Delete mlp_projector.py

mlp_projector.py

@@ -1,42 +0,0 @@
-import torch.nn as nn
-
-
-class MLPAudioProjector(nn.Module):
-    """2-layer MLP projector with Qwen-style 2x temporal downsampling."""
-
-    def __init__(self, config):
-        super().__init__()
-
-        encoder_dim = getattr(config, "encoder_dim", 768)
-        llm_dim = getattr(config, "llm_dim", 2048)
-
-        self.downsample = nn.Conv1d(
-            encoder_dim, encoder_dim, kernel_size=3, stride=2, padding=1, bias=False
-        )
-        self.linear_1 = nn.Linear(encoder_dim, llm_dim, bias=False)
-        self.act = nn.GELU()
-        self.linear_2 = nn.Linear(llm_dim, llm_dim, bias=False)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, module):
-        if isinstance(module, nn.Linear):
-            nn.init.normal_(module.weight, mean=0.0, std=0.02)
-        elif isinstance(module, nn.Conv1d):
-            nn.init.normal_(module.weight, mean=0.0, std=0.02)
-            if module.bias is not None:
-                nn.init.zeros_(module.bias)
-
-    def forward(self, x):
-        """
-        x: [Batch, Seq_Len, Dim]
-        Returns: [Batch, Seq_Len // 2, llm_dim]
-        """
-        # Conv1d expects [Batch, Channels, Seq_Len]
-        x = x.transpose(1, 2)
-        x = self.downsample(x)
-        x = x.transpose(1, 2)
-
-        x = self.linear_1(x)
-        x = self.act(x)
-        return self.linear_2(x)