When start firtst_train give errors. I have 96 Gb Ram and 3 P40/24GB/ 1 T4 /16GB/ ??

[casic]

LJSpeech-1.1/wavs/LJ043-0101.wav 22050
LJSpeech-1.1/wavs/LJ028-0188.wav 22050
LJSpeech-1.1/wavs/LJ018-0108.wav 22050
LJSpeech-1.1/wavs/LJ032-0211.wav 22050
LJSpeech-1.1/wavs/LJ034-0082.wav 22050
LJSpeech-1.1/wavs/LJ016-0002.wav 22050
LJSpeech-1.1/wavs/LJ013-0165.wav 22050
LJSpeech-1.1/wavs/LJ046-0247.wav 22050
LJSpeech-1.1/wavs/LJ017-0130.wav 22050
LJSpeech-1.1/wavs/LJ013-0176.wav 22050
LJSpeech-1.1/wavs/LJ042-0162.wav 22050
LJSpeech-1.1/wavs/LJ029-0201.wav 22050
LJSpeech-1.1/wavs/LJ016-0139.wav 22050
LJSpeech-1.1/wavs/LJ017-0258.wav 22050
LJSpeech-1.1/wavs/LJ004-0135.wav 22050
LJSpeech-1.1/wavs/LJ016-0149.wav 22050
LJSpeech-1.1/wavs/LJ024-0108.wav 22050
LJSpeech-1.1/wavs/LJ007-0078.wav 22050
LJSpeech-1.1/wavs/LJ014-0157.wav 22050
LJSpeech-1.1/wavs/LJ047-0208.wav 22050
LJSpeech-1.1/wavs/LJ013-0240.wav 22050
LJSpeech-1.1/wavs/LJ028-0059.wav 22050
Traceback (most recent call last):
File "/home/koce/StyleTTS/train_first.py", line 393, in
main()
File "/usr/local/lib/python3.10/dist-packages/click/core.py", line 1157, in call
return self.main(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/click/core.py", line 1078, in main
rv = self.invoke(ctx)
File "/usr/local/lib/python3.10/dist-packages/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/usr/local/lib/python3.10/dist-packages/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
Traceback (most recent call last):
File "/home/koce/StyleTTS/train_first.py", line 214, in main
File "/home/koce/StyleTTS/train_first.py", line 393, in
loss_reg = r1_reg(out, gt)
File "/home/koce/StyleTTS/utils.py", line 60, in r1_reg
grad_dout = torch.autograd.grad(
File "/usr/local/lib/python3.10/dist-packages/torch/autograd/init.py", line 394, in grad
main()
File "/usr/local/lib/python3.10/dist-packages/click/core.py", line 1157, in call
result = Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 78.00 MiB. GPU 0 has a total capacty of 23.87 GiB of which 25.62 MiB is free. Process 1178071 has 7.50 GiB memory in use. Process 1178069 has 10.88 GiB memory in use. Process 1178070 has 5.46 GiB memory in use. Of the allocated memory 10.44 GiB is allocated by PyTorch, and 249.36 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
return self.main(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/click/core.py", line 1078, in main
rv = self.invoke(ctx)
File "/usr/local/lib/python3.10/dist-packages/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/usr/local/lib/python3.10/dist-packages/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
File "/home/koce/StyleTTS/train_first.py", line 207, in main
mel_rec = model.decoder(en, F0_real, real_norm, s)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/koce/StyleTTS/models.py", line 461, in forward
x = block(x, s)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/koce/StyleTTS/models.py", line 407, in forward
out = self._residual(x, s)
File "/home/koce/StyleTTS/models.py", line 403, in _residual
x = self.conv2(self.dropout(x))
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1557, in _call_impl
args_result = hook(self, args)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py", line 67, in call
setattr(module, self.name, self.compute_weight(module))
File "/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py", line 26, in compute_weight
return _weight_norm(v, g, self.dim)
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacty of 23.87 GiB of which 9.62 MiB is free. Process 1178071 has 7.50 GiB memory in use. Process 1178069 has 10.89 GiB memory in use. Process 1178070 has 5.46 GiB memory in use. Of the allocated memory 7.08 GiB is allocated by PyTorch, and 254.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

[martinambrus]

You're running out of memory, quite possibly because of your config settings.

