## Results ### WenetSpeech char-based training results (Non-streaming and streaming) on zipformer model This is the [pull request](https://github.com/k2-fsa/icefall/pull/1130) in icefall. #### Non-streaming Best results (num of params : ~76M): Type | Greedy(dev & net & meeting) | Beam search(dev & net & meeting) |   -- | -- | -- | -- Non-streaming | 7.36 & 7.65 & 12.43 | 7.32 & 7.61 & 12.35 | --epoch=12 The training command: ``` ./zipformer/train.py \ --world-size 6 \ --num-epochs 12 \ --use-fp16 1 \ --max-duration 450 \ --training-subset L \ --lr-epochs 1.5 \ --context-size 2 \ --exp-dir zipformer/exp_L_context_2 \ --causal 0 \ --num-workers 8 ``` Listed best results for each epoch below: Epoch | Greedy search(dev & net & meeting) | Modified beam search(dev & net & meeting) |   -- | -- | -- | -- 4 | 7.83 & 8.86 &13.73 | 7.75 & 8.81 & 13.67 | avg=1;blank-penalty=2 5 | 7.75 & 8.46 & 13.38 | 7.68 & 8.41 & 13.27 | avg=1;blank-penalty=2 6 | 7.72 & 8.19 & 13.16 | 7.62 & 8.14 & 13.06 | avg=1;blank-penalty=2 7 | 7.59 & 8.08 & 12.97 | 7.53 & 8.01 & 12.87 | avg=2;blank-penalty=2 8 | 7.68 & 7.87 & 12.96 | 7.61 & 7.81 & 12.88 | avg=1;blank-penalty=2 9 | 7.57 & 7.77 & 12.87 | 7.5 & 7.71 & 12.77 | avg=1;blank-penalty=2 10 | 7.45 & 7.7 & 12.69 | 7.39 & 7.63 & 12.59 | avg=2;blank-penalty=2 11 | 7.35 & 7.67 & 12.46 | 7.31 & 7.63 & 12.43 | avg=3;blank-penalty=2 12 | 7.36 & 7.65 & 12.43 | 7.32 & 7.61 & 12.35 | avg=4;blank-penalty=2 The pre-trained model is available here : https://huggingface.co/pkufool/icefall-asr-zipformer-wenetspeech-20230615 #### Streaming Best results (num of params : ~76M): Type | Greedy(dev & net & meeting) | Beam search(dev & net & meeting) |   -- | -- | -- | -- Streaming | 8.45 & 9.89 & 16.46 | 8.21 & 9.77 & 16.07 | --epoch=12; --chunk-size=16; --left-context-frames=256 Streaming | 8.0 & 9.0 & 15.11 | 7.84 & 8.94 & 14.92 | --epoch=12; --chunk-size=32; --left-context-frames=256 The training command: ``` ./zipformer/train.py \ --world-size 8 \ --num-epochs 12 \ --use-fp16 1 \ --max-duration 450 \ --training-subset L \ --lr-epochs 1.5 \ --context-size 2 \ --exp-dir zipformer/exp_L_causal_context_2 \ --causal 1 \ --num-workers 8 ``` Best results for each epoch (--chunk-size=16; --left-context-frames=128) Epoch | Greedy search(dev & net & meeting) | Modified beam search(dev & net & meeting) |   -- | -- | -- | -- 6 | 9.14 & 10.75 & 18.15 | 8.79 & 10.54 & 17.64 | avg=1;blank-penalty=1.5 7 | 9.11 & 10.61 & 17.86 | 8.8 & 10.42 & 17.29 | avg=1;blank-penalty=1.5 8 | 8.89 & 10.32 & 17.44 | 8.59 & 10.09 & 16.9 | avg=1;blank-penalty=1.5 9 | 8.86 & 10.11 & 17.35 | 8.55 & 9.87 & 16.76 | avg=1;blank-penalty=1.5 10 | 8.66 & 10.0 & 16.94 | 8.39 & 9.83 & 16.47 | avg=2;blank-penalty=1.5 11 | 8.58 & 9.92 & 16.67 | 8.32 & 9.77 & 16.27 | avg=3;blank-penalty=1.5 12 | 8.45 & 9.89 & 16.46 | 8.21 & 9.77 & 16.07 | avg=4;blank-penalty=1.5 The pre-trained model is available here: https://huggingface.co/pkufool/icefall-asr-zipformer-streaming-wenetspeech-20230615 ### WenetSpeech char-based training results (offline and streaming) (Pruned Transducer 5) #### 2022-07-22 Using the codes from this PR https://github.com/k2-fsa/icefall/pull/447. When