Comments (1)
You're correct, right now the total loss is divided by the batch size. The reasoning behind this is that if we instead divide by the length of the examples we would be down weighting the gradient of longer utterances which isn't quite right semantically since every character in the output counts essentially the same amount toward the overall error rate.
Your proposal though would likely work fine and to be honest I haven't done a careful comparison of which is a better approach. There are likely trade-offs.
from speech.
Related Issues (20)
- ctc decoder with language model
- Pytest FAILURES
- Word Level LM at Line 96
- WARNING: Forward backward likelihood mismatch 0.000050 HOT 1
- make error from torch.utils.ffi import create_extension HOT 1
- your paper link
- Error in seq2seq.py of Method collate HOT 3
- Sample generation
- Make error HOT 1
- 请问有成功训练出中文汉语模型的么 HOT 1
- make error : system cannot find the path specified
- Out of memory Error HOT 1
- Multi Gpu Training support
- Loss is decrease but SER is increase
- Transducer: zip object is not subscriptable HOT 1
- Could you share your WER on librispeech?
- Make Requires Cuda
- TIMIT PER HOT 2
- Can this model convert Chinese audio to text?
- [CONTRIBUTION] Speech Dataset Generator
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from speech.