ESPnet v.0.1.5 (minor update)

• update the Librispeech ASR recipe and use subword modeling as default.
• attached Librispeech ASR model (librispeech_asr1.tgz):
  • RNNLM: exp/train_rnnlm_2layer_bs256_unigram2000/rnnlm.model.best
  • ASR models: exp/train_960_vggblstm_e4_subsample1_2_2_1_1_unit1024_proj1024_d1_unit1024_location1024_aconvc10_aconvf100_mtlalpha0.5_adadelta_bs30_mli800_mlo150_unigram2000/results/{model.acc.best,model.conf}
  • performance:

	WER (%)
Librispeech dev_clean	5.0
Librispeech test_clean	5.0

• • when we use the above models, please insert the ASR model directory (expdir) and RNNLM model directory (lmexpdir) in run.sh as follows:

expdir=exp/train_960_vggblstm_e4_subsample1_2_2_1_1_unit1024_proj1024_d1_unit1024_location1024_aconvc10_aconvf100_mtlalpha0.5_adadelta_bs30_mli800_mlo150_unigram2000
lmexpdir=exp/train_rnnlm_2layer_bs256_unigram2000

        ${decode_cmd} JOB=1:${nj} ${expdir}/${decode_dir}/log/decode.JOB.log \
            asr_recog.py \
            --ngpu ${ngpu} \
            --backend ${backend} \
            --recog-json ${feat_recog_dir}/split${nj}utt/data_${bpemode}${nbpe}.JOB.json \
            --result-label ${expdir}/${decode_dir}/data.JOB.json \
            --model ${expdir}/results/model.${recog_model}  \
            --model-conf ${expdir}/results/model.conf  \
            --beam-size ${beam_size} \
            --penalty ${penalty} \
            --maxlenratio ${maxlenratio} \
            --minlenratio ${minlenratio} \
            --ctc-weight ${ctc_weight} \
            --rnnlm ${lmexpdir}/rnnlm.model.best \
            --lm-weight ${lm_weight} \