How many time is required to train?

Archives TTS (Text-to-Speech)
#1

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:.

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

#2

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

python train.py --continue_path ā€˜path to modelā€™

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

1 Like
#3

Thanks! will look in to thatā€¦ 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)
#4

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

If you mean ask here regarding DeepSpeech. Itā€™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 ā€œgradual_trainingā€ 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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