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TTS/vocoder/models/multiband_melgan_generator.py

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