Toward a stable version

I think we have fixed many issues, and we can add a version 1.0 (or 0.1) as a stable version. Toward that we need to finish

• VGG2L for pytorch by @ShigekiKarita
• AN4 recipe by me
• AMI recipe
• swbd recipe
• fisher_swbd recipe
• LM integration @sw005320
• Attention/CTC joint decoding @takaaki-hori
• End detection
• Documentation by @sw005320 @kan-bayashi
• Modify L.embed to avoid the randomness @takaaki-hori
• Add WER scoring
• label smoothing by @takaaki-hori
• replace _ilens_to_index to np.cumsum
• refactor main training and recognition to be independent of pytorch and chainer backends.

If you have any action items, please add them in this issue. Then, we can move to more research-related implementation.

[sw005320]

Guys, by combining LSTMLM and joint attention/CTC decoding, we finally get CER 5.3 -> 3.8, WER 14.7 -> 9.3 in the WSJ task!!! The nice thing is that we don’t have to set min/maxlength and penalty (all set to 0.0), while we might need to tune the CTC and LM weights (0.3 and 1.0, respectively, see #76). @kan-bayashi, can you play with LSTMLM and joint decoding with the TEDLIUM recipe? You can train LSTMLM by using text data by referring tools/kaldi/egs/tedlium/s5_r2/local/ted_train_lm.sh and simply using

gunzip -c db/TEDLIUM_release2/LM/*.en.gz | sed 's/ <\/s>//g' | local/join_suffix.py | gzip -c  > ${dir}/data/text/train.txt.gz

[kan-bayashi]

The results of tedlium with ctc joint decoding and lm rescoring are as follows:

exp/train_trim_vggblstmp_e6_subsample1_2_2_1_1_unit320_proj320_d1_unit300_location_aconvc10_aconvf100_mtlalpha0.5_adadelta_bs30_mli800_mlo150/decode_dev_beam20_eacc.best_p0.1_len0.0-0.0_ctcw0.3_rnnlm1.0/result.txt:|        Sum/Avg                          |         507                 95429        |        91.8                  4.2                  4.0                  2.7                 10.8                 89.3        |
exp/train_trim_vggblstmp_e6_subsample1_2_2_1_1_unit320_proj320_d1_unit300_location_aconvc10_aconvf100_mtlalpha0.5_adadelta_bs30_mli800_mlo150/decode_test_beam20_eacc.best_p0.1_len0.0-0.0_ctcw0.3_rnnlm1.0/result.txt:|        Sum/Avg                       |        1155                145066         |        92.2                  3.7                   4.1                  2.4                  10.1                 85.3         |
exp/train_trim_vggblstmp_e6_subsample1_2_2_1_1_unit320_proj320_d1_unit300_location_aconvc10_aconvf100_mtlalpha0.5_adadelta_bs30_mli800_mlo150/decode_dev_beam20_eacc.best_p0.1_len0.0-0.0_ctcw0.3_rnnlm1.0/result.wrd.txt:|        Sum/Avg                           |         507                17783         |        83.2                 13.7                   3.1                  3.0                  19.8                 89.3         |
exp/train_trim_vggblstmp_e6_subsample1_2_2_1_1_unit320_proj320_d1_unit300_location_aconvc10_aconvf100_mtlalpha0.5_adadelta_bs30_mli800_mlo150/decode_test_beam20_eacc.best_p0.1_len0.0-0.0_ctcw0.3_rnnlm1.0/result.wrd.txt:|        Sum/Avg                       |        1155                 27500         |        84.0                   12.3                   3.7                   2.6                  18.6                  85.3         |

for dev set, CER 12.6 -> 10.8, WER 24.8 -> 19.8 for test set, CER 11.9 -> 10.1, WER 23.4 -> 18.6