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4890e27b45
* Use new APIs with k2.RaggedTensor * Fix style issues. * Update the installation doc, saying it requires at least k2 v1.7 * Extract framewise alignment information using CTC decoding. * Print environment information. Print information about k2, lhotse, PyTorch, and icefall. * Fix CI. * Fix CI. * Compute framewise alignment information of the LibriSpeech dataset. * Update comments for the time to compute alignments of train-960. * Preserve cut id in mix cut transformer. * Minor fixes. * Add doc about how to extract framewise alignments.
74 lines
2.6 KiB
Markdown
74 lines
2.6 KiB
Markdown
## Results
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### LibriSpeech BPE training results (Conformer-CTC)
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#### 2021-08-19
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(Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/13
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TensorBoard log is available at https://tensorboard.dev/experiment/GnRzq8WWQW62dK4bklXBTg/#scalars
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Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc
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The best decoding results (WER) are listed below, we got this results by averaging models from epoch 15 to 34, and using `attention-decoder` decoder with num_paths equals to 100.
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||test-clean|test-other|
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|--|--|--|
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|WER| 2.57% | 5.94% |
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To get more unique paths, we scaled the lattice.scores with 0.5 (see https://github.com/k2-fsa/icefall/pull/10#discussion_r690951662 for more details), we searched the lm_score_scale and attention_score_scale for best results, the scales that produced the WER above are also listed below.
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||lm_scale|attention_scale|
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|--|--|--|
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|test-clean|1.3|1.2|
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|test-other|1.2|1.1|
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You can use the following commands to reproduce our results:
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```bash
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git clone https://github.com/k2-fsa/icefall
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cd icefall
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# It was using ef233486, you may not need to switch to it
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# git checkout ef233486
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cd egs/librispeech/ASR
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./prepare.sh
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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python conformer_ctc/train.py --bucketing-sampler True \
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--concatenate-cuts False \
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--max-duration 200 \
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--full-libri True \
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--world-size 4 \
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--lang-dir data/lang_bpe_5000
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python conformer_ctc/decode.py --nbest-scale 0.5 \
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--epoch 34 \
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--avg 20 \
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--method attention-decoder \
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--max-duration 20 \
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--num-paths 100 \
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--lang-dir data/lang_bpe_5000
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```
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### LibriSpeech training results (Tdnn-Lstm)
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#### 2021-08-24
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(Wei Kang): Result of phone based Tdnn-Lstm model.
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Icefall version: https://github.com/k2-fsa/icefall/commit/caa0b9e9425af27e0c6211048acb55a76ed5d315
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Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc
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The best decoding results (WER) are listed below, we got this results by averaging models from epoch 19 to 14, and using `whole-lattice-rescoring` decoding method.
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||test-clean|test-other|
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|--|--|--|
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|WER| 6.59% | 17.69% |
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We searched the lm_score_scale for best results, the scales that produced the WER above are also listed below.
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||lm_scale|
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|--|--|
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|test-clean|0.8|
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|test-other|0.9|
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