RuntimeError: stack expects each tensor to be equal size, but got [1025, 1933] at entry 0 and [2, 80, 1933] at entry 8

[BranchScope]

Describe the bug

(so-vits-fork) C:\Users\BranchScope\so-vits-svc-fork\tests\dataset_raw\mina>svc train
Downloading D_0.pth: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 178M/178M [00:34<00:00, 5.41MiB/s]
Downloading G_0.pth: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200M/200M [00:43<00:00, 4.77MiB/s]
[20:27:25] INFO     [20:27:25] Using strategy: auto                                                                                                                     train.py:98
INFO: GPU available: True (cuda), used: True
           INFO     [20:27:25] GPU available: True (cuda), used: True                                                                                               rank_zero.py:53
INFO: TPU available: False, using: 0 TPU cores
           INFO     [20:27:25] TPU available: False, using: 0 TPU cores                                                                                             rank_zero.py:53
INFO: IPU available: False, using: 0 IPUs
           INFO     [20:27:25] IPU available: False, using: 0 IPUs                                                                                                  rank_zero.py:53
INFO: HPU available: False, using: 0 HPUs
           INFO     [20:27:25] HPU available: False, using: 0 HPUs                                                                                                  rank_zero.py:53
           WARNING  [20:27:25] C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\so_vits_svc_fork\modules\synthesizers.py:81: UserWarning: Unused  warnings.py:109
                    arguments: {'n_layers_q': 3, 'use_spectral_norm': False, 'pretrained': {'D_0.pth':
                    'https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_D_320000.pth', 'G_0.pth':
                    'https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_G_320000.pth'}}
                      warnings.warn(f"Unused arguments: {kwargs}")

           INFO     [20:27:25] Decoder type: hifi-gan                                                                                                           synthesizers.py:100
[20:27:26] WARNING  [20:27:26] C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\so_vits_svc_fork\utils.py:246: UserWarning: Keys not found in     warnings.py:109
                    checkpoint state dict:['emb_g.weight']
                      warnings.warn(f"Keys not found in checkpoint state dict:" f"{not_in_from}")

           WARNING  [20:27:26] C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\so_vits_svc_fork\utils.py:264: UserWarning: Shape mismatch:       warnings.py:109
                    ['dec.cond.weight: torch.Size([512, 256, 1]) -> torch.Size([512, 768, 1])', 'enc_q.enc.cond_layer.weight_v: torch.Size([6144, 256, 1]) ->
                    torch.Size([6144, 768, 1])', 'flow.flows.0.enc.cond_layer.weight_v: torch.Size([1536, 256, 1]) -> torch.Size([1536, 768, 1])',
                    'flow.flows.2.enc.cond_layer.weight_v: torch.Size([1536, 256, 1]) -> torch.Size([1536, 768, 1])', 'flow.flows.4.enc.cond_layer.weight_v:
                    torch.Size([1536, 256, 1]) -> torch.Size([1536, 768, 1])', 'flow.flows.6.enc.cond_layer.weight_v: torch.Size([1536, 256, 1]) ->
                    torch.Size([1536, 768, 1])', 'f0_decoder.cond.weight: torch.Size([192, 256, 1]) -> torch.Size([192, 768, 1])']
                      warnings.warn(

           INFO     [20:27:26] Loaded checkpoint 'logs\44k\G_0.pth' (epoch 0)                                                                                          utils.py:307
           INFO     [20:27:26] Loaded checkpoint 'logs\44k\D_0.pth' (epoch 0)                                                                                          utils.py:307
INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
           INFO     [20:27:26] LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]                                                                                                 cuda.py:58
┏━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃   ┃ Name  ┃ Type                     ┃ Params ┃
┡━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ 0 │ net_g │ SynthesizerTrn           │ 45.6 M │
│ 1 │ net_d │ MultiPeriodDiscriminator │ 46.7 M │
└───┴───────┴──────────────────────────┴────────┘
Trainable params: 92.4 M
Non-trainable params: 0
Total params: 92.4 M
Total estimated model params size (MB): 369
[20:27:31] WARNING  [20:27:31] C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\trainer\connectors\data_connector.py:442:       warnings.py:109
                    PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the
                    `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
                      rank_zero_warn(

           WARNING  [20:27:31] C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\loops\fit_loop.py:281: PossibleUserWarning: The warnings.py:109
                    number of training batches (17) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if
                    you want to see logs for the training epoch.
                      rank_zero_warn(

