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import torch |
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from TTS.vocoder.layers.pqmf import PQMF |
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from TTS.vocoder.models.melgan_generator import MelganGenerator |
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class MultibandMelganGenerator(MelganGenerator): |
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def __init__( |
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self, |
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in_channels=80, |
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out_channels=4, |
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proj_kernel=7, |
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base_channels=384, |
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upsample_factors=(2, 8, 2, 2), |
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res_kernel=3, |
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num_res_blocks=3, |
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): |
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super().__init__( |
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in_channels=in_channels, |
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out_channels=out_channels, |
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proj_kernel=proj_kernel, |
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base_channels=base_channels, |
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upsample_factors=upsample_factors, |
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res_kernel=res_kernel, |
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num_res_blocks=num_res_blocks, |
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) |
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self.pqmf_layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0) |
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def pqmf_analysis(self, x): |
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return self.pqmf_layer.analysis(x) |
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def pqmf_synthesis(self, x): |
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return self.pqmf_layer.synthesis(x) |
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@torch.no_grad() |
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def inference(self, cond_features): |
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cond_features = cond_features.to(self.layers[1].weight.device) |
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cond_features = torch.nn.functional.pad( |
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cond_features, (self.inference_padding, self.inference_padding), "replicate" |
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) |
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return self.pqmf_synthesis(self.layers(cond_features)) |
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