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| import torch
| import torch.nn as nn
|
| from funasr.register import tables
|
| @tables.register("adaptor_classes", "Linear")
| class Linear(nn.Module):
| def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
| super().__init__()
| self.k = downsample_rate
| self.encoder_dim = encoder_dim
| self.llm_dim = llm_dim
| self.linear1 = nn.Linear(self.encoder_dim * self.k, ffn_dim)
| self.relu = nn.ReLU()
| self.linear2 = nn.Linear(ffn_dim, self.llm_dim)
|
| def forward(self, x):
| batch_size, seq_len, dim = x.size()
| num_frames_to_discard = seq_len % self.k
| if num_frames_to_discard > 0:
| x = x[:, :-num_frames_to_discard, :]
| seq_len = x.size(1)
|
| x = x.contiguous()
| x = x.view(batch_size, seq_len // self.k, dim * self.k)
| x = self.linear1(x)
| x = self.relu(x)
| x = self.linear2(x)
| return x
|
|
