mirror of https://github.com/coqui-ai/TTS.git
98 lines
4.4 KiB
Python
98 lines
4.4 KiB
Python
# coding: utf-8
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import torch
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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.layers.gst_layers import GST
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from TTS.utils.generic_utils import sequence_mask
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from TTS.models.tacotron import Tacotron
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class TacotronGST(Tacotron):
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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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linear_dim=1025,
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mel_dim=80,
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memory_size=5,
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attn_win=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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separate_stopnet=True):
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super().__init__(num_chars,
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num_speakers,
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r,
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linear_dim,
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mel_dim,
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memory_size,
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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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separate_stopnet)
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gst_embedding_dim = 256
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decoder_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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self.decoder = Decoder(decoder_dim, mel_dim, r, memory_size, 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, separate_stopnet, proj_speaker_dim)
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self.gst = GST(num_mel=80, num_heads=4,
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num_style_tokens=10, embedding_dim=gst_embedding_dim)
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def forward(self, characters, text_lengths, mel_specs, speaker_ids=None):
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B = characters.size(0)
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mask = sequence_mask(text_lengths).to(characters.device)
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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._add_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.num_speakers > 1:
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encoder_outputs = self._add_speaker_embedding(encoder_outputs,
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self.speaker_embeddings)
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gst_outputs = self.gst(mel_specs)
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encoder_outputs = self._add_speaker_embedding(
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encoder_outputs, gst_outputs)
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mel_outputs, alignments, stop_tokens = self.decoder(
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encoder_outputs, mel_specs, mask, self.speaker_embeddings_projected)
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mel_outputs = mel_outputs.view(B, -1, self.mel_dim)
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linear_outputs = self.postnet(mel_outputs)
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linear_outputs = self.last_linear(linear_outputs)
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return mel_outputs, linear_outputs, alignments, stop_tokens
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def inference(self, characters, speaker_ids=None, style_mel=None):
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B = characters.size(0)
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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._add_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.num_speakers > 1:
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encoder_outputs = self._add_speaker_embedding(encoder_outputs,
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self.speaker_embeddings)
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if style_mel is not None:
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gst_outputs = self.gst(style_mel)
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gst_outputs = gst_outputs.expand(-1, encoder_outputs.size(1), -1)
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encoder_outputs = self._add_speaker_embedding(encoder_outputs,
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gst_outputs)
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mel_outputs, alignments, stop_tokens = self.decoder.inference(
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encoder_outputs, self.speaker_embeddings_projected)
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mel_outputs = mel_outputs.view(B, -1, self.mel_dim)
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linear_outputs = self.postnet(mel_outputs)
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linear_outputs = self.last_linear(linear_outputs)
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return mel_outputs, linear_outputs, alignments, stop_tokens
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