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Results

zipformer (zipformer + pruned stateless transducer)

See k2-fsa#1254 for more details.

zipformer

  • Non-streaming
  • normal-scaled model, number of model parameters: 65549011, i.e., 65.55 M

You can find a pretrained model, training logs, decoding logs, and decoding results at: https://huggingface.co/yfyeung/icefall-asr-gigaspeech-zipformer-2023-10-17

The tensorboard log for training is available at https://wandb.ai/yifanyeung/icefall-asr-gigaspeech-zipformer-2023-10-20

You can use https://github.com/k2-fsa/sherpa to deploy it.

decoding method test-clean test-other comment
greedy_search 10.31 10.50 --epoch 30 --avg 9
modified_beam_search 10.25 10.38 --epoch 30 --avg 9
fast_beam_search 10.26 10.48 --epoch 30 --avg 9

The training command is:

export CUDA_VISIBLE_DEVICES="0,1,2,3"
./zipformer/train.py \
  --world-size 4 \
  --num-epochs 30 \
  --start-epoch 1 \
  --use-fp16 1 \
  --exp-dir zipformer/exp \
  --causal 0 \
  --subset XL \
  --max-duration 700 \
  --use-transducer 1 \
  --use-ctc 0 \
  --lr-epochs 1 \
  --master-port 12345

The decoding command is:

export CUDA_VISIBLE_DEVICES=0

# greedy search
./zipformer/decode.py \
  --epoch 30 \
  --avg 9 \
  --exp-dir ./zipformer/exp \
  --max-duration 1000 \
  --decoding-method greedy_search

# modified beam search
./zipformer/decode.py \
  --epoch 30 \
  --avg 9 \
  --exp-dir ./zipformer/exp \
  --max-duration 1000 \
  --decoding-method modified_beam_search \
  --beam-size 4

# fast beam search (one best)
./zipformer/decode.py \
  --epoch 30 \
  --avg 9 \
  --exp-dir ./zipformer/exp \
  --max-duration 1000 \
  --decoding-method fast_beam_search \
  --beam 20.0 \
  --max-contexts 8 \
  --max-states 64

GigaSpeech BPE training results (Pruned Transducer 2)

2022-05-12

Conformer encoder + embedding decoder

Conformer encoder + non-recurrent decoder. The encoder is a reworked version of the conformer encoder, with many changes. The decoder contains only an embedding layer, a Conv1d (with kernel size 2) and a linear layer (to transform tensor dim). k2 pruned RNN-T loss is used.

The best WER, as of 2022-05-12, for the gigaspeech is below

Results are:

Dev Test
greedy search 10.51 10.73
fast beam search 10.50 10.69
modified beam search 10.40 10.51

To reproduce the above result, use the following commands for training:

cd egs/gigaspeech/ASR
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
./pruned_transducer_stateless2/train.py \
  --max-duration 120 \
  --num-workers 1 \
  --world-size 8 \
  --exp-dir pruned_transducer_stateless2/exp \
  --bpe-model data/lang_bpe_500/bpe.model \
  --use-fp16 True

and the following commands for decoding:

# greedy search
./pruned_transducer_stateless2/decode.py \
  --iter 3488000 \
  --avg 20 \
  --decoding-method greedy_search \
  --exp-dir pruned_transducer_stateless2/exp \
  --bpe-model data/lang_bpe_500/bpe.model \
  --max-duration 600

# fast beam search
./pruned_transducer_stateless2/decode.py \
  --iter 3488000 \
  --avg 20 \
  --decoding-method fast_beam_search \
  --exp-dir pruned_transducer_stateless2/exp \
  --bpe-model data/lang_bpe_500/bpe.model \
  --max-duration 600

# modified beam search
./pruned_transducer_stateless2/decode.py \
  --iter 3488000 \
  --avg 15 \
  --decoding-method modified_beam_search \
  --exp-dir pruned_transducer_stateless2/exp \
  --bpe-model data/lang_bpe_500/bpe.model \
  --max-duration 600

Pretrained model is available at https://huggingface.co/wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2

The tensorboard log for training is available at https://tensorboard.dev/experiment/zmmM0MLASnG1N2RmJ4MZBw/

GigaSpeech BPE training results (Conformer-CTC)

2022-04-06

The best WER, as of 2022-04-06, for the gigaspeech is below

Results using HLG decoding + n-gram LM rescoring + attention decoder rescoring:

Dev Test
WER 10.47 10.58

Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:

ngram_lm_scale attention_scale
0.5 1.3

To reproduce the above result, use the following commands for training:

cd egs/gigaspeech/ASR
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
./conformer_ctc/train.py \
  --max-duration 120 \
  --num-workers 1 \
  --world-size 8 \
  --exp-dir conformer_ctc/exp_500 \
  --lang-dir data/lang_bpe_500

and the following command for decoding:

./conformer_ctc/decode.py \
  --epoch 18 \
  --avg 6 \
  --method attention-decoder \
  --num-paths 1000 \
  --exp-dir conformer_ctc/exp_500 \
  --lang-dir data/lang_bpe_500 \
  --max-duration 20 \
  --num-workers 1

Results using HLG decoding + whole lattice rescoring:

Dev Test
WER 10.51 10.62

Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:

lm_scale
0.2

To reproduce the above result, use the training commands above, and the following command for decoding:

./conformer_ctc/decode.py \
  --epoch 18 \
  --avg 6 \
  --method whole-lattice-rescoring \
  --num-paths 1000 \
  --exp-dir conformer_ctc/exp_500 \
  --lang-dir data/lang_bpe_500 \
  --max-duration 20 \
  --num-workers 1

Note: the whole-lattice-rescoring method is about twice as fast as the attention-decoder method, with slightly worse WER.

Pretrained model is available at https://huggingface.co/wgb14/icefall-asr-gigaspeech-conformer-ctc

The tensorboard log for training is available at https://tensorboard.dev/experiment/rz63cmJXSK2fV9GceJtZXQ/