Comments (1)
AFAIK, normalizing to length doesn't make sense. RNN-T loss treats a single sequence as a single input object. In practice, it just scale your gradient values. In order to compare the normalized loss you need to scale the learning rate as well. Finally, you can specify this parameter if you like.
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Related Issues (20)
- 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
- Question about average_frames and reduction parmas HOT 1
- Not support for pytorch 1.7 HOT 1
- ninja: build stopped: subcommand failed. 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
- __version__ assignment breaks local build.
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