зеркало из https://github.com/mozilla/TTS.git
inherit TacotronAbstact with both tacotron and tacotron2
This commit is contained in:
Родитель
b4ac68df7b
Коммит
bd7237d916
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@ -1,23 +1,21 @@
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# 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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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(nn.Module):
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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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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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@ -27,38 +25,41 @@ class Tacotron(nn.Module):
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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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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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# embedding layer
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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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# 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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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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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 = 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(256, proj_speaker_dim), nn.Tanh())
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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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@ -68,28 +69,15 @@ class Tacotron(nn.Module):
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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 _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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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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@ -98,37 +86,50 @@ class Tacotron(nn.Module):
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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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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_dim
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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_dim x T_out
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# alignments: B x T_in x encoder_dim
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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, mask,
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encoder_outputs, mel_specs, input_mask,
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self.speaker_embeddings_projected)
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# B x T_out x decoder_dim
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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_dim
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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_inference(mel_specs, encoder_outputs, mask)
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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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@ -136,6 +137,7 @@ class Tacotron(nn.Module):
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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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@ -152,28 +154,3 @@ class Tacotron(nn.Module):
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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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@ -1,13 +1,15 @@
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import copy
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import torch
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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.tacotron2 import Encoder, Decoder, Postnet
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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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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(nn.Module):
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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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@ -25,16 +27,22 @@ class Tacotron2(nn.Module):
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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(Tacotron2, self).__init__()
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self.postnet_output_dim = postnet_output_dim
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self.decoder_output_dim = decoder_output_dim
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self.r = r
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self.bidirectional_decoder = bidirectional_decoder
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decoder_dim = 512 if num_speakers > 1 else 512
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encoder_dim = 512 if num_speakers > 1 else 512
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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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# embedding layer
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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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@ -42,20 +50,25 @@ class Tacotron2(nn.Module):
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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.speaker_embeddings = None
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self.speaker_embeddings_projected = None
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self.encoder = Encoder(encoder_dim)
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self.decoder = Decoder(decoder_dim, self.decoder_output_dim, r, attn_type, attn_win,
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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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if self.bidirectional_decoder:
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self.decoder_backward = copy.deepcopy(self.decoder)
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self.postnet = Postnet(self.postnet_output_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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# 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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@ -63,22 +76,48 @@ class Tacotron2(nn.Module):
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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, speaker_ids=None):
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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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mask = sequence_mask(text_lengths).to(text.device)
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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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speaker_ids)
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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, mask)
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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_inference(mel_specs, encoder_outputs, mask)
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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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@ -86,8 +125,11 @@ class Tacotron2(nn.Module):
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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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speaker_ids)
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self.speaker_embeddings)
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mel_outputs, alignments, stop_tokens = self.decoder.inference(
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encoder_outputs)
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mel_outputs_postnet = self.postnet(mel_outputs)
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@ -112,22 +154,16 @@ class Tacotron2(nn.Module):
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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 _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,
|
||||
self.speaker_embeddings_projected)
|
||||
decoder_outputs_b = decoder_outputs_b.transpose(1, 2)
|
||||
return decoder_outputs_b, alignments_b
|
||||
|
||||
def _add_speaker_embedding(self, encoder_outputs, speaker_ids):
|
||||
if hasattr(self, "speaker_embedding") and speaker_ids is None:
|
||||
raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
|
||||
if hasattr(self, "speaker_embedding") and speaker_ids is not None:
|
||||
speaker_embeddings = self.speaker_embedding(speaker_ids)
|
||||
def _speaker_embedding_pass(self, encoder_outputs, speaker_ids):
|
||||
# TODO: multi-speaker
|
||||
# if hasattr(self, "speaker_embedding") and speaker_ids is None:
|
||||
# raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
|
||||
# if hasattr(self, "speaker_embedding") and speaker_ids is not None:
|
||||
|
||||
speaker_embeddings.unsqueeze_(1)
|
||||
speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
|
||||
encoder_outputs.size(1),
|
||||
-1)
|
||||
encoder_outputs = encoder_outputs + speaker_embeddings
|
||||
return encoder_outputs
|
||||
# speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
|
||||
# encoder_outputs.size(1),
|
||||
# -1)
|
||||
# encoder_outputs = encoder_outputs + speaker_embeddings
|
||||
# return encoder_outputs
|
||||
pass
|
||||
|
|
Загрузка…
Ссылка в новой задаче