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https://github.com/k2-fsa/icefall.git
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50d2281524
* Add modified transducer for aishell. * Minor fixes. * Add extra data in transducer training. The extra data is from http://www.openslr.org/62/ * Update export.py and pretrained.py * Update CI to install pretrained models with aishell. * Update results. * Update results. * Update README. * Use symlinks to avoid copies.
317 lines
9.5 KiB
Markdown
317 lines
9.5 KiB
Markdown
## Results
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### Aishell training result(Transducer-stateless)
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#### 2022-03-01
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[./transducer_stateless_modified-2](./transducer_stateless_modified-2)
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Stateless transducer + modified transducer + using [aidatatang_200zh](http://www.openslr.org/62/) as extra training data.
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| | test |comment |
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|------------------------|------|----------------------------------------------------------------|
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| greedy search | 4.94 |--epoch 89, --avg 38, --max-duration 100, --max-sym-per-frame 1 |
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| modified beam search | 4.68 |--epoch 89, --avg 38, --max-duration 100 --beam-size 4 |
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The training commands are:
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```bash
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cd egs/aishell/ASR
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./prepare.sh --stop-stage 6
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./prepare_aidatatang_200zh.sh
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export CUDA_VISIBLE_DEVICES="0,1,2"
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./transducer_stateless_modified-2/train.py \
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--world-size 3 \
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--num-epochs 90 \
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--start-epoch 0 \
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--exp-dir transducer_stateless_modified-2/exp-2 \
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--max-duration 250 \
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--lr-factor 2.0 \
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--context-size 2 \
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--modified-transducer-prob 0.25 \
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--datatang-prob 0.2
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```
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The tensorboard log is available at
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<https://tensorboard.dev/experiment/oG72ZlWaSGua6fXkcGRRjA/>
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The commands for decoding are
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```bash
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# greedy search
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for epoch in 89; do
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for avg in 38; do
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./transducer_stateless_modified-2/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless_modified-2/exp-2 \
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--max-duration 100 \
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--context-size 2 \
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--decoding-method greedy_search \
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--max-sym-per-frame 1
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done
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done
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# modified beam search
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for epoch in 89; do
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for avg in 38; do
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./transducer_stateless_modified-2/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless_modified-2/exp-2 \
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--max-duration 100 \
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--context-size 2 \
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--decoding-method modified_beam_search \
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--beam-size 4
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done
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done
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```
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You can find a pre-trained model, decoding logs, and decoding results at
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<https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01>
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#### 2022-03-01
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[./transducer_stateless_modified](./transducer_stateless_modified)
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Stateless transducer + modified transducer.
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| | test |comment |
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|------------------------|------|----------------------------------------------------------------|
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| greedy search | 5.22 |--epoch 64, --avg 33, --max-duration 100, --max-sym-per-frame 1 |
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| modified beam search | 5.02 |--epoch 64, --avg 33, --max-duration 100 --beam-size 4 |
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The training commands are:
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```bash
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cd egs/aishell/ASR
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./prepare.sh --stop-stage 6
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export CUDA_VISIBLE_DEVICES="0,1,2"
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./transducer_stateless_modified/train.py \
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--world-size 3 \
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--num-epochs 90 \
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--start-epoch 0 \
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--exp-dir transducer_stateless_modified/exp-4 \
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--max-duration 250 \
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--lr-factor 2.0 \
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--context-size 2 \
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--modified-transducer-prob 0.25
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```
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The tensorboard log is available at
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<https://tensorboard.dev/experiment/C27M8YxRQCa1t2XglTqlWg/>
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The commands for decoding are
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```bash
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# greedy search
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for epoch in 64; do
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for avg in 33; do
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./transducer_stateless_modified/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless_modified/exp-4 \
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--max-duration 100 \
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--context-size 2 \
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--decoding-method greedy_search \
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--max-sym-per-frame 1
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done
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done
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# modified beam search
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for epoch in 64; do
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for avg in 33; do
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./transducer_stateless_modified/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless_modified/exp-4 \
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--max-duration 100 \
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--context-size 2 \
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--decoding-method modified_beam_search \
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--beam-size 4
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done
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done
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```
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You can find a pre-trained model, decoding logs, and decoding results at
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<https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01>
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#### 2022-2-19
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(Duo Ma): The tensorboard log for training is available at https://tensorboard.dev/experiment/25PmX3MxSVGTdvIdhOwllw/#scalars
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You can find a pretrained model by visiting https://huggingface.co/shuanguanma/icefall_aishell_transducer_stateless_context_size2_epoch60_2022_2_19
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| | test |comment |
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|---------------------------|------|-----------------------------------------|
