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