diff --git a/egs/timit/ASR/RESULTS.md b/egs/timit/ASR/RESULTS.md new file mode 100644 index 000000000..06ef5dec8 --- /dev/null +++ b/egs/timit/ASR/RESULTS.md @@ -0,0 +1,192 @@ +# results +# In this script, we use phone as modeling unit, so the PER equals to the WER. + +command: CUDA_VISIBLE_DEVICES='0' python tdnn_lstm_ctc/decode.py --epoch=59 --avg=1 + +2021-10-28 13:14:51,693 INFO [decode.py:387] Decoding started +2021-10-28 13:14:51,693 INFO [decode.py:388] {'exp_dir': PosixPath('tdnn_lstm_ctc/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 80, 'subsampling_factor': 3, 'search_beam': 20, 'output_beam': 5, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 59, 'avg': 1, 'method': 'whole-lattice-rescoring', 'num_paths': 100, 'nbest_scale': 0.5, 'export': False, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 200.0, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2} +2021-10-28 13:14:51,733 INFO [lexicon.py:176] Loading pre-compiled data/lang_phone/Linv.pt +2021-10-28 13:14:51,910 INFO [decode.py:397] device: cuda:0 +2021-10-28 13:14:58,958 INFO [decode.py:427] Loading pre-compiled G_4_gram.pt +2021-10-28 13:14:59,236 INFO [checkpoint.py:92] Loading checkpoint from tdnn_lstm_ctc/exp/epoch-59.pt +2021-10-28 13:15:01,789 INFO [decode.py:336] batch 0/?, cuts processed until now is 63 +2021-10-28 13:15:03,065 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.1.txt +2021-10-28 13:15:03,085 INFO [utils.py:469] [TEST-lm_scale_0.1] %WER 21.47% [1549 / 7215, 169 ins, 466 del, 914 sub ] +2021-10-28 13:15:03,118 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.1.txt +2021-10-28 13:15:03,146 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.2.txt +2021-10-28 13:15:03,166 INFO [utils.py:469] [TEST-lm_scale_0.2] %WER 21.26% [1534 / 7215, 150 ins, 490 del, 894 sub ] +2021-10-28 13:15:03,198 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.2.txt +2021-10-28 13:15:03,226 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.3.txt +2021-10-28 13:15:03,246 INFO [utils.py:469] [TEST-lm_scale_0.3] %WER 21.41% [1545 / 7215, 138 ins, 521 del, 886 sub ] +2021-10-28 13:15:03,279 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.3.txt +2021-10-28 13:15:03,307 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.4.txt +2021-10-28 13:15:03,327 INFO [utils.py:469] [TEST-lm_scale_0.4] %WER 21.73% [1568 / 7215, 127 ins, 566 del, 875 sub ] +2021-10-28 13:15:03,365 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.4.txt +2021-10-28 13:15:03,393 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.5.txt +2021-10-28 13:15:03,413 INFO [utils.py:469] [TEST-lm_scale_0.5] %WER 22.16% [1599 / 7215, 114 ins, 607 del, 878 sub ] +2021-10-28 13:15:03,445 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.5.txt +2021-10-28 13:15:03,474 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.6.txt +2021-10-28 13:15:03,494 INFO [utils.py:469] [TEST-lm_scale_0.6] %WER 22.76% [1642 / 7215, 109 ins, 638 del, 895 sub ] +2021-10-28 13:15:03,526 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.6.txt +2021-10-28 13:15:03,554 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.7.txt +2021-10-28 13:15:03,574 INFO [utils.py:469] [TEST-lm_scale_0.7] %WER 23.27% [1679 / 7215, 100 ins, 689 del, 890 sub ] +2021-10-28 13:15:03,611 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.7.txt +2021-10-28 13:15:03,639 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.8.txt +2021-10-28 13:15:03,660 INFO [utils.py:469] [TEST-lm_scale_0.8] %WER 24.21% [1747 / 7215, 96 ins, 745 del, 906 sub ] +2021-10-28 13:15:03,699 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.8.txt +2021-10-28 13:15:03,727 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.9.txt +2021-10-28 13:15:03,747 INFO [utils.py:469] [TEST-lm_scale_0.9] %WER 24.99% [1803 / 7215, 95 ins, 796 del, 912 sub ] +2021-10-28 13:15:03,783 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.9.txt +2021-10-28 13:15:03,811 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.0.txt +2021-10-28 13:15:03,830 INFO [utils.py:469] [TEST-lm_scale_1.0] %WER 25.61% [1848 / 7215, 92 ins, 844 del, 912 sub ] +2021-10-28 13:15:03,863 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.0.txt +2021-10-28 13:15:03,890 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.1.txt +2021-10-28 13:15:03,910 INFO [utils.py:469] [TEST-lm_scale_1.1] %WER 26.54% [1915 / 7215, 81 ins, 923 del, 911 sub ] +2021-10-28 13:15:03,943 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.1.txt +2021-10-28 13:15:03,971 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.2.txt +2021-10-28 13:15:03,991 INFO [utils.py:469] [TEST-lm_scale_1.2] %WER 27.50% [1984 / 7215, 76 ins, 986 del, 922 sub ] +2021-10-28 13:15:04,023 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.2.txt +2021-10-28 13:15:04,051 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.3.txt +2021-10-28 13:15:04,070 INFO [utils.py:469] [TEST-lm_scale_1.3] %WER 28.26% [2039 / 7215, 69 ins, 1046 del, 924 sub ] +2021-10-28 13:15:04,102 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.3.txt +2021-10-28 13:15:04,130 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.4.txt +2021-10-28 13:15:04,150 INFO [utils.py:469] [TEST-lm_scale_1.4] %WER 28.79% [2077 / 7215, 63 ins, 1100 del, 914 sub ] +2021-10-28 13:15:04,183 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.4.txt +2021-10-28 13:15:04,211 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.5.txt +2021-10-28 13:15:04,231 INFO [utils.py:469] [TEST-lm_scale_1.5] %WER 29.72% [2144 / 7215, 56 ins, 1178 del, 910 sub ] +2021-10-28 13:15:04,263 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.5.txt +2021-10-28 13:15:04,291 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.6.txt +2021-10-28 13:15:04,311 INFO [utils.py:469] [TEST-lm_scale_1.6] %WER 30.51% [2201 / 7215, 50 ins, 1250 del, 901 sub ] +2021-10-28 13:15:04,343 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.6.txt +2021-10-28 13:15:04,371 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.7.txt +2021-10-28 13:15:04,391 INFO [utils.py:469] [TEST-lm_scale_1.7] %WER 31.30% [2258 / 7215, 44 ins, 1317 del, 897 sub ] +2021-10-28 13:15:04,423 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.7.txt +2021-10-28 13:15:04,451 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.8.txt +2021-10-28 13:15:04,470 INFO [utils.py:469] [TEST-lm_scale_1.8] %WER 32.22% [2325 / 7215, 45 ins, 1374 del, 906 sub ] +2021-10-28 13:15:04,503 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.8.txt +2021-10-28 13:15:04,531 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.9.txt +2021-10-28 13:15:04,550 INFO [utils.py:469] [TEST-lm_scale_1.9] %WER 33.17% [2393 / 7215, 43 ins, 1444 del, 906 sub ] +2021-10-28 13:15:04,582 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.9.txt +2021-10-28 13:15:04,610 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_2.0.txt +2021-10-28 13:15:04,630 INFO [utils.py:469] [TEST-lm_scale_2.0] %WER 34.03% [2455 / 7215, 41 ins, 1510 del, 904 sub ] +2021-10-28 13:15:04,662 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_2.0.txt +2021-10-28 13:15:04,682 INFO [decode.py:374] +For TEST, PER of different settings are: +lm_scale_0.2 21.26 best for TEST +lm_scale_0.3 21.41 +lm_scale_0.1 21.47 +lm_scale_0.4 21.73 +lm_scale_0.5 22.16 +lm_scale_0.6 22.76 +lm_scale_0.7 23.27 +lm_scale_0.8 24.21 +lm_scale_0.9 24.99 +lm_scale_1.0 25.61 +lm_scale_1.1 26.54 +lm_scale_1.2 27.5 +lm_scale_1.3 28.26 +lm_scale_1.4 28.79 +lm_scale_1.5 29.72 +lm_scale_1.6 30.51 +lm_scale_1.7 31.3 +lm_scale_1.8 32.22 +lm_scale_1.9 33.17 +lm_scale_2.0 34.03 + +2021-10-28 13:15:04,682 INFO [decode.py:498] Done! + + +command: CUDA_VISIBLE_DEVICES='0' python tdnn_lstm_ctc/decode.py --epoch=59 --avg=5 + +2021-10-28 13:20:28,962 INFO [decode.py:387] Decoding started +2021-10-28 13:20:28,962 INFO [decode.py:388] {'exhell +_dir': PosixPath('tdnn_lstm_ctc/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 80, 'subsampling_factor': 3, 'search_beam': 20, 'output_beam': 5, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 59, 'avg': 5, 'method': 'whole-lattice-rescoring', 'num_paths': 100, 'nbest_scale': 0.5, 'export': False, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 200.0, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2} +2021-10-28 13:20:29,002 INFO [lexicon.py:176] Loading pre-compiled data/lang_phone/Linv.pt +2021-10-28 13:20:29,153 INFO [decode.py:397] device: cuda:0 +2021-10-28 13:20:35,947 INFO [decode.py:427] Loading pre-compiled G_4_gram.pt +2021-10-28 13:20:36,097 INFO [decode.py:458] averaging ['tdnn_lstm_ctc/exp/epoch-55.pt', 'tdnn_lstm_ctc/exp/epoch-56.pt', 'tdnn_lstm_ctc/exp/epoch-57.pt', 'tdnn_lstm_ctc/exp/epoch-58.pt', 'tdnn_lstm_ctc/exp/epoch-59.pt'] +2021-10-28 13:20:39,819 INFO [decode.py:336] batch 0/?, cuts processed until now is 63 +2021-10-28 13:20:41,218 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.1.txt +2021-10-28 13:20:41,239 INFO [utils.py:469] [TEST-lm_scale_0.1] %WER 20.82% [1502 / 7215, 144 ins, 478 del, 880 sub ] +2021-10-28 13:20:41,279 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.1.txt +2021-10-28 13:20:41,307 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.2.txt +2021-10-28 13:20:41,327 INFO [utils.py:469] [TEST-lm_scale_0.2] %WER 20.93% [1510 / 7215, 134 ins, 504 del, 872 sub ] +2021-10-28 13:20:41,365 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.2.txt +2021-10-28 13:20:41,395 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.3.txt +2021-10-28 13:20:41,415 INFO [utils.py:469] [TEST-lm_scale_0.3] %WER 21.33% [1539 / 7215, 122 ins, 541 del, 876 sub ] +2021-10-28 13:20:41,447 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.3.txt +2021-10-28 13:20:41,476 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.4.txt +2021-10-28 13:20:41,498 INFO [utils.py:469] [TEST-lm_scale_0.4] %WER 21.91% [1581 / 7215, 119 ins, 587 del, 875 sub ] +2021-10-28 13:20:41,530 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.4.txt +2021-10-28 13:20:41,563 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.5.txt +2021-10-28 13:20:41,591 INFO [utils.py:469] [TEST-lm_scale_0.5] %WER 22.58% [1629 / 7215, 116 ins, 636 del, 877 sub ] +2021-10-28 13:20:41,624 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.5.txt +2021-10-28 13:20:41,652 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.6.txt +2021-10-28 13:20:41,679 INFO [utils.py:469] [TEST-lm_scale_0.6] %WER 23.20% [1674 / 7215, 106 ins, 682 del, 886 sub ] +2021-10-28 13:20:41,712 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.6.txt +2021-10-28 13:20:41,740 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.7.txt +2021-10-28 13:20:41,768 INFO [utils.py:469] [TEST-lm_scale_0.7] %WER 23.76% [1714 / 7215, 92 ins, 738 del, 884 sub ] +2021-10-28 13:20:41,802 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.7.txt +2021-10-28 13:20:41,830 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.8.txt +2021-10-28 13:20:41,851 INFO [utils.py:469] [TEST-lm_scale_0.8] %WER 24.46% [1765 / 7215, 90 ins, 796 del, 879 sub ] +2021-10-28 13:20:41,892 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.8.txt +2021-10-28 13:20:41,920 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.9.txt +2021-10-28 13:20:41,940 INFO [utils.py:469] [TEST-lm_scale_0.9] %WER 25.16% [1815 / 7215, 81 ins, 843 del, 891 sub ] +2021-10-28 13:20:41,976 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.9.txt +2021-10-28 13:20:42,004 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.0.txt +2021-10-28 13:20:42,024 INFO [utils.py:469] [TEST-lm_scale_1.0] %WER 25.84% [1864 / 7215, 73 ins, 892 del, 899 sub ] +2021-10-28 13:20:42,067 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.0.txt +2021-10-28 13:20:42,099 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.1.txt +2021-10-28 13:20:42,119 INFO [utils.py:469] [TEST-lm_scale_1.1] %WER 26.46% [1909 / 7215, 69 ins, 932 del, 908 sub ] +2021-10-28 13:20:42,152 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.1.txt +2021-10-28 13:20:42,184 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.2.txt +2021-10-28 13:20:42,204 INFO [utils.py:469] [TEST-lm_scale_1.2] %WER 27.23% [1965 / 7215, 66 ins, 989 del, 910 sub ] +2021-10-28 13:20:42,241 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.2.txt +2021-10-28 13:20:42,280 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.3.txt +2021-10-28 13:20:42,300 INFO [utils.py:469] [TEST-lm_scale_1.3] %WER 28.01% [2021 / 7215, 60 ins, 1055 del, 906 sub ] +2021-10-28 13:20:42,332 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.3.txt +2021-10-28 13:20:42,360 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.4.txt +2021-10-28 13:20:42,386 INFO [utils.py:469] [TEST-lm_scale_1.4] %WER 29.04% [2095 / 7215, 54 ins, 1134 del, 907 sub ] +2021-10-28 13:20:42,425 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.4.txt +2021-10-28 13:20:42,454 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.5.txt +2021-10-28 13:20:42,477 INFO [utils.py:469] [TEST-lm_scale_1.5] %WER 30.08% [2170 / 7215, 48 ins, 1222 del, 900 sub ] +2021-10-28 13:20:42,516 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.5.txt +2021-10-28 13:20:42,544 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.6.txt +2021-10-28 13:20:42,567 INFO [utils.py:469] [TEST-lm_scale_1.6] %WER 31.02% [2238 / 7215, 41 ins, 1285 del, 912 sub ] +2021-10-28 13:20:42,602 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.6.txt +2021-10-28 13:20:42,630 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.7.txt +2021-10-28 13:20:42,650 INFO [utils.py:469] [TEST-lm_scale_1.7] %WER 31.73% [2289 / 7215, 40 ins, 1336 del, 913 sub ] +2021-10-28 13:20:42,692 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.7.txt +2021-10-28 13:20:42,720 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.8.txt +2021-10-28 13:20:42,740 INFO [utils.py:469] [TEST-lm_scale_1.8] %WER 32.52% [2346 / 7215, 39 ins, 1407 del, 900 sub ] +2021-10-28 13:20:42,780 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.8.txt +2021-10-28 13:20:42,808 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.9.txt +2021-10-28 13:20:42,828 INFO [utils.py:469] [TEST-lm_scale_1.9] %WER 33.35% [2406 / 7215, 40 ins, 1460 del, 906 sub ] +2021-10-28 13:20:42,865 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.9.txt +2021-10-28 13:20:42,899 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_2.0.txt +2021-10-28 13:20:42,919 INFO [utils.py:469] [TEST-lm_scale_2.0] %WER 33.97% [2451 / 7215, 39 ins, 1510 del, 902 sub ] +2021-10-28 13:20:42,952 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_2.0.txt +2021-10-28 13:20:42,986 INFO [decode.py:374] +For TEST, PER of different settings are: +lm_scale_0.1 20.82 best for TEST +lm_scale_0.2 20.93 +lm_scale_0.3 21.33 +lm_scale_0.4 21.91 +lm_scale_0.5 22.58 +lm_scale_0.6 23.2 +lm_scale_0.7 23.76 +lm_scale_0.8 24.46 +lm_scale_0.9 25.16 +lm_scale_1.0 25.84 +lm_scale_1.1 26.46 +lm_scale_1.2 27.23 +lm_scale_1.3 28.01 +lm_scale_1.4 29.04 +lm_scale_1.5 30.08 +lm_scale_1.6 31.02 +lm_scale_1.7 31.73 +lm_scale_1.8 32.52 +lm_scale_1.9 33.35 +lm_scale_2.0 33.97 + +2021-10-28 13:20:42,986 INFO [decode.py:498] Done! diff --git a/egs/timit/ASR/local/RESULTS.md b/egs/timit/ASR/local/RESULTS.md new file mode 100644 index 000000000..06ef5dec8 --- /dev/null +++ b/egs/timit/ASR/local/RESULTS.md @@ -0,0 +1,192 @@ +# results +# In this script, we use phone as modeling unit, so the PER equals to the WER. + +command: CUDA_VISIBLE_DEVICES='0' python tdnn_lstm_ctc/decode.py --epoch=59 --avg=1 + +2021-10-28 13:14:51,693 INFO [decode.py:387] Decoding started +2021-10-28 13:14:51,693 INFO [decode.py:388] {'exp_dir': PosixPath('tdnn_lstm_ctc/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 80, 'subsampling_factor': 3, 'search_beam': 20, 'output_beam': 5, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 59, 'avg': 1, 'method': 'whole-lattice-rescoring', 'num_paths': 100, 'nbest_scale': 0.5, 'export': False, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 200.0, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2} +2021-10-28 13:14:51,733 INFO [lexicon.py:176] Loading pre-compiled data/lang_phone/Linv.pt +2021-10-28 13:14:51,910 INFO [decode.py:397] device: cuda:0 +2021-10-28 13:14:58,958 INFO [decode.py:427] Loading pre-compiled G_4_gram.pt +2021-10-28 13:14:59,236 INFO [checkpoint.py:92] Loading checkpoint from tdnn_lstm_ctc/exp/epoch-59.pt +2021-10-28 13:15:01,789 INFO [decode.py:336] batch 0/?, cuts processed until now is 63 +2021-10-28 13:15:03,065 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.1.txt +2021-10-28 13:15:03,085 INFO [utils.py:469] [TEST-lm_scale_0.1] %WER 21.47% [1549 / 7215, 169 ins, 466 del, 914 sub ] +2021-10-28 13:15:03,118 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.1.txt +2021-10-28 13:15:03,146 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.2.txt +2021-10-28 13:15:03,166 INFO [utils.py:469] [TEST-lm_scale_0.2] %WER 21.26% [1534 / 7215, 150 ins, 490 del, 894 sub ] +2021-10-28 13:15:03,198 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.2.txt +2021-10-28 13:15:03,226 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.3.txt +2021-10-28 13:15:03,246 INFO [utils.py:469] [TEST-lm_scale_0.3] %WER 21.41% [1545 / 7215, 138 ins, 521 del, 886 sub ] +2021-10-28 13:15:03,279 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.3.txt +2021-10-28 13:15:03,307 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.4.txt +2021-10-28 13:15:03,327 INFO [utils.py:469] [TEST-lm_scale_0.4] %WER 21.73% [1568 / 7215, 127 ins, 566 del, 875 sub ] +2021-10-28 13:15:03,365 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.4.txt +2021-10-28 13:15:03,393 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.5.txt +2021-10-28 13:15:03,413 INFO [utils.py:469] [TEST-lm_scale_0.5] %WER 22.16% [1599 / 7215, 114 ins, 607 del, 878 sub ] +2021-10-28 13:15:03,445 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.5.txt +2021-10-28 13:15:03,474 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.6.txt +2021-10-28 13:15:03,494 INFO [utils.py:469] [TEST-lm_scale_0.6] %WER 22.76% [1642 / 7215, 109 ins, 638 del, 895 sub ] +2021-10-28 13:15:03,526 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.6.txt +2021-10-28 13:15:03,554 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.7.txt +2021-10-28 13:15:03,574 INFO [utils.py:469] [TEST-lm_scale_0.7] %WER 23.27% [1679 / 7215, 100 ins, 689 del, 890 sub ] +2021-10-28 13:15:03,611 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.7.txt +2021-10-28 13:15:03,639 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.8.txt +2021-10-28 13:15:03,660 INFO [utils.py:469] [TEST-lm_scale_0.8] %WER 24.21% [1747 / 7215, 96 ins, 745 del, 906 sub ] +2021-10-28 13:15:03,699 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.8.txt +2021-10-28 13:15:03,727 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.9.txt +2021-10-28 13:15:03,747 INFO [utils.py:469] [TEST-lm_scale_0.9] %WER 24.99% [1803 / 7215, 95 ins, 796 del, 912 sub ] +2021-10-28 13:15:03,783 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.9.txt +2021-10-28 13:15:03,811 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.0.txt +2021-10-28 13:15:03,830 