Comments (5)
Yes! It's worth trying and can lead to lower validation losses.
Let us know what your findings are.
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Yes! It's worth trying and can lead to lower validation losses.
Let us know what your findings are.
Is there any prior guidance for setting batch_size to 1?
I found that for my data, batch_size can be set to 16.
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@IsakWesterlundBitville Thanks for your comment, I tried your suggestion to compare training with batch size 4 against default batch size 1 when using 2 GPUs. However, when I use batch size 4, I just get the the diagonal line of the alignment till a much smaller decoder time step compared with the alignment obtained with batch size 1. Please could you tell if you changed any other config settings to take into account the change in batch size.
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@IsakWesterlundBitville Thanks for your comment, I tried your suggestion to compare training with batch size 4 against default batch size 1 when using 2 GPUs. However, when I use batch size 4, I just get the the diagonal line of the alignment till a much smaller decoder time step compared with the alignment obtained with batch size 1. Please could you tell if you changed any other config settings to take into account the change in batch size.
@rafaelvalle Do you have any idea why I get this difference in the alignment plots?
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The two samples are different, hence the alignment plots are different.
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Related Issues (20)
- Inference starting repeat itself. HOT 5
- List index out of range
- Request for clarification on some of the readme scripts. HOT 8
- Custom model resumed from pre-trained model has a stuttering problem.
- How would one keep the model loaded for immediate synthesis? HOT 17
- Inference on pre-trained model (flowtron_ljs) speaking nonsense. HOT 4
- Inference Demo "Hitting gate limit" HOT 2
- .
- inference speed on CPU
- Accelerated inference with TensorRT HOT 2
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- Error on loading training model "_pickle.UnpicklingError: invalid load key, '<'"
- Custom trained model and dataset problem
- Index out of range for custom dataset.
- value error while training custom dataset
- TypeError: guvectorize() missing 1 required positional argument 'signature' HOT 1
- _pickle.UnpicklingError: invalid load key, '<'. in inference.py in colab HOT 3
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