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Results

WenetSpeech char-based training results (Non-streaming and streaming) on zipformer model

This is the pull request 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 #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 https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_offline

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 https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_streaming

WenetSpeech char-based training results (Pruned Transducer 2)

2022-05-19

Using the codes from this PR #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 https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2