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Merge branch 'otc' of https://github.com/DongjiGao/icefall into otc
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# Introduction
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This is a weakly supervised ASR recipe for the LibriSpeech (clean 100 hours) dataset. We train a
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conformer model using Bypass Temporal Classification (BTC)/Omni-temporal Classification (OTC) with transcripts with synthetic errors. In this README, we will describe
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conformer model using [Bypass Temporal Classification](https://arxiv.org/pdf/2306.01031.pdf) (BTC)/Omni-temporal Classification (OTC) with transcripts with synthetic errors. In this README, we will describe
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the task and the BTC/OTC training process.
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Note that OTC is an extension of BTC and supports all BTC functions. Therefore, in the following, we only describe OTC.
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@ -124,13 +124,26 @@ The bypass arc deals with substitution and insertion errors, while the self-loop
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### Decoding
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```
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export CUDA_VISIBLE_DEVICES="0"
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python conformer_ctc2/decode.py \
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./conformer_ctc2/decode.py \
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--exp-dir "${exp_dir}" \
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--lang-dir "${otc_lang_dir}" \
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--lm-dir "${lm_dir}" \
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--otc-token "${otc_token}"
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```
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### Results (ctc-greedy-search)
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| Traning Criterion | test-clean | test-other |
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|------------|:-------:|:----:|
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| CTC |100.0|100.0 |
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| OTC | 11.89 | 25.46 |
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### Results (1best, blank_bias=-4)
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| Traning Criterion | test-clean | test-other |
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|------------|:-------:|:----:|
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| CTC |98.40|98.68 |
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| OTC | 6.59 | 15.98 |
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## Citations
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```
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@article{gao2023bypass,
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