RESULTS-100hours.md

Results for train-clean-100

This page shows the WERs for test-clean/test-other using only train-clean-100 subset as training data.

Distillation with hubert

2022-05-27

Related models/log/tensorboard: https://huggingface.co/GuoLiyong/stateless6_baseline_vs_disstillation

Following results are obtained by ./distillation_with_hubert.sh

The only differences is in pruned_transducer_stateless6/train.py.

For baseline: set enable_distillation=False

For distillation: set enable_distillation=True (the default)

Decoding method is modified beam search.

	test-clean	test-other	comment
baseline no vq distillation	7.09	18.88	--epoch 20, --avg 10, --max-duration 200
baseline no vq distillation	6.83	18.19	--epoch 30, --avg 10, --max-duration 200
baseline no vq distillation	6.73	17.79	--epoch 40, --avg 10, --max-duration 200
baseline no vq distillation	6.75	17.68	--epoch 50, --avg 10, --max-duration 200
distillation with hubert	5.82	15.98	--epoch 20, --avg 10, --max-duration 200
distillation with hubert	5.52	15.15	--epoch 30, --avg 10, --max-duration 200
distillation with hubert	5.45	14.94	--epoch 40, --avg 10, --max-duration 200
distillation with hubert	5.50	14.77	--epoch 50, --avg 10, --max-duration 200

Conformer encoder + embedding decoder

2022-02-21

Using commit 2332ba312d7ce72f08c7bac1e3312f7e3dd722dc.

	test-clean	test-other	comment
greedy search (max sym per frame 1)	6.34	16.7	--epoch 57, --avg 17, --max-duration 100
greedy search (max sym per frame 2)	6.34	16.7	--epoch 57, --avg 17, --max-duration 100
greedy search (max sym per frame 3)	6.34	16.7	--epoch 57, --avg 17, --max-duration 100
modified beam search (beam size 4)	6.31	16.3	--epoch 57, --avg 17, --max-duration 100

The training command for reproducing is given below:

cd egs/librispeech/ASR/
./prepare.sh
./prepare_giga_speech.sh

export CUDA_VISIBLE_DEVICES="0,1"

./transducer_stateless_multi_datasets/train.py \
  --world-size 2 \
  --num-epochs 60 \
  --start-epoch 0 \
  --exp-dir transducer_stateless_multi_datasets/exp-100-2 \
  --full-libri 0 \
  --max-duration 300 \
  --lr-factor 1 \
  --bpe-model data/lang_bpe_500/bpe.model \
  --modified-transducer-prob 0.25
  --giga-prob 0.2

The decoding command is given below:

for epoch in 57; do
  for avg in 17; do
    for sym in 1 2 3; do
    ./transducer_stateless_multi_datasets/decode.py \
      --epoch $epoch \
      --avg $avg \
      --exp-dir transducer_stateless_multi_datasets/exp-100-2 \
      --bpe-model ./data/lang_bpe_500/bpe.model \
      --max-duration 100 \
      --context-size 2 \
      --max-sym-per-frame $sym
    done
  done
done

epoch=57
avg=17
./transducer_stateless_multi_datasets/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir transducer_stateless_multi_datasets/exp-100-2 \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100 \
  --context-size 2 \
  --decoding-method modified_beam_search \
  --beam-size 4

The tensorboard log is available at https://tensorboard.dev/experiment/qUEKzMnrTZmOz1EXPda9RA/

A pre-trained model and decoding logs can be found at https://huggingface.co/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21