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)
      print("gen_args", gen_args)
      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),
          singing=torch.randn(2, 16 * upsample_factor),
          singing_lengths=torch.tensor(
              [16, 13] * upsample_factor, dtype=torch.long
          ),
          label={
              "lab": torch.randint(0, idim, (2, 8)),
              "score": torch.randint(0, idim, (2, 8)),
          },
          label_lengths={
              "lab": torch.tensor([8, 5], dtype=torch.long),
              "score": torch.tensor([8, 5], dtype=torch.long),
          },
          melody={
              "lab": torch.randint(0, 127, (2, 8)),
              "score": torch.randint(0, 127, (2, 8)),
          },
          duration={
              "lab": torch.tensor(
                  [[1, 2, 2, 3, 1, 3, 2, 2], [2, 2, 1, 4, 1, 2, 1, 3]],
                  dtype=torch.int64,
              ),
              "score_phn": torch.tensor(
                  [[1, 2, 2, 3, 1, 3, 2, 1], [2, 2, 1, 4, 1, 2, 1, 3]],
                  dtype=torch.int64,
              ),
              "score_syb": torch.tensor(
                  [[3, 3, 5, 5, 4, 4, 3, 3], [4, 4, 5, 5, 3, 3, 4, 4]],
                  dtype=torch.int64,
              ),
          },
          slur=torch.randint(0, 2, (2, 8)),
          pitch=torch.randn(2, 16, 1),
      )
      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,
                  (
                      1,
                      5,
                  ),
              ),
              label={
                  "lab": torch.randint(
                      0,
                      idim,
                      (
                          1,
                          5,
                      ),
                  ),
                  "score": torch.randint(
                      0,
                      idim,
                      (
                          1,
                          5,
                      ),
                  ),
              },
              melody={
                  "lab": torch.randint(
                      0,
                      127,
                      (
                          1,
                          5,
                      ),
                  ),
                  "score": torch.randint(
                      0,
                      127,
                      (
                          1,
                          5,
                      ),
                  ),
              },
              duration={
                  "lab": torch.tensor(
                      [[1, 2, 2, 3, 3]], dtype=torch.int64
                  ),
                  "score_phn": torch.tensor(
                      [[1, 2, 2, 3, 4]], dtype=torch.int64
                  ),
                  "score_syb": torch.tensor(
                      [[3, 3, 5, 5, 4]], dtype=torch.int64
                  ),
              },
              slur=torch.randint(0, 2, (1, 5)),
              pitch=torch.randn(16, 1),
          )
          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 teachder forcing
          inputs = dict(
              text=torch.randint(
                  0,
                  idim,
                  (
                      1,
                      5,
                  ),
              ),
              label={
                  "lab": torch.randint(
                      0,
                      idim,
                      (
                          1,
                          5,
                      ),
                  ),
                  "score": torch.randint(
                      0,
                      idim,
                      (
                          1,
                          5,
                      ),
                  ),
              },
              melody={
                  "lab": torch.randint(
                      0,
                      127,
                      (
                          1,
                          5,
                      ),
                  ),
                  "score": torch.randint(
                      0,
                      127,
                      (
                          1,
                          5,
                      ),
                  ),
              },
              duration={
                  "lab": torch.tensor(
                      [[1, 2, 2, 3, 3]], dtype=torch.int64
                  ),
                  "score_phn": torch.tensor(
                      [[1, 2, 2, 3, 4]], dtype=torch.int64
                  ),
                  "score_syb": torch.tensor(
                      [[3, 3, 5, 5, 4]], dtype=torch.int64
                  ),
              },
              slur=torch.randint(0, 2, (1, 5)),
              pitch=torch.randn(16, 1),
              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_svs/visinger/test_visinger.py
imports: import pytest
  import torch
  from espnet2.gan_svs.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