Comments (1)
Hi svenha,
thanks for the feedback and the pull request, very cool! :)
The WER rates you got are very interesting and probably accurate. It simply show how big of an impact the LM really has. We went from ~3-4% WER to ~7% WER with the modular approach and my suspicion is that this is because we add each speech corpora sentence only once to the language model (instead of as many times as it occurs in the training material as we did before - and especially german voxforge prompts appear multiple times). I have not verified that, though.
Regarding the question what is "realistic" I am not sure for now. Given the impact the LM has it really boils down to adapting the model to your specific application's needs - which is what I am working on at the moment (see speech_kaldi_adapt.py if you're interested - lacks documentation and is not finished, though).
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Related Issues (20)
- German Distant Speech Dataset HOT 2
- Training wav2letter++ streaming convnets (TDS + CTC) HOT 6
- Install on a Raspberry Pi 4 HOT 9
- Cite in a paper? HOT 1
- Question: Decoding with Zamia Speech's German wav2letter model using wav2letter Decoder executable HOT 8
- Errors while trying to add new words HOT 3
- retrain existing nnet3 model with more data HOT 3
- GMM decoding
- https://goofy.zamia.org/repo-ai/raspbian/stretch/armhf/bofh.asc - permission denied?
- Batch Inference HOT 2
- Est_republicaine Corpus not found HOT 6
- Speech corpus sentences extraction !
- Kaldi nnet3 without ivector model HOT 1
- Install Zamia-speech English nnet3-chain model on Ubuntu
- Change language - Zamia HOT 2
- Using Language Models to decode.
- Binaries are no longer available HOT 1
- Download page not available
- tdnn_f vs tdnn_fl
- Suggestion for extracting CNRTL Est Républicain Corpus HOT 1
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