INFO [utils.py:469] [TEST-lm_scale_1.0] %WER 25.61% [1848 / 7215, 92 ins, 844 del, 912 sub ] +2021-10-28 13:15:03,863 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.0.txt +2021-10-28 13:15:03,890 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.1.txt +2021-10-28 13:15:03,910 INFO [utils.py:469] [TEST-lm_scale_1.1] %WER 26.54% [1915 / 7215, 81 ins, 923 del, 911 sub ] +2021-10-28 13:15:03,943 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.1.txt +2021-10-28 13:15:03,971 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.2.txt +2021-10-28 13:15:03,991 INFO [utils.py:469] [TEST-lm_scale_1.2] %WER 27.50% [1984 / 7215, 76 ins, 986 del, 922 sub ] +2021-10-28 13:15:04,023 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.2.txt +2021-10-28 13:15:04,051 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.3.txt +2021-10-28 13:15:04,070 INFO [utils.py:469] [TEST-lm_scale_1.3] %WER 28.26% [2039 / 7215, 69 ins, 1046 del, 924 sub ] +2021-10-28 13:15:04,102 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.3.txt +2021-10-28 13:15:04,130 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.4.txt +2021-10-28 13:15:04,150 INFO [utils.py:469] [TEST-lm_scale_1.4] %WER 28.79% [2077 / 7215, 63 ins, 1100 del, 914 sub ] +2021-10-28 13:15:04,183 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.4.txt +2021-10-28 13:15:04,211 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.5.txt +2021-10-28 13:15:04,231 INFO [utils.py:469] [TEST-lm_scale_1.5] %WER 29.72% [2144 / 7215, 56 ins, 1178 del, 910 sub ] +2021-10-28 13:15:04,263 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.5.txt +2021-10-28 13:15:04,291 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.6.txt +2021-10-28 13:15:04,311 INFO [utils.py:469] [TEST-lm_scale_1.6] %WER 30.51% [2201 / 7215, 50 ins, 1250 del, 901 sub ] +2021-10-28 13:15:04,343 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.6.txt +2021-10-28 13:15:04,371 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.7.txt +2021-10-28 13:15:04,391 INFO [utils.py:469] [TEST-lm_scale_1.7] %WER 31.30% [2258 / 7215, 44 ins, 1317 del, 897 sub ] +2021-10-28 13:15:04,423 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.7.txt +2021-10-28 13:15:04,451 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.8.txt +2021-10-28 13:15:04,470 INFO [utils.py:469] [TEST-lm_scale_1.8] %WER 32.22% [2325 / 7215, 45 ins, 1374 del, 906 sub ] +2021-10-28 13:15:04,503 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.8.txt +2021-10-28 13:15:04,531 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.9.txt +2021-10-28 13:15:04,550 INFO [utils.py:469] [TEST-lm_scale_1.9] %WER 33.17% [2393 / 7215, 43 ins, 1444 del, 906 sub ] +2021-10-28 13:15:04,582 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.9.txt +2021-10-28 13:15:04,610 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_2.0.txt +2021-10-28 13:15:04,630 INFO [utils.py:469] [TEST-lm_scale_2.0] %WER 34.03% [2455 / 7215, 41 ins, 1510 del, 904 sub ] +2021-10-28 13:15:04,662 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_2.0.txt +2021-10-28 13:15:04,682 INFO [decode.py:374] +For TEST, PER of different settings are: +lm_scale_0.2 21.26 best for TEST +lm_scale_0.3 21.41 +lm_scale_0.1 21.47 +lm_scale_0.4 21.73 +lm_scale_0.5 22.16 +lm_scale_0.6 22.76 +lm_scale_0.7 23.27 +lm_scale_0.8 24.21 +lm_scale_0.9 24.99 +lm_scale_1.0 25.61 +lm_scale_1.1 26.54 +lm_scale_1.2 27.5 +lm_scale_1.3 28.26 +lm_scale_1.4 28.79 +lm_scale_1.5 29.72 +lm_scale_1.6 30.51 +lm_scale_1.7 31.3 +lm_scale_1.8 32.22 +lm_scale_1.9 33.17 +lm_scale_2.0 34.03 + +2021-10-28 13:15:04,682 INFO [decode.py:498] Done! + + +command: CUDA_VISIBLE_DEVICES='0' python tdnn_lstm_ctc/decode.py --epoch=59 --avg=5 + +2021-10-28 13:20:28,962 INFO [decode.py:387] Decoding started +2021-10-28 13:20:28,962 INFO [decode.py:388] {'exhell +_dir': PosixPath('tdnn_lstm_ctc/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 80, 'subsampling_factor': 3, 'search_beam': 20, 'output_beam': 5, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 59, 'avg': 5, 'method': 'whole-lattice-rescoring', 'num_paths': 100, 'nbest_scale': 0.5, 'export': False, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 200.0, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2} +2021-10-28 13:20:29,002 INFO [lexicon.py:176] Loading pre-compiled data/lang_phone/Linv.pt +2021-10-28 13:20:29,153 INFO [decode.py:397] device: cuda:0 +2021-10-28 13:20:35,947 INFO [decode.py:427] Loading pre-compiled G_4_gram.pt +2021-10-28 13:20:36,097 INFO [decode.py:458] averaging ['tdnn_lstm_ctc/exp/epoch-55.pt', 'tdnn_lstm_ctc/exp/epoch-56.pt', 'tdnn_lstm_ctc/exp/epoch-57.pt', 'tdnn_lstm_ctc/exp/epoch-58.pt', 'tdnn_lstm_ctc/exp/epoch-59.pt'] +2021-10-28 13:20:39,819 INFO [decode.py:336] batch 0/?, cuts processed until now is 63 +2021-10-28 13:20:41,218 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.1.txt +2021-10-28 13:20:41,239 INFO [utils.py:469] [TEST-lm_scale_0.1] %WER 20.82% [1502 / 7215, 144 ins, 478 del, 880 sub ] +2021-10-28 13:20:41,279 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.1.txt +2021-10-28 13:20:41,307 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.2.txt +2021-10-28 13:20:41,327 INFO [utils.py:469] [TEST-lm_scale_0.2] %WER 20.93% [1510 / 7215, 134 ins, 504 del, 872 sub ] +2021-10-28 13:20:41,365 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.2.txt +2021-10-28 13:20:41,395 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.3.txt +2021-10-28 13:20:41,415 INFO [utils.py:469] [TEST-lm_scale_0.3] %WER 21.33% [1539 / 7215, 122 ins, 541 del, 876 sub ] +2021-10-28 13:20:41,447 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.3.txt +2021-10-28 13:20:41,476 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.4.txt +2021-10-28 13:20:41,498 INFO [utils.py:469] [TEST-lm_scale_0.4] %WER 21.91% [1581 / 7215, 119 ins, 587 del, 875 sub ] +2021-10-28 13:20:41,530 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.4.txt +2021-10-28 13:20:41,563 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.5.txt +2021-10-28 13:20:41,591 INFO [utils.py:469] [TEST-lm_scale_0.5] %WER 22.58% [1629 / 7215, 116 ins, 636 del, 877 sub ] +2021-10-28 13:20:41,624 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.5.txt +2021-10-28 13:20:41,652 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.6.txt +2021-10-28 13:20:41,679 INFO [utils.py:469] [TEST-lm_scale_0.6] %WER 23.20% [1674 / 7215, 106 ins, 682 del, 886 sub ] +2021-10-28 13:20:41,712 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.6.txt +2021-10-28 13:20:41,740 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.7.txt +2021-10-28 13:20:41,768 INFO [utils.py:469] [TEST-lm_scale_0.7] %WER 23.76% [1714 / 7215, 92 ins, 738 del, 884 sub ] +2021-10-28 13:20:41,802 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.7.txt +2021-10-28 13:20:41,830 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.8.txt +2021-10-28 13:20:41,851 INFO [utils.py:469] [TEST-lm_scale_0.8] %WER 24.46% [1765 / 7215, 90 ins, 796 del, 879 sub ] +2021-10-28 13:20:41,892 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.8.txt +2021-10-28 13:20:41,920 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_0.9.txt +2021-10-28 13:20:41,940 INFO [utils.py:469] [TEST-lm_scale_0.9] %WER 25.16% [1815 / 7215, 81 ins, 843 del, 891 sub ] +2021-10-28 13:20:41,976 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_0.9.txt +2021-10-28 13:20:42,004 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.0.txt +2021-10-28 13:20:42,024 INFO [utils.py:469] [TEST-lm_scale_1.0] %WER 25.84% [1864 / 7215, 73 ins, 892 del, 899 sub ] +2021-10-28 13:20:42,067 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.0.txt +2021-10-28 13:20:42,099 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.1.txt +2021-10-28 13:20:42,119 INFO [utils.py:469] [TEST-lm_scale_1.1] %WER 26.46% [1909 / 7215, 69 ins, 932 del, 908 sub ] +2021-10-28 13:20:42,152 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.1.txt +2021-10-28 13:20:42,184 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.2.txt +2021-10-28 13:20:42,204 INFO [utils.py:469] [TEST-lm_scale_1.2] %WER 27.23% [1965 / 7215, 66 ins, 989 del, 910 sub ] +2021-10-28 13:20:42,241 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.2.txt +2021-10-28 13:20:42,280 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.3.txt +2021-10-28 13:20:42,300 INFO [utils.py:469] [TEST-lm_scale_1.3] %WER 28.01% [2021 / 7215, 60 ins, 1055 del, 906 sub ] +2021-10-28 13:20:42,332 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.3.txt +2021-10-28 13:20:42,360 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.4.txt +2021-10-28 13:20:42,386 INFO [utils.py:469] [TEST-lm_scale_1.4] %WER 29.04% [2095 / 7215, 54 ins, 1134 del, 907 sub ] +2021-10-28 13:20:42,425 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.4.txt +2021-10-28 13:20:42,454 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.5.txt +2021-10-28 13:20:42,477 INFO [utils.py:469] [TEST-lm_scale_1.5] %WER 30.08% [2170 / 7215, 48 ins, 1222 del, 900 sub ] +2021-10-28 13:20:42,516 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.5.txt +2021-10-28 13:20:42,544 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.6.txt +2021-10-28 13:20:42,567 INFO [utils.py:469] [TEST-lm_scale_1.6] %WER 31.02% [2238 / 7215, 41 ins, 1285 del, 912 sub ] +2021-10-28 13:20:42,602 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.6.txt +2021-10-28 13:20:42,630 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.7.txt +2021-10-28 13:20:42,650 INFO [utils.py:469] [TEST-lm_scale_1.7] %WER 31.73% [2289 / 7215, 40 ins, 1336 del, 913 sub ] +2021-10-28 13:20:42,692 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.7.txt +2021-10-28 13:20:42,720 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.8.txt +2021-10-28 13:20:42,740 INFO [utils.py:469] [TEST-lm_scale_1.8] %WER 32.52% [2346 / 7215, 39 ins, 1407 del, 900 sub ] +2021-10-28 13:20:42,780 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.8.txt +2021-10-28 13:20:42,808 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_1.9.txt +2021-10-28 13:20:42,828 INFO [utils.py:469] [TEST-lm_scale_1.9] %WER 33.35% [2406 / 7215, 40 ins, 1460 del, 906 sub ] +2021-10-28 13:20:42,865 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_1.9.txt +2021-10-28 13:20:42,899 INFO [decode.py:351] The transcripts are stored in tdnn_lstm_ctc/exp/recogs-TEST-lm_scale_2.0.txt +2021-10-28 13:20:42,919 INFO [utils.py:469] [TEST-lm_scale_2.0] %WER 33.97% [2451 / 7215, 39 ins, 1510 del, 902 sub ] +2021-10-28 13:20:42,952 INFO [decode.py:360] Wrote detailed error stats to tdnn_lstm_ctc/exp/errs-TEST-lm_scale_2.0.txt +2021-10-28 13:20:42,986 INFO [decode.py:374] +For TEST, PER of different settings are: +lm_scale_0.1 20.82 best for TEST +lm_scale_0.2 20.93 +lm_scale_0.3 21.33 +lm_scale_0.4 21.91 +lm_scale_0.5 22.58 +lm_scale_0.6 23.2 +lm_scale_0.7 23.76 +lm_scale_0.8 24.46 +lm_scale_0.9 25.16 +lm_scale_1.0 25.84 +lm_scale_1.1 26.46 +lm_scale_1.2 27.23 +lm_scale_1.3 28.01 +lm_scale_1.4 29.04 +lm_scale_1.5 30.08 +lm_scale_1.6 31.02 +lm_scale_1.7 31.73 +lm_scale_1.8 32.52 +lm_scale_1.9 33.35 +lm_scale_2.0 33.97 + +2021-10-28 13:20:42,986 INFO [decode.py:498] Done! diff --git a/egs/timit/ASR/local/__init__.py b/egs/timit/ASR/local/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/timit/ASR/local/compile_hlg.py b/egs/timit/ASR/local/compile_hlg.py new file mode 100644 index 000000000..ad8b41de1 --- /dev/null +++ b/egs/timit/ASR/local/compile_hlg.py @@ -0,0 +1,166 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input lang_dir and generates HLG from + + - H, the ctc topology, built from tokens contained in lang_dir/lexicon.txt + - L, the lexicon, built from lang_dir/L_disambig.pt + + Caution: We use a lexicon that contains disambiguation symbols + + - G, the LM, built from data/lm/G_3_gram.fst.txt + +The generated HLG is saved in $lang_dir/HLG.pt +""" +import argparse +import logging +from pathlib import Path + +import k2 +import torch + +from icefall.lexicon import Lexicon + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-dir", + type=str, + help="""Input and output directory. + """, + ) + + return parser.parse_args() + + +def compile_HLG(lang_dir: str) -> k2.Fsa: + """ + Args: + lang_dir: + The language directory, e.g., data/lang_phone or data/lang_bpe_5000. + + Return: + An FSA representing HLG. + """ + lexicon = Lexicon(lang_dir) + max_token_id = max(lexicon.tokens) + logging.info(f"Building ctc_topo. max_token_id: {max_token_id}") + H = k2.ctc_topo(max_token_id) + + if Path(lang_dir / "L_disambig.pt").is_file(): + logging.info("Loading L_disambig.pt") + d = torch.load(Path(lang_dir/"L_disambig.pt")) + L = k2.Fsa.from_dict(d) + else: + logging.info("Loading L_disambig.fst.txt") + with open(Path(lang_dir/"L_disambig.fst.txt")) as f: + L = k2.Fsa.from_openfst(f.read(), acceptor=False) + torch.save(L_disambig.as_dict(), Path(lang_dir / "L_disambig.pt")) + + #L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt")) + + if Path("data/lm/G.pt").is_file(): + logging.info("Loading pre-compiled G") + d = torch.load("data/lm/G.pt") + G = k2.Fsa.from_dict(d) + else: + logging.info("Loading G_3_gram.fst.txt") + with open("data/lm/G_3_gram.fst.txt") as f: + G = k2.Fsa.from_openfst(f.read(), acceptor=False) + torch.save(G.as_dict(), "data/lm/G.pt") + + first_token_disambig_id = lexicon.token_table["#0"] + first_word_disambig_id = lexicon.word_table["#0"] + + L = k2.arc_sort(L) + G = k2.arc_sort(G) + + logging.info("Intersecting L and G") + LG = k2.compose(L, G) + logging.info(f"LG shape: {LG.shape}") + + logging.info("Connecting LG") + LG = k2.connect(LG) + logging.info(f"LG shape after k2.connect: {LG.shape}") + + logging.info(type(LG.aux_labels)) + logging.info("Determinizing LG") + + LG = k2.determinize(LG) + logging.info(type(LG.aux_labels)) + + logging.info("Connecting LG after k2.determinize") + LG = k2.connect(LG) + + logging.info("Removing disambiguation symbols on LG") + + LG.labels[LG.labels >= first_token_disambig_id] = 0 + + LG.aux_labels.values[LG.aux_labels.values >= first_word_disambig_id] = 0 + + LG = k2.remove_epsilon(LG) + logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}") + + LG = k2.connect(LG) + LG.aux_labels = LG.aux_labels.remove_values_eq(0) + + logging.info("Arc sorting LG") + LG = k2.arc_sort(LG) + + logging.info("Composing H and LG") + # CAUTION: The name of the inner_labels is fixed + # to `tokens`. If you want to change it, please + # also change other places in icefall that are using + # it. + HLG = k2.compose(H, LG, inner_labels="tokens") + + logging.info("Connecting LG") + HLG = k2.connect(HLG) + + logging.info("Arc sorting LG") + HLG = k2.arc_sort(HLG) + logging.info(f"HLG.shape: {HLG.shape}") + + return HLG + + +def main(): + args = get_args() + lang_dir = Path(args.lang_dir) + + if (lang_dir / "HLG.pt").is_file(): + logging.info(f"{lang_dir}/HLG.pt already exists - skipping") + return + + logging.info(f"Processing {lang_dir}") + + HLG = compile_HLG(lang_dir) + logging.info(f"Saving HLG.pt to {lang_dir}") + torch.save(HLG.as_dict(), f"{lang_dir}/HLG.pt") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + main() diff --git a/egs/timit/ASR/local/compute_fbank_musan.py b/egs/timit/ASR/local/compute_fbank_musan.py new file mode 100644 index 000000000..d44524e70 --- /dev/null +++ b/egs/timit/ASR/local/compute_fbank_musan.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file computes fbank features of the musan dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import logging +import os +from pathlib import Path + +import torch +from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer, combine +from lhotse.recipes.utils import read_manifests_if_cached + +from icefall.utils import get_executor + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def compute_fbank_musan(): + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + num_jobs = min(15, os.cpu_count()) + num_mel_bins = 80 + + dataset_parts = ( + "music", + "speech", + "noise", + ) + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, output_dir=src_dir + ) + assert manifests is not None + + musan_cuts_path = output_dir / "cuts_musan.json.gz" + + if musan_cuts_path.is_file(): + logging.info(f"{musan_cuts_path} already exists - skipping") + return + + logging.info("Extracting features for Musan") + + extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins)) + + with get_executor() as ex: # Initialize the executor only once. + # create chunks of Musan with duration 5 - 10 seconds + musan_cuts = ( + CutSet.from_manifests( + recordings=combine( + part["recordings"] for part in manifests.values() + ) + ) + .cut_into_windows(10.0) + .filter(lambda c: c.duration > 5) + .compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/feats_musan", + num_jobs=num_jobs if ex is None else 80, + executor=ex, + storage_type=LilcomHdf5Writer, + ) + ) + musan_cuts.to_json(musan_cuts_path) + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + compute_fbank_musan() diff --git a/egs/timit/ASR/local/compute_fbank_timit.py b/egs/timit/ASR/local/compute_fbank_timit.py new file mode 100644 index 000000000..c59c81ec1 --- /dev/null +++ b/egs/timit/ASR/local/compute_fbank_timit.