mirror of https://github.com/coqui-ai/TTS.git
284 lines
12 KiB
Python
284 lines
12 KiB
Python
import torch
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from torch import nn
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from TTS.tts.layers.tacotron.gst_layers import GST
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from TTS.tts.layers.tacotron.tacotron2 import Decoder, Encoder, Postnet
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from TTS.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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"""Tacotron2 as in https://arxiv.org/abs/1712.05884
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It's an autoregressive encoder-attention-decoder-postnet architecture.
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Args:
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num_chars (int): number of input characters to define the size of embedding layer.
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num_speakers (int): number of speakers in the dataset. >1 enables multi-speaker training and model learns speaker embeddings.
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r (int): initial model reduction rate.
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postnet_output_dim (int, optional): postnet output channels. Defaults to 80.
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decoder_output_dim (int, optional): decoder output channels. Defaults to 80.
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attn_type (str, optional): attention type. Check ```TTS.tts.layers.tacotron.common_layers.init_attn```. Defaults to 'original'.
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attn_win (bool, optional): enable/disable attention windowing.
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It especially useful at inference to keep attention alignment diagonal. Defaults to False.
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attn_norm (str, optional): Attention normalization method. "sigmoid" or "softmax". Defaults to "softmax".
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prenet_type (str, optional): prenet type for the decoder. Defaults to "original".
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prenet_dropout (bool, optional): prenet dropout rate. Defaults to True.
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prenet_dropout_at_inference (bool, optional): use dropout at inference time. This leads to a better quality for
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some models. Defaults to False.
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forward_attn (bool, optional): enable/disable forward attention.
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It is only valid if ```attn_type``` is ```original```. Defaults to False.
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trans_agent (bool, optional): enable/disable transition agent in forward attention. Defaults to False.
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forward_attn_mask (bool, optional): enable/disable extra masking over forward attention. Defaults to False.
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location_attn (bool, optional): enable/disable location sensitive attention.
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It is only valid if ```attn_type``` is ```original```. Defaults to True.
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attn_K (int, optional): Number of attention heads for GMM attention. Defaults to 5.
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separate_stopnet (bool, optional): enable/disable separate stopnet training without only gradient
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flow from stopnet to the rest of the model. Defaults to True.
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bidirectional_decoder (bool, optional): enable/disable bidirectional decoding. Defaults to False.
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double_decoder_consistency (bool, optional): enable/disable double decoder consistency. Defaults to False.
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ddc_r (int, optional): reduction rate for the coarse decoder of double decoder consistency. Defaults to None.
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encoder_in_features (int, optional): input channels for the encoder. Defaults to 512.
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decoder_in_features (int, optional): input channels for the decoder. Defaults to 512.
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speaker_embedding_dim (int, optional): external speaker conditioning vector channels. Defaults to None.
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gst (Coqpit, optional): Coqpit to initialize the GST module. If `None`, GST is disabled. Defaults to None.
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"""
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def __init__(
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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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prenet_dropout_at_inference=False,
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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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encoder_in_features=512,
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decoder_in_features=512,
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speaker_embedding_dim=None,
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gst=None,
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):
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super().__init__(
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num_chars,
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num_speakers,
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r,
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postnet_output_dim,
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decoder_output_dim,
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attn_type,
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attn_win,
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attn_norm,
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prenet_type,
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prenet_dropout,
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prenet_dropout_at_inference,
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forward_attn,
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trans_agent,
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forward_attn_mask,
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location_attn,
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attn_K,
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separate_stopnet,
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bidirectional_decoder,
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double_decoder_consistency,
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ddc_r,
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encoder_in_features,
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decoder_in_features,
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speaker_embedding_dim,
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gst,
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)
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# speaker embedding layer
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if self.num_speakers > 1:
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if not self.embeddings_per_sample:
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speaker_embedding_dim = 512
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self.speaker_embedding = nn.Embedding(self.num_speakers, speaker_embedding_dim)
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self.speaker_embedding.weight.data.normal_(0, 0.3)
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# speaker and gst embeddings is concat in decoder input
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if self.num_speakers > 1:
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self.decoder_in_features += speaker_embedding_dim # add speaker embedding dim
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# embedding layer
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self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
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# base model layers
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self.encoder = Encoder(self.encoder_in_features)
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self.decoder = Decoder(
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self.decoder_in_features,
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self.decoder_output_dim,
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r,
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attn_type,
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attn_win,
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attn_norm,
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prenet_type,
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prenet_dropout,
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forward_attn,
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trans_agent,
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forward_attn_mask,
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location_attn,
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attn_K,
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separate_stopnet,
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)
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self.postnet = Postnet(self.postnet_output_dim)
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# setup prenet dropout
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self.decoder.prenet.dropout_at_inference = prenet_dropout_at_inference
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# global style token layers
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if self.gst:
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self.gst_layer = GST(
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num_mel=decoder_output_dim,
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speaker_embedding_dim=speaker_embedding_dim,
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num_heads=gst.gst_num_heads,
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num_style_tokens=gst.gst_num_style_tokens,
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gst_embedding_dim=gst.gst_embedding_dim,
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)
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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.coarse_decoder = Decoder(
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self.decoder_in_features,
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self.decoder_output_dim,
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ddc_r,
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attn_type,
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attn_win,
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attn_norm,
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prenet_type,
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prenet_dropout,
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forward_attn,
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trans_agent,
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forward_attn_mask,
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location_attn,
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attn_K,
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separate_stopnet,
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)
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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, speaker_embeddings=None):
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"""
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Shapes:
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text: [B, T_in]
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text_lengths: [B]
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mel_specs: [B, T_out, C]
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mel_lengths: [B]
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speaker_ids: [B, 1]
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speaker_embeddings: [B, C]
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"""
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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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if self.gst:
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# B x gst_dim
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encoder_outputs = self.compute_gst(encoder_outputs, mel_specs, speaker_embeddings)
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if self.num_speakers > 1:
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if not self.embeddings_per_sample:
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# B x 1 x speaker_embed_dim
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speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
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else:
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# B x 1 x speaker_embed_dim
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speaker_embeddings = torch.unsqueeze(speaker_embeddings, 1)
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encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
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encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
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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(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(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 (
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decoder_outputs,
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postnet_outputs,
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alignments,
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stop_tokens,
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decoder_outputs_backward,
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alignments_backward,
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)
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if self.double_decoder_consistency:
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decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(
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mel_specs, encoder_outputs, alignments, input_mask
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)
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return (
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decoder_outputs,
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postnet_outputs,
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alignments,
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stop_tokens,
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decoder_outputs_backward,
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alignments_backward,
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)
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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, style_mel=None, speaker_embeddings=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 self.gst:
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# B x gst_dim
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encoder_outputs = self.compute_gst(encoder_outputs, style_mel, speaker_embeddings)
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if self.num_speakers > 1:
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if not self.embeddings_per_sample:
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speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
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encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
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decoder_outputs, alignments, stop_tokens = self.decoder.inference(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(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, style_mel=None, speaker_embeddings=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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if self.gst:
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# B x gst_dim
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encoder_outputs = self.compute_gst(encoder_outputs, style_mel, speaker_embeddings)
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if self.num_speakers > 1:
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if not self.embeddings_per_sample:
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speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
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encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
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mel_outputs, alignments, stop_tokens = self.decoder.inference_truncated(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(mel_outputs, mel_outputs_postnet, alignments)
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return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
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