Online training RunTimeError

[Seped69]

I got an error when I tried to use the online training using colab

`Traceback (most recent call last): File "/content/vits-finetuning/train.py", line 302, in main() File "/content/vits-finetuning/train.py", line 56, in main mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,)) File "/usr/local/lib/python3.9/dist-packages/torch/multiprocessing/spawn.py", line 240, in spawn return start_processes(fn, args, nprocs, join, daemon, start_method='spawn') File "/usr/local/lib/python3.9/dist-packages/torch/multiprocessing/spawn.py", line 198, in start_processes while not context.join(): File "/usr/local/lib/python3.9/dist-packages/torch/multiprocessing/spawn.py", line 160, in join raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException:

-- Process 0 terminated with the following error: Traceback (most recent call last): File "/usr/local/lib/python3.9/dist-packages/torch/multiprocessing/spawn.py", line 69, in _wrap fn(i, args) File "/content/vits-finetuning/train.py", line 123, in run train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval]) File "/content/vits-finetuning/train.py", line 150, in train_and_evaluate (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths) File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(input, **kwargs) File "/usr/local/lib/python3.9/dist-packages/torch/nn/parallel/distributed.py", line 1026, in forward if torch.is_grad_enabled() and self.reducer._rebuild_buckets(): RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument find_unused_parameters=True to torch.nn.parallel.DistributedDataParallel, and by making sure all forward function outputs participate in calculating loss. If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's forward function. Please include the loss function and the structure of the return value of forward of your module when reporting this issue (e.g. list, dict, iterable). Parameter indices which did not receive grad for rank 0: 342 343 442 443 444 473 474 475 504 505 506 535 536 537 566 567 568 855 856 In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error`

[SayaSS]

If you are training a single speaker model, you also need to use configs/config-single-speaker.json

[Seped69]

Ah, it's working now, thanks!