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file computes fbank features of the LibriSpeech dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import logging +import os +from pathlib import Path + +import torch +from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer +from lhotse.recipes.utils import read_manifests_if_cached + +from icefall.utils import get_executor + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def compute_fbank_timit(): + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + num_jobs = min(15, os.cpu_count()) + num_mel_bins = 80 + + dataset_parts = ( + "TRAIN", + "DEV", + "TEST", + ) + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, output_dir=src_dir + ) + assert manifests is not None + + extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins)) + + with get_executor() as ex: # Initialize the executor only once. + for partition, m in manifests.items(): + if (output_dir / f"cuts_{partition}.json.gz").is_file(): + logging.info(f"{partition} already exists - skipping.") + continue + logging.info(f"Processing {partition}") + cut_set = CutSet.from_manifests( + recordings=m["recordings"], + supervisions=m["supervisions"], + ) + if "train" in partition: + cut_set = ( + cut_set + + cut_set.perturb_speed(0.9) + + cut_set.perturb_speed(1.1) + ) + cut_set = cut_set.compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/feats_{partition}", + # when an executor is specified, make more partitions + num_jobs=num_jobs if ex is None else 80, + executor=ex, + storage_type=LilcomHdf5Writer, + ) + cut_set.to_json(output_dir / f"cuts_{partition}.json.gz") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + compute_fbank_timit() diff --git a/egs/timit/ASR/local/prepare_lang.py b/egs/timit/ASR/local/prepare_lang.py new file mode 100644 index 000000000..80ba015cb --- /dev/null +++ b/egs/timit/ASR/local/prepare_lang.py @@ -0,0 +1,394 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input a lexicon file "data/lang_phone/lexicon.txt" +consisting of words and tokens (i.e., phones) and does the following: + +1. Add disambiguation symbols to the lexicon and generate lexicon_disambig.txt + +2. Generate tokens.txt, the token table mapping a token to a unique integer. + +3. Generate words.txt, the word table mapping a word to a unique integer. + +4. Generate L.pt, in k2 format. It can be loaded by + + d = torch.load("L.pt") + lexicon = k2.Fsa.from_dict(d) + +5. Generate L_disambig.pt, in k2 format. +""" +import argparse +import math +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List, Tuple + +import k2 +import torch + +from icefall.lexicon import read_lexicon, write_lexicon +from icefall.utils import str2bool + +Lexicon = List[Tuple[str, List[str]]] + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-dir", + type=str, + help="""Input and output directory. + It should contain a file lexicon.txt. + Generated files by this script are saved into this directory. + """, + ) + + parser.add_argument( + "--debug", + type=str2bool, + default=False, + help="""True for debugging, which will generate + a visualization of the lexicon FST. + + Caution: If your lexicon contains hundreds of thousands + of lines, please set it to False! + """, + ) + + return parser.parse_args() + + +def write_mapping(filename: str, sym2id: Dict[str, int]) -> None: + """Write a symbol to ID mapping to a file. + + Note: + No need to implement `read_mapping` as it can be done + through :func:`k2.SymbolTable.from_file`. + + Args: + filename: + Filename to save the mapping. + sym2id: + A dict mapping symbols to IDs. + Returns: + Return None. + """ + with open(filename, "w", encoding="utf-8") as f: + for sym, i in sym2id.items(): + f.write(f"{sym} {i}\n") + + +def get_tokens(lexicon: Lexicon) -> List[str]: + """Get tokens from a lexicon. + + Args: + lexicon: + It is the return value of :func:`read_lexicon`. + Returns: + Return a list of unique tokens. + """ + ans = set() + for _, tokens in lexicon: + ans.update(tokens) + #sorted_ans = sorted(list(ans)) + sorted_ans = list(ans) + return sorted_ans + + +def get_words(lexicon: Lexicon) -> List[str]: + """Get words from a lexicon. + + Args: + lexicon: + It is the return value of :func:`read_lexicon`. + Returns: + Return a list of unique words. + """ + ans = set() + for word, _ in lexicon: + ans.add(word) + sorted_ans = sorted(list(ans)) + return sorted_ans + + +def add_disambig_symbols(lexicon: Lexicon) -> Tuple[Lexicon, int]: + """It adds pseudo-token disambiguation symbols #1, #2 and so on + at the ends of tokens to ensure that all pronunciations are different, + and that none is a prefix of another. + + See also add_lex_disambig.pl from kaldi. + + Args: + lexicon: + It is returned by :func:`read_lexicon`. + Returns: + Return a tuple with two elements: + + - The output lexicon with disambiguation symbols + - The ID of the max disambiguation symbol that appears + in the lexicon + """ + + # (1) Work out the count of each token-sequence in the + # lexicon. + count = defaultdict(int) + for _, tokens in lexicon: + count[" ".join(tokens)] += 1 + + # (2) For each left sub-sequence of each token-sequence, note down + # that it exists (for identifying prefixes of longer strings). + issubseq = defaultdict(int) + for _, tokens in lexicon: + tokens = tokens.copy() + tokens.pop() + while tokens: + issubseq[" ".join(tokens)] = 1 + tokens.pop() + + # (3) For each entry in the lexicon: + # if the token sequence is unique and is not a + # prefix of another word, no disambig symbol. + # Else output #1, or #2, #3, ... if the same token-seq + # has already been assigned a disambig symbol. + ans = [] + + # We start with #1 since #0 has its own purpose + first_allowed_disambig = 1 + max_disambig = first_allowed_disambig - 1 + last_used_disambig_symbol_of = defaultdict(int) + + for word, tokens in lexicon: + tokenseq = " ".join(tokens) + assert tokenseq != "" + if issubseq[tokenseq] == 0 and count[tokenseq] == 1: + ans.append((word, tokens)) + continue + + cur_disambig = last_used_disambig_symbol_of[tokenseq] + if cur_disambig == 0: + cur_disambig = first_allowed_disambig + else: + cur_disambig += 1 + + if cur_disambig > max_disambig: + max_disambig = cur_disambig + last_used_disambig_symbol_of[tokenseq] = cur_disambig + tokenseq += f" #{cur_disambig}" + ans.append((word, tokenseq.split())) + return ans, max_disambig + + +def generate_id_map(symbols: List[str]) -> Dict[str, int]: + """Generate ID maps, i.e., map a symbol to a unique ID. + + Args: + symbols: + A list of unique symbols. + Returns: + A dict containing the mapping between symbols and IDs. + """ + return {sym: i for i, sym in enumerate(symbols)} + + +def add_self_loops( + arcs: List[List[Any]], disambig_token: int, disambig_word: int +) -> List[List[Any]]: + """Adds self-loops to states of an FST to propagate disambiguation symbols + through it. They are added on each state with non-epsilon output symbols + on at least one arc out of the state. + + See also fstaddselfloops.pl from Kaldi. One difference is that + Kaldi uses OpenFst style FSTs and it has multiple final states. + This function uses k2 style FSTs and it does not need to add self-loops + to the final state. + + The input label of a self-loop is `disambig_token`, while the output + label is `disambig_word`. + + Args: + arcs: + A list-of-list. The sublist contains + `[src_state, dest_state, label, aux_label, score]` + disambig_token: + It is the token ID of the symbol `#0`. + disambig_word: + It is the word ID of the symbol `#0`. + + Return: + Return new `arcs` containing self-loops. + """ + states_needs_self_loops = set() + for arc in arcs: + src, dst, ilabel, olabel, score = arc + if olabel != 0: + states_needs_self_loops.add(src) + + ans = [] + for s in states_needs_self_loops: + ans.append([s, s, disambig_token, disambig_word, 0]) + + return arcs + ans + + +def lexicon_to_fst( + lexicon: Lexicon, + token2id: Dict[str, int], + word2id: Dict[str, int], + need_self_loops: bool = False, +) -> k2.Fsa: + """Convert a lexicon to an FST (in k2 format) with optional silence at + the beginning and end of each word. + + Args: + lexicon: + The input lexicon. See also :func:`read_lexicon` + token2id: + A dict mapping tokens to IDs. + word2id: + A dict mapping words to IDs. + need_self_loops: + If True, add self-loop to states with non-epsilon output symbols + on at least one arc out of the state. The input label for this + self loop is `token2id["#0"]` and the output label is `word2id["#0"]`. + Returns: + Return an instance of `k2.Fsa` representing the given lexicon. + """ + pronprob = 1.0 + score = -math.log(pronprob) + + loop_state = 0 # words enter and leave from here + next_state = 1 # the next un-allocated state, will be incremented as we go. + arcs = [] + + print('token2id ori: ', token2id) + print('word2id ori: ', word2id) + + assert token2id[""] == 0 + assert word2id[""] == 0 + + eps = 0 + print('token2id new: ', token2id) + print('word2id new: ', word2id) + + print(lexicon) + for word, tokens in lexicon: + assert len(tokens) > 0, f"{word} has no pronunciations" + cur_state = loop_state + + word = word2id[word] + tokens = [token2id[i] for i in tokens] + + for i in range(len(tokens) - 1): + w = word if i == 0 else eps + arcs.append([cur_state, next_state, tokens[i], w, score]) + + cur_state = next_state + next_state += 1 + + # now for the last token of this word + # It has two out-going arcs, one to the loop state, + # the other one to the sil_state. + i = len(tokens) - 1 + w = word if i == 0 else eps + tokens[i] = tokens[i] if i >=0 else eps + arcs.append([cur_state, loop_state, tokens[i], w, score]) + + if need_self_loops: + disambig_token = token2id["#0"] + disambig_word = word2id["#0"] + arcs = add_self_loops( + arcs, + disambig_token=disambig_token, + disambig_word=disambig_word, + ) + + final_state = next_state + arcs.append([loop_state, final_state, -1, -1, 0]) + arcs.append([final_state]) + + arcs = sorted(arcs, key=lambda arc: arc[0]) + arcs = [[str(i) for i in arc] for arc in arcs] + arcs = [" ".join(arc) for arc in arcs] + arcs = "\n".join(arcs) + print(arcs) + fsa = k2.Fsa.from_str(arcs, acceptor=False) + return fsa + + +def main(): + args = get_args() + lang_dir = Path(args.lang_dir) + #out_dir = Path("data/lang_phone") + lexicon_filename = lang_dir / "lexicon.txt" + + lexicon = read_lexicon(lexicon_filename) + tokens = get_tokens(lexicon) + + words = get_words(lexicon) + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + + for i in range(max_disambig + 1): + disambig = f"#{i}" + assert disambig not in tokens + tokens.append(f"#{i}") + + assert "" not in tokens + tokens = [""] + tokens + + assert "" not in words + assert "#0" not in words + assert "" not in words + assert "" not in words + + words = [""] + words + ["#0", "", ""] + + token2id = generate_id_map(tokens) + word2id = generate_id_map(words) + + write_mapping(lang_dir / "tokens.txt", token2id) + write_mapping(lang_dir / "words.txt", word2id) + write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig) + + L = lexicon_to_fst( + lexicon, + token2id=token2id, + word2id=word2id, + ) + + L_disambig = lexicon_to_fst( + lexicon_disambig, + token2id=token2id, + word2id=word2id, + need_self_loops=True, + ) + torch.save(L.as_dict(), lang_dir / "L.pt") + torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt") + + if False: + # Just for debugging, will remove it + L.labels_sym = k2.SymbolTable.from_file(lang_dir / "tokens.txt") + L.aux_labels_sym = k2.SymbolTable.from_file(lang_dir / "words.txt") + L_disambig.labels_sym = L.labels_sym + L_disambig.aux_labels_sym = L.aux_labels_sym + L.draw(out_dir / "L.png", title="L") + L_disambig.draw(lang_dir / "L_disambig.png", title="L_disambig") + + +if __name__ == "__main__": + main() diff --git a/egs/timit/ASR/local/prepare_lexicon.py b/egs/timit/ASR/local/prepare_lexicon.py new file mode 100644 index 000000000..65c1dca44 --- /dev/null +++ b/egs/timit/ASR/local/prepare_lexicon.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input supervisions json dir "data/manifests" +consisting of supervisions_TRAIN.json and does the following: + +1. Generate lexicon.txt. + +""" +import argparse +import json +import logging +from pathlib import Path + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--manifests-dir", + type=str, + help="""Input directory. + """, + ) + parser.add_argument( + "--lang-dir", + type=str, + help="""Output directory. + """, + ) + + return parser.parse_args() + + +def prepare_lexicon(manifests_dir: str, lang_dir: str): + """ + Args: + manifests_dir: + The manifests directory, e.g., data/manifests. + lang_dir: + The language directory, e.g., data/lang_phone. + + Return: + The lexicon.txt file and the train.text in lang_dir. + """ + phones = [] + + supervisions_train = Path(manifests_dir) / "supervisions_TRAIN.json" + lexicon = Path(lang_dir) / "lexicon.txt" + + logging.info(f"Loading {supervisions_train}!") + with open(supervisions_train, 'r') as load_f: + load_dicts = json.load(load_f) + for load_dict in load_dicts: + idx = load_dict['id'] + text = load_dict['text'] + phones_list = list(filter(None, text.split(' '))) + + for phone in phones_list: + if phone not in phones: + phones.append(phone) + + with open(lexicon, 'w') as f: + for phone in sorted(phones): + f.write(str(phone) + " " + str(phone)) + f.write('\n') + f.write(" ") + f.write('\n') + + return lexicon + + +def main(): + args = get_args() + manifests_dir = Path(args.manifests_dir) + lang_dir = Path(args.lang_dir) + + logging.info(f"Generating lexicon.txt and train.text") + + lexicon_file = prepare_lexicon(manifests_dir, lang_dir) + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + main() + diff --git a/egs/timit/ASR/prepare.sh b/egs/timit/ASR/prepare.sh new file mode 100644 index 000000000..ddf860c02 --- /dev/null +++ b/egs/timit/ASR/prepare.sh @@ -0,0 +1,151 @@ +#!/usr/bin/env bash + +set -eou pipefail + +num_phones=39 +# Here we use num_phones=39 for modeling + +nj=15 +stage=-1 +stop_stage=100 + +# We assume dl_dir (download dir) contains the following +# directories and files. If not, they will be downloaded +# by this script automatically. +# +# - $dl_dir/timit +# You can find data, train_data.csv, test_data.csv, etc, inside it. +# You can download them from https://data.deepai.org/timit.zip +# +# - $dl_dir/lm +# This directory contains the language model(LM) downloaded from +# https://huggingface.co/luomingshuang/timit_lm, and the LM is based +# on 39 phones. +# +# - lm_tgmed.arpa +# +# - $dl_dir/musan +# This directory contains the following directories downloaded from +# http://www.openslr.org/17/ +# +# - music +# - noise +# - speech +dl_dir=$PWD/download +splits_dir=$PWD/splits_dir + +. shared/parse_options.sh || exit 1 + +# All files generated by this script are saved in "data". +# You can safely remove "data" and rerun this script to regenerate it. +mkdir -p data + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +log "dl_dir: $dl_dir" + +if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then + log "Stage -1: Download LM" + # We assume that you have installed the git-lfs, if not, you could install it + # using: `sudo apt-get install git-lfs && git-lfs install` + [ ! -e $dl_dir/lm ] && mkdir -p $dl_dir/lm + git clone https://huggingface.co/luomingshuang/timit_lm $dl_dir/lm +fi + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + log "Stage 0: Download data" + + # If you have pre-downloaded it to /path/to/timit, + # you can create a symlink + # + # ln -sfv /path/to/timit $dl_dir/timit + # + if [ ! -d $dl_dir/timit ]; then + lhotse download timit $dl_dir + fi + + # If you have pre-downloaded it to /path/to/musan, + # you can create a symlink + # + # ln -sfv /path/to/musan $dl_dir/ + # + if [ ! -d $dl_dir/musan ]; then + lhotse download musan $dl_dir + fi +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Prepare timit manifest" + # We assume that you have downloaded the timit corpus + # to $dl_dir/timit + mkdir -p data/manifests + lhotse prepare timit -p $num_phones -j $nj $dl_dir/timit/data $splits_dir data/manifests +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "Stage 2: Prepare musan manifest" + # We assume that you have downloaded the musan corpus + # to data/musan + mkdir -p data/manifests + lhotse prepare musan $dl_dir/musan data/manifests +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + log "Stage 3: Compute fbank for librispeech" + mkdir -p data/fbank + ./local/compute_fbank_timit.py +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + log "Stage 4: Compute fbank for musan" + mkdir -p data/fbank + ./local/compute_fbank_musan.py +fi + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + log "Stage 5: Prepare phone based lang" + lang_dir=data/lang_phone + mkdir -p $lang_dir + + ./local/prepare_lexicon.py \ + --manifests-dir data/manifests \ + --lang-dir $lang_dir + + if [ ! -f $lang_dir/L_disambig.pt ]; then + ./local/prepare_lang.py --lang-dir $lang_dir + fi +fi + +if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then + log "Stage 6: Prepare G" + # We assume you have install kaldilm, if not, please install + # it using: pip install kaldilm + + mkdir -p data/lm + if [ ! -f data/lm/G_3_gram.fst.txt ]; then + # It is used in building HLG + python3 -m kaldilm \ + --read-symbol-table="data/lang_phone/words.txt" \ + --disambig-symbol='#0' \ + --max-order=3 \ + $dl_dir/lm/lm_tgmed.arpa > data/lm/G_3_gram.fst.txt + fi + + if [ ! -f data/lm/G_4_gram.fst.txt ]; then + # It is used for LM rescoring + python3 -m kaldilm \ + --read-symbol-table="data/lang_phone/words.txt" \ + --disambig-symbol='#0' \ + --max-order=4 \ + $dl_dir/lm/lm_tgmed.arpa > data/lm/G_4_gram.fst.txt + fi +fi + +if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then + log "Stage 7: Compile HLG" + ./local/compile_hlg.py --lang-dir data/lang_phone +fi diff --git a/egs/timit/ASR/shared/parse_options.sh b/egs/timit/ASR/shared/parse_options.sh new file mode 100644 index 000000000..71fb9e5ea --- /dev/null +++ b/egs/timit/ASR/shared/parse_options.sh @@ -0,0 +1,97 @@ +#!/usr/bin/env bash + +# Copyright 2012 Johns Hopkins University (Author: Daniel Povey); +# Arnab Ghoshal, Karel Vesely + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED +# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE, +# MERCHANTABLITY OR NON-INFRINGEMENT. +# See the Apache 2 License for the specific language governing permissions and +# limitations under the License. + + +# Parse command-line options. +# To be sourced by another script (as in ". parse_options.sh"). +# Option format is: --option-name arg +# and shell variable "option_name" gets set to value "arg." +# The exception is --help, which takes no arguments, but prints the +# $help_message variable (if defined). + + +### +### The --config file options have lower priority to command line +### options, so we need to import them first... +### + +# Now import all the configs specified by command-line, in left-to-right order +for ((argpos=1; argpos<$#; argpos++)); do + if [ "${!argpos}" == "--config" ]; then + argpos_plus1=$((argpos+1)) + config=${!argpos_plus1} + [ ! -r $config ] && echo "$0: missing config '$config'" && exit 1 + . $config # source the config file. + fi +done + + +### +### Now we process the command line options +### +while true; do + [ -z "${1:-}" ] && break; # break if there are no arguments + case "$1" in + # If the enclosing script is called with --help option, print the help + # message and exit. Scripts should put help messages in $help_message + --help|-h) if [ -z "$help_message" ]; then echo "No help found." 