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