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| greedy search | 5.4 |--epoch 59, --avg 10, --max-duration 100 |
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| beam search | 5.05|--epoch 59, --avg 10, --max-duration 100 |
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You can use the following commands to reproduce our results:
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```bash
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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python3 ./transducer_stateless/train.py \
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--world-size 4 \
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--num-epochs 60 \
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--start-epoch 0 \
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--exp-dir exp/transducer_stateless_context_size2 \
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--max-duration 100 \
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--lr-factor 2.5 \
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--context-size 2
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lang_dir=data/lang_char
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dir=exp/transducer_stateless_context_size2
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python3 ./transducer_stateless/decode.py \
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--epoch 59 \
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--avg 10 \
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--exp-dir $dir \
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--lang-dir $lang_dir \
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--decoding-method greedy_search \
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--context-size 2 \
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--max-sym-per-frame 3
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lang_dir=data/lang_char
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dir=exp/transducer_stateless_context_size2
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python3 ./transducer_stateless/decode.py \
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--epoch 59 \
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--avg 10 \
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--exp-dir $dir \
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--lang-dir $lang_dir \
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--decoding-method beam_search \
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--context-size 2 \
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--max-sym-per-frame 3
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```
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### Aishell training results (Transducer-stateless)
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#### 2022-02-18
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(Pingfeng Luo) : The tensorboard log for training is available at <https://tensorboard.dev/experiment/k3QL6QMhRbCwCKYKM9po9w/>
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And pretrained model is available at <https://huggingface.co/pfluo/icefall-aishell-transducer-stateless-char-2021-12-29>
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||test|
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|--|--|
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|CER| 5.05% |
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You can use the following commands to reproduce our results:
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```bash
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export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7,8"
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./transducer_stateless/train.py \
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--bucketing-sampler True \
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--world-size 8 \
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--lang-dir data/lang_char \
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--num-epochs 60 \
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--start-epoch 0 \
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--exp-dir transducer_stateless/exp_rnnt_k2 \
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--max-duration 80 \
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--lr-factor 3
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./transducer_stateless/decode.py \
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--epoch 59 \
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--avg 10 \
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--lang-dir data/lang_char \
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--exp-dir transducer_stateless/exp_rnnt_k2 \
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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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### Aishell training results (Conformer-MMI)
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#### 2021-12-04
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(Pingfeng Luo): Result of <https://github.com/k2-fsa/icefall/pull/140>
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The tensorboard log for training is available at <https://tensorboard.dev/experiment/PSRYVbptRGynqpPRSykp1g>
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And pretrained model is available at <https://huggingface.co/pfluo/icefall_aishell_mmi_model>
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The best decoding results (CER) are listed below, we got this results by averaging models from epoch 61 to 85, and using `attention-decoder` decoder with num_paths equals to 100.
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||test|
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|--|--|
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|CER| 4.94% |
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||lm_scale|attention_scale|
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|--|--|--|
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|test|1.1|0.3|
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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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cd egs/aishell/ASR
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./prepare.sh
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export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7,8"
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python conformer_mmi/train.py --bucketing-sampler True \
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--max-duration 200 \
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--start-epoch 0 \
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--num-epochs 90 \
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--world-size 8
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python conformer_mmi/decode.py --nbest-scale 0.5 \
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--epoch 85 \
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--avg 25 \
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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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```
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### Aishell training results (Conformer-CTC)
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#### 2021-11-16
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(Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/30
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Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc
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The best decoding results (CER) are listed below, we got this results by averaging models from epoch 60 to 84, and using `attention-decoder` decoder with num_paths equals to 100.
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||test|
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|CER| 4.26% |
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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 CER above are also listed below.
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||lm_scale|attention_scale|
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|test|0.3|0.9|
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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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cd egs/aishell/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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--max-duration 200 \
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--start-epoch 0 \
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--num-epochs 90 \
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--world-size 4
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python conformer_ctc/decode.py --nbest-scale 0.5 \
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--epoch 84 \
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--avg 25 \
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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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```
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### Aishell training results (Tdnn-Lstm)
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#### 2021-09-13
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(Wei Kang): Result of phone based Tdnn-Lstm model, https://github.com/k2-fsa/icefall/pull/30
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Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc_lstm_ctc
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The best decoding results (CER) are listed below, we got this results by averaging models from epoch 19 to 8, and using `1best` decoding method.
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||test|
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|CER| 10.16% |
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