mirror of https://github.com/coqui-ai/TTS.git
365 lines
16 KiB
Python
365 lines
16 KiB
Python
# coding: utf-8
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import numpy as np
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import torch
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from torch import nn
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from TTS.tts.utils.measures import alignment_diagonal_score
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from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
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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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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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use_gst (bool, optional): enable/disable Global style token module.
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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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gradual_trainin (List): Gradual training schedule. If None or `[]`, no gradual training is used.
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Defaults to `[]`.
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"""
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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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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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use_gst=False,
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gst=None,
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gradual_training=[]):
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super().__init__(num_chars, num_speakers, r, postnet_output_dim,
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decoder_output_dim, attn_type, attn_win, attn_norm,
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prenet_type, prenet_dropout,
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prenet_dropout_at_inference, forward_attn,
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trans_agent, forward_attn_mask, location_attn, attn_K,
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separate_stopnet, bidirectional_decoder,
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double_decoder_consistency, ddc_r,
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encoder_in_features, decoder_in_features,
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speaker_embedding_dim, use_gst, gst, gradual_training)
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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,
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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 and use_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,
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text,
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text_lengths,
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mel_specs=None,
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mel_lengths=None,
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cond_input=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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cond_input: 'speaker_ids': [B, 1] and 'x_vectors':[B, C]
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"""
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outputs = {
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'alignments_backward': None,
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'decoder_outputs_backward': None
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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 and self.use_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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cond_input['x_vectors'])
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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(cond_input['speaker_ids'])[:,
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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(cond_input['x_vectors'], 1)
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encoder_outputs = self._concat_speaker_embedding(
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encoder_outputs, speaker_embeddings)
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encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(
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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(
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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(
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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(
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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(
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mel_specs, encoder_outputs, input_mask)
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outputs['alignments_backward'] = alignments_backward
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outputs['decoder_outputs_backward'] = decoder_outputs_backward
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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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outputs['alignments_backward'] = alignments_backward
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outputs['decoder_outputs_backward'] = decoder_outputs_backward
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outputs.update({
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'postnet_outputs': postnet_outputs,
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'decoder_outputs': decoder_outputs,
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'alignments': alignments,
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'stop_tokens': stop_tokens
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})
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return outputs
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@torch.no_grad()
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def inference(self, text, cond_input=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 and self.use_gst:
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# B x gst_dim
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encoder_outputs = self.compute_gst(encoder_outputs, cond_input['style_mel'],
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cond_input['x_vectors'])
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if self.num_speakers > 1:
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if not self.embeddings_per_sample:
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x_vector = self.speaker_embedding(cond_input['speaker_ids'])[:, None]
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x_vector = torch.unsqueeze(x_vector, 0).transpose(1, 2)
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else:
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x_vector = cond_input
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encoder_outputs = self._concat_speaker_embedding(
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encoder_outputs, x_vector)
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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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outputs = {
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'postnet_outputs': postnet_outputs,
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'decoder_outputs': decoder_outputs,
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'alignments': alignments,
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'stop_tokens': stop_tokens
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}
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return outputs
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def train_step(self, batch, criterion):
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"""Perform a single training step by fetching the right set if samples from the batch.
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Args:
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batch ([type]): [description]
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criterion ([type]): [description]
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"""
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text_input = batch['text_input']
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text_lengths = batch['text_lengths']
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mel_input = batch['mel_input']
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mel_lengths = batch['mel_lengths']
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linear_input = batch['linear_input']
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stop_targets = batch['stop_targets']
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speaker_ids = batch['speaker_ids']
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x_vectors = batch['x_vectors']
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# forward pass model
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outputs = self.forward(text_input,
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text_lengths,
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mel_input,
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mel_lengths,
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cond_input={
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'speaker_ids': speaker_ids,
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'x_vectors': x_vectors
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})
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# set the [alignment] lengths wrt reduction factor for guided attention
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if mel_lengths.max() % self.decoder.r != 0:
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alignment_lengths = (
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mel_lengths +
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(self.decoder.r -
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(mel_lengths.max() % self.decoder.r))) // self.decoder.r
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else:
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alignment_lengths = mel_lengths // self.decoder.r
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cond_input = {'speaker_ids': speaker_ids, 'x_vectors': x_vectors}
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outputs = self.forward(text_input, text_lengths, mel_input,
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mel_lengths, cond_input)
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# compute loss
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loss_dict = criterion(
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outputs['model_outputs'],
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outputs['decoder_outputs'],
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mel_input,
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linear_input,
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outputs['stop_tokens'],
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stop_targets,
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mel_lengths,
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outputs['decoder_outputs_backward'],
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outputs['alignments'],
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alignment_lengths,
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outputs['alignments_backward'],
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text_lengths,
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)
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# compute alignment error (the lower the better )
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align_error = 1 - alignment_diagonal_score(outputs['alignments'])
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loss_dict["align_error"] = align_error
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return outputs, loss_dict
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def train_log(self, ap, batch, outputs):
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postnet_outputs = outputs['model_outputs']
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alignments = outputs['alignments']
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alignments_backward = outputs['alignments_backward']
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mel_input = batch['mel_input']
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pred_spec = postnet_outputs[0].data.cpu().numpy()
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gt_spec = mel_input[0].data.cpu().numpy()
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align_img = alignments[0].data.cpu().numpy()
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figures = {
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"prediction": plot_spectrogram(pred_spec, ap, output_fig=False),
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"ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False),
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"alignment": plot_alignment(align_img, output_fig=False),
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}
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if self.bidirectional_decoder or self.double_decoder_consistency:
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figures["alignment_backward"] = plot_alignment(
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alignments_backward[0].data.cpu().numpy(), output_fig=False)
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# Sample audio
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train_audio = ap.inv_melspectrogram(pred_spec.T)
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return figures, train_audio
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def eval_step(self, batch, criterion):
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return self.train_step(batch, criterion)
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def eval_log(self, ap, batch, outputs):
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return self.train_log(ap, batch, outputs)
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