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- import torch
- from torch import nn
- from .espnet_positional_embedding import RelPositionalEncoding
- from .espnet_transformer_attn import RelPositionMultiHeadedAttention, MultiHeadedAttention
- from .layers import Swish, ConvolutionModule, EncoderLayer, MultiLayeredConv1d
- from ..layers import Embedding
- def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
- class ConformerLayers(nn.Module):
- def __init__(self, hidden_size, num_layers, kernel_size=9, dropout=0.0, num_heads=4, use_last_norm=True):
- super().__init__()
- self.use_last_norm = use_last_norm
- self.layers = nn.ModuleList()
- positionwise_layer = MultiLayeredConv1d
- positionwise_layer_args = (hidden_size, hidden_size * 4, 1, dropout)
- self.encoder_layers = nn.ModuleList([EncoderLayer(
- hidden_size,
- MultiHeadedAttention(num_heads, hidden_size, 0.0),
- positionwise_layer(*positionwise_layer_args),
- positionwise_layer(*positionwise_layer_args),
- ConvolutionModule(hidden_size, kernel_size, Swish()),
- dropout,
- ) for _ in range(num_layers)])
- if self.use_last_norm:
- self.layer_norm = nn.LayerNorm(hidden_size)
- else:
- self.layer_norm = nn.Linear(hidden_size, hidden_size)
- def forward(self, x, x_mask):
- """
- :param x: [B, T, H]
- :param padding_mask: [B, T]
- :return: [B, T, H]
- """
- for l in self.encoder_layers:
- x, mask = l(x, x_mask)
- x = self.layer_norm(x) * x_mask
- return x
- class ConformerEncoder(ConformerLayers):
- def __init__(self, hidden_size, dict_size=0, in_size=0, strides=[2,2], num_layers=None):
- conformer_enc_kernel_size = 9
- super().__init__(hidden_size, num_layers, conformer_enc_kernel_size)
- self.dict_size = dict_size
- if dict_size != 0:
- self.embed = Embedding(dict_size, hidden_size, padding_idx=0)
- else:
- self.seq_proj_in = torch.nn.Linear(in_size, hidden_size)
- self.seq_proj_out = torch.nn.Linear(hidden_size, in_size)
- self.mel_in = torch.nn.Linear(160, hidden_size)
- self.mel_pre_net = torch.nn.Sequential(*[
- torch.nn.Conv1d(hidden_size, hidden_size, kernel_size=s * 2, stride=s, padding=s // 2)
- for i, s in enumerate(strides)
- ])
- def forward(self, seq_out, mels_timbre, other_embeds=0):
- """
- :param src_tokens: [B, T]
- :return: [B x T x C]
- """
- x_lengths = (seq_out > 0).long().sum(-1)
- x = seq_out
- if self.dict_size != 0:
- x = self.embed(x) + other_embeds # [B, T, H]
- else:
- x = self.seq_proj_in(x) + other_embeds # [B, T, H]
- mels_timbre = self.mel_in(mels_timbre).transpose(1, 2)
- mels_timbre = self.mel_pre_net(mels_timbre).transpose(1, 2)
- T_out = x.size(1)
- if self.dict_size != 0:
- x_mask = torch.unsqueeze(sequence_mask(x_lengths + mels_timbre.size(1), x.size(1) + mels_timbre.size(1)), 2).to(x.dtype)
- else:
- x_mask = torch.cat((torch.ones(x.size(0), mels_timbre.size(1), 1).to(x.device), (x.abs().sum(2) > 0).float()[:, :, None]), dim=1)
- x = torch.cat((mels_timbre, x), 1)
- x = super(ConformerEncoder, self).forward(x, x_mask)
- if self.dict_size != 0:
- x = x[:, -T_out:, :]
- else:
- x = self.seq_proj_out(x[:, -T_out:, :])
- return x
- class ConformerDecoder(ConformerLayers):
- def __init__(self, hidden_size, num_layers):
- conformer_dec_kernel_size = 9
- super().__init__(hidden_size, num_layers, conformer_dec_kernel_size)
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