           INFO     [20:27:31] Setting current epoch to 0                                                                                                              train.py:311
           INFO     [20:27:31] Setting total batch idx to 0                                                                                                            train.py:327
           INFO     [20:27:31] Setting global step to 0                                                                                                                train.py:317
Epoch 0/9999 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/17 0:00:00 • -:--:-- 0.00it/s v_num: 0[20:27:34] WARNING  [20:27:34] C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\_utils.py:776: UserWarning: TypedStorage is deprecated. It  warnings.py:109
                    will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages
                    directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
                      return self.fget.__get__(instance, owner)()

[20:27:34] WARNING  [20:27:34] C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\_utils.py:776: UserWarning: TypedStorage is deprecated. It  warnings.py:109
                    will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages
                    directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
                      return self.fget.__get__(instance, owner)()

[20:27:34] WARNING  [20:27:34] C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\_utils.py:776: UserWarning: TypedStorage is deprecated. It  warnings.py:109
                    will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages
                    directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
                      return self.fget.__get__(instance, owner)()

[20:27:34] WARNING  [20:27:34] C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\_utils.py:776: UserWarning: TypedStorage is deprecated. It  warnings.py:109
                    will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages
                    directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
                      return self.fget.__get__(instance, owner)()

Epoch 0/9999 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/17 0:00:03 • -:--:-- 0.00it/s v_num: 0
Traceback (most recent call last):
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\Scripts\svc.exe\__main__.py", line 7, in <module>
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\click\core.py", line 1157, in __call__
    return self.main(*args, **kwargs)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\click\core.py", line 1078, in main
    rv = self.invoke(ctx)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\click\core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\click\core.py", line 1434, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\click\core.py", line 783, in invoke
    return __callback(*args, **kwargs)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\so_vits_svc_fork\__main__.py", line 128, in train
    train(
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\so_vits_svc_fork\train.py", line 149, in train
    trainer.fit(model, datamodule=datamodule)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\trainer\trainer.py", line 532, in fit
    call._call_and_handle_interrupt(
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\trainer\call.py", line 43, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\trainer\trainer.py", line 571, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\trainer\trainer.py", line 980, in _run
    results = self._run_stage()
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\trainer\trainer.py", line 1023, in _run_stage
    self.fit_loop.run()
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\loops\fit_loop.py", line 202, in run
    self.advance()
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\loops\fit_loop.py", line 355, in advance
    self.epoch_loop.run(self._data_fetcher)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\loops\training_epoch_loop.py", line 133, in run
    self.advance(data_fetcher)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\loops\training_epoch_loop.py", line 190, in advance
    batch = next(data_fetcher)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\loops\fetchers.py", line 126, in __next__
    batch = super().__next__()
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\loops\fetchers.py", line 58, in __next__
    batch = next(self.iterator)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\utilities\combined_loader.py", line 285, in __next__
    out = next(self._iterator)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\lightning\pytorch\utilities\combined_loader.py", line 65, in __next__
    out[i] = next(self.iterators[i])
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\utils\data\dataloader.py", line 633, in __next__
    data = self._next_data()
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\utils\data\dataloader.py", line 1345, in _next_data
    return self._process_data(data)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\utils\data\dataloader.py", line 1371, in _process_data
    data.reraise()
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\_utils.py", line 644, in reraise
    raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\utils\data\_utils\worker.py", line 308, in _worker_loop
    data = fetcher.fetch(index)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\utils\data\_utils\fetch.py", line 54, in fetch
    return self.collate_fn(data)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\so_vits_svc_fork\dataset.py", line 74, in forward
    results[key] = _pad_stack([b[key] for b in batch]).cpu()
  File "C:\Users\BranchScope\anaconda3\envs\so-vits-fork\lib\site-packages\so_vits_svc_fork\dataset.py", line 61, in _pad_stack
    return torch.stack(x_padded)
RuntimeError: stack expects each tensor to be equal size, but got [1025, 1933] at entry 0 and [2, 80, 1933] at entry 8

To Reproduce

svc train after all those commands to train new models from wav files

Additional context

Let me know if you need more logs.

Version

v4.1.11

Platform

Windows 11 using Anaconda

Code of Conduct

• I agree to follow this project's Code of Conduct.

No Duplicate

• I have checked existing issues to avoid duplicates.

[34j]

Maybe it's a problem with the number of channels?

[BranchScope]

wdym?