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abadc71415
* Use new APIs with k2.RaggedTensor * Fix style issues. * Update the installation doc, saying it requires at least k2 v1.7 * Use k2 v1.7
46 lines
1.6 KiB
Markdown
46 lines
1.6 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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### 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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|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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