1>&2; + else printf "$help_message\n" 1>&2 ; fi; + exit 0 ;; + --*=*) echo "$0: options to scripts must be of the form --name value, got '$1'" + exit 1 ;; + # If the first command-line argument begins with "--" (e.g. --foo-bar), + # then work out the variable name as $name, which will equal "foo_bar". + --*) name=`echo "$1" | sed s/^--// | sed s/-/_/g`; + # Next we test whether the variable in question is undefned-- if so it's + # an invalid option and we die. Note: $0 evaluates to the name of the + # enclosing script. + # The test [ -z ${foo_bar+xxx} ] will return true if the variable foo_bar + # is undefined. We then have to wrap this test inside "eval" because + # foo_bar is itself inside a variable ($name). + eval '[ -z "${'$name'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1; + + oldval="`eval echo \\$$name`"; + # Work out whether we seem to be expecting a Boolean argument. + if [ "$oldval" == "true" ] || [ "$oldval" == "false" ]; then + was_bool=true; + else + was_bool=false; + fi + + # Set the variable to the right value-- the escaped quotes make it work if + # the option had spaces, like --cmd "queue.pl -sync y" + eval $name=\"$2\"; + + # Check that Boolean-valued arguments are really Boolean. + if $was_bool && [[ "$2" != "true" && "$2" != "false" ]]; then + echo "$0: expected \"true\" or \"false\": $1 $2" 1>&2 + exit 1; + fi + shift 2; + ;; + *) break; + esac +done + + +# Check for an empty argument to the --cmd option, which can easily occur as a +# result of scripting errors. +[ ! -z "${cmd+xxx}" ] && [ -z "$cmd" ] && echo "$0: empty argument to --cmd option" 1>&2 && exit 1; + + +true; # so this script returns exit code 0. diff --git a/egs/timit/ASR/splits_dir/dev_samples.txt b/egs/timit/ASR/splits_dir/dev_samples.txt new file mode 100644 index 000000000..107da51a2 --- /dev/null +++ b/egs/timit/ASR/splits_dir/dev_samples.txt @@ -0,0 +1,400 @@ +FADG0_SI1279 TEST/DR4/FADG0/SI1279.WAV +FADG0_SI1909 TEST/DR4/FADG0/SI1909.WAV +FADG0_SI649 TEST/DR4/FADG0/SI649.WAV +FADG0_SX109 TEST/DR4/FADG0/SX109.WAV +FADG0_SX19 TEST/DR4/FADG0/SX19.WAV +FADG0_SX199 TEST/DR4/FADG0/SX199.WAV +FADG0_SX289 TEST/DR4/FADG0/SX289.WAV +FADG0_SX379 TEST/DR4/FADG0/SX379.WAV +FAKS0_SI1573 TEST/DR1/FAKS0/SI1573.WAV +FAKS0_SI2203 TEST/DR1/FAKS0/SI2203.WAV +FAKS0_SI943 TEST/DR1/FAKS0/SI943.WAV +FAKS0_SX133 TEST/DR1/FAKS0/SX133.WAV +FAKS0_SX223 TEST/DR1/FAKS0/SX223.WAV +FAKS0_SX313 TEST/DR1/FAKS0/SX313.WAV +FAKS0_SX403 TEST/DR1/FAKS0/SX403.WAV +FAKS0_SX43 TEST/DR1/FAKS0/SX43.WAV +FCAL1_SI1403 TEST/DR5/FCAL1/SI1403.WAV +FCAL1_SI2033 TEST/DR5/FCAL1/SI2033.WAV +FCAL1_SI773 TEST/DR5/FCAL1/SI773.WAV +FCAL1_SX143 TEST/DR5/FCAL1/SX143.WAV +FCAL1_SX233 TEST/DR5/FCAL1/SX233.WAV +FCAL1_SX323 TEST/DR5/FCAL1/SX323.WAV +FCAL1_SX413 TEST/DR5/FCAL1/SX413.WAV +FCAL1_SX53 TEST/DR5/FCAL1/SX53.WAV +FCMH0_SI1454 TEST/DR3/FCMH0/SI1454.WAV +FCMH0_SI2084 TEST/DR3/FCMH0/SI2084.WAV +FCMH0_SI824 TEST/DR3/FCMH0/SI824.WAV +FCMH0_SX104 TEST/DR3/FCMH0/SX104.WAV +FCMH0_SX14 TEST/DR3/FCMH0/SX14.WAV +FCMH0_SX194 TEST/DR3/FCMH0/SX194.WAV +FCMH0_SX284 TEST/DR3/FCMH0/SX284.WAV +FCMH0_SX374 TEST/DR3/FCMH0/SX374.WAV +FDAC1_SI1474 TEST/DR1/FDAC1/SI1474.WAV +FDAC1_SI2104 TEST/DR1/FDAC1/SI2104.WAV +FDAC1_SI844 TEST/DR1/FDAC1/SI844.WAV +FDAC1_SX124 TEST/DR1/FDAC1/SX124.WAV +FDAC1_SX214 TEST/DR1/FDAC1/SX214.WAV +FDAC1_SX304 TEST/DR1/FDAC1/SX304.WAV +FDAC1_SX34 TEST/DR1/FDAC1/SX34.WAV +FDAC1_SX394 TEST/DR1/FDAC1/SX394.WAV +FDMS0_SI1218 TEST/DR4/FDMS0/SI1218.WAV +FDMS0_SI1502 TEST/DR4/FDMS0/SI1502.WAV +FDMS0_SI1848 TEST/DR4/FDMS0/SI1848.WAV +FDMS0_SX138 TEST/DR4/FDMS0/SX138.WAV +FDMS0_SX228 TEST/DR4/FDMS0/SX228.WAV +FDMS0_SX318 TEST/DR4/FDMS0/SX318.WAV +FDMS0_SX408 TEST/DR4/FDMS0/SX408.WAV +FDMS0_SX48 TEST/DR4/FDMS0/SX48.WAV +FDRW0_SI1283 TEST/DR6/FDRW0/SI1283.WAV +FDRW0_SI1423 TEST/DR6/FDRW0/SI1423.WAV +FDRW0_SI653 TEST/DR6/FDRW0/SI653.WAV +FDRW0_SX113 TEST/DR6/FDRW0/SX113.WAV +FDRW0_SX203 TEST/DR6/FDRW0/SX203.WAV +FDRW0_SX23 TEST/DR6/FDRW0/SX23.WAV +FDRW0_SX293 TEST/DR6/FDRW0/SX293.WAV +FDRW0_SX383 TEST/DR6/FDRW0/SX383.WAV +FEDW0_SI1084 TEST/DR4/FEDW0/SI1084.WAV +FEDW0_SI1653 TEST/DR4/FEDW0/SI1653.WAV +FEDW0_SI1714 TEST/DR4/FEDW0/SI1714.WAV +FEDW0_SX184 TEST/DR4/FEDW0/SX184.WAV +FEDW0_SX274 TEST/DR4/FEDW0/SX274.WAV +FEDW0_SX364 TEST/DR4/FEDW0/SX364.WAV +FEDW0_SX4 TEST/DR4/FEDW0/SX4.WAV +FEDW0_SX94 TEST/DR4/FEDW0/SX94.WAV +FGJD0_SI1179 TEST/DR4/FGJD0/SI1179.WAV +FGJD0_SI549 TEST/DR4/FGJD0/SI549.WAV +FGJD0_SI818 TEST/DR4/FGJD0/SI818.WAV +FGJD0_SX189 TEST/DR4/FGJD0/SX189.WAV +FGJD0_SX279 TEST/DR4/FGJD0/SX279.WAV +FGJD0_SX369 TEST/DR4/FGJD0/SX369.WAV +FGJD0_SX9 TEST/DR4/FGJD0/SX9.WAV +FGJD0_SX99 TEST/DR4/FGJD0/SX99.WAV +FJEM0_SI1264 TEST/DR1/FJEM0/SI1264.WAV +FJEM0_SI1894 TEST/DR1/FJEM0/SI1894.WAV +FJEM0_SI634 TEST/DR1/FJEM0/SI634.WAV +FJEM0_SX184 TEST/DR1/FJEM0/SX184.WAV +FJEM0_SX274 TEST/DR1/FJEM0/SX274.WAV +FJEM0_SX364 TEST/DR1/FJEM0/SX364.WAV +FJEM0_SX4 TEST/DR1/FJEM0/SX4.WAV +FJEM0_SX94 TEST/DR1/FJEM0/SX94.WAV +FJMG0_SI1181 TEST/DR4/FJMG0/SI1181.WAV +FJMG0_SI1811 TEST/DR4/FJMG0/SI1811.WAV +FJMG0_SI551 TEST/DR4/FJMG0/SI551.WAV +FJMG0_SX101 TEST/DR4/FJMG0/SX101.WAV +FJMG0_SX11 TEST/DR4/FJMG0/SX11.WAV +FJMG0_SX191 TEST/DR4/FJMG0/SX191.WAV +FJMG0_SX281 TEST/DR4/FJMG0/SX281.WAV +FJMG0_SX371 TEST/DR4/FJMG0/SX371.WAV +FJSJ0_SI1484 TEST/DR8/FJSJ0/SI1484.WAV +FJSJ0_SI2114 TEST/DR8/FJSJ0/SI2114.WAV +FJSJ0_SI854 TEST/DR8/FJSJ0/SI854.WAV +FJSJ0_SX134 TEST/DR8/FJSJ0/SX134.WAV +FJSJ0_SX224 TEST/DR8/FJSJ0/SX224.WAV +FJSJ0_SX314 TEST/DR8/FJSJ0/SX314.WAV +FJSJ0_SX404 TEST/DR8/FJSJ0/SX404.WAV +FJSJ0_SX44 TEST/DR8/FJSJ0/SX44.WAV +FKMS0_SI1490 TEST/DR3/FKMS0/SI1490.WAV +FKMS0_SI2120 TEST/DR3/FKMS0/SI2120.WAV +FKMS0_SI860 TEST/DR3/FKMS0/SI860.WAV +FKMS0_SX140 TEST/DR3/FKMS0/SX140.WAV +FKMS0_SX230 TEST/DR3/FKMS0/SX230.WAV +FKMS0_SX320 TEST/DR3/FKMS0/SX320.WAV +FKMS0_SX410 TEST/DR3/FKMS0/SX410.WAV +FKMS0_SX50 TEST/DR3/FKMS0/SX50.WAV +FMAH0_SI1289 TEST/DR5/FMAH0/SI1289.WAV +FMAH0_SI1919 TEST/DR5/FMAH0/SI1919.WAV +FMAH0_SI659 TEST/DR5/FMAH0/SI659.WAV +FMAH0_SX119 TEST/DR5/FMAH0/SX119.WAV +FMAH0_SX209 TEST/DR5/FMAH0/SX209.WAV +FMAH0_SX29 TEST/DR5/FMAH0/SX29.WAV +FMAH0_SX299 TEST/DR5/FMAH0/SX299.WAV +FMAH0_SX389 TEST/DR5/FMAH0/SX389.WAV +FMML0_SI1040 TEST/DR7/FMML0/SI1040.WAV +FMML0_SI1670 TEST/DR7/FMML0/SI1670.WAV +FMML0_SI2300 TEST/DR7/FMML0/SI2300.WAV +FMML0_SX140 TEST/DR7/FMML0/SX140.WAV +FMML0_SX230 TEST/DR7/FMML0/SX230.WAV +FMML0_SX320 TEST/DR7/FMML0/SX320.WAV +FMML0_SX410 TEST/DR7/FMML0/SX410.WAV +FMML0_SX50 TEST/DR7/FMML0/SX50.WAV +FNMR0_SI1399 TEST/DR4/FNMR0/SI1399.WAV +FNMR0_SI2029 TEST/DR4/FNMR0/SI2029.WAV +FNMR0_SI769 TEST/DR4/FNMR0/SI769.WAV +FNMR0_SX139 TEST/DR4/FNMR0/SX139.WAV +FNMR0_SX229 TEST/DR4/FNMR0/SX229.WAV +FNMR0_SX319 TEST/DR4/FNMR0/SX319.WAV +FNMR0_SX409 TEST/DR4/FNMR0/SX409.WAV +FNMR0_SX49 TEST/DR4/FNMR0/SX49.WAV +FREW0_SI1030 TEST/DR4/FREW0/SI1030.WAV +FREW0_SI1280 TEST/DR4/FREW0/SI1280.WAV +FREW0_SI1910 TEST/DR4/FREW0/SI1910.WAV +FREW0_SX110 TEST/DR4/FREW0/SX110.WAV +FREW0_SX20 TEST/DR4/FREW0/SX20.WAV +FREW0_SX200 TEST/DR4/FREW0/SX200.WAV +FREW0_SX290 TEST/DR4/FREW0/SX290.WAV +FREW0_SX380 TEST/DR4/FREW0/SX380.WAV +FSEM0_SI1198 TEST/DR4/FSEM0/SI1198.WAV +FSEM0_SI1828 TEST/DR4/FSEM0/SI1828.WAV +FSEM0_SI568 TEST/DR4/FSEM0/SI568.WAV +FSEM0_SX118 TEST/DR4/FSEM0/SX118.WAV +FSEM0_SX208 TEST/DR4/FSEM0/SX208.WAV +FSEM0_SX28 TEST/DR4/FSEM0/SX28.WAV +FSEM0_SX298 TEST/DR4/FSEM0/SX298.WAV +FSEM0_SX388 TEST/DR4/FSEM0/SX388.WAV +MAJC0_SI1946 TEST/DR8/MAJC0/SI1946.WAV +MAJC0_SI2095 TEST/DR8/MAJC0/SI2095.WAV +MAJC0_SI835 TEST/DR8/MAJC0/SI835.WAV +MAJC0_SX115 TEST/DR8/MAJC0/SX115.WAV +MAJC0_SX205 TEST/DR8/MAJC0/SX205.WAV +MAJC0_SX25 TEST/DR8/MAJC0/SX25.WAV +MAJC0_SX295 TEST/DR8/MAJC0/SX295.WAV +MAJC0_SX385 TEST/DR8/MAJC0/SX385.WAV +MBDG0_SI1463 TEST/DR3/MBDG0/SI1463.WAV +MBDG0_SI2093 TEST/DR3/MBDG0/SI2093.WAV +MBDG0_SI833 TEST/DR3/MBDG0/SI833.WAV +MBDG0_SX113 TEST/DR3/MBDG0/SX113.WAV +MBDG0_SX203 TEST/DR3/MBDG0/SX203.WAV +MBDG0_SX23 TEST/DR3/MBDG0/SX23.WAV +MBDG0_SX293 TEST/DR3/MBDG0/SX293.WAV +MBDG0_SX383 TEST/DR3/MBDG0/SX383.WAV +MBNS0_SI1220 TEST/DR4/MBNS0/SI1220.WAV +MBNS0_SI1850 TEST/DR4/MBNS0/SI1850.WAV +MBNS0_SI590 TEST/DR4/MBNS0/SI590.WAV +MBNS0_SX140 TEST/DR4/MBNS0/SX140.WAV +MBNS0_SX230 TEST/DR4/MBNS0/SX230.WAV +MBNS0_SX320 TEST/DR4/MBNS0/SX320.WAV +MBNS0_SX410 TEST/DR4/MBNS0/SX410.WAV +MBNS0_SX50 TEST/DR4/MBNS0/SX50.WAV +MBWM0_SI1304 TEST/DR3/MBWM0/SI1304.WAV +MBWM0_SI1934 TEST/DR3/MBWM0/SI1934.WAV +MBWM0_SI674 TEST/DR3/MBWM0/SI674.WAV +MBWM0_SX134 TEST/DR3/MBWM0/SX134.WAV +MBWM0_SX224 TEST/DR3/MBWM0/SX224.WAV +MBWM0_SX314 TEST/DR3/MBWM0/SX314.WAV +MBWM0_SX404 TEST/DR3/MBWM0/SX404.WAV +MBWM0_SX44 TEST/DR3/MBWM0/SX44.WAV +MCSH0_SI1549 TEST/DR3/MCSH0/SI1549.WAV +MCSH0_SI2179 TEST/DR3/MCSH0/SI2179.WAV +MCSH0_SI919 TEST/DR3/MCSH0/SI919.WAV +MCSH0_SX109 TEST/DR3/MCSH0/SX109.WAV +MCSH0_SX19 TEST/DR3/MCSH0/SX19.WAV +MCSH0_SX199 TEST/DR3/MCSH0/SX199.WAV +MCSH0_SX289 TEST/DR3/MCSH0/SX289.WAV +MCSH0_SX379 TEST/DR3/MCSH0/SX379.WAV +MDLF0_SI1583 TEST/DR7/MDLF0/SI1583.WAV +MDLF0_SI2213 TEST/DR7/MDLF0/SI2213.WAV +MDLF0_SI953 TEST/DR7/MDLF0/SI953.WAV +MDLF0_SX143 TEST/DR7/MDLF0/SX143.WAV +MDLF0_SX233 TEST/DR7/MDLF0/SX233.WAV +MDLF0_SX323 TEST/DR7/MDLF0/SX323.WAV +MDLF0_SX413 TEST/DR7/MDLF0/SX413.WAV +MDLF0_SX53 TEST/DR7/MDLF0/SX53.WAV +MDLS0_SI1628 TEST/DR4/MDLS0/SI1628.WAV +MDLS0_SI2258 TEST/DR4/MDLS0/SI2258.WAV +MDLS0_SI998 TEST/DR4/MDLS0/SI998.WAV +MDLS0_SX188 TEST/DR4/MDLS0/SX188.WAV +MDLS0_SX278 TEST/DR4/MDLS0/SX278.WAV +MDLS0_SX368 TEST/DR4/MDLS0/SX368.WAV +MDLS0_SX8 TEST/DR4/MDLS0/SX8.WAV +MDLS0_SX98 TEST/DR4/MDLS0/SX98.WAV +MDVC0_SI2174 TEST/DR7/MDVC0/SI2174.WAV +MDVC0_SI2196 TEST/DR7/MDVC0/SI2196.WAV +MDVC0_SI936 TEST/DR7/MDVC0/SI936.WAV +MDVC0_SX126 TEST/DR7/MDVC0/SX126.WAV +MDVC0_SX216 TEST/DR7/MDVC0/SX216.WAV +MDVC0_SX306 TEST/DR7/MDVC0/SX306.WAV +MDVC0_SX36 TEST/DR7/MDVC0/SX36.WAV +MDVC0_SX396 TEST/DR7/MDVC0/SX396.WAV +MERS0_SI1019 TEST/DR7/MERS0/SI1019.WAV +MERS0_SI1649 TEST/DR7/MERS0/SI1649.WAV +MERS0_SI497 TEST/DR7/MERS0/SI497.WAV +MERS0_SX119 TEST/DR7/MERS0/SX119.WAV +MERS0_SX209 TEST/DR7/MERS0/SX209.WAV +MERS0_SX29 TEST/DR7/MERS0/SX29.WAV +MERS0_SX299 TEST/DR7/MERS0/SX299.WAV +MERS0_SX389 TEST/DR7/MERS0/SX389.WAV +MGJF0_SI1901 TEST/DR3/MGJF0/SI1901.WAV +MGJF0_SI641 TEST/DR3/MGJF0/SI641.WAV +MGJF0_SI776 TEST/DR3/MGJF0/SI776.WAV +MGJF0_SX101 TEST/DR3/MGJF0/SX101.WAV +MGJF0_SX11 TEST/DR3/MGJF0/SX11.WAV +MGJF0_SX191 TEST/DR3/MGJF0/SX191.WAV +MGJF0_SX281 TEST/DR3/MGJF0/SX281.WAV +MGJF0_SX371 TEST/DR3/MGJF0/SX371.WAV +MGLB0_SI1534 TEST/DR3/MGLB0/SI1534.WAV +MGLB0_SI2164 TEST/DR3/MGLB0/SI2164.WAV +MGLB0_SI904 TEST/DR3/MGLB0/SI904.WAV +MGLB0_SX184 TEST/DR3/MGLB0/SX184.WAV +MGLB0_SX274 TEST/DR3/MGLB0/SX274.WAV +MGLB0_SX364 TEST/DR3/MGLB0/SX364.WAV +MGLB0_SX4 TEST/DR3/MGLB0/SX4.WAV +MGLB0_SX94 TEST/DR3/MGLB0/SX94.WAV +MGWT0_SI1539 TEST/DR2/MGWT0/SI1539.WAV +MGWT0_SI2169 TEST/DR2/MGWT0/SI2169.WAV +MGWT0_SI909 TEST/DR2/MGWT0/SI909.WAV +MGWT0_SX189 TEST/DR2/MGWT0/SX189.WAV +MGWT0_SX279 TEST/DR2/MGWT0/SX279.WAV +MGWT0_SX369 TEST/DR2/MGWT0/SX369.WAV +MGWT0_SX9 TEST/DR2/MGWT0/SX9.WAV +MGWT0_SX99 TEST/DR2/MGWT0/SX99.WAV +MJAR0_SI1988 TEST/DR2/MJAR0/SI1988.WAV +MJAR0_SI2247 TEST/DR2/MJAR0/SI2247.WAV +MJAR0_SI728 TEST/DR2/MJAR0/SI728.WAV +MJAR0_SX188 TEST/DR2/MJAR0/SX188.WAV +MJAR0_SX278 TEST/DR2/MJAR0/SX278.WAV +MJAR0_SX368 TEST/DR2/MJAR0/SX368.WAV +MJAR0_SX8 TEST/DR2/MJAR0/SX8.WAV +MJAR0_SX98 TEST/DR2/MJAR0/SX98.WAV +MJFC0_SI1033 TEST/DR6/MJFC0/SI1033.WAV +MJFC0_SI1663 TEST/DR6/MJFC0/SI1663.WAV +MJFC0_SI2293 TEST/DR6/MJFC0/SI2293.WAV +MJFC0_SX133 TEST/DR6/MJFC0/SX133.WAV +MJFC0_SX223 TEST/DR6/MJFC0/SX223.WAV +MJFC0_SX313 TEST/DR6/MJFC0/SX313.WAV +MJFC0_SX403 TEST/DR6/MJFC0/SX403.WAV +MJFC0_SX43 TEST/DR6/MJFC0/SX43.WAV +MJSW0_SI1010 TEST/DR1/MJSW0/SI1010.WAV +MJSW0_SI1640 TEST/DR1/MJSW0/SI1640.WAV +MJSW0_SI2270 TEST/DR1/MJSW0/SI2270.WAV +MJSW0_SX110 TEST/DR1/MJSW0/SX110.WAV +MJSW0_SX20 TEST/DR1/MJSW0/SX20.WAV +MJSW0_SX200 TEST/DR1/MJSW0/SX200.WAV +MJSW0_SX290 TEST/DR1/MJSW0/SX290.WAV +MJSW0_SX380 TEST/DR1/MJSW0/SX380.WAV +MMDB1_SI1625 TEST/DR2/MMDB1/SI1625.WAV +MMDB1_SI2255 TEST/DR2/MMDB1/SI2255.WAV +MMDB1_SI995 TEST/DR2/MMDB1/SI995.WAV +MMDB1_SX185 TEST/DR2/MMDB1/SX185.WAV +MMDB1_SX275 TEST/DR2/MMDB1/SX275.WAV +MMDB1_SX365 TEST/DR2/MMDB1/SX365.WAV +MMDB1_SX5 TEST/DR2/MMDB1/SX5.WAV +MMDB1_SX95 TEST/DR2/MMDB1/SX95.WAV +MMDM2_SI1452 TEST/DR2/MMDM2/SI1452.WAV +MMDM2_SI1555 TEST/DR2/MMDM2/SI1555.WAV +MMDM2_SI2082 TEST/DR2/MMDM2/SI2082.WAV +MMDM2_SX102 TEST/DR2/MMDM2/SX102.WAV +MMDM2_SX12 TEST/DR2/MMDM2/SX12.WAV +MMDM2_SX192 TEST/DR2/MMDM2/SX192.WAV +MMDM2_SX282 TEST/DR2/MMDM2/SX282.WAV +MMDM2_SX372 TEST/DR2/MMDM2/SX372.WAV +MMJR0_SI1648 TEST/DR3/MMJR0/SI1648.WAV +MMJR0_SI2166 TEST/DR3/MMJR0/SI2166.WAV +MMJR0_SI2278 TEST/DR3/MMJR0/SI2278.WAV +MMJR0_SX118 TEST/DR3/MMJR0/SX118.WAV +MMJR0_SX208 TEST/DR3/MMJR0/SX208.WAV +MMJR0_SX28 TEST/DR3/MMJR0/SX28.WAV +MMJR0_SX298 TEST/DR3/MMJR0/SX298.WAV +MMJR0_SX388 TEST/DR3/MMJR0/SX388.WAV +MMWH0_SI1089 TEST/DR3/MMWH0/SI1089.WAV +MMWH0_SI1301 TEST/DR3/MMWH0/SI1301.WAV +MMWH0_SI459 TEST/DR3/MMWH0/SI459.WAV +MMWH0_SX189 TEST/DR3/MMWH0/SX189.WAV +MMWH0_SX279 TEST/DR3/MMWH0/SX279.WAV +MMWH0_SX369 TEST/DR3/MMWH0/SX369.WAV +MMWH0_SX9 TEST/DR3/MMWH0/SX9.WAV +MMWH0_SX99 TEST/DR3/MMWH0/SX99.WAV +MPDF0_SI1542 TEST/DR2/MPDF0/SI1542.WAV +MPDF0_SI2172 TEST/DR2/MPDF0/SI2172.WAV +MPDF0_SI912 TEST/DR2/MPDF0/SI912.WAV +MPDF0_SX102 TEST/DR2/MPDF0/SX102.WAV +MPDF0_SX12 TEST/DR2/MPDF0/SX12.WAV +MPDF0_SX192 TEST/DR2/MPDF0/SX192.WAV +MPDF0_SX282 TEST/DR2/MPDF0/SX282.WAV +MPDF0_SX372 TEST/DR2/MPDF0/SX372.WAV +MRCS0_SI1223 TEST/DR7/MRCS0/SI1223.WAV +MRCS0_SI1853 TEST/DR7/MRCS0/SI1853.WAV +MRCS0_SI593 TEST/DR7/MRCS0/SI593.WAV +MRCS0_SX143 TEST/DR7/MRCS0/SX143.WAV +MRCS0_SX233 TEST/DR7/MRCS0/SX233.WAV +MRCS0_SX323 TEST/DR7/MRCS0/SX323.WAV +MRCS0_SX413 TEST/DR7/MRCS0/SX413.WAV +MRCS0_SX53 TEST/DR7/MRCS0/SX53.WAV +MREB0_SI1375 TEST/DR1/MREB0/SI1375.WAV +MREB0_SI2005 TEST/DR1/MREB0/SI2005.WAV +MREB0_SI745 TEST/DR1/MREB0/SI745.WAV +MREB0_SX115 TEST/DR1/MREB0/SX115.WAV +MREB0_SX205 TEST/DR1/MREB0/SX205.WAV +MREB0_SX25 TEST/DR1/MREB0/SX25.WAV +MREB0_SX295 TEST/DR1/MREB0/SX295.WAV +MREB0_SX385 TEST/DR1/MREB0/SX385.WAV +MRJM4_SI1489 TEST/DR7/MRJM4/SI1489.WAV +MRJM4_SI2119 TEST/DR7/MRJM4/SI2119.WAV +MRJM4_SI859 TEST/DR7/MRJM4/SI859.WAV +MRJM4_SX139 TEST/DR7/MRJM4/SX139.WAV +MRJM4_SX229 TEST/DR7/MRJM4/SX229.WAV +MRJM4_SX319 TEST/DR7/MRJM4/SX319.WAV +MRJM4_SX409 TEST/DR7/MRJM4/SX409.WAV +MRJM4_SX49 TEST/DR7/MRJM4/SX49.WAV +MRJR0_SI1182 TEST/DR6/MRJR0/SI1182.WAV +MRJR0_SI1812 TEST/DR6/MRJR0/SI1812.WAV +MRJR0_SI2313 TEST/DR6/MRJR0/SI2313.WAV +MRJR0_SX102 TEST/DR6/MRJR0/SX102.WAV +MRJR0_SX12 TEST/DR6/MRJR0/SX12.WAV +MRJR0_SX192 TEST/DR6/MRJR0/SX192.WAV +MRJR0_SX282 TEST/DR6/MRJR0/SX282.WAV +MRJR0_SX372 TEST/DR6/MRJR0/SX372.WAV +MROA0_SI1307 TEST/DR4/MROA0/SI1307.WAV +MROA0_SI1970 TEST/DR4/MROA0/SI1970.WAV +MROA0_SI677 TEST/DR4/MROA0/SI677.WAV +MROA0_SX137 TEST/DR4/MROA0/SX137.WAV +MROA0_SX227 TEST/DR4/MROA0/SX227.WAV +MROA0_SX317 TEST/DR4/MROA0/SX317.WAV +MROA0_SX407 TEST/DR4/MROA0/SX407.WAV +MROA0_SX47 TEST/DR4/MROA0/SX47.WAV +MRTK0_SI1093 TEST/DR3/MRTK0/SI1093.WAV +MRTK0_SI1723 TEST/DR3/MRTK0/SI1723.WAV +MRTK0_SI1750 TEST/DR3/MRTK0/SI1750.WAV +MRTK0_SX103 TEST/DR3/MRTK0/SX103.WAV +MRTK0_SX13 TEST/DR3/MRTK0/SX13.WAV +MRTK0_SX193 TEST/DR3/MRTK0/SX193.WAV +MRTK0_SX283 TEST/DR3/MRTK0/SX283.WAV +MRTK0_SX373 TEST/DR3/MRTK0/SX373.WAV +MRWS1_SI1130 TEST/DR5/MRWS1/SI1130.WAV +MRWS1_SI1496 TEST/DR5/MRWS1/SI1496.WAV +MRWS1_SI500 TEST/DR5/MRWS1/SI500.WAV +MRWS1_SX140 TEST/DR5/MRWS1/SX140.WAV +MRWS1_SX230 TEST/DR5/MRWS1/SX230.WAV +MRWS1_SX320 TEST/DR5/MRWS1/SX320.WAV +MRWS1_SX410 TEST/DR5/MRWS1/SX410.WAV +MRWS1_SX50 TEST/DR5/MRWS1/SX50.WAV +MTAA0_SI1285 TEST/DR3/MTAA0/SI1285.WAV +MTAA0_SI1915 TEST/DR3/MTAA0/SI1915.WAV +MTAA0_SI596 TEST/DR3/MTAA0/SI596.WAV +MTAA0_SX115 TEST/DR3/MTAA0/SX115.WAV +MTAA0_SX205 TEST/DR3/MTAA0/SX205.WAV +MTAA0_SX25 TEST/DR3/MTAA0/SX25.WAV +MTAA0_SX295 TEST/DR3/MTAA0/SX295.WAV +MTAA0_SX385 TEST/DR3/MTAA0/SX385.WAV +MTDT0_SI1994 TEST/DR3/MTDT0/SI1994.WAV +MTDT0_SI2254 TEST/DR3/MTDT0/SI2254.WAV +MTDT0_SI994 TEST/DR3/MTDT0/SI994.WAV +MTDT0_SX184 TEST/DR3/MTDT0/SX184.WAV +MTDT0_SX274 TEST/DR3/MTDT0/SX274.WAV +MTDT0_SX364 TEST/DR3/MTDT0/SX364.WAV +MTDT0_SX4 TEST/DR3/MTDT0/SX4.WAV +MTDT0_SX94 TEST/DR3/MTDT0/SX94.WAV +MTEB0_SI1133 TEST/DR4/MTEB0/SI1133.WAV +MTEB0_SI2064 TEST/DR4/MTEB0/SI2064.WAV +MTEB0_SI503 TEST/DR4/MTEB0/SI503.WAV +MTEB0_SX143 TEST/DR4/MTEB0/SX143.WAV +MTEB0_SX233 TEST/DR4/MTEB0/SX233.WAV +MTEB0_SX323 TEST/DR4/MTEB0/SX323.WAV +MTEB0_SX413 TEST/DR4/MTEB0/SX413.WAV +MTEB0_SX53 TEST/DR4/MTEB0/SX53.WAV +MTHC0_SI1015 TEST/DR3/MTHC0/SI1015.WAV +MTHC0_SI1645 TEST/DR3/MTHC0/SI1645.WAV +MTHC0_SI2275 TEST/DR3/MTHC0/SI2275.WAV +MTHC0_SX115 TEST/DR3/MTHC0/SX115.WAV +MTHC0_SX205 TEST/DR3/MTHC0/SX205.WAV +MTHC0_SX25 TEST/DR3/MTHC0/SX25.WAV +MTHC0_SX295 TEST/DR3/MTHC0/SX295.WAV +MTHC0_SX385 TEST/DR3/MTHC0/SX385.WAV +MWJG0_SI1124 TEST/DR3/MWJG0/SI1124.WAV +MWJG0_SI1754 TEST/DR3/MWJG0/SI1754.WAV +MWJG0_SI494 TEST/DR3/MWJG0/SI494.WAV +MWJG0_SX134 TEST/DR3/MWJG0/SX134.WAV +MWJG0_SX224 TEST/DR3/MWJG0/SX224.WAV +MWJG0_SX314 TEST/DR3/MWJG0/SX314.WAV +MWJG0_SX404 TEST/DR3/MWJG0/SX404.WAV +MWJG0_SX44 TEST/DR3/MWJG0/SX44.WAV diff --git a/egs/timit/ASR/splits_dir/train_samples.txt b/egs/timit/ASR/splits_dir/train_samples.txt new file mode 100644 index 000000000..9f3c824c6 --- /dev/null +++ b/egs/timit/ASR/splits_dir/train_samples.txt @@ -0,0 +1,3696 @@ +FAEM0_SI1392 TRAIN/DR2/FAEM0/SI1392.WAV +FAEM0_SI2022 TRAIN/DR2/FAEM0/SI2022.WAV +FAEM0_SI762 TRAIN/DR2/FAEM0/SI762.WAV +FAEM0_SX132 TRAIN/DR2/FAEM0/SX132.WAV +FAEM0_SX222 TRAIN/DR2/FAEM0/SX222.WAV +FAEM0_SX312 TRAIN/DR2/FAEM0/SX312.WAV +FAEM0_SX402 TRAIN/DR2/FAEM0/SX402.WAV +FAEM0_SX42 TRAIN/DR2/FAEM0/SX42.WAV +FAJW0_SI1263 TRAIN/DR2/FAJW0/SI1263.WAV +FAJW0_SI1893 TRAIN/DR2/FAJW0/SI1893.WAV +FAJW0_SI633 TRAIN/DR2/FAJW0/SI633.WAV +FAJW0_SX183 TRAIN/DR2/FAJW0/SX183.WAV +FAJW0_SX273 TRAIN/DR2/FAJW0/SX273.WAV +FAJW0_SX3 TRAIN/DR2/FAJW0/SX3.WAV +FAJW0_SX363 TRAIN/DR2/FAJW0/SX363.WAV +FAJW0_SX93 TRAIN/DR2/FAJW0/SX93.WAV +FALK0_SI1086 TRAIN/DR3/FALK0/SI1086.WAV +FALK0_SI456 TRAIN/DR3/FALK0/SI456.WAV +FALK0_SI658 TRAIN/DR3/FALK0/SI658.WAV +FALK0_SX186 TRAIN/DR3/FALK0/SX186.WAV +FALK0_SX276 TRAIN/DR3/FALK0/SX276.WAV +FALK0_SX366 TRAIN/DR3/FALK0/SX366.WAV +FALK0_SX6 TRAIN/DR3/FALK0/SX6.WAV +FALK0_SX96 TRAIN/DR3/FALK0/SX96.WAV +FALR0_SI1325 TRAIN/DR4/FALR0/SI1325.WAV +FALR0_SI1955 TRAIN/DR4/FALR0/SI1955.WAV +FALR0_SI695 TRAIN/DR4/FALR0/SI695.WAV +FALR0_SX155 TRAIN/DR4/FALR0/SX155.WAV +FALR0_SX245 TRAIN/DR4/FALR0/SX245.WAV +FALR0_SX335 TRAIN/DR4/FALR0/SX335.WAV +FALR0_SX425 TRAIN/DR4/FALR0/SX425.WAV +FALR0_SX65 TRAIN/DR4/FALR0/SX65.WAV +FAPB0_SI1063 TRAIN/DR6/FAPB0/SI1063.WAV +FAPB0_SI1693 TRAIN/DR6/FAPB0/SI1693.WAV +FAPB0_SI2323 TRAIN/DR6/FAPB0/SI2323.WAV +FAPB0_SX163 TRAIN/DR6/FAPB0/SX163.WAV +FAPB0_SX253 TRAIN/DR6/FAPB0/SX253.WAV +FAPB0_SX343 TRAIN/DR6/FAPB0/SX343.WAV +FAPB0_SX433 TRAIN/DR6/FAPB0/SX433.WAV +FAPB0_SX73 TRAIN/DR6/FAPB0/SX73.WAV +FBAS0_SI1387 TRAIN/DR4/FBAS0/SI1387.WAV +FBAS0_SI1472 TRAIN/DR4/FBAS0/SI1472.WAV +FBAS0_SI2066 TRAIN/DR4/FBAS0/SI2066.WAV +FBAS0_SX127 TRAIN/DR4/FBAS0/SX127.WAV +FBAS0_SX217 TRAIN/DR4/FBAS0/SX217.WAV +FBAS0_SX307 TRAIN/DR4/FBAS0/SX307.WAV +FBAS0_SX37 TRAIN/DR4/FBAS0/SX37.WAV +FBAS0_SX397 TRAIN/DR4/FBAS0/SX397.WAV +FBCG1_SI1612 TRAIN/DR8/FBCG1/SI1612.WAV +FBCG1_SI2242 TRAIN/DR8/FBCG1/SI2242.WAV +FBCG1_SI982 TRAIN/DR8/FBCG1/SI982.WAV +FBCG1_SX172 TRAIN/DR8/FBCG1/SX172.WAV +FBCG1_SX262 TRAIN/DR8/FBCG1/SX262.WAV +FBCG1_SX352 TRAIN/DR8/FBCG1/SX352.WAV +FBCG1_SX442 TRAIN/DR8/FBCG1/SX442.WAV +FBCG1_SX82 TRAIN/DR8/FBCG1/SX82.WAV +FBCH0_SI1586 TRAIN/DR6/FBCH0/SI1586.WAV +FBCH0_SI956 TRAIN/DR6/FBCH0/SI956.WAV +FBCH0_SI959 TRAIN/DR6/FBCH0/SI959.WAV +FBCH0_SX146 TRAIN/DR6/FBCH0/SX146.WAV +FBCH0_SX236 TRAIN/DR6/FBCH0/SX236.WAV +FBCH0_SX326 TRAIN/DR6/FBCH0/SX326.WAV +FBCH0_SX416 TRAIN/DR6/FBCH0/SX416.WAV +FBCH0_SX56 TRAIN/DR6/FBCH0/SX56.WAV +FBJL0_SI1552 TRAIN/DR5/FBJL0/SI1552.WAV +FBJL0_SI2182 TRAIN/DR5/FBJL0/SI2182.WAV +FBJL0_SI922 TRAIN/DR5/FBJL0/SI922.WAV +FBJL0_SX112 TRAIN/DR5/FBJL0/SX112.WAV +FBJL0_SX202 TRAIN/DR5/FBJL0/SX202.WAV +FBJL0_SX22 TRAIN/DR5/FBJL0/SX22.WAV +FBJL0_SX292 TRAIN/DR5/FBJL0/SX292.WAV +FBJL0_SX382 TRAIN/DR5/FBJL0/SX382.WAV +FBLV0_SI1058 TRAIN/DR7/FBLV0/SI1058.WAV +FBLV0_SI1688 TRAIN/DR7/FBLV0/SI1688.WAV +FBLV0_SI2318 TRAIN/DR7/FBLV0/SI2318.WAV +FBLV0_SX158 TRAIN/DR7/FBLV0/SX158.WAV +FBLV0_SX248 TRAIN/DR7/FBLV0/SX248.WAV +FBLV0_SX338 TRAIN/DR7/FBLV0/SX338.WAV +FBLV0_SX428 TRAIN/DR7/FBLV0/SX428.WAV +FBLV0_SX68 TRAIN/DR7/FBLV0/SX68.WAV +FBMH0_SI1136 TRAIN/DR5/FBMH0/SI1136.WAV +FBMH0_SI1766 TRAIN/DR5/FBMH0/SI1766.WAV +FBMH0_SI970 TRAIN/DR5/FBMH0/SI970.WAV +FBMH0_SX146 TRAIN/DR5/FBMH0/SX146.WAV +FBMH0_SX236 TRAIN/DR5/FBMH0/SX236.WAV +FBMH0_SX326 TRAIN/DR5/FBMH0/SX326.WAV +FBMH0_SX416 TRAIN/DR5/FBMH0/SX416.WAV +FBMH0_SX56 TRAIN/DR5/FBMH0/SX56.WAV +FBMJ0_SI1776 TRAIN/DR4/FBMJ0/SI1776.WAV +FBMJ0_SI516 TRAIN/DR4/FBMJ0/SI516.WAV +FBMJ0_SI815 TRAIN/DR4/FBMJ0/SI815.WAV +FBMJ0_SX156 TRAIN/DR4/FBMJ0/SX156.WAV +FBMJ0_SX246 TRAIN/DR4/FBMJ0/SX246.WAV +FBMJ0_SX336 TRAIN/DR4/FBMJ0/SX336.WAV +FBMJ0_SX426 TRAIN/DR4/FBMJ0/SX426.WAV +FBMJ0_SX66 TRAIN/DR4/FBMJ0/SX66.WAV +FCAG0_SI1503 TRAIN/DR4/FCAG0/SI1503.WAV +FCAG0_SI1641 TRAIN/DR4/FCAG0/SI1641.WAV +FCAG0_SI2133 TRAIN/DR4/FCAG0/SI2133.WAV +FCAG0_SX153 TRAIN/DR4/FCAG0/SX153.WAV +FCAG0_SX243 TRAIN/DR4/FCAG0/SX243.WAV +FCAG0_SX333 TRAIN/DR4/FCAG0/SX333.WAV +FCAG0_SX423 TRAIN/DR4/FCAG0/SX423.WAV +FCAG0_SX63 TRAIN/DR4/FCAG0/SX63.WAV +FCAJ0_SI1479 TRAIN/DR2/FCAJ0/SI1479.WAV +FCAJ0_SI1804 TRAIN/DR2/FCAJ0/SI1804.WAV +FCAJ0_SI849 TRAIN/DR2/FCAJ0/SI849.WAV +FCAJ0_SX129 TRAIN/DR2/FCAJ0/SX129.WAV +FCAJ0_SX219 TRAIN/DR2/FCAJ0/SX219.WAV +FCAJ0_SX309 TRAIN/DR2/FCAJ0/SX309.WAV +FCAJ0_SX39 TRAIN/DR2/FCAJ0/SX39.WAV +FCAJ0_SX399 TRAIN/DR2/FCAJ0/SX399.WAV +FCDR1_SI1186 TRAIN/DR5/FCDR1/SI1186.WAV +FCDR1_SI1816 TRAIN/DR5/FCDR1/SI1816.WAV +FCDR1_SI556 TRAIN/DR5/FCDR1/SI556.WAV +FCDR1_SX106 TRAIN/DR5/FCDR1/SX106.WAV +FCDR1_SX16 TRAIN/DR5/FCDR1/SX16.WAV +FCDR1_SX196 TRAIN/DR5/FCDR1/SX196.WAV +FCDR1_SX286 TRAIN/DR5/FCDR1/SX286.WAV +FCDR1_SX376 TRAIN/DR5/FCDR1/SX376.WAV +FCEG0_SI1248 TRAIN/DR8/FCEG0/SI1248.WAV +FCEG0_SI1878 TRAIN/DR8/FCEG0/SI1878.WAV +FCEG0_SI618 TRAIN/DR8/FCEG0/SI618.WAV +FCEG0_SX168 TRAIN/DR8/FCEG0/SX168.WAV +FCEG0_SX258 TRAIN/DR8/FCEG0/SX258.WAV +FCEG0_SX348 TRAIN/DR8/FCEG0/SX348.WAV +FCEG0_SX438 TRAIN/DR8/FCEG0/SX438.WAV +FCEG0_SX78 TRAIN/DR8/FCEG0/SX78.WAV +FCJF0_SI1027 TRAIN/DR1/FCJF0/SI1027.WAV +FCJF0_SI1657 TRAIN/DR1/FCJF0/SI1657.WAV +FCJF0_SI648 TRAIN/DR1/FCJF0/SI648.WAV +FCJF0_SX127 TRAIN/DR1/FCJF0/SX127.WAV +FCJF0_SX217 TRAIN/DR1/FCJF0/SX217.WAV +FCJF0_SX307 TRAIN/DR1/FCJF0/SX307.WAV +FCJF0_SX37 TRAIN/DR1/FCJF0/SX37.WAV +FCJF0_SX397 TRAIN/DR1/FCJF0/SX397.WAV +FCJS0_SI1607 TRAIN/DR7/FCJS0/SI1607.WAV +FCJS0_SI2237 TRAIN/DR7/FCJS0/SI2237.WAV +FCJS0_SI977 TRAIN/DR7/FCJS0/SI977.WAV +FCJS0_SX167 TRAIN/DR7/FCJS0/SX167.WAV +FCJS0_SX257 TRAIN/DR7/FCJS0/SX257.WAV +FCJS0_SX347 TRAIN/DR7/FCJS0/SX347.WAV +FCJS0_SX437 TRAIN/DR7/FCJS0/SX437.WAV +FCJS0_SX77 TRAIN/DR7/FCJS0/SX77.WAV +FCKE0_SI1111 TRAIN/DR3/FCKE0/SI1111.WAV +FCKE0_SI1741 TRAIN/DR3/FCKE0/SI1741.WAV +FCKE0_SI481 TRAIN/DR3/FCKE0/SI481.WAV +FCKE0_SX121 TRAIN/DR3/FCKE0/SX121.WAV +FCKE0_SX211 TRAIN/DR3/FCKE0/SX211.WAV +FCKE0_SX301 TRAIN/DR3/FCKE0/SX301.WAV +FCKE0_SX31 TRAIN/DR3/FCKE0/SX31.WAV +FCKE0_SX391 TRAIN/DR3/FCKE0/SX391.WAV +FCLT0_SI1438 TRAIN/DR8/FCLT0/SI1438.WAV +FCLT0_SI2068 TRAIN/DR8/FCLT0/SI2068.WAV +FCLT0_SI808 TRAIN/DR8/FCLT0/SI808.WAV +FCLT0_SX178 TRAIN/DR8/FCLT0/SX178.WAV +FCLT0_SX268 TRAIN/DR8/FCLT0/SX268.WAV +FCLT0_SX358 TRAIN/DR8/FCLT0/SX358.WAV +FCLT0_SX448 TRAIN/DR8/FCLT0/SX448.WAV +FCLT0_SX88 TRAIN/DR8/FCLT0/SX88.WAV +FCMG0_SI1142 TRAIN/DR3/FCMG0/SI1142.WAV +FCMG0_SI1242 TRAIN/DR3/FCMG0/SI1242.WAV +FCMG0_SI1872 TRAIN/DR3/FCMG0/SI1872.WAV +FCMG0_SX162 TRAIN/DR3/FCMG0/SX162.WAV +FCMG0_SX252 TRAIN/DR3/FCMG0/SX252.WAV +FCMG0_SX342 TRAIN/DR3/FCMG0/SX342.WAV +FCMG0_SX432 TRAIN/DR3/FCMG0/SX432.WAV +FCMG0_SX72 TRAIN/DR3/FCMG0/SX72.WAV +FCMM0_SI1083 TRAIN/DR2/FCMM0/SI1083.WAV +FCMM0_SI1957 TRAIN/DR2/FCMM0/SI1957.WAV +FCMM0_SI453 TRAIN/DR2/FCMM0/SI453.WAV +FCMM0_SX183 TRAIN/DR2/FCMM0/SX183.WAV +FCMM0_SX273 TRAIN/DR2/FCMM0/SX273.WAV +FCMM0_SX363 TRAIN/DR2/FCMM0/SX363.WAV +FCMM0_SX420 TRAIN/DR2/FCMM0/SX420.WAV +FCMM0_SX93 TRAIN/DR2/FCMM0/SX93.WAV +FCRZ0_SI1913 TRAIN/DR7/FCRZ0/SI1913.WAV +FCRZ0_SI2053 TRAIN/DR7/FCRZ0/SI2053.WAV +FCRZ0_SI793 TRAIN/DR7/FCRZ0/SI793.WAV +FCRZ0_SX163 TRAIN/DR7/FCRZ0/SX163.WAV +FCRZ0_SX253 TRAIN/DR7/FCRZ0/SX253.WAV +FCRZ0_SX343 TRAIN/DR7/FCRZ0/SX343.WAV +FCRZ0_SX433 TRAIN/DR7/FCRZ0/SX433.WAV +FCRZ0_SX73 TRAIN/DR7/FCRZ0/SX73.WAV +FCYL0_SI1297 TRAIN/DR2/FCYL0/SI1297.WAV +FCYL0_SI1927 TRAIN/DR2/FCYL0/SI1927.WAV +FCYL0_SI667 TRAIN/DR2/FCYL0/SI667.WAV +FCYL0_SX127 TRAIN/DR2/FCYL0/SX127.WAV +FCYL0_SX217 TRAIN/DR2/FCYL0/SX217.WAV +FCYL0_SX349 TRAIN/DR2/FCYL0/SX349.WAV +FCYL0_SX37 TRAIN/DR2/FCYL0/SX37.WAV +FCYL0_SX397 TRAIN/DR2/FCYL0/SX397.WAV +FDAS1_SI1461 TRAIN/DR2/FDAS1/SI1461.WAV +FDAS1_SI2091 TRAIN/DR2/FDAS1/SI2091.WAV +FDAS1_SI831 TRAIN/DR2/FDAS1/SI831.WAV +FDAS1_SX111 TRAIN/DR2/FDAS1/SX111.WAV +FDAS1_SX201 TRAIN/DR2/FDAS1/SX201.WAV +FDAS1_SX21 TRAIN/DR2/FDAS1/SX21.WAV +FDAS1_SX291 TRAIN/DR2/FDAS1/SX291.WAV +FDAS1_SX381 TRAIN/DR2/FDAS1/SX381.WAV +FDAW0_SI1271 TRAIN/DR1/FDAW0/SI1271.WAV +FDAW0_SI1406 TRAIN/DR1/FDAW0/SI1406.WAV +FDAW0_SI2036 TRAIN/DR1/FDAW0/SI2036.WAV +FDAW0_SX146 TRAIN/DR1/FDAW0/SX146.WAV +FDAW0_SX236 TRAIN/DR1/FDAW0/SX236.WAV +FDAW0_SX326 TRAIN/DR1/FDAW0/SX326.WAV +FDAW0_SX416 TRAIN/DR1/FDAW0/SX416.WAV +FDAW0_SX56 TRAIN/DR1/FDAW0/SX56.WAV +FDFB0_SI1318 TRAIN/DR3/FDFB0/SI1318.WAV +FDFB0_SI1948 TRAIN/DR3/FDFB0/SI1948.WAV +FDFB0_SI2010 TRAIN/DR3/FDFB0/SI2010.WAV +FDFB0_SX148 TRAIN/DR3/FDFB0/SX148.WAV +FDFB0_SX238 TRAIN/DR3/FDFB0/SX238.WAV +FDFB0_SX328 TRAIN/DR3/FDFB0/SX328.WAV +FDFB0_SX418 TRAIN/DR3/FDFB0/SX418.WAV +FDFB0_SX58 TRAIN/DR3/FDFB0/SX58.WAV +FDJH0_SI1565 TRAIN/DR3/FDJH0/SI1565.WAV +FDJH0_SI2195 TRAIN/DR3/FDJH0/SI2195.WAV +FDJH0_SI935 TRAIN/DR3/FDJH0/SI935.WAV +FDJH0_SX125 TRAIN/DR3/FDJH0/SX125.WAV +FDJH0_SX215 TRAIN/DR3/FDJH0/SX215.WAV +FDJH0_SX305 TRAIN/DR3/FDJH0/SX305.WAV +FDJH0_SX35 TRAIN/DR3/FDJH0/SX35.WAV +FDJH0_SX395 TRAIN/DR3/FDJH0/SX395.WAV +FDKN0_SI1081 TRAIN/DR4/FDKN0/SI1081.WAV +FDKN0_SI1202 TRAIN/DR4/FDKN0/SI1202.WAV +FDKN0_SI1711 TRAIN/DR4/FDKN0/SI1711.WAV +FDKN0_SX181 TRAIN/DR4/FDKN0/SX181.WAV +FDKN0_SX271 TRAIN/DR4/FDKN0/SX271.WAV +FDKN0_SX361 TRAIN/DR4/FDKN0/SX361.WAV +FDKN0_SX451 TRAIN/DR4/FDKN0/SX451.WAV +FDKN0_SX91 TRAIN/DR4/FDKN0/SX91.WAV +FDML0_SI1149 TRAIN/DR1/FDML0/SI1149.WAV +FDML0_SI1779 TRAIN/DR1/FDML0/SI1779.WAV +FDML0_SI2075 TRAIN/DR1/FDML0/SI2075.WAV +FDML0_SX159 TRAIN/DR1/FDML0/SX159.WAV +FDML0_SX249 TRAIN/DR1/FDML0/SX249.WAV +FDML0_SX339 TRAIN/DR1/FDML0/SX339.WAV +FDML0_SX429 TRAIN/DR1/FDML0/SX429.WAV +FDML0_SX69 TRAIN/DR1/FDML0/SX69.WAV +FDMY0_SI1197 TRAIN/DR5/FDMY0/SI1197.WAV +FDMY0_SI567 TRAIN/DR5/FDMY0/SI567.WAV +FDMY0_SI714 TRAIN/DR5/FDMY0/SI714.WAV +FDMY0_SX117 TRAIN/DR5/FDMY0/SX117.WAV +FDMY0_SX207 TRAIN/DR5/FDMY0/SX207.WAV +FDMY0_SX27 TRAIN/DR5/FDMY0/SX27.WAV +FDMY0_SX297 TRAIN/DR5/FDMY0/SX297.WAV +FDMY0_SX387 TRAIN/DR5/FDMY0/SX387.WAV +FDNC0_SI1278 TRAIN/DR2/FDNC0/SI1278.WAV +FDNC0_SI1908 TRAIN/DR2/FDNC0/SI1908.WAV +FDNC0_SI2287 TRAIN/DR2/FDNC0/SI2287.WAV +FDNC0_SX108 TRAIN/DR2/FDNC0/SX108.WAV +FDNC0_SX18 TRAIN/DR2/FDNC0/SX18.WAV +FDNC0_SX198 TRAIN/DR2/FDNC0/SX198.WAV +FDNC0_SX288 TRAIN/DR2/FDNC0/SX288.WAV +FDNC0_SX378 TRAIN/DR2/FDNC0/SX378.WAV +FDTD0_SI1561 TRAIN/DR5/FDTD0/SI1561.WAV +FDTD0_SI2191 TRAIN/DR5/FDTD0/SI2191.WAV +FDTD0_SI931 TRAIN/DR5/FDTD0/SI931.WAV +FDTD0_SX121 TRAIN/DR5/FDTD0/SX121.WAV +FDTD0_SX211 TRAIN/DR5/FDTD0/SX211.WAV +FDTD0_SX301 TRAIN/DR5/FDTD0/SX301.WAV +FDTD0_SX321 TRAIN/DR5/FDTD0/SX321.WAV +FDTD0_SX391 TRAIN/DR5/FDTD0/SX391.WAV +FDXW0_SI1511 TRAIN/DR2/FDXW0/SI1511.WAV +FDXW0_SI2141 TRAIN/DR2/FDXW0/SI2141.WAV +FDXW0_SI881 TRAIN/DR2/FDXW0/SI881.WAV +FDXW0_SX161 TRAIN/DR2/FDXW0/SX161.WAV +FDXW0_SX251 TRAIN/DR2/FDXW0/SX251.WAV +FDXW0_SX341 TRAIN/DR2/FDXW0/SX341.WAV +FDXW0_SX431 TRAIN/DR2/FDXW0/SX431.WAV +FDXW0_SX71 TRAIN/DR2/FDXW0/SX71.WAV +FEAC0_SI1245 TRAIN/DR2/FEAC0/SI1245.WAV +FEAC0_SI1875 TRAIN/DR2/FEAC0/SI1875.WAV +FEAC0_SI615 TRAIN/DR2/FEAC0/SI615.WAV +FEAC0_SX165 TRAIN/DR2/FEAC0/SX165.WAV +FEAC0_SX255 TRAIN/DR2/FEAC0/SX255.WAV +FEAC0_SX345 TRAIN/DR2/FEAC0/SX345.WAV +FEAC0_SX435 TRAIN/DR2/FEAC0/SX435.WAV +FEAC0_SX75 TRAIN/DR2/FEAC0/SX75.WAV +FEAR0_SI1252 TRAIN/DR5/FEAR0/SI1252.WAV +FEAR0_SI1882 TRAIN/DR5/FEAR0/SI1882.WAV +FEAR0_SI622 TRAIN/DR5/FEAR0/SI622.WAV +FEAR0_SX172 TRAIN/DR5/FEAR0/SX172.WAV +FEAR0_SX262 TRAIN/DR5/FEAR0/SX262.WAV +FEAR0_SX352 TRAIN/DR5/FEAR0/SX352.WAV +FEAR0_SX442 TRAIN/DR5/FEAR0/SX442.WAV +FEAR0_SX82 TRAIN/DR5/FEAR0/SX82.WAV +FECD0_SI1418 TRAIN/DR1/FECD0/SI1418.WAV +FECD0_SI2048 TRAIN/DR1/FECD0/SI2048.WAV +FECD0_SI788 TRAIN/DR1/FECD0/SI788.WAV +FECD0_SX158 TRAIN/DR1/FECD0/SX158.WAV +FECD0_SX248 TRAIN/DR1/FECD0/SX248.WAV +FECD0_SX338 TRAIN/DR1/FECD0/SX338.WAV +FECD0_SX428 TRAIN/DR1/FECD0/SX428.WAV +FECD0_SX68 TRAIN/DR1/FECD0/SX68.WAV +FEEH0_SI1112 TRAIN/DR4/FEEH0/SI1112.WAV +FEEH0_SI1742 TRAIN/DR4/FEEH0/SI1742.WAV +FEEH0_SI471 TRAIN/DR4/FEEH0/SI471.WAV +FEEH0_SX122 TRAIN/DR4/FEEH0/SX122.WAV +FEEH0_SX212 TRAIN/DR4/FEEH0/SX212.WAV +FEEH0_SX302 TRAIN/DR4/FEEH0/SX302.WAV +FEEH0_SX32 TRAIN/DR4/FEEH0/SX32.WAV +FEEH0_SX392 TRAIN/DR4/FEEH0/SX392.WAV +FEME0_SI1505 TRAIN/DR3/FEME0/SI1505.WAV +FEME0_SI2135 TRAIN/DR3/FEME0/SI2135.WAV +FEME0_SI875 TRAIN/DR3/FEME0/SI875.WAV +FEME0_SX155 TRAIN/DR3/FEME0/SX155.WAV +FEME0_SX245 TRAIN/DR3/FEME0/SX245.WAV +FEME0_SX335 TRAIN/DR3/FEME0/SX335.WAV +FEME0_SX425 TRAIN/DR3/FEME0/SX425.WAV +FEME0_SX65 TRAIN/DR3/FEME0/SX65.WAV +FETB0_SI1148 TRAIN/DR1/FETB0/SI1148.WAV +FETB0_SI1778 TRAIN/DR1/FETB0/SI1778.WAV +FETB0_SI518 TRAIN/DR1/FETB0/SI518.WAV +FETB0_SX158 TRAIN/DR1/FETB0/SX158.WAV +FETB0_SX248 TRAIN/DR1/FETB0/SX248.WAV +FETB0_SX338 TRAIN/DR1/FETB0/SX338.WAV +FETB0_SX428 TRAIN/DR1/FETB0/SX428.WAV +FETB0_SX68 TRAIN/DR1/FETB0/SX68.WAV +FEXM0_SI1101 TRAIN/DR5/FEXM0/SI1101.WAV +FEXM0_SI1731 TRAIN/DR5/FEXM0/SI1731.WAV +FEXM0_SI482 TRAIN/DR5/FEXM0/SI482.WAV +FEXM0_SX111 TRAIN/DR5/FEXM0/SX111.WAV +FEXM0_SX201 TRAIN/DR5/FEXM0/SX201.WAV +FEXM0_SX291 TRAIN/DR5/FEXM0/SX291.WAV +FEXM0_SX366 TRAIN/DR5/FEXM0/SX366.WAV +FEXM0_SX381 TRAIN/DR5/FEXM0/SX381.WAV +FGCS0_SI1486 TRAIN/DR3/FGCS0/SI1486.WAV +FGCS0_SI2116 TRAIN/DR3/FGCS0/SI2116.WAV +FGCS0_SI856 TRAIN/DR3/FGCS0/SI856.WAV +FGCS0_SX136 TRAIN/DR3/FGCS0/SX136.WAV +FGCS0_SX226 TRAIN/DR3/FGCS0/SX226.WAV +FGCS0_SX316 TRAIN/DR3/FGCS0/SX316.WAV +FGCS0_SX406 TRAIN/DR3/FGCS0/SX406.WAV +FGCS0_SX46 TRAIN/DR3/FGCS0/SX46.WAV +FGDP0_SI1618 TRAIN/DR5/FGDP0/SI1618.WAV +FGDP0_SI2248 TRAIN/DR5/FGDP0/SI2248.WAV +FGDP0_SI988 TRAIN/DR5/FGDP0/SI988.WAV +FGDP0_SX178 TRAIN/DR5/FGDP0/SX178.WAV +FGDP0_SX268 TRAIN/DR5/FGDP0/SX268.WAV +FGDP0_SX358 TRAIN/DR5/FGDP0/SX358.WAV +FGDP0_SX448 TRAIN/DR5/FGDP0/SX448.WAV +FGDP0_SX88 TRAIN/DR5/FGDP0/SX88.WAV +FGMB0_SI1145 TRAIN/DR5/FGMB0/SI1145.WAV +FGMB0_SI1775 TRAIN/DR5/FGMB0/SI1775.WAV +FGMB0_SI515 TRAIN/DR5/FGMB0/SI515.WAV +FGMB0_SX155 TRAIN/DR5/FGMB0/SX155.WAV +FGMB0_SX245 TRAIN/DR5/FGMB0/SX245.WAV +FGMB0_SX335 TRAIN/DR5/FGMB0/SX335.WAV +FGMB0_SX425 TRAIN/DR5/FGMB0/SX425.WAV +FGMB0_SX65 TRAIN/DR5/FGMB0/SX65.WAV +FGRW0_SI1152 TRAIN/DR3/FGRW0/SI1152.WAV +FGRW0_SI1782 TRAIN/DR3/FGRW0/SI1782.WAV +FGRW0_SI1990 TRAIN/DR3/FGRW0/SI1990.WAV +FGRW0_SX162 TRAIN/DR3/FGRW0/SX162.WAV +FGRW0_SX252 TRAIN/DR3/FGRW0/SX252.WAV +FGRW0_SX342 TRAIN/DR3/FGRW0/SX342.WAV +FGRW0_SX432 TRAIN/DR3/FGRW0/SX432.WAV +FGRW0_SX72 TRAIN/DR3/FGRW0/SX72.WAV +FHLM0_SI1560 TRAIN/DR2/FHLM0/SI1560.WAV +FHLM0_SI2190 TRAIN/DR2/FHLM0/SI2190.WAV +FHLM0_SI930 TRAIN/DR2/FHLM0/SI930.WAV +FHLM0_SX120 TRAIN/DR2/FHLM0/SX120.WAV +FHLM0_SX210 TRAIN/DR2/FHLM0/SX210.WAV +FHLM0_SX300 TRAIN/DR2/FHLM0/SX300.WAV +FHLM0_SX349 TRAIN/DR2/FHLM0/SX349.WAV +FHLM0_SX390 TRAIN/DR2/FHLM0/SX390.WAV +FHXS0_SI1075 TRAIN/DR6/FHXS0/SI1075.WAV +FHXS0_SI2302 TRAIN/DR6/FHXS0/SI2302.WAV +FHXS0_SI2335 TRAIN/DR6/FHXS0/SI2335.WAV +FHXS0_SX175 TRAIN/DR6/FHXS0/SX175.WAV +FHXS0_SX265 TRAIN/DR6/FHXS0/SX265.WAV +FHXS0_SX355 TRAIN/DR6/FHXS0/SX355.WAV +FHXS0_SX445 TRAIN/DR6/FHXS0/SX445.WAV +FHXS0_SX85 TRAIN/DR6/FHXS0/SX85.WAV +FJDM2_SI1582 TRAIN/DR6/FJDM2/SI1582.WAV +FJDM2_SI1964 TRAIN/DR6/FJDM2/SI1964.WAV +FJDM2_SI2212 TRAIN/DR6/FJDM2/SI2212.WAV +FJDM2_SX142 TRAIN/DR6/FJDM2/SX142.WAV +FJDM2_SX232 TRAIN/DR6/FJDM2/SX232.WAV +FJDM2_SX322 TRAIN/DR6/FJDM2/SX322.WAV +FJDM2_SX412 TRAIN/DR6/FJDM2/SX412.WAV +FJDM2_SX52 TRAIN/DR6/FJDM2/SX52.WAV +FJEN0_SI1047 TRAIN/DR7/FJEN0/SI1047.WAV +FJEN0_SI1677 TRAIN/DR7/FJEN0/SI1677.WAV +FJEN0_SI2307 TRAIN/DR7/FJEN0/SI2307.WAV +FJEN0_SX147 TRAIN/DR7/FJEN0/SX147.WAV +FJEN0_SX237 TRAIN/DR7/FJEN0/SX237.WAV +FJEN0_SX327 TRAIN/DR7/FJEN0/SX327.WAV +FJEN0_SX417 TRAIN/DR7/FJEN0/SX417.WAV +FJEN0_SX57 TRAIN/DR7/FJEN0/SX57.WAV +FJHK0_SI1022 TRAIN/DR7/FJHK0/SI1022.WAV +FJHK0_SI1652 TRAIN/DR7/FJHK0/SI1652.WAV +FJHK0_SI2282 TRAIN/DR7/FJHK0/SI2282.WAV +FJHK0_SX122 TRAIN/DR7/FJHK0/SX122.WAV +FJHK0_SX212 TRAIN/DR7/FJHK0/SX212.WAV +FJHK0_SX302 TRAIN/DR7/FJHK0/SX302.WAV +FJHK0_SX32 TRAIN/DR7/FJHK0/SX32.WAV +FJHK0_SX392 TRAIN/DR7/FJHK0/SX392.WAV +FJKL0_SI1562 TRAIN/DR2/FJKL0/SI1562.WAV +FJKL0_SI2192 TRAIN/DR2/FJKL0/SI2192.WAV +FJKL0_SI932 TRAIN/DR2/FJKL0/SI932.WAV +FJKL0_SX122 TRAIN/DR2/FJKL0/SX122.WAV +FJKL0_SX212 TRAIN/DR2/FJKL0/SX212.WAV +FJKL0_SX302 TRAIN/DR2/FJKL0/SX302.WAV +FJKL0_SX32 TRAIN/DR2/FJKL0/SX32.WAV +FJKL0_SX392 TRAIN/DR2/FJKL0/SX392.WAV +FJLG0_SI1506 TRAIN/DR3/FJLG0/SI1506.WAV +FJLG0_SI1889 TRAIN/DR3/FJLG0/SI1889.WAV +FJLG0_SI2306 TRAIN/DR3/FJLG0/SI2306.WAV +FJLG0_SX179 TRAIN/DR3/FJLG0/SX179.WAV +FJLG0_SX269 TRAIN/DR3/FJLG0/SX269.WAV +FJLG0_SX359 TRAIN/DR3/FJLG0/SX359.WAV +FJLG0_SX449 TRAIN/DR3/FJLG0/SX449.WAV +FJLG0_SX89 TRAIN/DR3/FJLG0/SX89.WAV +FJLR0_SI1231 TRAIN/DR3/FJLR0/SI1231.WAV +FJLR0_SI1861 TRAIN/DR3/FJLR0/SI1861.WAV +FJLR0_SI601 TRAIN/DR3/FJLR0/SI601.WAV +FJLR0_SX151 TRAIN/DR3/FJLR0/SX151.WAV +FJLR0_SX241 TRAIN/DR3/FJLR0/SX241.WAV +FJLR0_SX331 TRAIN/DR3/FJLR0/SX331.WAV +FJLR0_SX421 TRAIN/DR3/FJLR0/SX421.WAV +FJLR0_SX61 TRAIN/DR3/FJLR0/SX61.WAV +FJRB0_SI1302 TRAIN/DR8/FJRB0/SI1302.WAV +FJRB0_SI1932 TRAIN/DR8/FJRB0/SI1932.WAV +FJRB0_SI672 TRAIN/DR8/FJRB0/SI672.WAV +FJRB0_SX132 TRAIN/DR8/FJRB0/SX132.WAV +FJRB0_SX222 TRAIN/DR8/FJRB0/SX222.WAV +FJRB0_SX312 TRAIN/DR8/FJRB0/SX312.WAV +FJRB0_SX402 TRAIN/DR8/FJRB0/SX402.WAV +FJRB0_SX42 TRAIN/DR8/FJRB0/SX42.WAV +FJRP1_SI1432 TRAIN/DR7/FJRP1/SI1432.WAV +FJRP1_SI2062 TRAIN/DR7/FJRP1/SI2062.WAV +FJRP1_SI802 TRAIN/DR7/FJRP1/SI802.WAV +FJRP1_SX172 TRAIN/DR7/FJRP1/SX172.WAV +FJRP1_SX262 TRAIN/DR7/FJRP1/SX262.WAV +FJRP1_SX352 TRAIN/DR7/FJRP1/SX352.WAV +FJRP1_SX442 TRAIN/DR7/FJRP1/SX442.WAV +FJRP1_SX82 TRAIN/DR7/FJRP1/SX82.WAV +FJSK0_SI1052 TRAIN/DR7/FJSK0/SI1052.WAV +FJSK0_SI1682 TRAIN/DR7/FJSK0/SI1682.WAV +FJSK0_SI2312 TRAIN/DR7/FJSK0/SI2312.WAV +FJSK0_SX152 TRAIN/DR7/FJSK0/SX152.WAV +FJSK0_SX242 TRAIN/DR7/FJSK0/SX242.WAV +FJSK0_SX332 TRAIN/DR7/FJSK0/SX332.WAV +FJSK0_SX422 TRAIN/DR7/FJSK0/SX422.WAV +FJSK0_SX62 TRAIN/DR7/FJSK0/SX62.WAV +FJSP0_SI1434 TRAIN/DR1/FJSP0/SI1434.WAV +FJSP0_SI1763 TRAIN/DR1/FJSP0/SI1763.WAV +FJSP0_SI804 TRAIN/DR1/FJSP0/SI804.WAV +FJSP0_SX174 TRAIN/DR1/FJSP0/SX174.WAV +FJSP0_SX264 TRAIN/DR1/FJSP0/SX264.WAV +FJSP0_SX354 TRAIN/DR1/FJSP0/SX354.WAV +FJSP0_SX444 TRAIN/DR1/FJSP0/SX444.WAV +FJSP0_SX84 TRAIN/DR1/FJSP0/SX84.WAV +FJWB1_SI2055 TRAIN/DR4/FJWB1/SI2055.WAV +FJWB1_SI748 TRAIN/DR4/FJWB1/SI748.WAV +FJWB1_SI795 TRAIN/DR4/FJWB1/SI795.WAV +FJWB1_SX165 TRAIN/DR4/FJWB1/SX165.WAV +FJWB1_SX255 TRAIN/DR4/FJWB1/SX255.WAV +FJWB1_SX345 TRAIN/DR4/FJWB1/SX345.WAV +FJWB1_SX435 TRAIN/DR4/FJWB1/SX435.WAV +FJWB1_SX75 TRAIN/DR4/FJWB1/SX75.WAV +FJXM0_SI1211 TRAIN/DR5/FJXM0/SI1211.WAV +FJXM0_SI1971 TRAIN/DR5/FJXM0/SI1971.WAV +FJXM0_SI581 TRAIN/DR5/FJXM0/SI581.WAV +FJXM0_SX131 TRAIN/DR5/FJXM0/SX131.WAV +FJXM0_SX221 TRAIN/DR5/FJXM0/SX221.WAV +FJXM0_SX311 TRAIN/DR5/FJXM0/SX311.WAV +FJXM0_SX401 TRAIN/DR5/FJXM0/SX401.WAV +FJXM0_SX41 TRAIN/DR5/FJXM0/SX41.WAV +FJXP0_SI1122 TRAIN/DR4/FJXP0/SI1122.WAV +FJXP0_SI1752 TRAIN/DR4/FJXP0/SI1752.WAV +FJXP0_SI492 TRAIN/DR4/FJXP0/SI492.WAV +FJXP0_SX132 TRAIN/DR4/FJXP0/SX132.WAV +FJXP0_SX222 TRAIN/DR4/FJXP0/SX222.WAV +FJXP0_SX312 TRAIN/DR4/FJXP0/SX312.WAV +FJXP0_SX402 TRAIN/DR4/FJXP0/SX402.WAV +FJXP0_SX42 TRAIN/DR4/FJXP0/SX42.WAV +FKAA0_SI1208 TRAIN/DR2/FKAA0/SI1208.WAV +FKAA0_SI1838 TRAIN/DR2/FKAA0/SI1838.WAV +FKAA0_SI578 TRAIN/DR2/FKAA0/SI578.WAV +FKAA0_SX128 TRAIN/DR2/FKAA0/SX128.WAV +FKAA0_SX218 TRAIN/DR2/FKAA0/SX218.WAV +FKAA0_SX308 TRAIN/DR2/FKAA0/SX308.WAV +FKAA0_SX38 TRAIN/DR2/FKAA0/SX38.WAV +FKAA0_SX398 TRAIN/DR2/FKAA0/SX398.WAV +FKDE0_SI1141 TRAIN/DR7/FKDE0/SI1141.WAV +FKDE0_SI1771 TRAIN/DR7/FKDE0/SI1771.WAV +FKDE0_SI2221 TRAIN/DR7/FKDE0/SI2221.WAV +FKDE0_SX151 TRAIN/DR7/FKDE0/SX151.WAV +FKDE0_SX241 TRAIN/DR7/FKDE0/SX241.WAV +FKDE0_SX331 TRAIN/DR7/FKDE0/SX331.WAV +FKDE0_SX421 TRAIN/DR7/FKDE0/SX421.WAV +FKDE0_SX61 TRAIN/DR7/FKDE0/SX61.WAV +FKDW0_SI1207 TRAIN/DR4/FKDW0/SI1207.WAV +FKDW0_SI1891 TRAIN/DR4/FKDW0/SI1891.WAV +FKDW0_SI577 TRAIN/DR4/FKDW0/SI577.WAV +FKDW0_SX127 TRAIN/DR4/FKDW0/SX127.WAV +FKDW0_SX217 TRAIN/DR4/FKDW0/SX217.WAV +FKDW0_SX307 TRAIN/DR4/FKDW0/SX307.WAV +FKDW0_SX37 TRAIN/DR4/FKDW0/SX37.WAV +FKDW0_SX397 TRAIN/DR4/FKDW0/SX397.WAV +FKFB0_SI1608 TRAIN/DR1/FKFB0/SI1608.WAV +FKFB0_SI2238 TRAIN/DR1/FKFB0/SI2238.WAV +FKFB0_SI978 TRAIN/DR1/FKFB0/SI978.WAV +FKFB0_SX168 TRAIN/DR1/FKFB0/SX168.WAV +FKFB0_SX258 TRAIN/DR1/FKFB0/SX258.WAV +FKFB0_SX348 TRAIN/DR1/FKFB0/SX348.WAV +FKFB0_SX438 TRAIN/DR1/FKFB0/SX438.WAV +FKFB0_SX78 TRAIN/DR1/FKFB0/SX78.WAV +FKKH0_SI1290 TRAIN/DR5/FKKH0/SI1290.WAV +FKKH0_SI1920 TRAIN/DR5/FKKH0/SI1920.WAV +FKKH0_SI660 TRAIN/DR5/FKKH0/SI660.WAV +FKKH0_SX120 TRAIN/DR5/FKKH0/SX120.WAV +FKKH0_SX210 TRAIN/DR5/FKKH0/SX210.WAV +FKKH0_SX30 TRAIN/DR5/FKKH0/SX30.WAV +FKKH0_SX300 TRAIN/DR5/FKKH0/SX300.WAV +FKKH0_SX390 TRAIN/DR5/FKKH0/SX390.WAV +FKLC0_SI1615 TRAIN/DR4/FKLC0/SI1615.WAV +FKLC0_SI2245 TRAIN/DR4/FKLC0/SI2245.WAV +FKLC0_SI985 TRAIN/DR4/FKLC0/SI985.WAV +FKLC0_SX175 TRAIN/DR4/FKLC0/SX175.WAV +FKLC0_SX265 TRAIN/DR4/FKLC0/SX265.WAV +FKLC0_SX355 TRAIN/DR4/FKLC0/SX355.WAV +FKLC0_SX445 TRAIN/DR4/FKLC0/SX445.WAV +FKLC0_SX85 TRAIN/DR4/FKLC0/SX85.WAV +FKLC1_SI1048 TRAIN/DR6/FKLC1/SI1048.WAV +FKLC1_SI1678 TRAIN/DR6/FKLC1/SI1678.WAV +FKLC1_SI2308 TRAIN/DR6/FKLC1/SI2308.WAV +FKLC1_SX148 TRAIN/DR6/FKLC1/SX148.WAV +FKLC1_SX238 TRAIN/DR6/FKLC1/SX238.WAV +FKLC1_SX328 TRAIN/DR6/FKLC1/SX328.WAV +FKLC1_SX418 TRAIN/DR6/FKLC1/SX418.WAV +FKLC1_SX58 TRAIN/DR6/FKLC1/SX58.WAV +FKLH0_SI1257 TRAIN/DR8/FKLH0/SI1257.WAV +FKLH0_SI1887 TRAIN/DR8/FKLH0/SI1887.WAV +FKLH0_SI627 TRAIN/DR8/FKLH0/SI627.WAV +FKLH0_SX177 TRAIN/DR8/FKLH0/SX177.WAV +FKLH0_SX267 TRAIN/DR8/FKLH0/SX267.WAV +FKLH0_SX357 TRAIN/DR8/FKLH0/SX357.WAV +FKLH0_SX447 TRAIN/DR8/FKLH0/SX447.WAV +FKLH0_SX87 TRAIN/DR8/FKLH0/SX87.WAV +FKSR0_SI1117 TRAIN/DR7/FKSR0/SI1117.WAV +FKSR0_SI1747 TRAIN/DR7/FKSR0/SI1747.WAV +FKSR0_SI487 TRAIN/DR7/FKSR0/SI487.WAV +FKSR0_SX161 TRAIN/DR7/FKSR0/SX161.WAV +FKSR0_SX217 TRAIN/DR7/FKSR0/SX217.WAV +FKSR0_SX366 TRAIN/DR7/FKSR0/SX366.WAV +FKSR0_SX37 TRAIN/DR7/FKSR0/SX37.WAV +FKSR0_SX397 TRAIN/DR7/FKSR0/SX397.WAV +FLAC0_SI1339 TRAIN/DR3/FLAC0/SI1339.WAV +FLAC0_SI2161 TRAIN/DR3/FLAC0/SI2161.WAV +FLAC0_SI901 TRAIN/DR3/FLAC0/SI901.WAV +FLAC0_SX181 TRAIN/DR3/FLAC0/SX181.WAV +FLAC0_SX271 TRAIN/DR3/FLAC0/SX271.WAV +FLAC0_SX361 TRAIN/DR3/FLAC0/SX361.WAV +FLAC0_SX451 TRAIN/DR3/FLAC0/SX451.WAV +FLAC0_SX91 TRAIN/DR3/FLAC0/SX91.WAV +FLAG0_SI1464 TRAIN/DR6/FLAG0/SI1464.WAV +FLAG0_SI2094 TRAIN/DR6/FLAG0/SI2094.WAV +FLAG0_SI834 TRAIN/DR6/FLAG0/SI834.WAV +FLAG0_SX114 TRAIN/DR6/FLAG0/SX114.WAV +FLAG0_SX204 TRAIN/DR6/FLAG0/SX204.WAV +FLAG0_SX24 TRAIN/DR6/FLAG0/SX24.WAV +FLAG0_SX294 TRAIN/DR6/FLAG0/SX294.WAV +FLAG0_SX384 TRAIN/DR6/FLAG0/SX384.WAV +FLEH0_SI1051 TRAIN/DR7/FLEH0/SI1051.WAV +FLEH0_SI1681 TRAIN/DR7/FLEH0/SI1681.WAV +FLEH0_SI2311 TRAIN/DR7/FLEH0/SI2311.WAV +FLEH0_SX151 TRAIN/DR7/FLEH0/SX151.WAV +FLEH0_SX241 TRAIN/DR7/FLEH0/SX241.WAV +FLEH0_SX331 TRAIN/DR7/FLEH0/SX331.WAV +FLEH0_SX421 