mirror of https://github.com/coqui-ai/TTS.git
53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
# coding: utf-8
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import torch
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from torch import nn
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from math import sqrt
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from layers.tacotron import Prenet, Encoder, Decoder, PostCBHG
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from utils.generic_utils import sequence_mask
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class Tacotron(nn.Module):
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def __init__(self,
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num_chars,
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linear_dim=1025,
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mel_dim=80,
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r=5,
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padding_idx=None,
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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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super(Tacotron, self).__init__()
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self.r = r
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self.mel_dim = mel_dim
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self.linear_dim = linear_dim
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self.embedding = nn.Embedding(num_chars, 256, padding_idx=padding_idx)
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self.embedding.weight.data.normal_(0, 0.3)
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self.encoder = Encoder(256)
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self.decoder = Decoder(256, mel_dim, r, memory_size, attn_win, attn_norm)
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self.postnet = PostCBHG(mel_dim)
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self.last_linear = nn.Sequential(
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nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim),
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nn.Sigmoid())
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def forward(self, characters, text_lengths, mel_specs):
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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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encoder_outputs = self.encoder(inputs)
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mel_outputs, alignments, stop_tokens = self.decoder(
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encoder_outputs, mel_specs, mask)
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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):
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B = characters.size(0)
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inputs = self.embedding(characters)
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encoder_outputs = self.encoder(inputs)
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mel_outputs, alignments, stop_tokens = self.decoder.inference(
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encoder_outputs)
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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 |