Comments (3)
Maybe just an --eval-precision
flag that defaults to fp32
?
from speechbrain.
@Adel-Moumen, what do you think about that?
from speechbrain.
I think that we can have a new flag that check if we apply it at test time or not. We can improve the data class https://github.com/speechbrain/speechbrain/blob/develop/speechbrain/core.py#L90 to have new args such as enable_testing
so that by default enable_testing=False
and we can turn it to True
by using a new flag --amp_testing=True
or smth like that. Wdyt ?
CC: @asumagic you might want to be part of this discussion.
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Related Issues (20)
- Training twice as long with Torch > 1.11 HOT 10
- Training regression for Conformer-Transducer models HOT 2
- Math Domain Error in Pretraining tutorial. HOT 1
- Typing syntax not supported in 3.7/3.8 HOT 8
- Potential `SpectrogramDrop` bugs HOT 2
- dtype mismatch in AttentiveStatisticsPooling with FP16 training mode HOT 1
- Task ASR Reported: Caught ZeroDivisionError in DataLoader worker process 0. HOT 4
- Huggingface-Aishell get wrong prediction HOT 5
- AMD ROCm: Conformer-transducer diverges HOT 2
- AMD ROCm: `torch.backends.cudnn.benchmark` should be set to `False` by default on ROCm
- Wav2Vec2WordpieceTokenizer' object has no attribute '_create_trie' HOT 2
- Same result for different samples (with same name) using speech separation HOT 1
- Cannot reproduce the result in speech translation HOT 6
- [Bug] RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument mat1 in method wrapper_CUDA_addmm) HOT 1
- pip install --editable . didn't run in VS code HOT 3
- SoundChoice G2P seems to be broken HOT 1
- Couldn't find appropriate backend to handle uri example.wav and format None. HOT 3
- Should not include punctuation in the G2P samples ? HOT 1
- Valid Loss Increasing While Train Loss Decreases Using Custom Training Data with Speaker ID
- `compute_STFT` performs HtoD transfer at every `forward` call HOT 1
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