Comments (1)
You shouldn't average over frames. If I remember correctly, theoretically it doesn't make sense. The loss is calculated for the entire utterance.
There is no a direct link between the RNN-T loss value and WER. I think a good analogue would be the negative log-likelihood and the accuracy of a classifier.
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Related Issues (20)
- question about the rnnt loss arguments HOT 4
- WARNING: sample 0 [42, 26] has a forward/backward mismatch -52.543503 / 0.000000 HOT 3
- ImportError: libcudart.so.10.2: cannot open shared object file: No such file or directory HOT 3
- Transducer loss leads to memory leak HOT 4
- RuntimeError: rnnt_loss status 1 HOT 5
- warning that forward/backward mismatch HOT 3
- question about the gather arguments HOT 1
- Not support for pytorch 1.7 HOT 1
- ninja: build stopped: subcommand failed. HOT 1
- Normalize the RNN-T Loss with input seq length HOT 1
- PyTorch 1.9 Support HOT 5
- Strange behavior using PyTorch DDP HOT 7
- undefined symbol: _ZNSt19basic_ostringstreamIcSt11char_traitsIcESaIcEEC1Ev HOT 2
- rnnt_loss status 1 HOT 2
- THC/THC.h: No such file or directory HOT 7
- can't install warp-rnnt HOT 2
- Exception: CPU version is not implemented HOT 2
- improve efficiency of warps HOT 4
- ImportError: libcudart.so.10.1: cannot open shared object file: No such file or directory HOT 2
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