training with the L subset, the CERs are **Offline**: |decoding-method| epoch | avg | use-averaged-model | DEV | TEST-NET | TEST-MEETING| |-- | -- | -- | -- | -- | -- | --| |greedy_search | 4 | 1 | True | 8.22 | 9.03 | 14.54| |modified_beam_search | 4 | 1 | True | **8.17** | **9.04** | **14.44**| |fast_beam_search | 4 | 1 | True | 8.29 | 9.00 | 14.93| The offline training command for reproducing is given below: ``` export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" ./pruned_transducer_stateless5/train.py \ --lang-dir data/lang_char \ --exp-dir pruned_transducer_stateless5/exp_L_offline \ --world-size 8 \ --num-epochs 15 \ --start-epoch 2 \ --max-duration 120 \ --valid-interval 3000 \ --model-warm-step 3000 \ --save-every-n 8000 \ --average-period 1000 \ --training-subset L ``` The tensorboard training log can be found at https://tensorboard.dev/experiment/SvnN2jfyTB2Hjqu22Z7ZoQ/#scalars . A pre-trained offline model and decoding logs can be found at **Streaming**: |decoding-method| epoch | avg | use-averaged-model | DEV | TEST-NET | TEST-MEETING| |--|--|--|--|--|--|--| | greedy_search | 7| 1| True | 8.78 | 10.12 | 16.16 | | modified_beam_search | 7| 1| True| **8.53**| **9.95** | **15.81** | | fast_beam_search | 7 | 1| True | 9.01 | 10.47 | 16.28 | The streaming training command for reproducing is given below: ``` export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" ./pruned_transducer_stateless5/train.py \ --lang-dir data/lang_char \ --exp-dir pruned_transducer_stateless5/exp_L_streaming \ --world-size 8 \ --num-epochs 15 \ --start-epoch 1 \ --max-duration 140 \ --valid-interval 3000 \ --model-warm-step 3000 \ --save-every-n 8000 \ --average-period 1000 \ --training-subset L \ --dynamic-chunk-training True \ --causal-convolution True \ --short-chunk-size 25 \ --num-left-chunks 4 ``` The tensorboard training log can be found at https://tensorboard.dev/experiment/E2NXPVflSOKWepzJ1a1uDQ/#scalars . A pre-trained offline model and decoding logs can be found at ### WenetSpeech char-based training results (Pruned Transducer 2) #### 2022-05-19 Using the codes from this PR https://github.com/k2-fsa/icefall/pull/349. When training with the L subset, the CERs are | | dev | test-net | test-meeting | comment | |------------------------------------|-------|----------|--------------|------------------------------------------| | greedy search | 7.80 | 8.75 | 13.49 | --epoch 10, --avg 2, --max-duration 100 | | modified beam search (beam size 4) | 7.76 | 8.71 | 13.41 | --epoch 10, --avg 2, --max-duration 100 | | fast beam search (1best) | 7.94 | 8.74 | 13.80 | --epoch 10, --avg 2, --max-duration 1500 | | fast beam search (nbest) | 9.82 | 10.98 | 16.37 | --epoch 10, --avg 2, --max-duration 600 | | fast beam search (nbest oracle) | 6.88 | 7.18 | 11.77 | --epoch 10, --avg 2, --max-duration 600 | | fast beam search (nbest LG, ngram_lm_scale=0.35) | 8.83 | 9.88 | 15.47 | --epoch 10, --avg 2, --max-duration 600 | The training command for reproducing is given below: ``` export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" ./pruned_transducer_stateless2/train.py \ --lang-dir data/lang_char \ --exp-dir pruned_transducer_stateless2/exp \ --world-size 8 \ --num-epochs 15 \ --start-epoch 0 \ --max-duration 180 \ --valid-interval 3000 \ --model-warm-step 3000 \ --save-every-n 8000 \ --training-subset L ``` The tensorboard training log can be found at https://tensorboard.dev/experiment/wM4ZUNtASRavJx79EOYYcg/#scalars The decoding command is: ``` epoch=10 avg=2 ## greedy search ./pruned_transducer_stateless2/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir ./pruned_transducer_stateless2/exp \ --lang-dir data/lang_char \ --max-duration 100 \ --decoding-method greedy_search ## modified beam search ./pruned_transducer_stateless2/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir ./pruned_transducer_stateless2/exp \ --lang-dir data/lang_char \ --max-duration 100 \ --decoding-method modified_beam_search \ --beam-size 4 ## fast beam search (1best) ./pruned_transducer_stateless2/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir ./pruned_transducer_stateless2/exp \ --lang-dir data/lang_char \ --max-duration 1500 \ --decoding-method fast_beam_search \ --beam 4 \ --max-contexts 4 \ --max-states 8 ## fast beam search (nbest) ./pruned_transducer_stateless2/decode.py \ --epoch 10 \ --avg 2 \ --exp-dir ./pruned_transducer_stateless2/exp \ --lang-dir data/lang_char \ --max-duration 600 \ --decoding-method fast_beam_search_nbest \ --beam 20.0 \ --max-contexts 8 \ --max-states 64 \ --num-paths 200 \ --nbest-scale 0.5 ## fast beam search (nbest oracle WER) ./pruned_transducer_stateless2/decode.py \ --epoch 10 \ --avg 2 \ --exp-dir ./pruned_transducer_stateless2/exp \ --lang-dir data/lang_char \ --max-duration 600 \ --decoding-method fast_beam_search_nbest_oracle \ --beam 20.0 \ --max-contexts 8 \ --max-states 64 \ --num-paths 200 \ --nbest-scale 0.5 ## fast beam search (with LG) ./pruned_transducer_stateless2/decode.py \ --epoch 10 \ --avg 2 \ --exp-dir ./pruned_transducer_stateless2/exp \ --lang-dir data/lang_char \ --max-duration 600 \ --decoding-method fast_beam_search_nbest_LG \ --ngram-lm-scale 0.35 \ --beam 20.0 \ --max-contexts 8 \ --max-states 64 ``` When training with the M subset, the CERs are | | dev | test-net | test-meeting | comment | |------------------------------------|--------|-----------|---------------|-------------------------------------------| | greedy search | 10.40 | 11.31 | 19.64 | --epoch 29, --avg 11, --max-duration 100 | | modified beam search (beam size 4) | 9.85 | 11.04 | 18.20 | --epoch 29, --avg 11, --max-duration 100 | | fast beam search (set as default) | 10.18 | 11.10 | 19.32 | --epoch 29, --avg 11, --max-duration 1500 | When training with the S subset, the CERs are | | dev | test-net | test-meeting | comment | |------------------------------------|--------|-----------|---------------|-------------------------------------------| | greedy search | 19.92 | 25.20 | 35.35 | --epoch 29, --avg 24, --max-duration 100 | | modified beam search (beam size 4) | 18.62 | 23.88 | 33.80 | --epoch 29, --avg 24, --max-duration 100 | | fast beam search (set as default) | 19.31 | 24.41 | 34.87 | --epoch 29, --avg 24, --max-duration 1500 | A pre-trained model and decoding logs can be found at