ESPnet2 ASR model

`espnet/shihlun_asr_whisper_medium_finetuned_chime4`

This model was trained by Shih-Lun Wu (slseanwu) using the chime4 recipe in espnet.

Demo: How to use in ESPnet2

cd espnet
pip install -e .
cd egs2/chime4/asr1

train_set=tr05_multi_noisy_si284 # tr05_multi_noisy (original training data) or tr05_multi_noisy_si284 (add si284 data)
valid_set=dt05_multi_isolated_1ch_track
test_sets="dt05_real_isolated_1ch_track dt05_simu_isolated_1ch_track et05_real_isolated_1ch_track et05_simu_isolated_1ch_track"

asr_tag=whisper_medium_finetune_lr1e-5_adamw_wd1e-2_3epochs
asr_config=conf/tuning/train_asr_whisper_full.yaml
inference_config=conf/decode_asr_whisper_noctc_greedy.yaml

./asr.sh \
    --skip_data_prep false \
    --skip_train true \
    --skip_eval false \
    --lang en \
    --ngpu 1 \
    --nj 4 \
    --stage 1 \
    --stop_stage 13 \
    --gpu_inference true \
    --inference_nj 1 \
    --token_type whisper_multilingual \
    --feats_normalize '' \
    --max_wav_duration 30 \
    --feats_type raw \
    --use_lm false \
    --cleaner whisper_en \
    --asr_tag "${asr_tag}" \
    --asr_config "${asr_config}" \
    --inference_config "${inference_config}" \
    --inference_asr_model valid.acc.ave.pth \
    --train_set "${train_set}" \
    --valid_set "${valid_set}" \
    --test_sets "${test_sets}" "$@"

RESULTS

Environments

date: Tue Jan 10 04:15:30 CST 2023
python version: 3.9.13 (main, Aug 25 2022, 23:26:10) [GCC 11.2.0]
espnet version: espnet 202211
pytorch version: pytorch 1.12.1
Git hash: d89be931dcc8f61437ac49cbe39a773f2054c50c
- Commit date: Mon Jan 9 11:06:45 2023 -0600

asr_whisper_medium_finetune_lr1e-5_adamw_wd1e-2_3epochs

WER

dataset	Snt	Wrd	Corr	Sub	Del	Ins	Err	S.Err
decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track	1640	24791	97.8	1.7	0.5	0.3	2.5	24.5
decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track	1640	24792	96.1	3.0	0.9	0.5	4.4	35.6
decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_real_isolated_1ch_track	1320	19341	96.4	2.9	0.7	0.5	4.1	33.0
decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track	1320	19344	93.4	5.0	1.7	0.8	7.4	41.8
decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track	1640	24791	97.7	1.8	0.5	0.4	2.8	25.5
decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track	1640	24792	96.0	3.3	0.8	0.7	4.8	36.0
decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_real_isolated_1ch_track	1320	19341	96.1	3.3	0.6	0.7	4.6	34.9
decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track	1320	19344	92.9	5.8	1.3	1.2	8.3	43.2

CER

dataset	Snt	Wrd	Corr	Sub	Del	Ins	Err	S.Err
decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track	1640	141889	99.1	0.3	0.5	0.3	1.2	24.5
decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track	1640	141900	98.2	0.8	1.0	0.5	2.3	35.6
decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_real_isolated_1ch_track	1320	110558	98.5	0.7	0.8	0.5	1.9	33.0
decode_asr_whisper_noctc_beam20_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track	1320	110572	96.5	1.6	1.9	0.8	4.3	41.8
decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_real_isolated_1ch_track	1640	141889	99.1	0.4	0.5	0.5	1.3	25.5
decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/dt05_simu_isolated_1ch_track	1640	141900	98.2	0.9	0.9	0.6	2.4	36.0
decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_real_isolated_1ch_track	1320	110558	98.4	0.9	0.7	0.6	2.2	34.9
decode_asr_whisper_noctc_greedy_asr_model_valid.acc.ave/et05_simu_isolated_1ch_track	1320	110572	96.3	2.0	1.7	1.2	4.9	43.2