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72c09ec0b7* indextts2 * update lfs for audio files --------- Co-authored-by: wangyining02 <wangyining02@bilibili.com>
234 lines
7 KiB
Python
234 lines
7 KiB
Python
# Copyright (c) 2023 Amphion.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import numpy as np
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import torch
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import torch.nn as nn
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import math
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from torch.nn import functional as F
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class StyleAdaptiveLayerNorm(nn.Module):
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def __init__(self, normalized_shape, eps=1e-5):
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super().__init__()
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self.in_dim = normalized_shape
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self.norm = nn.LayerNorm(self.in_dim, eps=eps, elementwise_affine=False)
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self.style = nn.Linear(self.in_dim, self.in_dim * 2)
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self.style.bias.data[: self.in_dim] = 1
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self.style.bias.data[self.in_dim :] = 0
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def forward(self, x, condition):
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# x: (B, T, d); condition: (B, T, d)
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style = self.style(torch.mean(condition, dim=1, keepdim=True))
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gamma, beta = style.chunk(2, -1)
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out = self.norm(x)
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out = gamma * out + beta
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return out
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class PositionalEncoding(nn.Module):
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def __init__(self, d_model, dropout, max_len=5000):
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super().__init__()
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self.dropout = dropout
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position = torch.arange(max_len).unsqueeze(1)
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div_term = torch.exp(
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torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
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)
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pe = torch.zeros(max_len, 1, d_model)
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pe[:, 0, 0::2] = torch.sin(position * div_term)
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pe[:, 0, 1::2] = torch.cos(position * div_term)
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self.register_buffer("pe", pe)
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def forward(self, x):
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x = x + self.pe[: x.size(0)]
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return F.dropout(x, self.dropout, training=self.training)
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class TransformerFFNLayer(nn.Module):
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def __init__(
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self, encoder_hidden, conv_filter_size, conv_kernel_size, encoder_dropout
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):
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super().__init__()
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self.encoder_hidden = encoder_hidden
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self.conv_filter_size = conv_filter_size
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self.conv_kernel_size = conv_kernel_size
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self.encoder_dropout = encoder_dropout
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self.ffn_1 = nn.Conv1d(
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self.encoder_hidden,
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self.conv_filter_size,
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self.conv_kernel_size,
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padding=self.conv_kernel_size // 2,
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)
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self.ffn_1.weight.data.normal_(0.0, 0.02)
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self.ffn_2 = nn.Linear(self.conv_filter_size, self.encoder_hidden)
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self.ffn_2.weight.data.normal_(0.0, 0.02)
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def forward(self, x):
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# x: (B, T, d)
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x = self.ffn_1(x.permute(0, 2, 1)).permute(
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0, 2, 1
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) # (B, T, d) -> (B, d, T) -> (B, T, d)
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x = F.relu(x)
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x = F.dropout(x, self.encoder_dropout, training=self.training)
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x = self.ffn_2(x)
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return x
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class TransformerEncoderLayer(nn.Module):
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def __init__(
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self,
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encoder_hidden,
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encoder_head,
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conv_filter_size,
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conv_kernel_size,
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encoder_dropout,
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use_cln,
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):
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super().__init__()
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self.encoder_hidden = encoder_hidden
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self.encoder_head = encoder_head
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self.conv_filter_size = conv_filter_size
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self.conv_kernel_size = conv_kernel_size
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self.encoder_dropout = encoder_dropout
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self.use_cln = use_cln
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if not self.use_cln:
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self.ln_1 = nn.LayerNorm(self.encoder_hidden)
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self.ln_2 = nn.LayerNorm(self.encoder_hidden)
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else:
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self.ln_1 = StyleAdaptiveLayerNorm(self.encoder_hidden)
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self.ln_2 = StyleAdaptiveLayerNorm(self.encoder_hidden)
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self.self_attn = nn.MultiheadAttention(
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self.encoder_hidden, self.encoder_head, batch_first=True
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)
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self.ffn = TransformerFFNLayer(
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self.encoder_hidden,
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self.conv_filter_size,
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self.conv_kernel_size,
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self.encoder_dropout,
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)
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def forward(self, x, key_padding_mask, conditon=None):
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# x: (B, T, d); key_padding_mask: (B, T), mask is 0; condition: (B, T, d)
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# self attention
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residual = x
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if self.use_cln:
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x = self.ln_1(x, conditon)
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else:
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x = self.ln_1(x)
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if key_padding_mask != None:
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key_padding_mask_input = ~(key_padding_mask.bool())
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else:
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key_padding_mask_input = None
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x, _ = self.self_attn(
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query=x, key=x, value=x, key_padding_mask=key_padding_mask_input
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)
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x = F.dropout(x, self.encoder_dropout, training=self.training)
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x = residual + x
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# ffn
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residual = x
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if self.use_cln:
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x = self.ln_2(x, conditon)
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else:
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x = self.ln_2(x)
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x = self.ffn(x)
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x = residual + x
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return x
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class TransformerEncoder(nn.Module):
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def __init__(
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self,
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enc_emb_tokens=None,
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encoder_layer=4,
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encoder_hidden=256,
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encoder_head=4,
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conv_filter_size=1024,
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conv_kernel_size=5,
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encoder_dropout=0.1,
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use_cln=False,
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cfg=None,
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):
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super().__init__()
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self.encoder_layer = (
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encoder_layer if encoder_layer is not None else cfg.encoder_layer
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)
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self.encoder_hidden = (
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encoder_hidden if encoder_hidden is not None else cfg.encoder_hidden
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)
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self.encoder_head = (
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encoder_head if encoder_head is not None else cfg.encoder_head
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)
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self.conv_filter_size = (
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conv_filter_size if conv_filter_size is not None else cfg.conv_filter_size
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)
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self.conv_kernel_size = (
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conv_kernel_size if conv_kernel_size is not None else cfg.conv_kernel_size
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)
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self.encoder_dropout = (
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encoder_dropout if encoder_dropout is not None else cfg.encoder_dropout
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)
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self.use_cln = use_cln if use_cln is not None else cfg.use_cln
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if enc_emb_tokens != None:
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self.use_enc_emb = True
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self.enc_emb_tokens = enc_emb_tokens
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else:
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self.use_enc_emb = False
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self.position_emb = PositionalEncoding(
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self.encoder_hidden, self.encoder_dropout
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)
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self.layers = nn.ModuleList([])
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self.layers.extend(
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[
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TransformerEncoderLayer(
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self.encoder_hidden,
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self.encoder_head,
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self.conv_filter_size,
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self.conv_kernel_size,
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self.encoder_dropout,
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self.use_cln,
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)
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for i in range(self.encoder_layer)
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]
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)
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if self.use_cln:
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self.last_ln = StyleAdaptiveLayerNorm(self.encoder_hidden)
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else:
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self.last_ln = nn.LayerNorm(self.encoder_hidden)
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def forward(self, x, key_padding_mask, condition=None):
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if len(x.shape) == 2 and self.use_enc_emb:
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x = self.enc_emb_tokens(x)
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x = self.position_emb(x)
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else:
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x = self.position_emb(x) # (B, T, d)
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for layer in self.layers:
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x = layer(x, key_padding_mask, condition)
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if self.use_cln:
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x = self.last_ln(x, condition)
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else:
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x = self.last_ln(x)
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return x
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