Alterations to support VQVAE on mel spectrograms

codes/models/vqvae/vqvae.py

@@ -271,6 +271,6 @@ def register_vqvae_audio(opt_net, opt):
 
 
 if __name__ == '__main__':
-    model = VQVAE(in_channel=-1, conv_module=nn.Conv1d, conv_transpose_module=nn.ConvTranspose1d)
+    model = VQVAE(in_channel=80, conv_module=nn.Conv1d, conv_transpose_module=nn.ConvTranspose1d)
-    res=model(torch.randn(1,224))
+    res=model(torch.randn(1,80,224))
     print(res[0].shape)

codes/train.py

@@ -300,7 +300,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_tts_lj.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vqvae_audio_lj.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()

codes/trainer/injectors/base_injectors.py

@@ -10,6 +10,20 @@ from utils.util import opt_get
 from utils.weight_scheduler import get_scheduler_for_opt
 
 
+class PadInjector(Injector):
+    def __init__(self, opt, env):
+        super().__init__(opt, env)
+        self.multiple = opt['multiple']
+
+    def forward(self, state):
+        ldim = state[self.input].shape[-1]
+        mod = self.multiple-(ldim % self.multiple)
+        t = state[self.input]
+        if mod != 0:
+            t = torch.nn.functional.pad(t, (0, mod))
+        return {self.output: t}
+
+
 class SqueezeInjector(Injector):
     def __init__(self, opt, env):
         super().__init__(opt, env)