icefall/egs/libritts/ASR/RESULTS.md

Results

zipformer (zipformer + pruned stateless transducer)

See https://github.com/k2-fsa/icefall/pull/1746 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/zrjin/icefall-asr-libritts-zipformer-2024-10-20

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

decoding method	test-clean	test-other	comment
greedy_search	2.83	5.91	--epoch 30 --avg 5
modified_beam_search	2.80	5.87	--epoch 30 --avg 5
fast_beam_search	2.87	5.86	--epoch 30 --avg 5
greedy_search	2.76	5.68	--epoch 40 --avg 16
modified_beam_search	2.74	5.66	--epoch 40 --avg 16
fast_beam_search	2.75	5.67	--epoch 40 --avg 16
greedy_search	2.74	5.67	--epoch 50 --avg 30
modified_beam_search	2.73	5.58	--epoch 50 --avg 30
fast_beam_search	2.78	5.61	--epoch 50 --avg 30

The training command is:

export CUDA_VISIBLE_DEVICES="0,1"
./zipformer/train.py \
  --world-size 2 \
  --num-epochs 50 \
  --start-epoch 1 \
  --use-fp16 1 \
  --exp-dir zipformer/exp \
  --causal 0 \
  --full-libri 1 \
  --max-duration 3600

This was used on 2 Nvidia A800 GPUs, you'll need to adjust the CUDA_VISIBLE_DEVICES, --world-size and --max-duration according to your hardware.

The decoding command is:

export CUDA_VISIBLE_DEVICES="0"
for m in greedy_search modified_beam_search fast_beam_search; do
  ./zipformer/decode.py \
    --epoch 50 \
    --avg 30 \
    --use-averaged-model 1 \
    --exp-dir ./zipformer/exp \
    --max-duration 600 \
    --decoding-method $m
done