On a T4 GPU, you won't be able to train much, since it has a very limited VRAM capacity (16GB). The best I could do with a T4 on Google Colab was to fine-tune the LJSpeech model with my own set of 1 - 1.25 seconds long WAV files and settings batch_size to 2 and max_len to 100 in config.yml

You can check out the https://github.com/yl4579/StyleTTS2/blob/main/Colab/StyleTTS2_Finetune_Demo.ipynb Colab notebook for an example of how to fine-tune on a T4, or the other Colab notebooks ( https://github.com/yl4579/StyleTTS2/tree/main/Colab ) for an inspiration there.

[casic]

Thanks.
But is there a way to fine tune on all GPUS ???
Thanks in advance!

[martinambrus]

No, this is not yet possible due to issue #7 and because fine-tuning script is built on top of phase 2 training script which suffers this issue. The best you can get is accelerated fine-tuning on a single processor which is marginally faster and uses a little bit less memory, but it's not a lot: accelerate launch --mixed_precision=fp16 --num_processes=1 train_finetune_accelerate.py --config_path ./Configs/config_ft.yml

[casic]

Thanks. One more , I have started first stage and is running about 10 hours , then terminal break process. I see it was stopped on 7/200 . Is there a way to continue after stop, not start from zero every time ???

[martinambrus]

Yes, the training process saves checkpoint files - as many of them as you set in config via the save_freq option. It defaults to 2, so it saves files such as StyleTTS2/Models/LJSpeech/epoch_1st_00002.pth and you can use those to continue from where you finished.

To do so, just set the pretrained_model config option to a full path of the last checkpoint file (e.g. /home/user/StyleTTS2/Models/LJSpeech/epoch_1st_00007.pth), second_stage_load_pretrained parameter to false and load_only_params option to false. Then start training and it should pick up from the given checkpoint file.

If you wanted to resume 2nd stage training, you'll need to provide 2nd stage checkpoint file and set second_stage_load_pretrained parameter to true.

[casic]

Thank you very much Martin. Have i nice YEAR.

[yl4579]

Sorry for the late reply. I was quite busy recently. Finetuning should use all GPUs. I have tested the finetuning script on 4 NVidia A100 and it worked perfectly well. Have you checked using nvidia-smi that all GPUs were being used when you run the script, or only one of them?

[martinambrus]

Sorry for the late reply. I was quite busy recently. Finetuning should use all GPUs. I have tested the finetuning script on 4 NVidia A100 and it worked perfectly well. Have you checked using nvidia-smi that all GPUs were being used when you run the script, or only one of them?

Then is the following statement from the README incorrect or did I simply misunderstand what you meant there?

The script is modified from train_second.py which uses DP, as DDP does not work for train_second.py. Please see the bold section above if you are willing to help with this problem. ... If you are using a single GPU (because the script doesn't work with DDP) and want to save training speed and VRAM, you can do (thank @korakoe for making the script at #100): accelerate launch --mixed_precision=fp16 --num_processes=1 train_finetune_accelerate.py --config_path ./Configs/config_ft.yml

[teamblubee]

The fine tuning is a modified second training so it might work but I don't think so. If the author can create a post showing where the train second fails.

Once that's posted then I can take some time to examine it and see if I can debug the issue.

[casic]

I have started without the accelerator and it starts. Thanks for the replies !!! На сб, 20.01.2024 г. в 18:16 blubee ***@***.***> написа:

…

The fine tuning is a modified second training so it might work but I don't think so. If the author can create a post showing where the train second fails. Once that's posted then I can take some time to examine it and see if I can debug the issue. — Reply to this email directly, view it on GitHub <#178 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAYTSUJFS6IFNHU2TI7NZZDYPPUULAVCNFSM6AAAAABBIJ5EH2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMBSGE3DKMZTGM> . You are receiving this because you authored the thread.Message ID: ***@***.***>