Comments (3)
We just checked in fixes to Eesen that would solve the CPU compilation issue. Please update to the latest.
Please stick to the original ExpA() function (rather than exp()) which deals with some critical extreme cases.
CPU-based CTC training is NOT available in Eesen. You have to rely on GPUs for training.
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Thank you for reply. I confirmed the updated code is compiled well in CPU only mode.
By the way, is there any reason the CTC cost function doesn't support the CPU only mode? Too slow?
from eesen.
Yes, for most scenarios, CPU training is not useful, given the data size of ASR tasks.
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Related Issues (20)
- Clean up v2 for swb
- DeepBiLSTM HOT 2
- Missing label.counts HOT 3
- Query on LibriSpeech Character Error Rate HOT 2
- difference in output labels HOT 1
- Memory Leak HOT 1
- failed: Dim() == v.Dim() HOT 2
- Potential overflow when calculating exp
- Clarification Regarding Using WFST decoding HOT 1
- Installing error HOT 8
- Training Error when run tedlium recipe HOT 2
- LatticeFasterDecoder failed with "link_extra_cost == link_extra_cost" HOT 1
- Cannot install openfst-1.4.1 HOT 2
- Read failure in ReadBasicType, file position is -1, next char is -1
- KALDI_ASSERT: at train-ctc-parallel:AddMatMat:cuda-matrix.cc:570, failed: m == NumCols()
- Why do we need space and unk symbols in the char mode for acoustic model? HOT 6
- Why do we need to compile the tokens to FST in wsj recipe?
- Can not run training program with cuda 10.2 HOT 3
- Librispeech - Training starting error HOT 3
- Determinizability of TLG.fst in the phonetic case
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