How many time is required to train?

Hi there, Im running https://colab.research.google.com/gist/erogol/97516ad65b44dbddb8cd694953187c5b/tts_example.ipynb but I wonder how many time it is needed to run in collab? it seems that it will take some hours? Maybe more than 12?

Just starting at TTS, I want to construct a MVP where it can speack the words the text I write and the other way around so I will love if you have any suguestions for tackle the inverse problem STT would be nice.

Im trying to run diverse things I have found on internet, but for the moment no luck in constructing that MVP :slight_smile:.


And currently

I dont think it will end the 1000 epoch before 12 hours.

You could save the model to gdrive and continue training with a new session.

python train.py --continue_path ‘path to model’

Also for STT see here https://github.com/mozilla/DeepSpeech.

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Thanks! will look in to that… can I ask things here even that they are not of the models used in mozilla?

Anyway, I tried to run on my computer (I have a 2080 with 8Gb RAM) got this on first step of the 1000, it is a OOM, is there a way I can train it with a parameter?

  --> STEP: 149/195 -- GLOBAL_STEP: 150
     | > decoder_loss: 1.54959  (2.75099)
     | > postnet_loss: 1.65185  (3.31417)
     | > stopnet_loss: 0.33840  (0.53356)
     | > ga_loss: 0.02369  (0.04075)
     | > loss: 3.22514 
     | > align_error: 0.99233  (0.99067)
     | > avg_spec_len: 705.203125
     | > avg_text_len: 126.171875
     | > step_time: 1.02
     | > loader_time: 0.01
     | > lr: 0.00010
 ! Run is removed from ../ljspeech-July-03-2020_03+56PM-3366328
Traceback (most recent call last):
  File "train.py", line 676, in 
    main(args)
  File "train.py", line 591, in main
    global_step, epoch)
  File "train.py", line 191, in train
    loss_dict['loss'].backward()
  File "/home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/tensor.py", line 198, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/autograd/__init__.py", line 100, in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: CUDA out of memory. Tried to allocate 110.00 MiB (GPU 0; 7.79 GiB total capacity; 4.37 GiB already allocated; 116.88 MiB free; 4.64 GiB reserved in total by PyTorch) (malloc at /opt/conda/conda-bld/pytorch_1587428398394/work/c10/cuda/CUDACachingAllocator.cpp:289)
frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x4e (0x7f1127c94b5e in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1:  + 0x1f39d (0x7f1127a5639d in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
frame #2:  + 0x2058e (0x7f1127a5758e in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
frame #3: at::native::empty_cuda(c10::ArrayRef, c10::TensorOptions const&, c10::optional) + 0x291 (0x7f112a9ed461 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #4:  + 0xddcb6b (0x7f1128c9db6b in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #5:  + 0xe26457 (0x7f1128ce7457 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #6:  + 0xdd3999 (0x7f114fc49999 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #7:  + 0xdd3cd7 (0x7f114fc49cd7 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #8:  + 0xd77a7e (0x7f1128c38a7e in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #9:  + 0xd7a543 (0x7f1128c3b543 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #10: at::native::cudnn_convolution_backward_input(c10::ArrayRef, at::Tensor const&, at::Tensor const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, long, bool, bool) + 0xb2 (0x7f1128c3bd82 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #11:  + 0xde18a0 (0x7f1128ca28a0 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #12:  + 0xe26138 (0x7f1128ce7138 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #13: at::native::cudnn_convolution_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, long, bool, bool, std::array) + 0x4fa (0x7f1128c3d41a in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #14:  + 0xde1bcb (0x7f1128ca2bcb in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #15:  + 0xe26194 (0x7f1128ce7194 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #16:  + 0x29defc6 (0x7f1151854fc6 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #17:  + 0x2a2ea54 (0x7f11518a4a54 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #18: torch::autograd::generated::CudnnConvolutionBackward::apply(std::vector >&&) + 0x378 (0x7f115146cf28 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #19:  + 0x2ae8215 (0x7f115195e215 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #20: torch::autograd::Engine::evaluate_function(std::shared_ptr&, torch::autograd::Node*, torch::autograd::InputBuffer&) + 0x16f3 (0x7f115195b513 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #21: torch::autograd::Engine::thread_main(std::shared_ptr const&, bool) + 0x3d2 (0x7f115195c2f2 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #22: torch::autograd::Engine::thread_init(int) + 0x39 (0x7f1151954969 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #23: torch::autograd::python::PythonEngine::thread_init(int) + 0x38 (0x7f1154c9b558 in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #24:  + 0xc819d (0x7f115770319d in /home/tyoc213/miniconda3/envs/fastai2/lib/python3.7/site-packages/torch/lib/../../../.././libstdc++.so.6)
frame #25:  + 0x76db (0x7f1175e296db in /lib/x86_64-linux-gnu/libpthread.so.0)
frame #26: clone + 0x3f (0x7f1175b5288f in /lib/x86_64-linux-gnu/libc.so.6)

Thanks! will look in to that… can I ask things here even that they are not of the models used in mozilla?

If you mean ask here regarding DeepSpeech. It’s better you use the forum for the given topic -> https://discourse.mozilla.org/c/deep-speech

Anyway, I tried to run on my computer (I have a 2080 with 8Gb RAM) got this on first step of the 1000, it is a OOM, is there a way I can train it with a parameter?

You can reduce the batch_size. Check the “gradual_training” setting in config.json.
If its something like this [[0, 7, 64], [1, 5, 64], [50000, 3, 64], [130000, 2, 32], [290000, 1, 32]]. In this case 64/64/64/32/32 are the batch_sizes -> reduce them.
Try something like this -> [[0, 7, 32], [1, 5, 32], [50000, 3, 32], [130000, 2, 32], [290000, 1, 16]]

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