code: def test_multi_speaker_vits_is_trainable_and_decodable(
      gen_dict, dis_dict, loss_dict, spks, spk_embed_dim, langs
  ):
      idim = 10
      odim = 5
      global_channels = 8
      gen_args = make_vits_generator_args(**gen_dict)
      gen_args["generator_params"]["spks"] = spks
      gen_args["generator_params"]["langs"] = langs
      gen_args["generator_params"]["spk_embed_dim"] = spk_embed_dim
      gen_args["generator_params"]["global_channels"] = global_channels
      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
          ),
      )
      if spks > 0:
          inputs["sids"] = torch.randint(0, spks, (2, 1))
      if langs > 0:
          inputs["lids"] = torch.randint(0, langs, (2, 1))
      if spk_embed_dim > 0:
          inputs["spembs"] = torch.randn(2, spk_embed_dim)
      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,),
              ),
          )
          if spks > 0:
              inputs["sids"] = torch.randint(0, spks, (1,))
          if langs > 0:
              inputs["lids"] = torch.randint(0, langs, (1,))
          if spk_embed_dim > 0:
              inputs["spembs"] = torch.randn(spk_embed_dim)
          model.inference(**inputs)
  
          # check inference with predefined duration
          inputs = dict(
              text=torch.randint(
                  0,
                  idim,
                  (5,),
              ),
              durations=torch.tensor([1, 2, 3, 4, 5], dtype=torch.long),
          )
          if spks > 0:
              inputs["sids"] = torch.randint(0, spks, (1,))
          if langs > 0:
              inputs["lids"] = torch.randint(0, langs, (1,))
          if spk_embed_dim > 0:
              inputs["spembs"] = torch.randn(spk_embed_dim)
          output_dict = model.inference(**inputs)
          assert (
              output_dict["wav"].size(0)
              == inputs["durations"].sum() * upsample_factor
          )
  
          # check inference with teachder forcing
          inputs = dict(
              text=torch.randint(
                  0,
                  idim,
                  (5,),
              ),
              feats=torch.randn(16, odim),
          )
          if spks > 0:
              inputs["sids"] = torch.randint(0, spks, (1,))
          if langs > 0:
              inputs["lids"] = torch.randint(0, langs, (1,))
          if spk_embed_dim > 0:
              inputs["spembs"] = torch.randn(spk_embed_dim)
          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 6 for calling a global function with example usage: test_multi_speaker_vits_is_trainable_and_decodable(gen_dict, dis_dict, loss_dict, spks, spk_embed_dim, langs) without return types