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4c1b3665ee
* WIP: Use optimized_transducer to compute transducer loss. * Minor fixes. * Fix decoding. * Fix decoding. * Add RESULTS. * Update RESULTS. * Update CI. * Fix sampling rate for yesno recipe.
237 lines
6.5 KiB
Markdown
237 lines
6.5 KiB
Markdown
## Results
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### LibriSpeech BPE training results (Transducer)
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#### Conformer encoder + embedding decoder
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Using commit `TODO`.
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Conformer encoder + non-current decoder. The decoder
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contains only an embedding layer and a Conv1d (with kernel size 2).
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The WERs are
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| | test-clean | test-other | comment |
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|---------------------------|------------|------------|------------------------------------------|
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| greedy search | 2.77 | 7.07 | --epoch 30, --avg 13, --max-duration 100 |
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| beam search (beam size 4) | 2.76 | 6.97 | |
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The training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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./transducer_stateless/train.py \
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--world-size 4 \
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--num-epochs 30 \
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--start-epoch 0 \
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--exp-dir transducer_stateless/exp-full \
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--full-libri 1 \
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--max-duration 250 \
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--lr-factor 3
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```
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The tensorboard training log can be found at
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<https://tensorboard.dev/experiment/6fnVojoUQTmEJVq1yG34Vw/>
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The decoding command is:
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```
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epoch=36
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avg=13
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## greedy search
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./transducer_stateless/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless/exp-full \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100
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## beam search
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./transducer_stateless/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless/exp-full \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100 \
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--decoding-method beam_search \
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--beam-size 4
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```
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#### Conformer encoder + LSTM decoder
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Using commit `8187d6236c2926500da5ee854f758e621df803cc`.
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Conformer encoder + LSTM decoder.
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The best WER is
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| | test-clean | test-other |
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|-----|------------|------------|
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| WER | 3.07 | 7.51 |
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using `--epoch 34 --avg 11` with **greedy search**.
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The training command to reproduce the above WER is:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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./transducer/train.py \
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--world-size 4 \
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--num-epochs 35 \
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--start-epoch 0 \
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--exp-dir transducer/exp-lr-2.5-full \
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--full-libri 1 \
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--max-duration 180 \
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--lr-factor 2.5
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```
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The decoding command is:
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```
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epoch=34
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avg=11
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./transducer/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer/exp-lr-2.5-full \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100
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```
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You can find the tensorboard log at: <https://tensorboard.dev/experiment/D7NQc3xqTpyVmWi5FnWjrA>
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### LibriSpeech BPE training results (Conformer-CTC)
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#### 2021-11-09
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The best WER, as of 2021-11-09, for the librispeech test dataset is below
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(using HLG decoding + n-gram LM rescoring + attention decoder rescoring):
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| | test-clean | test-other |
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|-----|------------|------------|
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| WER | 2.42 | 5.73 |
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Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
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| ngram_lm_scale | attention_scale |
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|----------------|-----------------|
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| 2.0 | 2.0 |
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To reproduce the above result, use the following commands for training:
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```
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cd egs/librispeech/ASR/conformer_ctc
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./prepare.sh
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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./conformer_ctc/train.py \
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--exp-dir conformer_ctc/exp_500_att0.8 \
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--lang-dir data/lang_bpe_500 \
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--att-rate 0.8 \
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--full-libri 1 \
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--max-duration 200 \
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--concatenate-cuts 0 \
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--world-size 4 \
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--bucketing-sampler 1 \
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--start-epoch 0 \
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--num-epochs 90
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# Note: It trains for 90 epochs, but the best WER is at epoch-77.pt
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```
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and the following command for decoding
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```
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./conformer_ctc/decode.py \
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--exp-dir conformer_ctc/exp_500_att0.8 \
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--lang-dir data/lang_bpe_500 \
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--max-duration 30 \
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--concatenate-cuts 0 \
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--bucketing-sampler 1 \
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--num-paths 1000 \
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--epoch 77 \
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--avg 55 \
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--method attention-decoder \
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--nbest-scale 0.5
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```
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You can find the pre-trained model by visiting
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<https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09>
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The tensorboard log for training is available at
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<https://tensorboard.dev/experiment/hZDWrZfaSqOMqtW0NEfXKg/#scalars>
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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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