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modeling_rvc.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import json
+import os
+
+class ResidualBlock(nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super().__init__()
+        self.convs1 = nn.ModuleList([
+            nn.Conv1d(channels, channels, kernel_size, 1, dilation=d, padding=d)
+            for d in dilation
+        ])
+        self.convs2 = nn.ModuleList([
+            nn.Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=1)
+            for _ in dilation
+        ])
+
+    def forward(self, x):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, 0.1)
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, 0.1)
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+class RVCModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        model_cfg = config["model"]
+        
+        self.encoder = nn.Sequential(
+            nn.Conv1d(128, model_cfg["upsample_initial_channel"], 7, 1, 3),
+            *[ResidualBlock(model_cfg["upsample_initial_channel"]) for _ in range(3)]
+        )
+        
+        self.decoder = nn.Sequential(
+            nn.Conv1d(model_cfg["upsample_initial_channel"], 128, 7, 1, 3),
+        )
+        
+    def forward(self, x):
+        encoded = self.encoder(x)
+        decoded = self.decoder(encoded)
+        return decoded
+    
+    def convert_voice(self, audio_path):
+        return audio_path
+    
+    @classmethod
+    def from_pretrained(cls, model_path):
+        config_path = os.path.join(model_path, "config.json")
+        with open(config_path, "r") as f:
+            config = json.load(f)
+        
+        model = cls(config)
+        
+        model_file = os.path.join(model_path, "model.pth")
+        if os.path.exists(model_file):
+            model.load_state_dict(torch.load(model_file, map_location="cpu"))
+        
+        return model