code: def test_vits_is_trainable_and_decodable(
      gen_dict, dis_dict, loss_dict
  ):
      idim = 10
      odim = 5
      gen_args = make_vits_generator_args(**gen_dict)
      dis_args = make_vits_discriminator_args(**dis_dict)
      loss_args = make_vits_loss_args(**loss_dict)
      model = VITS(
          idim=idim,
          odim=odim,
          **gen_args,
          **dis_args,
          **loss_args,
      )
      model.train()
      upsample_factor = model.generator.upsample_factor
      inputs = dict(
          text=torch.randint(0, idim, (2, 8)),
          text_lengths=torch.tensor([8, 5], dtype=torch.long),
          feats=torch.randn(2, 16, odim),
          feats_lengths=torch.tensor([16, 13], dtype=torch.long),
          speech=torch.randn(2, 16 * upsample_factor),
          speech_lengths=torch.tensor(
              [16, 13] * upsample_factor, dtype=torch.long
          ),
      )
      gen_loss = model(forward_generator=True, **inputs)["loss"]
      gen_loss.backward()
      dis_loss = model(forward_generator=False, **inputs)["loss"]
      dis_loss.backward()
  
      with torch.no_grad():
          model.eval()
  
          # check inference
          inputs = dict(
              text=torch.randint(
                  0,
                  idim,
                  (5,),
              )
          )
          model.inference(**inputs)
  
          # check inference with predefined durations
          inputs = dict(
              text=torch.randint(
                  0,
                  idim,
                  (5,),
              ),
              durations=torch.tensor([1, 2, 3, 4, 5], dtype=torch.long),
          )
          output_dict = model.inference(**inputs)
          assert (
              output_dict["wav"].size(0)
              == inputs["durations"].sum() * upsample_factor
          )
  
          # check inference with teacher forcing
          inputs = dict(
              text=torch.randint(
                  0,
                  idim,
                  (5,),
              ),
              feats=torch.randn(16, odim),
          )
          output_dict = model.inference(
              **inputs, use_teacher_forcing=True
          )
          assert (
              output_dict["wav"].size(0)
              == inputs["feats"].size(0) * upsample_factor
          )
  
file_path: espnet/test/espnet2/gan_tts/vits/test_vits.py
imports: import pytest
  import torch
  from espnet2.gan_tts.vits import VITS
label_desc: Use image node_id 5 for calling a global function with example usage: test_vits_is_trainable_and_decodable(gen_dict, dis_dict, loss_dict) without return types