Add time_embed_dim_multiplier
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@ -90,6 +90,7 @@ class DiffusionVocoderWithRef(nn.Module):
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scale_factor=2,
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conditioning_inputs_provided=True,
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conditioning_input_dim=80,
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time_embed_dim_multiplier=4,
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):
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super().__init__()
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@ -111,7 +112,7 @@ class DiffusionVocoderWithRef(nn.Module):
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padding = 1 if kernel_size == 3 else 2
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time_embed_dim = model_channels * 4
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time_embed_dim = model_channels * time_embed_dim_multiplier
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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@ -332,5 +333,5 @@ if __name__ == '__main__':
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spec = torch.randn(2,512,160)
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cond = torch.randn(2, 3, 80, 173)
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ts = torch.LongTensor([555, 556])
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model = DiffusionVocoderWithRef(32, conditioning_inputs_provided=True)
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model = DiffusionVocoderWithRef(32, conditioning_inputs_provided=True, time_embed_dim_multiplier=8)
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print(model(clip, ts, spec, cond, 3).shape)
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