaker_embedding_pass(self, encoder_outputs, speaker_ids):
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# TODO: multi-speaker
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# if hasattr(self, "speaker_embedding") and speaker_ids is None:
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# raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
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# if hasattr(self, "speaker_embedding") and speaker_ids is not None:
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# speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
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# encoder_outputs.size(1),
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# -1)
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# encoder_outputs = encoder_outputs + speaker_embeddings
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# return encoder_outputs
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pass
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