mirror of https://github.com/coqui-ai/TTS.git
180 lines
7.8 KiB
Python
180 lines
7.8 KiB
Python
# coding: utf-8
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import torch
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import copy
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from torch import nn
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from TTS.layers.tacotron import Encoder, Decoder, PostCBHG
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from TTS.utils.generic_utils import sequence_mask
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from TTS.layers.gst_layers import GST
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class Tacotron(nn.Module):
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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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memory_size=5,
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attn_type='original',
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attn_win=False,
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gst=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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super(Tacotron, self).__init__()
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self.r = r
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self.decoder_output_dim = decoder_output_dim
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self.postnet_output_dim = postnet_output_dim
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self.gst = gst
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self.num_speakers = num_speakers
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self.bidirectional_decoder = bidirectional_decoder
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decoder_dim = 512 if num_speakers > 1 else 256
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encoder_dim = 512 if num_speakers > 1 else 256
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proj_speaker_dim = 80 if num_speakers > 1 else 0
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# embedding layer
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self.embedding = nn.Embedding(num_chars, 256)
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self.embedding.weight.data.normal_(0, 0.3)
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# boilerplate model
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self.encoder = Encoder(encoder_dim)
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self.decoder = Decoder(decoder_dim, 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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if self.bidirectional_decoder:
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self.decoder_backward = copy.deepcopy(self.decoder)
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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, 256)
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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(256, 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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def _init_states(self):
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self.speaker_embeddings = None
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self.speaker_embeddings_projected = None
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def compute_speaker_embedding(self, speaker_ids):
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if hasattr(self, "speaker_embedding") and speaker_ids is None:
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raise RuntimeError(
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" [!] Model has speaker embedding layer but speaker_id is not provided"
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)
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if hasattr(self, "speaker_embedding") and speaker_ids is not None:
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self.speaker_embeddings = self._compute_speaker_embedding(
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speaker_ids)
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self.speaker_embeddings_projected = self.speaker_project_mel(
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self.speaker_embeddings).squeeze(1)
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def compute_gst(self, inputs, mel_specs):
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gst_outputs = self.gst_layer(mel_specs)
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inputs = self._add_speaker_embedding(inputs, gst_outputs)
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return inputs
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def forward(self, characters, text_lengths, mel_specs, 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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mask = sequence_mask(text_lengths).to(characters.device)
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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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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_dim
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encoder_outputs = self.encoder(inputs)
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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_dim x T_out
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# alignments: B x T_in x encoder_dim
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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, mask,
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self.speaker_embeddings_projected)
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# B x T_out x decoder_dim
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postnet_outputs = self.postnet(decoder_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_dim
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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_inference(mel_specs, encoder_outputs, 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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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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def _backward_inference(self, mel_specs, encoder_outputs, mask):
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decoder_outputs_b, alignments_b, _ = self.decoder_backward(
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encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
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self.speaker_embeddings_projected)
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decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous()
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return decoder_outputs_b, alignments_b
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def _compute_speaker_embedding(self, speaker_ids):
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speaker_embeddings = self.speaker_embedding(speaker_ids)
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return speaker_embeddings.unsqueeze_(1)
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@staticmethod
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def _add_speaker_embedding(outputs, speaker_embeddings):
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speaker_embeddings_ = speaker_embeddings.expand(
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outputs.size(0), outputs.size(1), -1)
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outputs = outputs + speaker_embeddings_
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return outputs
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@staticmethod
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def _concat_speaker_embedding(outputs, speaker_embeddings):
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speaker_embeddings_ = speaker_embeddings.expand(
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outputs.size(0), outputs.size(1), -1)
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outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
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return outputs
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