TRAIN/DR7/FLEH0/SX421.WAV +FLEH0_SX61 TRAIN/DR7/FLEH0/SX61.WAV +FLET0_SI1137 TRAIN/DR7/FLET0/SI1137.WAV +FLET0_SI1767 TRAIN/DR7/FLET0/SI1767.WAV +FLET0_SI507 TRAIN/DR7/FLET0/SI507.WAV +FLET0_SX147 TRAIN/DR7/FLET0/SX147.WAV +FLET0_SX237 TRAIN/DR7/FLET0/SX237.WAV +FLET0_SX277 TRAIN/DR7/FLET0/SX277.WAV +FLET0_SX417 TRAIN/DR7/FLET0/SX417.WAV +FLET0_SX57 TRAIN/DR7/FLET0/SX57.WAV +FLHD0_SI1344 TRAIN/DR4/FLHD0/SI1344.WAV +FLHD0_SI1827 TRAIN/DR4/FLHD0/SI1827.WAV +FLHD0_SI1974 TRAIN/DR4/FLHD0/SI1974.WAV +FLHD0_SX174 TRAIN/DR4/FLHD0/SX174.WAV +FLHD0_SX264 TRAIN/DR4/FLHD0/SX264.WAV +FLHD0_SX354 TRAIN/DR4/FLHD0/SX354.WAV +FLHD0_SX444 TRAIN/DR4/FLHD0/SX444.WAV +FLHD0_SX84 TRAIN/DR4/FLHD0/SX84.WAV +FLJA0_SI1078 TRAIN/DR5/FLJA0/SI1078.WAV +FLJA0_SI1708 TRAIN/DR5/FLJA0/SI1708.WAV +FLJA0_SI2338 TRAIN/DR5/FLJA0/SI2338.WAV +FLJA0_SX178 TRAIN/DR5/FLJA0/SX178.WAV +FLJA0_SX268 TRAIN/DR5/FLJA0/SX268.WAV +FLJA0_SX358 TRAIN/DR5/FLJA0/SX358.WAV +FLJA0_SX448 TRAIN/DR5/FLJA0/SX448.WAV +FLJA0_SX88 TRAIN/DR5/FLJA0/SX88.WAV +FLJD0_SI1516 TRAIN/DR3/FLJD0/SI1516.WAV +FLJD0_SI2146 TRAIN/DR3/FLJD0/SI2146.WAV +FLJD0_SI886 TRAIN/DR3/FLJD0/SI886.WAV +FLJD0_SX166 TRAIN/DR3/FLJD0/SX166.WAV +FLJD0_SX256 TRAIN/DR3/FLJD0/SX256.WAV +FLJD0_SX346 TRAIN/DR3/FLJD0/SX346.WAV +FLJD0_SX436 TRAIN/DR3/FLJD0/SX436.WAV +FLJD0_SX76 TRAIN/DR3/FLJD0/SX76.WAV +FLJG0_SI1611 TRAIN/DR5/FLJG0/SI1611.WAV +FLJG0_SI2241 TRAIN/DR5/FLJG0/SI2241.WAV +FLJG0_SI981 TRAIN/DR5/FLJG0/SI981.WAV +FLJG0_SX171 TRAIN/DR5/FLJG0/SX171.WAV +FLJG0_SX261 TRAIN/DR5/FLJG0/SX261.WAV +FLJG0_SX351 TRAIN/DR5/FLJG0/SX351.WAV +FLJG0_SX441 TRAIN/DR5/FLJG0/SX441.WAV +FLJG0_SX81 TRAIN/DR5/FLJG0/SX81.WAV +FLKM0_SI1880 TRAIN/DR4/FLKM0/SI1880.WAV +FLKM0_SI620 TRAIN/DR4/FLKM0/SI620.WAV +FLKM0_SI686 TRAIN/DR4/FLKM0/SI686.WAV +FLKM0_SX116 TRAIN/DR4/FLKM0/SX116.WAV +FLKM0_SX260 TRAIN/DR4/FLKM0/SX260.WAV +FLKM0_SX350 TRAIN/DR4/FLKM0/SX350.WAV +FLKM0_SX440 TRAIN/DR4/FLKM0/SX440.WAV +FLKM0_SX80 TRAIN/DR4/FLKM0/SX80.WAV +FLMA0_SI1243 TRAIN/DR2/FLMA0/SI1243.WAV +FLMA0_SI1873 TRAIN/DR2/FLMA0/SI1873.WAV +FLMA0_SI613 TRAIN/DR2/FLMA0/SI613.WAV +FLMA0_SX163 TRAIN/DR2/FLMA0/SX163.WAV +FLMA0_SX253 TRAIN/DR2/FLMA0/SX253.WAV +FLMA0_SX343 TRAIN/DR2/FLMA0/SX343.WAV +FLMA0_SX433 TRAIN/DR2/FLMA0/SX433.WAV +FLMA0_SX73 TRAIN/DR2/FLMA0/SX73.WAV +FLMC0_SI1372 TRAIN/DR2/FLMC0/SI1372.WAV +FLMC0_SI2002 TRAIN/DR2/FLMC0/SI2002.WAV +FLMC0_SI742 TRAIN/DR2/FLMC0/SI742.WAV +FLMC0_SX112 TRAIN/DR2/FLMC0/SX112.WAV +FLMC0_SX22 TRAIN/DR2/FLMC0/SX22.WAV +FLMC0_SX292 TRAIN/DR2/FLMC0/SX292.WAV +FLMC0_SX336 TRAIN/DR2/FLMC0/SX336.WAV +FLMC0_SX382 TRAIN/DR2/FLMC0/SX382.WAV +FLMK0_SI1035 TRAIN/DR5/FLMK0/SI1035.WAV +FLMK0_SI1229 TRAIN/DR5/FLMK0/SI1229.WAV +FLMK0_SI2295 TRAIN/DR5/FLMK0/SI2295.WAV +FLMK0_SX135 TRAIN/DR5/FLMK0/SX135.WAV +FLMK0_SX225 TRAIN/DR5/FLMK0/SX225.WAV +FLMK0_SX315 TRAIN/DR5/FLMK0/SX315.WAV +FLMK0_SX405 TRAIN/DR5/FLMK0/SX405.WAV +FLMK0_SX45 TRAIN/DR5/FLMK0/SX45.WAV +FLOD0_SI1287 TRAIN/DR5/FLOD0/SI1287.WAV +FLOD0_SI1917 TRAIN/DR5/FLOD0/SI1917.WAV +FLOD0_SI657 TRAIN/DR5/FLOD0/SI657.WAV +FLOD0_SX117 TRAIN/DR5/FLOD0/SX117.WAV +FLOD0_SX171 TRAIN/DR5/FLOD0/SX171.WAV +FLOD0_SX207 TRAIN/DR5/FLOD0/SX207.WAV +FLOD0_SX297 TRAIN/DR5/FLOD0/SX297.WAV +FLOD0_SX387 TRAIN/DR5/FLOD0/SX387.WAV +FLTM0_SI1070 TRAIN/DR3/FLTM0/SI1070.WAV +FLTM0_SI1700 TRAIN/DR3/FLTM0/SI1700.WAV +FLTM0_SI2330 TRAIN/DR3/FLTM0/SI2330.WAV +FLTM0_SX170 TRAIN/DR3/FLTM0/SX170.WAV +FLTM0_SX260 TRAIN/DR3/FLTM0/SX260.WAV +FLTM0_SX350 TRAIN/DR3/FLTM0/SX350.WAV +FLTM0_SX440 TRAIN/DR3/FLTM0/SX440.WAV +FLTM0_SX80 TRAIN/DR3/FLTM0/SX80.WAV +FMAH1_SI1509 TRAIN/DR7/FMAH1/SI1509.WAV +FMAH1_SI2139 TRAIN/DR7/FMAH1/SI2139.WAV +FMAH1_SI879 TRAIN/DR7/FMAH1/SI879.WAV +FMAH1_SX159 TRAIN/DR7/FMAH1/SX159.WAV +FMAH1_SX249 TRAIN/DR7/FMAH1/SX249.WAV +FMAH1_SX339 TRAIN/DR7/FMAH1/SX339.WAV +FMAH1_SX429 TRAIN/DR7/FMAH1/SX429.WAV +FMAH1_SX69 TRAIN/DR7/FMAH1/SX69.WAV +FMBG0_SI1160 TRAIN/DR8/FMBG0/SI1160.WAV +FMBG0_SI1790 TRAIN/DR8/FMBG0/SI1790.WAV +FMBG0_SI2264 TRAIN/DR8/FMBG0/SI2264.WAV +FMBG0_SX260 TRAIN/DR8/FMBG0/SX260.WAV +FMBG0_SX3 TRAIN/DR8/FMBG0/SX3.WAV +FMBG0_SX350 TRAIN/DR8/FMBG0/SX350.WAV +FMBG0_SX440 TRAIN/DR8/FMBG0/SX440.WAV +FMBG0_SX80 TRAIN/DR8/FMBG0/SX80.WAV +FMEM0_SI1377 TRAIN/DR1/FMEM0/SI1377.WAV +FMEM0_SI2007 TRAIN/DR1/FMEM0/SI2007.WAV +FMEM0_SI747 TRAIN/DR1/FMEM0/SI747.WAV +FMEM0_SX117 TRAIN/DR1/FMEM0/SX117.WAV +FMEM0_SX207 TRAIN/DR1/FMEM0/SX207.WAV +FMEM0_SX297 TRAIN/DR1/FMEM0/SX297.WAV +FMEM0_SX333 TRAIN/DR1/FMEM0/SX333.WAV +FMEM0_SX387 TRAIN/DR1/FMEM0/SX387.WAV +FMJB0_SI1177 TRAIN/DR2/FMJB0/SI1177.WAV +FMJB0_SI1807 TRAIN/DR2/FMJB0/SI1807.WAV +FMJB0_SI547 TRAIN/DR2/FMJB0/SI547.WAV +FMJB0_SX187 TRAIN/DR2/FMJB0/SX187.WAV +FMJB0_SX277 TRAIN/DR2/FMJB0/SX277.WAV +FMJB0_SX367 TRAIN/DR2/FMJB0/SX367.WAV +FMJB0_SX7 TRAIN/DR2/FMJB0/SX7.WAV +FMJB0_SX97 TRAIN/DR2/FMJB0/SX97.WAV +FMJF0_SI1254 TRAIN/DR3/FMJF0/SI1254.WAV +FMJF0_SI1884 TRAIN/DR3/FMJF0/SI1884.WAV +FMJF0_SI624 TRAIN/DR3/FMJF0/SI624.WAV +FMJF0_SX174 TRAIN/DR3/FMJF0/SX174.WAV +FMJF0_SX264 TRAIN/DR3/FMJF0/SX264.WAV +FMJF0_SX354 TRAIN/DR3/FMJF0/SX354.WAV +FMJF0_SX444 TRAIN/DR3/FMJF0/SX444.WAV +FMJF0_SX84 TRAIN/DR3/FMJF0/SX84.WAV +FMJU0_SI1389 TRAIN/DR6/FMJU0/SI1389.WAV +FMJU0_SI2019 TRAIN/DR6/FMJU0/SI2019.WAV +FMJU0_SI759 TRAIN/DR6/FMJU0/SI759.WAV +FMJU0_SX129 TRAIN/DR6/FMJU0/SX129.WAV +FMJU0_SX219 TRAIN/DR6/FMJU0/SX219.WAV +FMJU0_SX309 TRAIN/DR6/FMJU0/SX309.WAV +FMJU0_SX39 TRAIN/DR6/FMJU0/SX39.WAV +FMJU0_SX399 TRAIN/DR6/FMJU0/SX399.WAV +FMKC0_SI1041 TRAIN/DR7/FMKC0/SI1041.WAV +FMKC0_SI1072 TRAIN/DR7/FMKC0/SI1072.WAV +FMKC0_SI1702 TRAIN/DR7/FMKC0/SI1702.WAV +FMKC0_SX172 TRAIN/DR7/FMKC0/SX172.WAV +FMKC0_SX262 TRAIN/DR7/FMKC0/SX262.WAV +FMKC0_SX352 TRAIN/DR7/FMKC0/SX352.WAV +FMKC0_SX442 TRAIN/DR7/FMKC0/SX442.WAV +FMKC0_SX82 TRAIN/DR7/FMKC0/SX82.WAV +FMKF0_SI1018 TRAIN/DR2/FMKF0/SI1018.WAV +FMKF0_SI1536 TRAIN/DR2/FMKF0/SI1536.WAV +FMKF0_SI906 TRAIN/DR2/FMKF0/SI906.WAV +FMKF0_SX186 TRAIN/DR2/FMKF0/SX186.WAV +FMKF0_SX276 TRAIN/DR2/FMKF0/SX276.WAV +FMKF0_SX366 TRAIN/DR2/FMKF0/SX366.WAV +FMKF0_SX6 TRAIN/DR2/FMKF0/SX6.WAV +FMKF0_SX96 TRAIN/DR2/FMKF0/SX96.WAV +FMMH0_SI1537 TRAIN/DR2/FMMH0/SI1537.WAV +FMMH0_SI2167 TRAIN/DR2/FMMH0/SI2167.WAV +FMMH0_SI907 TRAIN/DR2/FMMH0/SI907.WAV +FMMH0_SX187 TRAIN/DR2/FMMH0/SX187.WAV +FMMH0_SX367 TRAIN/DR2/FMMH0/SX367.WAV +FMMH0_SX420 TRAIN/DR2/FMMH0/SX420.WAV +FMMH0_SX7 TRAIN/DR2/FMMH0/SX7.WAV +FMMH0_SX97 TRAIN/DR2/FMMH0/SX97.WAV +FMPG0_SI1602 TRAIN/DR5/FMPG0/SI1602.WAV +FMPG0_SI2232 TRAIN/DR5/FMPG0/SI2232.WAV +FMPG0_SI972 TRAIN/DR5/FMPG0/SI972.WAV +FMPG0_SX162 TRAIN/DR5/FMPG0/SX162.WAV +FMPG0_SX252 TRAIN/DR5/FMPG0/SX252.WAV +FMPG0_SX342 TRAIN/DR5/FMPG0/SX342.WAV +FMPG0_SX432 TRAIN/DR5/FMPG0/SX432.WAV +FMPG0_SX72 TRAIN/DR5/FMPG0/SX72.WAV +FNKL0_SI1522 TRAIN/DR8/FNKL0/SI1522.WAV +FNKL0_SI2152 TRAIN/DR8/FNKL0/SI2152.WAV +FNKL0_SI892 TRAIN/DR8/FNKL0/SI892.WAV +FNKL0_SX172 TRAIN/DR8/FNKL0/SX172.WAV +FNKL0_SX196 TRAIN/DR8/FNKL0/SX196.WAV +FNKL0_SX262 TRAIN/DR8/FNKL0/SX262.WAV +FNKL0_SX442 TRAIN/DR8/FNKL0/SX442.WAV +FNKL0_SX82 TRAIN/DR8/FNKL0/SX82.WAV +FNTB0_SI1203 TRAIN/DR3/FNTB0/SI1203.WAV +FNTB0_SI573 TRAIN/DR3/FNTB0/SI573.WAV +FNTB0_SI679 TRAIN/DR3/FNTB0/SI679.WAV +FNTB0_SX123 TRAIN/DR3/FNTB0/SX123.WAV +FNTB0_SX213 TRAIN/DR3/FNTB0/SX213.WAV +FNTB0_SX303 TRAIN/DR3/FNTB0/SX303.WAV +FNTB0_SX33 TRAIN/DR3/FNTB0/SX33.WAV +FNTB0_SX393 TRAIN/DR3/FNTB0/SX393.WAV +FPAB1_SI1471 TRAIN/DR7/FPAB1/SI1471.WAV +FPAB1_SI2101 TRAIN/DR7/FPAB1/SI2101.WAV +FPAB1_SI841 TRAIN/DR7/FPAB1/SI841.WAV +FPAB1_SX121 TRAIN/DR7/FPAB1/SX121.WAV +FPAB1_SX211 TRAIN/DR7/FPAB1/SX211.WAV +FPAB1_SX301 TRAIN/DR7/FPAB1/SX301.WAV +FPAB1_SX31 TRAIN/DR7/FPAB1/SX31.WAV +FPAB1_SX391 TRAIN/DR7/FPAB1/SX391.WAV +FPAC0_SI1921 TRAIN/DR7/FPAC0/SI1921.WAV +FPAC0_SI2011 TRAIN/DR7/FPAC0/SI2011.WAV +FPAC0_SI661 TRAIN/DR7/FPAC0/SI661.WAV +FPAC0_SX121 TRAIN/DR7/FPAC0/SX121.WAV +FPAC0_SX211 TRAIN/DR7/FPAC0/SX211.WAV +FPAC0_SX301 TRAIN/DR7/FPAC0/SX301.WAV +FPAC0_SX31 TRAIN/DR7/FPAC0/SX31.WAV +FPAC0_SX391 TRAIN/DR7/FPAC0/SX391.WAV +FPAD0_SI1346 TRAIN/DR6/FPAD0/SI1346.WAV +FPAD0_SI1976 TRAIN/DR6/FPAD0/SI1976.WAV +FPAD0_SI716 TRAIN/DR6/FPAD0/SI716.WAV +FPAD0_SX176 TRAIN/DR6/FPAD0/SX176.WAV +FPAD0_SX266 TRAIN/DR6/FPAD0/SX266.WAV +FPAD0_SX356 TRAIN/DR6/FPAD0/SX356.WAV +FPAD0_SX446 TRAIN/DR6/FPAD0/SX446.WAV +FPAD0_SX86 TRAIN/DR6/FPAD0/SX86.WAV +FPAF0_SI1054 TRAIN/DR4/FPAF0/SI1054.WAV +FPAF0_SI1684 TRAIN/DR4/FPAF0/SI1684.WAV +FPAF0_SI2314 TRAIN/DR4/FPAF0/SI2314.WAV +FPAF0_SX154 TRAIN/DR4/FPAF0/SX154.WAV +FPAF0_SX244 TRAIN/DR4/FPAF0/SX244.WAV +FPAF0_SX334 TRAIN/DR4/FPAF0/SX334.WAV +FPAF0_SX424 TRAIN/DR4/FPAF0/SX424.WAV +FPAF0_SX64 TRAIN/DR4/FPAF0/SX64.WAV +FPAZ0_SI1593 TRAIN/DR3/FPAZ0/SI1593.WAV +FPAZ0_SI2223 TRAIN/DR3/FPAZ0/SI2223.WAV +FPAZ0_SI963 TRAIN/DR3/FPAZ0/SI963.WAV +FPAZ0_SX153 TRAIN/DR3/FPAZ0/SX153.WAV +FPAZ0_SX243 TRAIN/DR3/FPAZ0/SX243.WAV +FPAZ0_SX27 TRAIN/DR3/FPAZ0/SX27.WAV +FPAZ0_SX423 TRAIN/DR3/FPAZ0/SX423.WAV +FPAZ0_SX63 TRAIN/DR3/FPAZ0/SX63.WAV +FPJF0_SI1046 TRAIN/DR2/FPJF0/SI1046.WAV +FPJF0_SI1259 TRAIN/DR2/FPJF0/SI1259.WAV +FPJF0_SI1676 TRAIN/DR2/FPJF0/SI1676.WAV +FPJF0_SX146 TRAIN/DR2/FPJF0/SX146.WAV +FPJF0_SX236 TRAIN/DR2/FPJF0/SX236.WAV +FPJF0_SX326 TRAIN/DR2/FPJF0/SX326.WAV +FPJF0_SX352 TRAIN/DR2/FPJF0/SX352.WAV +FPJF0_SX56 TRAIN/DR2/FPJF0/SX56.WAV +FPLS0_SI1590 TRAIN/DR8/FPLS0/SI1590.WAV +FPLS0_SI2220 TRAIN/DR8/FPLS0/SI2220.WAV +FPLS0_SI960 TRAIN/DR8/FPLS0/SI960.WAV +FPLS0_SX150 TRAIN/DR8/FPLS0/SX150.WAV +FPLS0_SX240 TRAIN/DR8/FPLS0/SX240.WAV +FPLS0_SX3 TRAIN/DR8/FPLS0/SX3.WAV +FPLS0_SX330 TRAIN/DR8/FPLS0/SX330.WAV +FPLS0_SX60 TRAIN/DR8/FPLS0/SX60.WAV +FPMY0_SI1153 TRAIN/DR5/FPMY0/SI1153.WAV +FPMY0_SI1783 TRAIN/DR5/FPMY0/SI1783.WAV +FPMY0_SI523 TRAIN/DR5/FPMY0/SI523.WAV +FPMY0_SX163 TRAIN/DR5/FPMY0/SX163.WAV +FPMY0_SX196 TRAIN/DR5/FPMY0/SX196.WAV +FPMY0_SX253 TRAIN/DR5/FPMY0/SX253.WAV +FPMY0_SX343 TRAIN/DR5/FPMY0/SX343.WAV +FPMY0_SX73 TRAIN/DR5/FPMY0/SX73.WAV +FREH0_SI1315 TRAIN/DR7/FREH0/SI1315.WAV +FREH0_SI1945 TRAIN/DR7/FREH0/SI1945.WAV +FREH0_SI685 TRAIN/DR7/FREH0/SI685.WAV +FREH0_SX145 TRAIN/DR7/FREH0/SX145.WAV +FREH0_SX235 TRAIN/DR7/FREH0/SX235.WAV +FREH0_SX325 TRAIN/DR7/FREH0/SX325.WAV +FREH0_SX415 TRAIN/DR7/FREH0/SX415.WAV +FREH0_SX55 TRAIN/DR7/FREH0/SX55.WAV +FRJB0_SI1427 TRAIN/DR6/FRJB0/SI1427.WAV +FRJB0_SI1470 TRAIN/DR6/FRJB0/SI1470.WAV +FRJB0_SI1794 TRAIN/DR6/FRJB0/SI1794.WAV +FRJB0_SX167 TRAIN/DR6/FRJB0/SX167.WAV +FRJB0_SX257 TRAIN/DR6/FRJB0/SX257.WAV +FRJB0_SX347 TRAIN/DR6/FRJB0/SX347.WAV +FRJB0_SX437 TRAIN/DR6/FRJB0/SX437.WAV +FRJB0_SX77 TRAIN/DR6/FRJB0/SX77.WAV +FRLL0_SI1514 TRAIN/DR2/FRLL0/SI1514.WAV +FRLL0_SI805 TRAIN/DR2/FRLL0/SI805.WAV +FRLL0_SI884 TRAIN/DR2/FRLL0/SI884.WAV +FRLL0_SX164 TRAIN/DR2/FRLL0/SX164.WAV +FRLL0_SX254 TRAIN/DR2/FRLL0/SX254.WAV +FRLL0_SX344 TRAIN/DR2/FRLL0/SX344.WAV +FRLL0_SX434 TRAIN/DR2/FRLL0/SX434.WAV +FRLL0_SX74 TRAIN/DR2/FRLL0/SX74.WAV +FSAG0_SI1323 TRAIN/DR5/FSAG0/SI1323.WAV +FSAG0_SI1953 TRAIN/DR5/FSAG0/SI1953.WAV +FSAG0_SI693 TRAIN/DR5/FSAG0/SI693.WAV +FSAG0_SX153 TRAIN/DR5/FSAG0/SX153.WAV +FSAG0_SX243 TRAIN/DR5/FSAG0/SX243.WAV +FSAG0_SX333 TRAIN/DR5/FSAG0/SX333.WAV +FSAG0_SX423 TRAIN/DR5/FSAG0/SX423.WAV +FSAG0_SX63 TRAIN/DR5/FSAG0/SX63.WAV +FSAH0_SI1244 TRAIN/DR1/FSAH0/SI1244.WAV +FSAH0_SI1874 TRAIN/DR1/FSAH0/SI1874.WAV +FSAH0_SI614 TRAIN/DR1/FSAH0/SI614.WAV +FSAH0_SX164 TRAIN/DR1/FSAH0/SX164.WAV +FSAH0_SX327 TRAIN/DR1/FSAH0/SX327.WAV +FSAH0_SX344 TRAIN/DR1/FSAH0/SX344.WAV +FSAH0_SX434 TRAIN/DR1/FSAH0/SX434.WAV +FSAH0_SX74 TRAIN/DR1/FSAH0/SX74.WAV +FSAK0_SI1300 TRAIN/DR4/FSAK0/SI1300.WAV +FSAK0_SI1930 TRAIN/DR4/FSAK0/SI1930.WAV +FSAK0_SI670 TRAIN/DR4/FSAK0/SI670.WAV +FSAK0_SX130 TRAIN/DR4/FSAK0/SX130.WAV +FSAK0_SX220 TRAIN/DR4/FSAK0/SX220.WAV +FSAK0_SX310 TRAIN/DR4/FSAK0/SX310.WAV +FSAK0_SX40 TRAIN/DR4/FSAK0/SX40.WAV +FSAK0_SX400 TRAIN/DR4/FSAK0/SX400.WAV +FSBK0_SI1069 TRAIN/DR6/FSBK0/SI1069.WAV +FSBK0_SI1699 TRAIN/DR6/FSBK0/SI1699.WAV +FSBK0_SI2329 TRAIN/DR6/FSBK0/SI2329.WAV +FSBK0_SX169 TRAIN/DR6/FSBK0/SX169.WAV +FSBK0_SX259 TRAIN/DR6/FSBK0/SX259.WAV +FSBK0_SX349 TRAIN/DR6/FSBK0/SX349.WAV +FSBK0_SX439 TRAIN/DR6/FSBK0/SX439.WAV +FSBK0_SX79 TRAIN/DR6/FSBK0/SX79.WAV +FSCN0_SI1886 TRAIN/DR2/FSCN0/SI1886.WAV +FSCN0_SI626 TRAIN/DR2/FSCN0/SI626.WAV +FSCN0_SI705 TRAIN/DR2/FSCN0/SI705.WAV +FSCN0_SX176 TRAIN/DR2/FSCN0/SX176.WAV +FSCN0_SX266 TRAIN/DR2/FSCN0/SX266.WAV +FSCN0_SX356 TRAIN/DR2/FSCN0/SX356.WAV +FSCN0_SX446 TRAIN/DR2/FSCN0/SX446.WAV +FSCN0_SX86 TRAIN/DR2/FSCN0/SX86.WAV +FSDC0_SI1312 TRAIN/DR5/FSDC0/SI1312.WAV +FSDC0_SI1942 TRAIN/DR5/FSDC0/SI1942.WAV +FSDC0_SI2234 TRAIN/DR5/FSDC0/SI2234.WAV +FSDC0_SX142 TRAIN/DR5/FSDC0/SX142.WAV +FSDC0_SX232 TRAIN/DR5/FSDC0/SX232.WAV +FSDC0_SX322 TRAIN/DR5/FSDC0/SX322.WAV +FSDC0_SX412 TRAIN/DR5/FSDC0/SX412.WAV +FSDC0_SX52 TRAIN/DR5/FSDC0/SX52.WAV +FSDJ0_SI1115 TRAIN/DR6/FSDJ0/SI1115.WAV +FSDJ0_SI1745 TRAIN/DR6/FSDJ0/SI1745.WAV +FSDJ0_SI485 TRAIN/DR6/FSDJ0/SI485.WAV +FSDJ0_SX125 TRAIN/DR6/FSDJ0/SX125.WAV +FSDJ0_SX215 TRAIN/DR6/FSDJ0/SX215.WAV +FSDJ0_SX305 TRAIN/DR6/FSDJ0/SX305.WAV +FSDJ0_SX35 TRAIN/DR6/FSDJ0/SX35.WAV +FSDJ0_SX395 TRAIN/DR6/FSDJ0/SX395.WAV +FSGF0_SI1557 TRAIN/DR6/FSGF0/SI1557.WAV +FSGF0_SI2187 TRAIN/DR6/FSGF0/SI2187.WAV +FSGF0_SI927 TRAIN/DR6/FSGF0/SI927.WAV +FSGF0_SX117 TRAIN/DR6/FSGF0/SX117.WAV +FSGF0_SX207 TRAIN/DR6/FSGF0/SX207.WAV +FSGF0_SX27 TRAIN/DR6/FSGF0/SX27.WAV +FSGF0_SX297 TRAIN/DR6/FSGF0/SX297.WAV +FSGF0_SX387 TRAIN/DR6/FSGF0/SX387.WAV +FSJG0_SI1570 TRAIN/DR5/FSJG0/SI1570.WAV +FSJG0_SI2200 TRAIN/DR5/FSJG0/SI2200.WAV +FSJG0_SI940 TRAIN/DR5/FSJG0/SI940.WAV +FSJG0_SX130 TRAIN/DR5/FSJG0/SX130.WAV +FSJG0_SX220 TRAIN/DR5/FSJG0/SX220.WAV +FSJG0_SX310 TRAIN/DR5/FSJG0/SX310.WAV +FSJG0_SX40 TRAIN/DR5/FSJG0/SX40.WAV +FSJG0_SX400 TRAIN/DR5/FSJG0/SX400.WAV +FSJK1_SI1025 TRAIN/DR1/FSJK1/SI1025.WAV +FSJK1_SI2285 TRAIN/DR1/FSJK1/SI2285.WAV +FSJK1_SI696 TRAIN/DR1/FSJK1/SI696.WAV +FSJK1_SX125 TRAIN/DR1/FSJK1/SX125.WAV +FSJK1_SX215 TRAIN/DR1/FSJK1/SX215.WAV +FSJK1_SX305 TRAIN/DR1/FSJK1/SX305.WAV +FSJK1_SX35 TRAIN/DR1/FSJK1/SX35.WAV +FSJK1_SX395 TRAIN/DR1/FSJK1/SX395.WAV +FSJS0_SI1171 TRAIN/DR3/FSJS0/SI1171.WAV +FSJS0_SI1801 TRAIN/DR3/FSJS0/SI1801.WAV +FSJS0_SI541 TRAIN/DR3/FSJS0/SI541.WAV +FSJS0_SX181 TRAIN/DR3/FSJS0/SX181.WAV +FSJS0_SX271 TRAIN/DR3/FSJS0/SX271.WAV +FSJS0_SX361 TRAIN/DR3/FSJS0/SX361.WAV +FSJS0_SX451 TRAIN/DR3/FSJS0/SX451.WAV +FSJS0_SX91 TRAIN/DR3/FSJS0/SX91.WAV +FSJW0_SI1333 TRAIN/DR3/FSJW0/SI1333.WAV +FSJW0_SI1963 TRAIN/DR3/FSJW0/SI1963.WAV +FSJW0_SI703 TRAIN/DR3/FSJW0/SI703.WAV +FSJW0_SX163 TRAIN/DR3/FSJW0/SX163.WAV +FSJW0_SX253 TRAIN/DR3/FSJW0/SX253.WAV +FSJW0_SX343 TRAIN/DR3/FSJW0/SX343.WAV +FSJW0_SX433 TRAIN/DR3/FSJW0/SX433.WAV +FSJW0_SX73 TRAIN/DR3/FSJW0/SX73.WAV +FSKC0_SI1416 TRAIN/DR3/FSKC0/SI1416.WAV +FSKC0_SI2046 TRAIN/DR3/FSKC0/SI2046.WAV +FSKC0_SI786 TRAIN/DR3/FSKC0/SI786.WAV +FSKC0_SX156 TRAIN/DR3/FSKC0/SX156.WAV +FSKC0_SX246 TRAIN/DR3/FSKC0/SX246.WAV +FSKC0_SX336 TRAIN/DR3/FSKC0/SX336.WAV +FSKC0_SX426 TRAIN/DR3/FSKC0/SX426.WAV +FSKC0_SX66 TRAIN/DR3/FSKC0/SX66.WAV +FSKL0_SI1529 TRAIN/DR2/FSKL0/SI1529.WAV +FSKL0_SI2159 TRAIN/DR2/FSKL0/SI2159.WAV +FSKL0_SI899 TRAIN/DR2/FSKL0/SI899.WAV +FSKL0_SX179 TRAIN/DR2/FSKL0/SX179.WAV +FSKL0_SX269 TRAIN/DR2/FSKL0/SX269.WAV +FSKL0_SX359 TRAIN/DR2/FSKL0/SX359.WAV +FSKL0_SX449 TRAIN/DR2/FSKL0/SX449.WAV +FSKL0_SX89 TRAIN/DR2/FSKL0/SX89.WAV +FSKP0_SI1098 TRAIN/DR5/FSKP0/SI1098.WAV +FSKP0_SI1728 TRAIN/DR5/FSKP0/SI1728.WAV +FSKP0_SI468 TRAIN/DR5/FSKP0/SI468.WAV +FSKP0_SX108 TRAIN/DR5/FSKP0/SX108.WAV +FSKP0_SX18 TRAIN/DR5/FSKP0/SX18.WAV +FSKP0_SX198 TRAIN/DR5/FSKP0/SX198.WAV +FSKP0_SX288 TRAIN/DR5/FSKP0/SX288.WAV +FSKP0_SX378 TRAIN/DR5/FSKP0/SX378.WAV +FSLS0_SI1056 TRAIN/DR3/FSLS0/SI1056.WAV +FSLS0_SI1686 TRAIN/DR3/FSLS0/SI1686.WAV +FSLS0_SI2316 TRAIN/DR3/FSLS0/SI2316.WAV +FSLS0_SX156 TRAIN/DR3/FSLS0/SX156.WAV +FSLS0_SX202 TRAIN/DR3/FSLS0/SX202.WAV +FSLS0_SX246 TRAIN/DR3/FSLS0/SX246.WAV +FSLS0_SX426 TRAIN/DR3/FSLS0/SX426.WAV +FSLS0_SX66 TRAIN/DR3/FSLS0/SX66.WAV +FSMA0_SI1621 TRAIN/DR1/FSMA0/SI1621.WAV +FSMA0_SI2251 TRAIN/DR1/FSMA0/SI2251.WAV +FSMA0_SI991 TRAIN/DR1/FSMA0/SI991.WAV +FSMA0_SX181 TRAIN/DR1/FSMA0/SX181.WAV +FSMA0_SX271 TRAIN/DR1/FSMA0/SX271.WAV +FSMA0_SX361 TRAIN/DR1/FSMA0/SX361.WAV +FSMA0_SX451 TRAIN/DR1/FSMA0/SX451.WAV +FSMA0_SX91 TRAIN/DR1/FSMA0/SX91.WAV +FSMM0_SI1314 TRAIN/DR5/FSMM0/SI1314.WAV +FSMM0_SI1944 TRAIN/DR5/FSMM0/SI1944.WAV +FSMM0_SI684 TRAIN/DR5/FSMM0/SI684.WAV +FSMM0_SX144 TRAIN/DR5/FSMM0/SX144.WAV +FSMM0_SX234 TRAIN/DR5/FSMM0/SX234.WAV +FSMM0_SX324 TRAIN/DR5/FSMM0/SX324.WAV +FSMM0_SX414 TRAIN/DR5/FSMM0/SX414.WAV +FSMM0_SX54 TRAIN/DR5/FSMM0/SX54.WAV +FSMS1_SI1504 TRAIN/DR5/FSMS1/SI1504.WAV +FSMS1_SI2134 TRAIN/DR5/FSMS1/SI2134.WAV +FSMS1_SI874 TRAIN/DR5/FSMS1/SI874.WAV +FSMS1_SX154 TRAIN/DR5/FSMS1/SX154.WAV +FSMS1_SX244 TRAIN/DR5/FSMS1/SX244.WAV +FSMS1_SX334 TRAIN/DR5/FSMS1/SX334.WAV +FSMS1_SX347 TRAIN/DR5/FSMS1/SX347.WAV +FSMS1_SX64 TRAIN/DR5/FSMS1/SX64.WAV +FSPM0_SI1241 TRAIN/DR7/FSPM0/SI1241.WAV +FSPM0_SI1871 TRAIN/DR7/FSPM0/SI1871.WAV +FSPM0_SI611 TRAIN/DR7/FSPM0/SI611.WAV +FSPM0_SX161 TRAIN/DR7/FSPM0/SX161.WAV +FSPM0_SX251 TRAIN/DR7/FSPM0/SX251.WAV +FSPM0_SX341 TRAIN/DR7/FSPM0/SX341.WAV +FSPM0_SX431 TRAIN/DR7/FSPM0/SX431.WAV +FSPM0_SX71 TRAIN/DR7/FSPM0/SX71.WAV +FSRH0_SI1719 TRAIN/DR2/FSRH0/SI1719.WAV +FSRH0_SI1931 TRAIN/DR2/FSRH0/SI1931.WAV +FSRH0_SI671 TRAIN/DR2/FSRH0/SI671.WAV +FSRH0_SX131 TRAIN/DR2/FSRH0/SX131.WAV +FSRH0_SX221 TRAIN/DR2/FSRH0/SX221.WAV +FSRH0_SX311 TRAIN/DR2/FSRH0/SX311.WAV +FSRH0_SX401 TRAIN/DR2/FSRH0/SX401.WAV +FSRH0_SX41 TRAIN/DR2/FSRH0/SX41.WAV +FSSB0_SI1082 TRAIN/DR4/FSSB0/SI1082.WAV +FSSB0_SI1712 TRAIN/DR4/FSSB0/SI1712.WAV +FSSB0_SI2342 TRAIN/DR4/FSSB0/SI2342.WAV +FSSB0_SX182 TRAIN/DR4/FSSB0/SX182.WAV +FSSB0_SX272 TRAIN/DR4/FSSB0/SX272.WAV +FSSB0_SX362 TRAIN/DR4/FSSB0/SX362.WAV +FSSB0_SX452 TRAIN/DR4/FSSB0/SX452.WAV +FSSB0_SX92 TRAIN/DR4/FSSB0/SX92.WAV +FTAJ0_SI1329 TRAIN/DR6/FTAJ0/SI1329.WAV +FTAJ0_SI474 TRAIN/DR6/FTAJ0/SI474.WAV +FTAJ0_SI699 TRAIN/DR6/FTAJ0/SI699.WAV +FTAJ0_SX159 TRAIN/DR6/FTAJ0/SX159.WAV +FTAJ0_SX249 TRAIN/DR6/FTAJ0/SX249.WAV +FTAJ0_SX339 TRAIN/DR6/FTAJ0/SX339.WAV +FTAJ0_SX429 TRAIN/DR6/FTAJ0/SX429.WAV +FTAJ0_SX69 TRAIN/DR6/FTAJ0/SX69.WAV +FTBR0_SI1402 TRAIN/DR1/FTBR0/SI1402.WAV +FTBR0_SI2181 TRAIN/DR1/FTBR0/SI2181.WAV +FTBR0_SI921 TRAIN/DR1/FTBR0/SI921.WAV +FTBR0_SX111 TRAIN/DR1/FTBR0/SX111.WAV +FTBR0_SX201 TRAIN/DR1/FTBR0/SX201.WAV +FTBR0_SX21 TRAIN/DR1/FTBR0/SX21.WAV +FTBR0_SX291 TRAIN/DR1/FTBR0/SX291.WAV +FTBR0_SX381 TRAIN/DR1/FTBR0/SX381.WAV +FTBW0_SI1345 TRAIN/DR5/FTBW0/SI1345.WAV +FTBW0_SI1975 TRAIN/DR5/FTBW0/SI1975.WAV +FTBW0_SI715 TRAIN/DR5/FTBW0/SI715.WAV +FTBW0_SX175 TRAIN/DR5/FTBW0/SX175.WAV +FTBW0_SX265 TRAIN/DR5/FTBW0/SX265.WAV +FTBW0_SX355 TRAIN/DR5/FTBW0/SX355.WAV +FTBW0_SX445 TRAIN/DR5/FTBW0/SX445.WAV +FTBW0_SX85 TRAIN/DR5/FTBW0/SX85.WAV +FTLG0_SI1743 TRAIN/DR5/FTLG0/SI1743.WAV +FTLG0_SI483 TRAIN/DR5/FTLG0/SI483.WAV +FTLG0_SI840 TRAIN/DR5/FTLG0/SI840.WAV +FTLG0_SX123 TRAIN/DR5/FTLG0/SX123.WAV +FTLG0_SX213 TRAIN/DR5/FTLG0/SX213.WAV +FTLG0_SX303 TRAIN/DR5/FTLG0/SX303.WAV +FTLG0_SX33 TRAIN/DR5/FTLG0/SX33.WAV +FTLG0_SX393 TRAIN/DR5/FTLG0/SX393.WAV +FTMG0_SI1532 TRAIN/DR2/FTMG0/SI1532.WAV +FTMG0_SI2162 TRAIN/DR2/FTMG0/SI2162.WAV +FTMG0_SI902 TRAIN/DR2/FTMG0/SI902.WAV +FTMG0_SX182 TRAIN/DR2/FTMG0/SX182.WAV +FTMG0_SX272 TRAIN/DR2/FTMG0/SX272.WAV +FTMG0_SX362 TRAIN/DR2/FTMG0/SX362.WAV +FTMG0_SX452 TRAIN/DR2/FTMG0/SX452.WAV +FTMG0_SX92 TRAIN/DR2/FTMG0/SX92.WAV +FVFB0_SI1032 TRAIN/DR1/FVFB0/SI1032.WAV +FVFB0_SI1510 TRAIN/DR1/FVFB0/SI1510.WAV +FVFB0_SI2292 TRAIN/DR1/FVFB0/SI2292.WAV +FVFB0_SX132 TRAIN/DR1/FVFB0/SX132.WAV +FVFB0_SX222 TRAIN/DR1/FVFB0/SX222.WAV +FVFB0_SX312 TRAIN/DR1/FVFB0/SX312.WAV +FVFB0_SX402 TRAIN/DR1/FVFB0/SX402.WAV +FVFB0_SX42 TRAIN/DR1/FVFB0/SX42.WAV +FVKB0_SI1159 TRAIN/DR7/FVKB0/SI1159.WAV +FVKB0_SI1789 TRAIN/DR7/FVKB0/SI1789.WAV +FVKB0_SI529 TRAIN/DR7/FVKB0/SI529.WAV +FVKB0_SX169 TRAIN/DR7/FVKB0/SX169.WAV +FVKB0_SX259 TRAIN/DR7/FVKB0/SX259.WAV +FVKB0_SX349 TRAIN/DR7/FVKB0/SX349.WAV +FVKB0_SX439 TRAIN/DR7/FVKB0/SX439.WAV +FVKB0_SX79 TRAIN/DR7/FVKB0/SX79.WAV +FVMH0_SI1466 TRAIN/DR1/FVMH0/SI1466.WAV +FVMH0_SI2096 TRAIN/DR1/FVMH0/SI2096.WAV +FVMH0_SI836 TRAIN/DR1/FVMH0/SI836.WAV +FVMH0_SX116 TRAIN/DR1/FVMH0/SX116.WAV +FVMH0_SX206 TRAIN/DR1/FVMH0/SX206.WAV +FVMH0_SX26 TRAIN/DR1/FVMH0/SX26.WAV +FVMH0_SX296 TRAIN/DR1/FVMH0/SX296.WAV +FVMH0_SX386 TRAIN/DR1/FVMH0/SX386.WAV +MABC0_SI1620 TRAIN/DR6/MABC0/SI1620.WAV +MABC0_SI2041 TRAIN/DR6/MABC0/SI2041.WAV +MABC0_SI781 TRAIN/DR6/MABC0/SI781.WAV +MABC0_SX151 TRAIN/DR6/MABC0/SX151.WAV +MABC0_SX241 TRAIN/DR6/MABC0/SX241.WAV +MABC0_SX331 TRAIN/DR6/MABC0/SX331.WAV +MABC0_SX421 TRAIN/DR6/MABC0/SX421.WAV +MABC0_SX61 TRAIN/DR6/MABC0/SX61.WAV +MADC0_SI1367 TRAIN/DR3/MADC0/SI1367.WAV +MADC0_SI1997 TRAIN/DR3/MADC0/SI1997.WAV +MADC0_SI737 TRAIN/DR3/MADC0/SI737.WAV +MADC0_SX107 TRAIN/DR3/MADC0/SX107.WAV +MADC0_SX17 TRAIN/DR3/MADC0/SX17.WAV +MADC0_SX197 TRAIN/DR3/MADC0/SX197.WAV +MADC0_SX287 TRAIN/DR3/MADC0/SX287.WAV +MADC0_SX377 TRAIN/DR3/MADC0/SX377.WAV +MADD0_SI1295 TRAIN/DR7/MADD0/SI1295.WAV +MADD0_SI1798 TRAIN/DR7/MADD0/SI1798.WAV +MADD0_SI538 TRAIN/DR7/MADD0/SI538.WAV +MADD0_SX178 TRAIN/DR7/MADD0/SX178.WAV +MADD0_SX268 TRAIN/DR7/MADD0/SX268.WAV +MADD0_SX358 TRAIN/DR7/MADD0/SX358.WAV +MADD0_SX448 TRAIN/DR7/MADD0/SX448.WAV +MADD0_SX88 TRAIN/DR7/MADD0/SX88.WAV +MAEB0_SI1411 TRAIN/DR4/MAEB0/SI1411.WAV +MAEB0_SI2250 TRAIN/DR4/MAEB0/SI2250.WAV +MAEB0_SI990 TRAIN/DR4/MAEB0/SI990.WAV +MAEB0_SX180 TRAIN/DR4/MAEB0/SX180.WAV +MAEB0_SX270 TRAIN/DR4/MAEB0/SX270.WAV +MAEB0_SX360 TRAIN/DR4/MAEB0/SX360.WAV +MAEB0_SX450 TRAIN/DR4/MAEB0/SX450.WAV +MAEB0_SX90 TRAIN/DR4/MAEB0/SX90.WAV +MAEO0_SI1326 TRAIN/DR7/MAEO0/SI1326.WAV +MAEO0_SI1655 TRAIN/DR7/MAEO0/SI1655.WAV +MAEO0_SI1956 TRAIN/DR7/MAEO0/SI1956.WAV +MAEO0_SX156 TRAIN/DR7/MAEO0/SX156.WAV +MAEO0_SX246 TRAIN/DR7/MAEO0/SX246.WAV +MAEO0_SX336 TRAIN/DR7/MAEO0/SX336.WAV +MAEO0_SX426 TRAIN/DR7/MAEO0/SX426.WAV +MAEO0_SX66 TRAIN/DR7/MAEO0/SX66.WAV +MAFM0_SI1569 TRAIN/DR7/MAFM0/SI1569.WAV +MAFM0_SI2199 TRAIN/DR7/MAFM0/SI2199.WAV +MAFM0_SI939 TRAIN/DR7/MAFM0/SI939.WAV +MAFM0_SX129 TRAIN/DR7/MAFM0/SX129.WAV +MAFM0_SX219 TRAIN/DR7/MAFM0/SX219.WAV +MAFM0_SX309 TRAIN/DR7/MAFM0/SX309.WAV +MAFM0_SX39 TRAIN/DR7/MAFM0/SX39.WAV +MAFM0_SX399 TRAIN/DR7/MAFM0/SX399.WAV +MAJP0_SI1074 TRAIN/DR6/MAJP0/SI1074.WAV +MAJP0_SI1704 TRAIN/DR6/MAJP0/SI1704.WAV +MAJP0_SI2334 TRAIN/DR6/MAJP0/SI2334.WAV +MAJP0_SX174 TRAIN/DR6/MAJP0/SX174.WAV +MAJP0_SX264 TRAIN/DR6/MAJP0/SX264.WAV +MAJP0_SX354 TRAIN/DR6/MAJP0/SX354.WAV +MAJP0_SX444 TRAIN/DR6/MAJP0/SX444.WAV +MAJP0_SX84 TRAIN/DR6/MAJP0/SX84.WAV +MAKB0_SI1016 TRAIN/DR3/MAKB0/SI1016.WAV +MAKB0_SI1646 TRAIN/DR3/MAKB0/SI1646.WAV +MAKB0_SI2276 TRAIN/DR3/MAKB0/SI2276.WAV +MAKB0_SX116 TRAIN/DR3/MAKB0/SX116.WAV +MAKB0_SX206 TRAIN/DR3/MAKB0/SX206.WAV +MAKB0_SX26 TRAIN/DR3/MAKB0/SX26.WAV +MAKB0_SX296 TRAIN/DR3/MAKB0/SX296.WAV +MAKB0_SX386 TRAIN/DR3/MAKB0/SX386.WAV +MAKR0_SI1352 TRAIN/DR3/MAKR0/SI1352.WAV +MAKR0_SI1982 TRAIN/DR3/MAKR0/SI1982.WAV +MAKR0_SI722 TRAIN/DR3/MAKR0/SI722.WAV +MAKR0_SX182 TRAIN/DR3/MAKR0/SX182.WAV +MAKR0_SX272 TRAIN/DR3/MAKR0/SX272.WAV +MAKR0_SX362 TRAIN/DR3/MAKR0/SX362.WAV +MAKR0_SX452 TRAIN/DR3/MAKR0/SX452.WAV +MAKR0_SX92 TRAIN/DR3/MAKR0/SX92.WAV +MAPV0_SI1293 TRAIN/DR3/MAPV0/SI1293.WAV +MAPV0_SI1923 TRAIN/DR3/MAPV0/SI1923.WAV +MAPV0_SI663 TRAIN/DR3/MAPV0/SI663.WAV +MAPV0_SX123 TRAIN/DR3/MAPV0/SX123.WAV +MAPV0_SX213 TRAIN/DR3/MAPV0/SX213.WAV +MAPV0_SX303 TRAIN/DR3/MAPV0/SX303.WAV +MAPV0_SX33 TRAIN/DR3/MAPV0/SX33.WAV +MAPV0_SX393 TRAIN/DR3/MAPV0/SX393.WAV +MARC0_SI1188 TRAIN/DR2/MARC0/SI1188.WAV +MARC0_SI1818 TRAIN/DR2/MARC0/SI1818.WAV +MARC0_SI558 TRAIN/DR2/MARC0/SI558.WAV +MARC0_SX108 TRAIN/DR2/MARC0/SX108.WAV +MARC0_SX18 TRAIN/DR2/MARC0/SX18.WAV +MARC0_SX198 TRAIN/DR2/MARC0/SX198.WAV +MARC0_SX288 TRAIN/DR2/MARC0/SX288.WAV +MARC0_SX378 TRAIN/DR2/MARC0/SX378.WAV +MARW0_SI1276 TRAIN/DR4/MARW0/SI1276.WAV +MARW0_SI1906 TRAIN/DR4/MARW0/SI1906.WAV +MARW0_SI646 TRAIN/DR4/MARW0/SI646.WAV +MARW0_SX106 TRAIN/DR4/MARW0/SX106.WAV +MARW0_SX16 TRAIN/DR4/MARW0/SX16.WAV +MARW0_SX286 TRAIN/DR4/MARW0/SX286.WAV +MARW0_SX349 TRAIN/DR4/MARW0/SX349.WAV +MARW0_SX376 TRAIN/DR4/MARW0/SX376.WAV +MBAR0_SI1319 TRAIN/DR7/MBAR0/SI1319.WAV +MBAR0_SI1949 TRAIN/DR7/MBAR0/SI1949.WAV +MBAR0_SI689 TRAIN/DR7/MBAR0/SI689.WAV +MBAR0_SX149 TRAIN/DR7/MBAR0/SX149.WAV +MBAR0_SX239 TRAIN/DR7/MBAR0/SX239.WAV +MBAR0_SX329 TRAIN/DR7/MBAR0/SX329.WAV +MBAR0_SX419 TRAIN/DR7/MBAR0/SX419.WAV +MBAR0_SX59 TRAIN/DR7/MBAR0/SX59.WAV +MBBR0_SI1055 TRAIN/DR7/MBBR0/SI1055.WAV +MBBR0_SI1685 TRAIN/DR7/MBBR0/SI1685.WAV +MBBR0_SI2315 TRAIN/DR7/MBBR0/SI2315.WAV +MBBR0_SX155 TRAIN/DR7/MBBR0/SX155.WAV +MBBR0_SX245 TRAIN/DR7/MBBR0/SX245.WAV +MBBR0_SX335 TRAIN/DR7/MBBR0/SX335.WAV +MBBR0_SX425 TRAIN/DR7/MBBR0/SX425.WAV +MBBR0_SX65 TRAIN/DR7/MBBR0/SX65.WAV +MBCG0_SI2217 TRAIN/DR8/MBCG0/SI2217.WAV +MBCG0_SI486 TRAIN/DR8/MBCG0/SI486.WAV +MBCG0_SI957 TRAIN/DR8/MBCG0/SI957.WAV +MBCG0_SX147 TRAIN/DR8/MBCG0/SX147.WAV +MBCG0_SX237 TRAIN/DR8/MBCG0/SX237.WAV +MBCG0_SX327 TRAIN/DR8/MBCG0/SX327.WAV +MBCG0_SX417 TRAIN/DR8/MBCG0/SX417.WAV +MBCG0_SX57 TRAIN/DR8/MBCG0/SX57.WAV +MBEF0_SI1281 TRAIN/DR3/MBEF0/SI1281.WAV +MBEF0_SI1911 TRAIN/DR3/MBEF0/SI1911.WAV +MBEF0_SI651 TRAIN/DR3/MBEF0/SI651.WAV +MBEF0_SX111 TRAIN/DR3/MBEF0/SX111.WAV +MBEF0_SX201 TRAIN/DR3/MBEF0/SX201.WAV +MBEF0_SX21 TRAIN/DR3/MBEF0/SX21.WAV +MBEF0_SX291 TRAIN/DR3/MBEF0/SX291.WAV +MBEF0_SX381 TRAIN/DR3/MBEF0/SX381.WAV +MBGT0_SI1341 TRAIN/DR5/MBGT0/SI1341.WAV +MBGT0_SI1841 TRAIN/DR5/MBGT0/SI1841.WAV +MBGT0_SI711 TRAIN/DR5/MBGT0/SI711.WAV +MBGT0_SX171 TRAIN/DR5/MBGT0/SX171.WAV +MBGT0_SX261 TRAIN/DR5/MBGT0/SX261.WAV +MBGT0_SX351 TRAIN/DR5/MBGT0/SX351.WAV +MBGT0_SX441 TRAIN/DR5/MBGT0/SX441.WAV +MBGT0_SX81 TRAIN/DR5/MBGT0/SX81.WAV +MBJV0_SI1247 TRAIN/DR2/MBJV0/SI1247.WAV +MBJV0_SI1877 TRAIN/DR2/MBJV0/SI1877.WAV +MBJV0_SI617 TRAIN/DR2/MBJV0/SI617.WAV +MBJV0_SX167 TRAIN/DR2/MBJV0/SX167.WAV +MBJV0_SX257 TRAIN/DR2/MBJV0/SX257.WAV +MBJV0_SX347 TRAIN/DR2/MBJV0/SX347.WAV +MBJV0_SX437 TRAIN/DR2/MBJV0/SX437.WAV +MBJV0_SX77 TRAIN/DR2/MBJV0/SX77.WAV +MBMA0_SI1222 TRAIN/DR4/MBMA0/SI1222.WAV +MBMA0_SI1852 TRAIN/DR4/MBMA0/SI1852.WAV +MBMA0_SI592 TRAIN/DR4/MBMA0/SI592.WAV +MBMA0_SX142 TRAIN/DR4/MBMA0/SX142.WAV +MBMA0_SX232 TRAIN/DR4/MBMA0/SX232.WAV +MBMA0_SX322 TRAIN/DR4/MBMA0/SX322.WAV +MBMA0_SX412 TRAIN/DR4/MBMA0/SX412.WAV +MBMA0_SX52 TRAIN/DR4/MBMA0/SX52.WAV +MBMA1_SI2207 TRAIN/DR6/MBMA1/SI2207.WAV +MBMA1_SI2214 TRAIN/DR6/MBMA1/SI2214.WAV +MBMA1_SI954 TRAIN/DR6/MBMA1/SI954.WAV +MBMA1_SX144 TRAIN/DR6/MBMA1/SX144.WAV +MBMA1_SX234 TRAIN/DR6/MBMA1/SX234.WAV +MBMA1_SX324 TRAIN/DR6/MBMA1/SX324.WAV +MBMA1_SX414 TRAIN/DR6/MBMA1/SX414.WAV +MBMA1_SX54 TRAIN/DR6/MBMA1/SX54.WAV +MBML0_SI1169 TRAIN/DR7/MBML0/SI1169.WAV +MBML0_SI1799 TRAIN/DR7/MBML0/SI1799.WAV +MBML0_SI539 TRAIN/DR7/MBML0/SI539.WAV +MBML0_SX179 TRAIN/DR7/MBML0/SX179.WAV +MBML0_SX269 TRAIN/DR7/MBML0/SX269.WAV +MBML0_SX359 TRAIN/DR7/MBML0/SX359.WAV +MBML0_SX449 TRAIN/DR7/MBML0/SX449.WAV +MBML0_SX89 TRAIN/DR7/MBML0/SX89.WAV +MBOM0_SI1014 TRAIN/DR7/MBOM0/SI1014.WAV +MBOM0_SI1644 TRAIN/DR7/MBOM0/SI1644.WAV +MBOM0_SI2274 TRAIN/DR7/MBOM0/SI2274.WAV +MBOM0_SX114 TRAIN/DR7/MBOM0/SX114.WAV +MBOM0_SX204 TRAIN/DR7/MBOM0/SX204.WAV +MBOM0_SX294 TRAIN/DR7/MBOM0/SX294.WAV +MBOM0_SX311 TRAIN/DR7/MBOM0/SX311.WAV +MBOM0_SX384 TRAIN/DR7/MBOM0/SX384.WAV +MBSB0_SI1353 TRAIN/DR8/MBSB0/SI1353.WAV +MBSB0_SI1983 TRAIN/DR8/MBSB0/SI1983.WAV +MBSB0_SI723 TRAIN/DR8/MBSB0/SI723.WAV +MBSB0_SX183 TRAIN/DR8/MBSB0/SX183.WAV +MBSB0_SX273 TRAIN/DR8/MBSB0/SX273.WAV +MBSB0_SX3 TRAIN/DR8/MBSB0/SX3.WAV +MBSB0_SX363 TRAIN/DR8/MBSB0/SX363.WAV +MBSB0_SX93 TRAIN/DR8/MBSB0/SX93.WAV +MBTH0_SI2102 TRAIN/DR7/MBTH0/SI2102.WAV +MBTH0_SI505 TRAIN/DR7/MBTH0/SI505.WAV +MBTH0_SI757 TRAIN/DR7/MBTH0/SI757.WAV +MBTH0_SX122 TRAIN/DR7/MBTH0/SX122.WAV +MBTH0_SX212 TRAIN/DR7/MBTH0/SX212.WAV +MBTH0_SX302 TRAIN/DR7/MBTH0/SX302.WAV +MBTH0_SX32 TRAIN/DR7/MBTH0/SX32.WAV +MBTH0_SX392 TRAIN/DR7/MBTH0/SX392.WAV +MBWP0_SI1531 TRAIN/DR4/MBWP0/SI1531.WAV +MBWP0_SI1969 TRAIN/DR4/MBWP0/SI1969.WAV +MBWP0_SI709 TRAIN/DR4/MBWP0/SI709.WAV +MBWP0_SX169 TRAIN/DR4/MBWP0/SX169.WAV +MBWP0_SX259 TRAIN/DR4/MBWP0/SX259.WAV +MBWP0_SX349 TRAIN/DR4/MBWP0/SX349.WAV +MBWP0_SX439 TRAIN/DR4/MBWP0/SX439.WAV +MBWP0_SX79 TRAIN/DR4/MBWP0/SX79.WAV +MCAE0_SI1447 TRAIN/DR6/MCAE0/SI1447.WAV +MCAE0_SI2077 TRAIN/DR6/MCAE0/SI2077.WAV +MCAE0_SI817 TRAIN/DR6/MCAE0/SI817.WAV +MCAE0_SX187 TRAIN/DR6/MCAE0/SX187.WAV +MCAE0_SX277 TRAIN/DR6/MCAE0/SX277.WAV +MCAE0_SX367 TRAIN/DR6/MCAE0/SX367.WAV +MCAE0_SX7 TRAIN/DR6/MCAE0/SX7.WAV +MCAE0_SX97 TRAIN/DR6/MCAE0/SX97.WAV +MCAL0_SI1138 TRAIN/DR3/MCAL0/SI1138.WAV +MCAL0_SI1768 TRAIN/DR3/MCAL0/SI1768.WAV +MCAL0_SI508 TRAIN/DR3/MCAL0/SI508.WAV +MCAL0_SX148 TRAIN/DR3/MCAL0/SX148.WAV +MCAL0_SX238 TRAIN/DR3/MCAL0/SX238.WAV +MCAL0_SX328 TRAIN/DR3/MCAL0/SX328.WAV +MCAL0_SX418 TRAIN/DR3/MCAL0/SX418.WAV +MCAL0_SX58 TRAIN/DR3/MCAL0/SX58.WAV +MCDC0_SI1292 TRAIN/DR3/MCDC0/SI1292.WAV +MCDC0_SI1922 TRAIN/DR3/MCDC0/SI1922.WAV +MCDC0_SI662 TRAIN/DR3/MCDC0/SI662.WAV +MCDC0_SX122 TRAIN/DR3/MCDC0/SX122.WAV +MCDC0_SX212 TRAIN/DR3/MCDC0/SX212.WAV +MCDC0_SX302 TRAIN/DR3/MCDC0/SX302.WAV +MCDC0_SX32 TRAIN/DR3/MCDC0/SX32.WAV +MCDC0_SX392 TRAIN/DR3/MCDC0/SX392.WAV +MCDD0_SI1513 TRAIN/DR3/MCDD0/SI1513.WAV +MCDD0_SI2143 TRAIN/DR3/MCDD0/SI2143.WAV +MCDD0_SI883 TRAIN/DR3/MCDD0/SI883.WAV +MCDD0_SX163 TRAIN/DR3/MCDD0/SX163.WAV +MCDD0_SX253 TRAIN/DR3/MCDD0/SX253.WAV +MCDD0_SX343 TRAIN/DR3/MCDD0/SX343.WAV +MCDD0_SX433 TRAIN/DR3/MCDD0/SX433.WAV +MCDD0_SX73 TRAIN/DR3/MCDD0/SX73.WAV +MCDR0_SI1154 TRAIN/DR4/MCDR0/SI1154.WAV +MCDR0_SI1784 TRAIN/DR4/MCDR0/SI1784.WAV +MCDR0_SI524 TRAIN/DR4/MCDR0/SI524.WAV +MCDR0_SX164 TRAIN/DR4/MCDR0/SX164.WAV +MCDR0_SX254 TRAIN/DR4/MCDR0/SX254.WAV +MCDR0_SX344 TRAIN/DR4/MCDR0/SX344.WAV +MCDR0_SX434 TRAIN/DR4/MCDR0/SX434.WAV +MCDR0_SX74 TRAIN/DR4/MCDR0/SX74.WAV +MCEF0_SI1135 TRAIN/DR3/MCEF0/SI1135.WAV +MCEF0_SI1765 TRAIN/DR3/MCEF0/SI1765.WAV +MCEF0_SI842 TRAIN/DR3/MCEF0/SI842.WAV +MCEF0_SX145 TRAIN/DR3/MCEF0/SX145.WAV +MCEF0_SX235 TRAIN/DR3/MCEF0/SX235.WAV +MCEF0_SX325 TRAIN/DR3/MCEF0/SX325.WAV +MCEF0_SX415 TRAIN/DR3/MCEF0/SX415.WAV +MCEF0_SX55 TRAIN/DR3/MCEF0/SX55.WAV +MCEW0_SI1442 TRAIN/DR2/MCEW0/SI1442.WAV +MCEW0_SI2072 TRAIN/DR2/MCEW0/SI2072.WAV +MCEW0_SI812 TRAIN/DR2/MCEW0/SI812.WAV +MCEW0_SX182 TRAIN/DR2/MCEW0/SX182.WAV +MCEW0_SX272 TRAIN/DR2/MCEW0/SX272.WAV +MCEW0_SX362 TRAIN/DR2/MCEW0/SX362.WAV +MCEW0_SX452 TRAIN/DR2/MCEW0/SX452.WAV +MCEW0_SX92 TRAIN/DR2/MCEW0/SX92.WAV +MCHL0_SI1347 TRAIN/DR5/MCHL0/SI1347.WAV +MCHL0_SI1404 TRAIN/DR5/MCHL0/SI1404.WAV +MCHL0_SI1977 TRAIN/DR5/MCHL0/SI1977.WAV +MCHL0_SX177 TRAIN/DR5/MCHL0/SX177.WAV +MCHL0_SX267 TRAIN/DR5/MCHL0/SX267.WAV +MCHL0_SX357 TRAIN/DR5/MCHL0/SX357.WAV +MCHL0_SX447 TRAIN/DR5/MCHL0/SX447.WAV +MCHL0_SX87 TRAIN/DR5/MCHL0/SX87.WAV +MCLK0_SI1660 TRAIN/DR7/MCLK0/SI1660.WAV +MCLK0_SI2290 TRAIN/DR7/MCLK0/SI2290.WAV +MCLK0_SI650 TRAIN/DR7/MCLK0/SI650.WAV +MCLK0_SX130 TRAIN/DR7/MCLK0/SX130.WAV +MCLK0_SX220 TRAIN/DR7/MCLK0/SX220.WAV +MCLK0_SX310 TRAIN/DR7/MCLK0/SX310.WAV +MCLK0_SX40 TRAIN/DR7/MCLK0/SX40.WAV +MCLK0_SX400 TRAIN/DR7/MCLK0/SX400.WAV +MCLM0_SI1456 TRAIN/DR5/MCLM0/SI1456.WAV +MCLM0_SI2086 TRAIN/DR5/MCLM0/SI2086.WAV +MCLM0_SI826 TRAIN/DR5/MCLM0/SI826.WAV +MCLM0_SX106 TRAIN/DR5/MCLM0/SX106.WAV +MCLM0_SX16 TRAIN/DR5/MCLM0/SX16.WAV +MCLM0_SX196 TRAIN/DR5/MCLM0/SX196.WAV +MCLM0_SX286 TRAIN/DR5/MCLM0/SX286.WAV +MCLM0_SX376 TRAIN/DR5/MCLM0/SX376.WAV +MCPM0_SI1194 TRAIN/DR1/MCPM0/SI1194.WAV +MCPM0_SI1824 TRAIN/DR1/MCPM0/SI1824.WAV +MCPM0_SI564 TRAIN/DR1/MCPM0/SI564.WAV +MCPM0_SX114 TRAIN/DR1/MCPM0/SX114.WAV +MCPM0_SX204 TRAIN/DR1/MCPM0/SX204.WAV +MCPM0_SX24 TRAIN/DR1/MCPM0/SX24.WAV +MCPM0_SX294 TRAIN/DR1/MCPM0/SX294.WAV +MCPM0_SX384 TRAIN/DR1/MCPM0/SX384.WAV +MCRE0_SI1121 TRAIN/DR7/MCRE0/SI1121.WAV +MCRE0_SI1725 TRAIN/DR7/MCRE0/SI1725.WAV +MCRE0_SI1751 TRAIN/DR7/MCRE0/SI1751.WAV +MCRE0_SX131 TRAIN/DR7/MCRE0/SX131.WAV +MCRE0_SX221 TRAIN/DR7/MCRE0/SX221.WAV +MCRE0_SX24 TRAIN/DR7/MCRE0/SX24.WAV +MCRE0_SX401 TRAIN/DR7/MCRE0/SX401.WAV +MCRE0_SX41 TRAIN/DR7/MCRE0/SX41.WAV +MCSS0_SI1380 TRAIN/DR4/MCSS0/SI1380.WAV +MCSS0_SI688 TRAIN/DR4/MCSS0/SI688.WAV +MCSS0_SI750 TRAIN/DR4/MCSS0/SI750.WAV +MCSS0_SX120 TRAIN/DR4/MCSS0/SX120.WAV +MCSS0_SX210 TRAIN/DR4/MCSS0/SX210.WAV +MCSS0_SX30 TRAIN/DR4/MCSS0/SX30.WAV +MCSS0_SX300 TRAIN/DR4/MCSS0/SX300.WAV +MCSS0_SX390 TRAIN/DR4/MCSS0/SX390.WAV +MCTH0_SI1209 TRAIN/DR7/MCTH0/SI1209.WAV +MCTH0_SI1839 TRAIN/DR7/MCTH0/SI1839.WAV +MCTH0_SI579 TRAIN/DR7/MCTH0/SI579.WAV +MCTH0_SX129 TRAIN/DR7/MCTH0/SX129.WAV +MCTH0_SX219 TRAIN/DR7/MCTH0/SX219.WAV +MCTH0_SX309 TRAIN/DR7/MCTH0/SX309.WAV +MCTH0_SX39 TRAIN/DR7/MCTH0/SX39.WAV +MCTH0_SX399 TRAIN/DR7/MCTH0/SX399.WAV +MCTM0_SI1350 TRAIN/DR2/MCTM0/SI1350.WAV +MCTM0_SI1980 TRAIN/DR2/MCTM0/SI1980.WAV +MCTM0_SI720 TRAIN/DR2/MCTM0/SI720.WAV +MCTM0_SX180 TRAIN/DR2/MCTM0/SX180.WAV +MCTM0_SX270 TRAIN/DR2/MCTM0/SX270.WAV +MCTM0_SX360 TRAIN/DR2/MCTM0/SX360.WAV +MCTM0_SX450 TRAIN/DR2/MCTM0/SX450.WAV +MCTM0_SX90 TRAIN/DR2/MCTM0/SX90.WAV +MCXM0_SI1351 TRAIN/DR8/MCXM0/SI1351.WAV +MCXM0_SI1981 TRAIN/DR8/MCXM0/SI1981.WAV +MCXM0_SI721 TRAIN/DR8/MCXM0/SI721.WAV +MCXM0_SX181 TRAIN/DR8/MCXM0/SX181.WAV +MCXM0_SX271 TRAIN/DR8/MCXM0/SX271.WAV +MCXM0_SX361 TRAIN/DR8/MCXM0/SX361.WAV +MCXM0_SX451 TRAIN/DR8/MCXM0/SX451.WAV +MCXM0_SX91 TRAIN/DR8/MCXM0/SX91.WAV +MDAC0_SI1261 TRAIN/DR1/MDAC0/SI1261.WAV +MDAC0_SI1837 TRAIN/DR1/MDAC0/SI1837.WAV +MDAC0_SI631 TRAIN/DR1/MDAC0/SI631.WAV +MDAC0_SX181 TRAIN/DR1/MDAC0/SX181.WAV +MDAC0_SX271 TRAIN/DR1/MDAC0/SX271.WAV +MDAC0_SX361 TRAIN/DR1/MDAC0/SX361.WAV +MDAC0_SX451 TRAIN/DR1/MDAC0/SX451.WAV +MDAC0_SX91 TRAIN/DR1/MDAC0/SX91.WAV +MDAS0_SI1266 TRAIN/DR5/MDAS0/SI1266.WAV +MDAS0_SI1896 TRAIN/DR5/MDAS0/SI1896.WAV +MDAS0_SI636 TRAIN/DR5/MDAS0/SI636.WAV +MDAS0_SX186 TRAIN/DR5/MDAS0/SX186.WAV +MDAS0_SX21 TRAIN/DR5/MDAS0/SX21.WAV +MDAS0_SX276 TRAIN/DR5/MDAS0/SX276.WAV +MDAS0_SX6 TRAIN/DR5/MDAS0/SX6.WAV +MDAS0_SX96 TRAIN/DR5/MDAS0/SX96.WAV +MDBB1_SI1006 TRAIN/DR3/MDBB1/SI1006.WAV +MDBB1_SI1636 TRAIN/DR3/MDBB1/SI1636.WAV +MDBB1_SI2056 TRAIN/DR3/MDBB1/SI2056.WAV +MDBB1_SX106 TRAIN/DR3/MDBB1/SX106.WAV +MDBB1_SX16 TRAIN/DR3/MDBB1/SX16.WAV +MDBB1_SX196 TRAIN/DR3/MDBB1/SX196.WAV +MDBB1_SX286 TRAIN/DR3/MDBB1/SX286.WAV +MDBB1_SX376 TRAIN/DR3/MDBB1/SX376.WAV +MDBP0_SI1158 TRAIN/DR2/MDBP0/SI1158.WAV +MDBP0_SI1788 TRAIN/DR2/MDBP0/SI1788.WAV +MDBP0_SI528 TRAIN/DR2/MDBP0/SI528.WAV +MDBP0_SX168 TRAIN/DR2/MDBP0/SX168.WAV +MDBP0_SX258 TRAIN/DR2/MDBP0/SX258.WAV +MDBP0_SX348 TRAIN/DR2/MDBP0/SX348.WAV +MDBP0_SX438 TRAIN/DR2/MDBP0/SX438.WAV +MDBP0_SX78 TRAIN/DR2/MDBP0/SX78.WAV +MDCD0_SI1415 TRAIN/DR4/MDCD0/SI1415.WAV +MDCD0_SI2045 TRAIN/DR4/MDCD0/SI2045.WAV +MDCD0_SI785 TRAIN/DR4/MDCD0/SI785.WAV +MDCD0_SX155 TRAIN/DR4/MDCD0/SX155.WAV +MDCD0_SX245 TRAIN/DR4/MDCD0/SX245.WAV +MDCD0_SX335 TRAIN/DR4/MDCD0/SX335.WAV +MDCD0_SX425 TRAIN/DR4/MDCD0/SX425.WAV +MDCD0_SX65 TRAIN/DR4/MDCD0/SX65.WAV +MDCM0_SI1480 TRAIN/DR7/MDCM0/SI1480.WAV +MDCM0_SI2110 TRAIN/DR7/MDCM0/SI2110.WAV +MDCM0_SI850 TRAIN/DR7/MDCM0/SI850.WAV +MDCM0_SX130 TRAIN/DR7/MDCM0/SX130.WAV +MDCM0_SX220 TRAIN/DR7/MDCM0/SX220.WAV +MDCM0_SX310 TRAIN/DR7/MDCM0/SX310.WAV +MDCM0_SX40 TRAIN/DR7/MDCM0/SX40.WAV +MDCM0_SX400 TRAIN/DR7/MDCM0/SX400.WAV +MDDC0_SI1419 TRAIN/DR3/MDDC0/SI1419.WAV +MDDC0_SI2049 TRAIN/DR3/MDDC0/SI2049.WAV +MDDC0_SI789 TRAIN/DR3/MDDC0/SI789.WAV +MDDC0_SX159 TRAIN/DR3/MDDC0/SX159.WAV +MDDC0_SX249 TRAIN/DR3/MDDC0/SX249.WAV +MDDC0_SX339 TRAIN/DR3/MDDC0/SX339.WAV +MDDC0_SX429 TRAIN/DR3/MDDC0/SX429.WAV +MDDC0_SX69 TRAIN/DR3/MDDC0/SX69.WAV +MDED0_SI1170 TRAIN/DR7/MDED0/SI1170.WAV +MDED0_SI1800 TRAIN/DR7/MDED0/SI1800.WAV +MDED0_SI540 TRAIN/DR7/MDED0/SI540.WAV +MDED0_SX180 TRAIN/DR7/MDED0/SX180.WAV +MDED0_SX270 TRAIN/DR7/MDED0/SX270.WAV +MDED0_SX360 TRAIN/DR7/MDED0/SX360.WAV +MDED0_SX450 TRAIN/DR7/MDED0/SX450.WAV +MDED0_SX90 TRAIN/DR7/MDED0/SX90.WAV +MDEF0_SI1123 TRAIN/DR3/MDEF0/SI1123.WAV +MDEF0_SI1563 TRAIN/DR3/MDEF0/SI1563.WAV +MDEF0_SI2193 TRAIN/DR3/MDEF0/SI2193.WAV +MDEF0_SX123 TRAIN/DR3/MDEF0/SX123.WAV +MDEF0_SX213 TRAIN/DR3/MDEF0/SX213.WAV +MDEF0_SX303 TRAIN/DR3/MDEF0/SX303.WAV +MDEF0_SX33 TRAIN/DR3/MDEF0/SX33.WAV +MDEF0_SX393 TRAIN/DR3/MDEF0/SX393.WAV +MDEM0_SI1868 TRAIN/DR2/MDEM0/SI1868.WAV +MDEM0_SI608 TRAIN/DR2/MDEM0/SI608.WAV +MDEM0_SI800 TRAIN/DR2/MDEM0/SI800.WAV +MDEM0_SX158 TRAIN/DR2/MDEM0/SX158.WAV +MDEM0_SX248 TRAIN/DR2/MDEM0/SX248.WAV +MDEM0_SX338 TRAIN/DR2/MDEM0/SX338.WAV +MDEM0_SX428 TRAIN/DR2/MDEM0/SX428.WAV +MDEM0_SX68 TRAIN/DR2/MDEM0/SX68.WAV +MDHL0_SI1439 TRAIN/DR5/MDHL0/SI1439.WAV +MDHL0_SI2069 TRAIN/DR5/MDHL0/SI2069.WAV +MDHL0_SI809 TRAIN/DR5/MDHL0/SI809.WAV +MDHL0_SX179 TRAIN/DR5/MDHL0/SX179.WAV +MDHL0_SX269 TRAIN/DR5/MDHL0/SX269.WAV +MDHL0_SX359 TRAIN/DR5/MDHL0/SX359.WAV +MDHL0_SX449 TRAIN/DR5/MDHL0/SX449.WAV +MDHL0_SX89 TRAIN/DR5/MDHL0/SX89.WAV +MDHS0_SI1530 TRAIN/DR3/MDHS0/SI1530.WAV +MDHS0_SI2160 TRAIN/DR3/MDHS0/SI2160.WAV +MDHS0_SI900 TRAIN/DR3/MDHS0/SI900.WAV +MDHS0_SX180 TRAIN/DR3/MDHS0/SX180.WAV +MDHS0_SX270 TRAIN/DR3/MDHS0/SX270.WAV +MDHS0_SX360 TRAIN/DR3/MDHS0/SX360.WAV +MDHS0_SX450 TRAIN/DR3/MDHS0/SX450.WAV +MDHS0_SX90 TRAIN/DR3/MDHS0/SX90.WAV +MDJM0_SI1455 TRAIN/DR3/MDJM0/SI1455.WAV +MDJM0_SI2085 TRAIN/DR3/MDJM0/SI2085.WAV +MDJM0_SI825 TRAIN/DR3/MDJM0/SI825.WAV +MDJM0_SX105 TRAIN/DR3/MDJM0/SX105.WAV +MDJM0_SX15 TRAIN/DR3/MDJM0/SX15.WAV +MDJM0_SX195 TRAIN/DR3/MDJM0/SX195.WAV +MDJM0_SX285 TRAIN/DR3/MDJM0/SX285.WAV +MDJM0_SX375 TRAIN/DR3/MDJM0/SX375.WAV +MDKS0_SI1066 TRAIN/DR7/MDKS0/SI1066.WAV +MDKS0_SI1696 TRAIN/DR7/MDKS0/SI1696.WAV +MDKS0_SI2326 TRAIN/DR7/MDKS0/SI2326.WAV +MDKS0_SX166 TRAIN/DR7/MDKS0/SX166.WAV +MDKS0_SX256 TRAIN/DR7/MDKS0/SX256.WAV +MDKS0_SX346 TRAIN/DR7/MDKS0/SX346.WAV +MDKS0_SX436 TRAIN/DR7/MDKS0/SX436.WAV +MDKS0_SX76 TRAIN/DR7/MDKS0/SX76.WAV +MDLB0_SI1306 TRAIN/DR2/MDLB0/SI1306.WAV +MDLB0_SI1936 TRAIN/DR2/MDLB0/SI1936.WAV +MDLB0_SI676 TRAIN/DR2/MDLB0/SI676.WAV +MDLB0_SX136 TRAIN/DR2/MDLB0/SX136.WAV +MDLB0_SX226 TRAIN/DR2/MDLB0/SX226.WAV +MDLB0_SX316 TRAIN/DR2/MDLB0/SX316.WAV +MDLB0_SX406 TRAIN/DR2/MDLB0/SX406.WAV +MDLB0_SX46 TRAIN/DR2/MDLB0/SX46.WAV +MDLC0_SI1395 TRAIN/DR3/MDLC0/SI1395.WAV +MDLC0_SI2025 TRAIN/DR3/MDLC0/SI2025.WAV +MDLC0_SI765 TRAIN/DR3/MDLC0/SI765.WAV +MDLC0_SX135 TRAIN/DR3/MDLC0/SX135.WAV +MDLC0_SX225 TRAIN/DR3/MDLC0/SX225.WAV +MDLC0_SX315 TRAIN/DR3/MDLC0/SX315.WAV +MDLC0_SX405 TRAIN/DR3/MDLC0/SX405.WAV +MDLC0_SX45 TRAIN/DR3/MDLC0/SX45.WAV +MDLC1_SI1435 TRAIN/DR7/MDLC1/SI1435.WAV +MDLC1_SI2065 TRAIN/DR7/MDLC1/SI2065.WAV +MDLC1_SI2144 TRAIN/DR7/MDLC1/SI2144.WAV +MDLC1_SX175 TRAIN/DR7/MDLC1/SX175.WAV +MDLC1_SX265 TRAIN/DR7/MDLC1/SX265.WAV +MDLC1_SX355 TRAIN/DR7/MDLC1/SX355.WAV +MDLC1_SX445 TRAIN/DR7/MDLC1/SX445.WAV +MDLC1_SX85 TRAIN/DR7/MDLC1/SX85.WAV +MDLC2_SI1614 TRAIN/DR2/MDLC2/SI1614.WAV +MDLC2_SI2244 TRAIN/DR2/MDLC2/SI2244.WAV +MDLC2_SI984 TRAIN/DR2/MDLC2/SI984.WAV +MDLC2_SX174 TRAIN/DR2/MDLC2/SX174.WAV +MDLC2_SX264 TRAIN/DR2/MDLC2/SX264.WAV +MDLC2_SX354 TRAIN/DR2/MDLC2/SX354.WAV +MDLC2_SX444 TRAIN/DR2/MDLC2/SX444.WAV +MDLC2_SX84 TRAIN/DR2/MDLC2/SX84.WAV +MDLH0_SI1960 TRAIN/DR3/MDLH0/SI1960.WAV +MDLH0_SI574 TRAIN/DR3/MDLH0/SI574.WAV +MDLH0_SI700 TRAIN/DR3/MDLH0/SI700.WAV +MDLH0_SX160 TRAIN/DR3/MDLH0/SX160.WAV +MDLH0_SX250 TRAIN/DR3/MDLH0/SX250.WAV +MDLH0_SX340 TRAIN/DR3/MDLH0/SX340.WAV +MDLH0_SX430 TRAIN/DR3/MDLH0/SX430.WAV +MDLH0_SX70 TRAIN/DR3/MDLH0/SX70.WAV +MDLM0_SI1234 TRAIN/DR7/MDLM0/SI1234.WAV +MDLM0_SI1864 TRAIN/DR7/MDLM0/SI1864.WAV +MDLM0_SI604 TRAIN/DR7/MDLM0/SI604.WAV +MDLM0_SX154 TRAIN/DR7/MDLM0/SX154.WAV +MDLM0_SX244 TRAIN/DR7/MDLM0/SX244.WAV +MDLM0_SX334 TRAIN/DR7/MDLM0/SX334.WAV +MDLM0_SX424 TRAIN/DR7/MDLM0/SX424.WAV +MDLM0_SX64 TRAIN/DR7/MDLM0/SX64.WAV +MDLR0_SI1233 TRAIN/DR7/MDLR0/SI1233.WAV +MDLR0_SI1863 TRAIN/DR7/MDLR0/SI1863.WAV +MDLR0_SI603 TRAIN/DR7/MDLR0/SI603.WAV +MDLR0_SX153 TRAIN/DR7/MDLR0/SX153.WAV +MDLR0_SX243 TRAIN/DR7/MDLR0/SX243.WAV +MDLR0_SX333 TRAIN/DR7/MDLR0/SX333.WAV +MDLR0_SX423 TRAIN/DR7/MDLR0/SX423.WAV +MDLR0_SX63 TRAIN/DR7/MDLR0/SX63.WAV +MDLR1_SI1299 TRAIN/DR7/MDLR1/SI1299.WAV +MDLR1_SI1929 TRAIN/DR7/MDLR1/SI1929.WAV +MDLR1_SI669 TRAIN/DR7/MDLR1/SI669.WAV +MDLR1_SX129 TRAIN/DR7/MDLR1/SX129.WAV +MDLR1_SX219 TRAIN/DR7/MDLR1/SX219.WAV +MDLR1_SX309 TRAIN/DR7/MDLR1/SX309.WAV +MDLR1_SX39 TRAIN/DR7/MDLR1/SX39.WAV +MDLR1_SX399 TRAIN/DR7/MDLR1/SX399.WAV +MDMA0_SI1238 TRAIN/DR4/MDMA0/SI1238.WAV +MDMA0_SI1430 TRAIN/DR4/MDMA0/SI1430.WAV +MDMA0_SI2060 TRAIN/DR4/MDMA0/SI2060.WAV +MDMA0_SX170 TRAIN/DR4/MDMA0/SX170.WAV +MDMA0_SX260 TRAIN/DR4/MDMA0/SX260.WAV +MDMA0_SX350 TRAIN/DR4/MDMA0/SX350.WAV +MDMA0_SX440 TRAIN/DR4/MDMA0/SX440.WAV +MDMA0_SX80 TRAIN/DR4/MDMA0/SX80.WAV +MDMT0_SI1832 TRAIN/DR2/MDMT0/SI1832.WAV +MDMT0_SI2341 TRAIN/DR2/MDMT0/SI2341.WAV +MDMT0_SI572 TRAIN/DR2/MDMT0/SI572.WAV +MDMT0_SX122 TRAIN/DR2/MDMT0/SX122.WAV +MDMT0_SX212 TRAIN/DR2/MDMT0/SX212.WAV +MDMT0_SX302 TRAIN/DR2/MDMT0/SX302.WAV +MDMT0_SX32 TRAIN/DR2/MDMT0/SX32.WAV +MDMT0_SX392 TRAIN/DR2/MDMT0/SX392.WAV +MDNS0_SI1011 TRAIN/DR3/MDNS0/SI1011.WAV +MDNS0_SI2271 TRAIN/DR3/MDNS0/SI2271.WAV +MDNS0_SI873 TRAIN/DR3/MDNS0/SI873.WAV +MDNS0_SX111 TRAIN/DR3/MDNS0/SX111.WAV +MDNS0_SX201 TRAIN/DR3/MDNS0/SX201.WAV +MDNS0_SX21 TRAIN/DR3/MDNS0/SX21.WAV +MDNS0_SX291 TRAIN/DR3/MDNS0/SX291.WAV +MDNS0_SX381 TRAIN/DR3/MDNS0/SX381.WAV +MDPB0_SI1760 TRAIN/DR7/MDPB0/SI1760.WAV +MDPB0_SI2126 TRAIN/DR7/MDPB0/SI2126.WAV +MDPB0_SI866 TRAIN/DR7/MDPB0/SI866.WAV +MDPB0_SX146 TRAIN/DR7/MDPB0/SX146.WAV +MDPB0_SX236 TRAIN/DR7/MDPB0/SX236.WAV +MDPB0_SX326 TRAIN/DR7/MDPB0/SX326.WAV +MDPB0_SX416 TRAIN/DR7/MDPB0/SX416.WAV +MDPB0_SX56 TRAIN/DR7/MDPB0/SX56.WAV +MDPK0_SI1053 TRAIN/DR1/MDPK0/SI1053.WAV +MDPK0_SI1683 TRAIN/DR1/MDPK0/SI1683.WAV +MDPK0_SI552 TRAIN/DR1/MDPK0/SI552.WAV +MDPK0_SX153 TRAIN/DR1/MDPK0/SX153.WAV +MDPK0_SX243 TRAIN/DR1/MDPK0/SX243.WAV +MDPK0_SX333 TRAIN/DR1/MDPK0/SX333.WAV +MDPK0_SX423 TRAIN/DR1/MDPK0/SX423.WAV +MDPK0_SX63 TRAIN/DR1/MDPK0/SX63.WAV +MDPS0_SI1651 TRAIN/DR2/MDPS0/SI1651.WAV +MDPS0_SI1979 TRAIN/DR2/MDPS0/SI1979.WAV +MDPS0_SI719 TRAIN/DR2/MDPS0/SI719.WAV +MDPS0_SX179 TRAIN/DR2/MDPS0/SX179.WAV +MDPS0_SX269 TRAIN/DR2/MDPS0/SX269.WAV +MDPS0_SX359 TRAIN/DR2/MDPS0/SX359.WAV +MDPS0_SX449 TRAIN/DR2/MDPS0/SX449.WAV +MDPS0_SX89 TRAIN/DR2/MDPS0/SX89.WAV +MDRD0_SI1382 TRAIN/DR6/MDRD0/SI1382.WAV +MDRD0_SI2012 TRAIN/DR6/MDRD0/SI2012.WAV +MDRD0_SI752 TRAIN/DR6/MDRD0/SI752.WAV +MDRD0_SX122 TRAIN/DR6/MDRD0/SX122.WAV +MDRD0_SX212 TRAIN/DR6/MDRD0/SX212.WAV +MDRD0_SX302 TRAIN/DR6/MDRD0/SX302.WAV +MDRD0_SX32 TRAIN/DR6/MDRD0/SX32.WAV +MDRD0_SX392 TRAIN/DR6/MDRD0/SX392.WAV +MDSJ0_SI1462 TRAIN/DR5/MDSJ0/SI1462.WAV +MDSJ0_SI2092 TRAIN/DR5/MDSJ0/SI2092.WAV +MDSJ0_SI832 TRAIN/DR5/MDSJ0/SI832.WAV +MDSJ0_SX112 TRAIN/DR5/MDSJ0/SX112.WAV +MDSJ0_SX22 TRAIN/DR5/MDSJ0/SX22.WAV +MDSJ0_SX292 TRAIN/DR5/MDSJ0/SX292.WAV +MDSJ0_SX382 TRAIN/DR5/MDSJ0/SX382.WAV +MDSJ0_SX438 TRAIN/DR5/MDSJ0/SX438.WAV +MDSS0_SI1881 TRAIN/DR2/MDSS0/SI1881.WAV +MDSS0_SI2087 TRAIN/DR2/MDSS0/SI2087.WAV +MDSS0_SI621 TRAIN/DR2/MDSS0/SI621.WAV +MDSS0_SX171 TRAIN/DR2/MDSS0/SX171.WAV +MDSS0_SX261 TRAIN/DR2/MDSS0/SX261.WAV +MDSS0_SX351 TRAIN/DR2/MDSS0/SX351.WAV +MDSS0_SX441 TRAIN/DR2/MDSS0/SX441.WAV +MDSS0_SX81 TRAIN/DR2/MDSS0/SX81.WAV +MDSS1_SI1327 TRAIN/DR3/MDSS1/SI1327.WAV +MDSS1_SI1713 TRAIN/DR3/MDSS1/SI1713.WAV +MDSS1_SI697 TRAIN/DR3/MDSS1/SI697.WAV +MDSS1_SX157 TRAIN/DR3/MDSS1/SX157.WAV +MDSS1_SX247 TRAIN/DR3/MDSS1/SX247.WAV +MDSS1_SX337 TRAIN/DR3/MDSS1/SX337.WAV +MDSS1_SX427 TRAIN/DR3/MDSS1/SX427.WAV +MDSS1_SX67 TRAIN/DR3/MDSS1/SX67.WAV +MDTB0_SI1200 TRAIN/DR3/MDTB0/SI1200.WAV +MDTB0_SI1830 TRAIN/DR3/MDTB0/SI1830.WAV +MDTB0_SI570 TRAIN/DR3/MDTB0/SI570.WAV +MDTB0_SX120 TRAIN/DR3/MDTB0/SX120.WAV +MDTB0_SX210 TRAIN/DR3/MDTB0/SX210.WAV +MDTB0_SX300 TRAIN/DR3/MDTB0/SX300.WAV +MDTB0_SX321 TRAIN/DR3/MDTB0/SX321.WAV +MDTB0_SX390 TRAIN/DR3/MDTB0/SX390.WAV +MDWD0_SI1260 TRAIN/DR2/MDWD0/SI1260.WAV +MDWD0_SI1890 TRAIN/DR2/MDWD0/SI1890.WAV +MDWD0_SI557 TRAIN/DR2/MDWD0/SI557.WAV +MDWD0_SX180 TRAIN/DR2/MDWD0/SX180.WAV +MDWD0_SX270 TRAIN/DR2/MDWD0/SX270.WAV +MDWD0_SX360 TRAIN/DR2/MDWD0/SX360.WAV +MDWD0_SX450 TRAIN/DR2/MDWD0/SX450.WAV +MDWD0_SX90 TRAIN/DR2/MDWD0/SX90.WAV +MDWH0_SI1168 TRAIN/DR5/MDWH0/SI1168.WAV +MDWH0_SI1925 TRAIN/DR5/MDWH0/SI1925.WAV +MDWH0_SI665 TRAIN/DR5/MDWH0/SI665.WAV +MDWH0_SX125 TRAIN/DR5/MDWH0/SX125.WAV +MDWH0_SX215 TRAIN/DR5/MDWH0/SX215.WAV +MDWH0_SX305 TRAIN/DR5/MDWH0/SX305.WAV +MDWH0_SX35 TRAIN/DR5/MDWH0/SX35.WAV +MDWH0_SX395 TRAIN/DR5/MDWH0/SX395.WAV +MDWM0_SI1546 TRAIN/DR3/MDWM0/SI1546.WAV +MDWM0_SI2176 TRAIN/DR3/MDWM0/SI2176.WAV +MDWM0_SI916 TRAIN/DR3/MDWM0/SI916.WAV +MDWM0_SX106 TRAIN/DR3/MDWM0/SX106.WAV +MDWM0_SX16 TRAIN/DR3/MDWM0/SX16.WAV +MDWM0_SX286 TRAIN/DR3/MDWM0/SX286.WAV +MDWM0_SX376 TRAIN/DR3/MDWM0/SX376.WAV +MDWM0_SX433 TRAIN/DR3/MDWM0/SX433.WAV +MEAL0_SI1547 TRAIN/DR6/MEAL0/SI1547.WAV +MEAL0_SI2177 TRAIN/DR6/MEAL0/SI2177.WAV +MEAL0_SI917 TRAIN/DR6/MEAL0/SI917.WAV +MEAL0_SX107 TRAIN/DR6/MEAL0/SX107.WAV +MEAL0_SX197 TRAIN/DR6/MEAL0/SX197.WAV +MEAL0_SX287 TRAIN/DR6/MEAL0/SX287.WAV +MEAL0_SX347 TRAIN/DR6/MEAL0/SX347.WAV +MEAL0_SX377 TRAIN/DR6/MEAL0/SX377.WAV +MEDR0_SI1374 TRAIN/DR1/MEDR0/SI1374.WAV +MEDR0_SI2004 TRAIN/DR1/MEDR0/SI2004.WAV +MEDR0_SI744 TRAIN/DR1/MEDR0/SI744.WAV +MEDR0_SX114 TRAIN/DR1/MEDR0/SX114.WAV +MEDR0_SX204 TRAIN/DR1/MEDR0/SX204.WAV +MEDR0_SX24 TRAIN/DR1/MEDR0/SX24.WAV +MEDR0_SX294 TRAIN/DR1/MEDR0/SX294.WAV +MEDR0_SX384 TRAIN/DR1/MEDR0/SX384.WAV +MEFG0_SI465 TRAIN/DR2/MEFG0/SI465.WAV +MEFG0_SI491 TRAIN/DR2/MEFG0/SI491.WAV +MEFG0_SI598 TRAIN/DR2/MEFG0/SI598.WAV +MEFG0_SX105 TRAIN/DR2/MEFG0/SX105.WAV +MEFG0_SX15 TRAIN/DR2/MEFG0/SX15.WAV +MEFG0_SX195 TRAIN/DR2/MEFG0/SX195.WAV +MEFG0_SX285 TRAIN/DR2/MEFG0/SX285.WAV +MEFG0_SX375 TRAIN/DR2/MEFG0/SX375.WAV +MEGJ0_SI1337 TRAIN/DR5/MEGJ0/SI1337.WAV +MEGJ0_SI1967 TRAIN/DR5/MEGJ0/SI1967.WAV +MEGJ0_SI707 TRAIN/DR5/MEGJ0/SI707.WAV +MEGJ0_SX167 TRAIN/DR5/MEGJ0/SX167.WAV +MEGJ0_SX257 TRAIN/DR5/MEGJ0/SX257.WAV +MEGJ0_SX3 TRAIN/DR5/MEGJ0/SX3.WAV +MEGJ0_SX437 TRAIN/DR5/MEGJ0/SX437.WAV +MEGJ0_SX77 TRAIN/DR5/MEGJ0/SX77.WAV +MEJL0_SI1592 TRAIN/DR6/MEJL0/SI1592.WAV +MEJL0_SI1654 TRAIN/DR6/MEJL0/SI1654.WAV +MEJL0_SI962 TRAIN/DR6/MEJL0/SI962.WAV +MEJL0_SX152 TRAIN/DR6/MEJL0/SX152.WAV +MEJL0_SX242 TRAIN/DR6/MEJL0/SX242.WAV +MEJL0_SX332 TRAIN/DR6/MEJL0/SX332.WAV +MEJL0_SX422 TRAIN/DR6/MEJL0/SX422.WAV +MEJL0_SX62 TRAIN/DR6/MEJL0/SX62.WAV +MEJS0_SI1240 TRAIN/DR8/MEJS0/SI1240.WAV +MEJS0_SI1870 TRAIN/DR8/MEJS0/SI1870.WAV +MEJS0_SI610 TRAIN/DR8/MEJS0/SI610.WAV +MEJS0_SX160 TRAIN/DR8/MEJS0/SX160.WAV +MEJS0_SX250 TRAIN/DR8/MEJS0/SX250.WAV +MEJS0_SX340 TRAIN/DR8/MEJS0/SX340.WAV +MEJS0_SX430 TRAIN/DR8/MEJS0/SX430.WAV +MEJS0_SX70 TRAIN/DR8/MEJS0/SX70.WAV +MESG0_SI1332 TRAIN/DR4/MESG0/SI1332.WAV +MESG0_SI1962 TRAIN/DR4/MESG0/SI1962.WAV +MESG0_SI702 TRAIN/DR4/MESG0/SI702.WAV +MESG0_SX162 TRAIN/DR4/MESG0/SX162.WAV +MESG0_SX252 TRAIN/DR4/MESG0/SX252.WAV +MESG0_SX342 TRAIN/DR4/MESG0/SX342.WAV +MESG0_SX432 TRAIN/DR4/MESG0/SX432.WAV +MESG0_SX72 TRAIN/DR4/MESG0/SX72.WAV +MESJ0_SI2039 TRAIN/DR6/MESJ0/SI2039.WAV +MESJ0_SI2257 TRAIN/DR6/MESJ0/SI2257.WAV +MESJ0_SI997 TRAIN/DR6/MESJ0/SI997.WAV +MESJ0_SX187 TRAIN/DR6/MESJ0/SX187.WAV +MESJ0_SX277 TRAIN/DR6/MESJ0/SX277.WAV +MESJ0_SX367 TRAIN/DR6/MESJ0/SX367.WAV +MESJ0_SX7 TRAIN/DR6/MESJ0/SX7.WAV +MESJ0_SX97 TRAIN/DR6/MESJ0/SX97.WAV +MEWM0_SI1348 TRAIN/DR5/MEWM0/SI1348.WAV +MEWM0_SI1978 TRAIN/DR5/MEWM0/SI1978.WAV +MEWM0_SI718 TRAIN/DR5/MEWM0/SI718.WAV +MEWM0_SX178 TRAIN/DR5/MEWM0/SX178.WAV +MEWM0_SX268 TRAIN/DR5/MEWM0/SX268.WAV +MEWM0_SX358 TRAIN/DR5/MEWM0/SX358.WAV +MEWM0_SX448 TRAIN/DR5/MEWM0/SX448.WAV +MEWM0_SX88 TRAIN/DR5/MEWM0/SX88.WAV +MFER0_SI1492 TRAIN/DR5/MFER0/SI1492.WAV +MFER0_SI2122 TRAIN/DR5/MFER0/SI2122.WAV +MFER0_SI862 TRAIN/DR5/MFER0/SI862.WAV +MFER0_SX142 TRAIN/DR5/MFER0/SX142.WAV +MFER0_SX232 TRAIN/DR5/MFER0/SX232.WAV +MFER0_SX322 TRAIN/DR5/MFER0/SX322.WAV +MFER0_SX412 TRAIN/DR5/MFER0/SX412.WAV +MFER0_SX52 TRAIN/DR5/MFER0/SX52.WAV +MFMC0_SI1132 TRAIN/DR3/MFMC0/SI1132.WAV +MFMC0_SI1762 TRAIN/DR3/MFMC0/SI1762.WAV +MFMC0_SI502 TRAIN/DR3/MFMC0/SI502.WAV +MFMC0_SX142 TRAIN/DR3/MFMC0/SX142.WAV +MFMC0_SX232 TRAIN/DR3/MFMC0/SX232.WAV +MFMC0_SX322 TRAIN/DR3/MFMC0/SX322.WAV +MFMC0_SX412 TRAIN/DR3/MFMC0/SX412.WAV +MFMC0_SX52 TRAIN/DR3/MFMC0/SX52.WAV +MFRM0_SI1155 TRAIN/DR4/MFRM0/SI1155.WAV +MFRM0_SI1717 TRAIN/DR4/MFRM0/SI1717.WAV +MFRM0_SI1785 TRAIN/DR4/MFRM0/SI1785.WAV +MFRM0_SX165 TRAIN/DR4/MFRM0/SX165.WAV +MFRM0_SX255 TRAIN/DR4/MFRM0/SX255.WAV +MFRM0_SX345 TRAIN/DR4/MFRM0/SX345.WAV +MFRM0_SX435 TRAIN/DR4/MFRM0/SX435.WAV +MFRM0_SX75 TRAIN/DR4/MFRM0/SX75.WAV +MFWK0_SI1249 TRAIN/DR4/MFWK0/SI1249.WAV +MFWK0_SI1879 TRAIN/DR4/MFWK0/SI1879.WAV +MFWK0_SI619 TRAIN/DR4/MFWK0/SI619.WAV +MFWK0_SX169 TRAIN/DR4/MFWK0/SX169.WAV +MFWK0_SX259 TRAIN/DR4/MFWK0/SX259.WAV +MFWK0_SX349 TRAIN/DR4/MFWK0/SX349.WAV +MFWK0_SX439 TRAIN/DR4/MFWK0/SX439.WAV +MFWK0_SX79 TRAIN/DR4/MFWK0/SX79.WAV +MFXS0_SI1674 TRAIN/DR7/MFXS0/SI1674.WAV +MFXS0_SI2225 TRAIN/DR7/MFXS0/SI2225.WAV +MFXS0_SI2304 TRAIN/DR7/MFXS0/SI2304.WAV +MFXS0_SX144 TRAIN/DR7/MFXS0/SX144.WAV +MFXS0_SX234 TRAIN/DR7/MFXS0/SX234.WAV +MFXS0_SX324 TRAIN/DR7/MFXS0/SX324.WAV +MFXS0_SX414 TRAIN/DR7/MFXS0/SX414.WAV +MFXS0_SX54 TRAIN/DR7/MFXS0/SX54.WAV +MFXV0_SI1005 TRAIN/DR7/MFXV0/SI1005.WAV +MFXV0_SI1342 TRAIN/DR7/MFXV0/SI1342.WAV +MFXV0_SI1635 TRAIN/DR7/MFXV0/SI1635.WAV +MFXV0_SX105 TRAIN/DR7/MFXV0/SX105.WAV +MFXV0_SX15 TRAIN/DR7/MFXV0/SX15.WAV +MFXV0_SX195 TRAIN/DR7/MFXV0/SX195.WAV +MFXV0_SX285 TRAIN/DR7/MFXV0/SX285.WAV +MFXV0_SX375 TRAIN/DR7/MFXV0/SX375.WAV +MGAF0_SI1282 TRAIN/DR3/MGAF0/SI1282.WAV +MGAF0_SI1912 TRAIN/DR3/MGAF0/SI1912.WAV +MGAF0_SI652 TRAIN/DR3/MGAF0/SI652.WAV +MGAF0_SX112 TRAIN/DR3/MGAF0/SX112.WAV +MGAF0_SX202 TRAIN/DR3/MGAF0/SX202.WAV +MGAF0_SX22 TRAIN/DR3/MGAF0/SX22.WAV +MGAF0_SX292 TRAIN/DR3/MGAF0/SX292.WAV +MGAF0_SX382 TRAIN/DR3/MGAF0/SX382.WAV +MGAG0_SI1321 TRAIN/DR4/MGAG0/SI1321.WAV +MGAG0_SI645 TRAIN/DR4/MGAG0/SI645.WAV +MGAG0_SI691 TRAIN/DR4/MGAG0/SI691.WAV +MGAG0_SX151 TRAIN/DR4/MGAG0/SX151.WAV +MGAG0_SX241 TRAIN/DR4/MGAG0/SX241.WAV +MGAG0_SX331 TRAIN/DR4/MGAG0/SX331.WAV +MGAG0_SX421 TRAIN/DR4/MGAG0/SX421.WAV +MGAG0_SX61 TRAIN/DR4/MGAG0/SX61.WAV +MGAK0_SI1036 TRAIN/DR7/MGAK0/SI1036.WAV +MGAK0_SI1666 TRAIN/DR7/MGAK0/SI1666.WAV +MGAK0_SI2296 TRAIN/DR7/MGAK0/SI2296.WAV +MGAK0_SX136 TRAIN/DR7/MGAK0/SX136.WAV +MGAK0_SX226 TRAIN/DR7/MGAK0/SX226.WAV +MGAK0_SX316 TRAIN/DR7/MGAK0/SX316.WAV +MGAK0_SX406 TRAIN/DR7/MGAK0/SX406.WAV +MGAK0_SX46 TRAIN/DR7/MGAK0/SX46.WAV +MGAR0_SI1212 TRAIN/DR7/MGAR0/SI1212.WAV +MGAR0_SI1694 TRAIN/DR7/MGAR0/SI1694.WAV +MGAR0_SI1842 TRAIN/DR7/MGAR0/SI1842.WAV +MGAR0_SX132 TRAIN/DR7/MGAR0/SX132.WAV +MGAR0_SX222 TRAIN/DR7/MGAR0/SX222.WAV +MGAR0_SX312 TRAIN/DR7/MGAR0/SX312.WAV +MGAR0_SX402 TRAIN/DR7/MGAR0/SX402.WAV +MGAR0_SX42 TRAIN/DR7/MGAR0/SX42.WAV +MGAW0_SI1165 TRAIN/DR7/MGAW0/SI1165.WAV +MGAW0_SI1802 TRAIN/DR7/MGAW0/SI1802.WAV +MGAW0_SI535 TRAIN/DR7/MGAW0/SI535.WAV +MGAW0_SX175 TRAIN/DR7/MGAW0/SX175.WAV +MGAW0_SX265 TRAIN/DR7/MGAW0/SX265.WAV +MGAW0_SX355 TRAIN/DR7/MGAW0/SX355.WAV +MGAW0_SX445 TRAIN/DR7/MGAW0/SX445.WAV +MGAW0_SX85 TRAIN/DR7/MGAW0/SX85.WAV +MGES0_SI1481 TRAIN/DR5/MGES0/SI1481.WAV +MGES0_SI2111 TRAIN/DR5/MGES0/SI2111.WAV +MGES0_SI851 TRAIN/DR5/MGES0/SI851.WAV +MGES0_SX131 TRAIN/DR5/MGES0/SX131.WAV +MGES0_SX221 TRAIN/DR5/MGES0/SX221.WAV +MGES0_SX311 TRAIN/DR5/MGES0/SX311.WAV +MGES0_SX401 TRAIN/DR5/MGES0/SX401.WAV +MGES0_SX41 TRAIN/DR5/MGES0/SX41.WAV +MGJC0_SI1256 TRAIN/DR4/MGJC0/SI1256.WAV +MGJC0_SI1335 TRAIN/DR4/MGJC0/SI1335.WAV +MGJC0_SI1965 TRAIN/DR4/MGJC0/SI1965.WAV +MGJC0_SX165 TRAIN/DR4/MGJC0/SX165.WAV +MGJC0_SX255 TRAIN/DR4/MGJC0/SX255.WAV +MGJC0_SX345 TRAIN/DR4/MGJC0/SX345.WAV +MGJC0_SX435 TRAIN/DR4/MGJC0/SX435.WAV +MGJC0_SX75 TRAIN/DR4/MGJC0/SX75.WAV +MGRL0_SI1497 TRAIN/DR1/MGRL0/SI1497.WAV +MGRL0_SI2127 TRAIN/DR1/MGRL0/SI2127.WAV +MGRL0_SI867 TRAIN/DR1/MGRL0/SI867.WAV +MGRL0_SX147 TRAIN/DR1/MGRL0/SX147.WAV +MGRL0_SX237 TRAIN/DR1/MGRL0/SX237.WAV +MGRL0_SX327 TRAIN/DR1/MGRL0/SX327.WAV +MGRL0_SX417 TRAIN/DR1/MGRL0/SX417.WAV +MGRL0_SX57 TRAIN/DR1/MGRL0/SX57.WAV +MGRP0_SI1317 TRAIN/DR4/MGRP0/SI1317.WAV +MGRP0_SI1947 TRAIN/DR4/MGRP0/SI1947.WAV +MGRP0_SI687 TRAIN/DR4/MGRP0/SI687.WAV +MGRP0_SX147 TRAIN/DR4/MGRP0/SX147.WAV +MGRP0_SX237 TRAIN/DR4/MGRP0/SX237.WAV +MGRP0_SX327 TRAIN/DR4/MGRP0/SX327.WAV +MGRP0_SX417 TRAIN/DR4/MGRP0/SX417.WAV +MGRP0_SX57 TRAIN/DR4/MGRP0/SX57.WAV +MGSH0_SI1176 TRAIN/DR5/MGSH0/SI1176.WAV +MGSH0_SI1806 TRAIN/DR5/MGSH0/SI1806.WAV +MGSH0_SI546 TRAIN/DR5/MGSH0/SI546.WAV +MGSH0_SX127 TRAIN/DR5/MGSH0/SX127.WAV +MGSH0_SX186 TRAIN/DR5/MGSH0/SX186.WAV +MGSH0_SX276 TRAIN/DR5/MGSH0/SX276.WAV +MGSH0_SX6 TRAIN/DR5/MGSH0/SX6.WAV +MGSH0_SX96 TRAIN/DR5/MGSH0/SX96.WAV +MGSL0_SI1164 TRAIN/DR7/MGSL0/SI1164.WAV +MGSL0_SI534 TRAIN/DR7/MGSL0/SI534.WAV +MGSL0_SI797 TRAIN/DR7/MGSL0/SI797.WAV +MGSL0_SX174 TRAIN/DR7/MGSL0/SX174.WAV +MGSL0_SX264 TRAIN/DR7/MGSL0/SX264.WAV +MGSL0_SX354 TRAIN/DR7/MGSL0/SX354.WAV +MGSL0_SX444 TRAIN/DR7/MGSL0/SX444.WAV +MGSL0_SX84 TRAIN/DR7/MGSL0/SX84.WAV +MGXP0_SI1087 TRAIN/DR4/MGXP0/SI1087.WAV +MGXP0_SI457 TRAIN/DR4/MGXP0/SI457.WAV +MGXP0_SI525 TRAIN/DR4/MGXP0/SI525.WAV +MGXP0_SX187 TRAIN/DR4/MGXP0/SX187.WAV +MGXP0_SX277 TRAIN/DR4/MGXP0/SX277.WAV +MGXP0_SX367 TRAIN/DR4/MGXP0/SX367.WAV +MGXP0_SX7 TRAIN/DR4/MGXP0/SX7.WAV +MGXP0_SX97 TRAIN/DR4/MGXP0/SX97.WAV +MHBS0_SI1575 TRAIN/DR7/MHBS0/SI1575.WAV +MHBS0_SI2205 TRAIN/DR7/MHBS0/SI2205.WAV +MHBS0_SI945 TRAIN/DR7/MHBS0/SI945.WAV +MHBS0_SX135 TRAIN/DR7/MHBS0/SX135.WAV +MHBS0_SX225 TRAIN/DR7/MHBS0/SX225.WAV +MHBS0_SX315 TRAIN/DR7/MHBS0/SX315.WAV +MHBS0_SX405 TRAIN/DR7/MHBS0/SX405.WAV +MHBS0_SX45 TRAIN/DR7/MHBS0/SX45.WAV +MHIT0_SI1613 TRAIN/DR5/MHIT0/SI1613.WAV +MHIT0_SI2243 TRAIN/DR5/MHIT0/SI2243.WAV +MHIT0_SI983 TRAIN/DR5/MHIT0/SI983.WAV +MHIT0_SX173 TRAIN/DR5/MHIT0/SX173.WAV +MHIT0_SX263 TRAIN/DR5/MHIT0/SX263.WAV +MHIT0_SX353 TRAIN/DR5/MHIT0/SX353.WAV +MHIT0_SX443 TRAIN/DR5/MHIT0/SX443.WAV +MHIT0_SX83 TRAIN/DR5/MHIT0/SX83.WAV +MHJB0_SI1017 TRAIN/DR3/MHJB0/SI1017.WAV +MHJB0_SI1647 TRAIN/DR3/MHJB0/SI1647.WAV +MHJB0_SI2277 TRAIN/DR3/MHJB0/SI2277.WAV +MHJB0_SX117 TRAIN/DR3/MHJB0/SX117.WAV +MHJB0_SX207 TRAIN/DR3/MHJB0/SX207.WAV +MHJB0_SX27 TRAIN/DR3/MHJB0/SX27.WAV +MHJB0_SX297 TRAIN/DR3/MHJB0/SX297.WAV +MHJB0_SX387 TRAIN/DR3/MHJB0/SX387.WAV +MHMG0_SI1365 TRAIN/DR5/MHMG0/SI1365.WAV +MHMG0_SI1995 TRAIN/DR5/MHMG0/SI1995.WAV +MHMG0_SI735 TRAIN/DR5/MHMG0/SI735.WAV +MHMG0_SX105 TRAIN/DR5/MHMG0/SX105.WAV +MHMG0_SX15 TRAIN/DR5/MHMG0/SX15.WAV +MHMG0_SX195 TRAIN/DR5/MHMG0/SX195.WAV +MHMG0_SX285 TRAIN/DR5/MHMG0/SX285.WAV +MHMG0_SX375 TRAIN/DR5/MHMG0/SX375.WAV +MHMR0_SI1119 TRAIN/DR3/MHMR0/SI1119.WAV +MHMR0_SI1692 TRAIN/DR3/MHMR0/SI1692.WAV +MHMR0_SI489 TRAIN/DR3/MHMR0/SI489.WAV +MHMR0_SX129 TRAIN/DR3/MHMR0/SX129.WAV +MHMR0_SX219 TRAIN/DR3/MHMR0/SX219.WAV +MHMR0_SX309 TRAIN/DR3/MHMR0/SX309.WAV +MHMR0_SX39 TRAIN/DR3/MHMR0/SX39.WAV +MHMR0_SX399 TRAIN/DR3/MHMR0/SX399.WAV +MHRM0_SI1475 TRAIN/DR2/MHRM0/SI1475.WAV +MHRM0_SI2218 TRAIN/DR2/MHRM0/SI2218.WAV +MHRM0_SI958 TRAIN/DR2/MHRM0/SI958.WAV +MHRM0_SX148 TRAIN/DR2/MHRM0/SX148.WAV +MHRM0_SX238 TRAIN/DR2/MHRM0/SX238.WAV +MHRM0_SX328 TRAIN/DR2/MHRM0/SX328.WAV +MHRM0_SX418 TRAIN/DR2/MHRM0/SX418.WAV +MHRM0_SX58 TRAIN/DR2/MHRM0/SX58.WAV +MHXL0_SI1772 TRAIN/DR7/MHXL0/SI1772.WAV +MHXL0_SI512 TRAIN/DR7/MHXL0/SI512.WAV +MHXL0_SI612 TRAIN/DR7/MHXL0/SI612.WAV +MHXL0_SX152 TRAIN/DR7/MHXL0/SX152.WAV +MHXL0_SX242 TRAIN/DR7/MHXL0/SX242.WAV +MHXL0_SX332 TRAIN/DR7/MHXL0/SX332.WAV +MHXL0_SX422 TRAIN/DR7/MHXL0/SX422.WAV +MHXL0_SX62 TRAIN/DR7/MHXL0/SX62.WAV +MILB0_SI2163 TRAIN/DR3/MILB0/SI2163.WAV +MILB0_SI807 TRAIN/DR3/MILB0/SI807.WAV +MILB0_SI903 TRAIN/DR3/MILB0/SI903.WAV +MILB0_SX183 TRAIN/DR3/MILB0/SX183.WAV +MILB0_SX273 TRAIN/DR3/MILB0/SX273.WAV +MILB0_SX3 TRAIN/DR3/MILB0/SX3.WAV +MILB0_SX363 TRAIN/DR3/MILB0/SX363.WAV +MILB0_SX93 TRAIN/DR3/MILB0/SX93.WAV +MJAC0_SI1331 TRAIN/DR4/MJAC0/SI1331.WAV +MJAC0_SI2148 TRAIN/DR4/MJAC0/SI2148.WAV +MJAC0_SI701 TRAIN/DR4/MJAC0/SI701.WAV +MJAC0_SX251 TRAIN/DR4/MJAC0/SX251.WAV +MJAC0_SX307 TRAIN/DR4/MJAC0/SX307.WAV +MJAC0_SX341 TRAIN/DR4/MJAC0/SX341.WAV +MJAC0_SX431 TRAIN/DR4/MJAC0/SX431.WAV +MJAC0_SX71 TRAIN/DR4/MJAC0/SX71.WAV +MJAE0_SI1524 TRAIN/DR2/MJAE0/SI1524.WAV +MJAE0_SI1999 TRAIN/DR2/MJAE0/SI1999.WAV +MJAE0_SI2154 TRAIN/DR2/MJAE0/SI2154.WAV +MJAE0_SX174 TRAIN/DR2/MJAE0/SX174.WAV +MJAE0_SX264 TRAIN/DR2/MJAE0/SX264.WAV +MJAE0_SX354 TRAIN/DR2/MJAE0/SX354.WAV +MJAE0_SX444 TRAIN/DR2/MJAE0/SX444.WAV +MJAE0_SX84 TRAIN/DR2/MJAE0/SX84.WAV +MJAI0_SI1604 TRAIN/DR7/MJAI0/SI1604.WAV +MJAI0_SI682 TRAIN/DR7/MJAI0/SI682.WAV +MJAI0_SI710 TRAIN/DR7/MJAI0/SI710.WAV +MJAI0_SX164 TRAIN/DR7/MJAI0/SX164.WAV +MJAI0_SX254 TRAIN/DR7/MJAI0/SX254.WAV +MJAI0_SX344 TRAIN/DR7/MJAI0/SX344.WAV +MJAI0_SX434 TRAIN/DR7/MJAI0/SX434.WAV +MJAI0_SX74 TRAIN/DR7/MJAI0/SX74.WAV +MJBG0_SI1232 TRAIN/DR2/MJBG0/SI1232.WAV +MJBG0_SI1724 TRAIN/DR2/MJBG0/SI1724.WAV +MJBG0_SI1862 TRAIN/DR2/MJBG0/SI1862.WAV +MJBG0_SX152 TRAIN/DR2/MJBG0/SX152.WAV +MJBG0_SX242 TRAIN/DR2/MJBG0/SX242.WAV +MJBG0_SX332 TRAIN/DR2/MJBG0/SX332.WAV +MJBG0_SX422 TRAIN/DR2/MJBG0/SX422.WAV +MJBG0_SX62 TRAIN/DR2/MJBG0/SX62.WAV +MJDA0_SI1031 TRAIN/DR3/MJDA0/SI1031.WAV +MJDA0_SI1661 TRAIN/DR3/MJDA0/SI1661.WAV +MJDA0_SI2291 TRAIN/DR3/MJDA0/SI2291.WAV +MJDA0_SX131 TRAIN/DR3/MJDA0/SX131.WAV +MJDA0_SX221 TRAIN/DR3/MJDA0/SX221.WAV +MJDA0_SX311 TRAIN/DR3/MJDA0/SX311.WAV +MJDA0_SX401 TRAIN/DR3/MJDA0/SX401.WAV +MJDA0_SX41 TRAIN/DR3/MJDA0/SX41.WAV +MJDC0_SI1161 TRAIN/DR4/MJDC0/SI1161.WAV +MJDC0_SI2165 TRAIN/DR4/MJDC0/SI2165.WAV +MJDC0_SI531 TRAIN/DR4/MJDC0/SI531.WAV +MJDC0_SX171 TRAIN/DR4/MJDC0/SX171.WAV +MJDC0_SX261 TRAIN/DR4/MJDC0/SX261.WAV +MJDC0_SX351 TRAIN/DR4/MJDC0/SX351.WAV +MJDC0_SX441 TRAIN/DR4/MJDC0/SX441.WAV +MJDC0_SX81 TRAIN/DR4/MJDC0/SX81.WAV +MJDE0_SI1120 TRAIN/DR2/MJDE0/SI1120.WAV +MJDE0_SI463 TRAIN/DR2/MJDE0/SI463.WAV +MJDE0_SI490 TRAIN/DR2/MJDE0/SI490.WAV +MJDE0_SX130 TRAIN/DR2/MJDE0/SX130.WAV +MJDE0_SX220 TRAIN/DR2/MJDE0/SX220.WAV +MJDE0_SX310 TRAIN/DR2/MJDE0/SX310.WAV +MJDE0_SX40 TRAIN/DR2/MJDE0/SX40.WAV +MJDE0_SX400 TRAIN/DR2/MJDE0/SX400.WAV +MJDG0_SI1042 TRAIN/DR7/MJDG0/SI1042.WAV +MJDG0_SI1672 TRAIN/DR7/MJDG0/SI1672.WAV +MJDG0_SI1705 TRAIN/DR7/MJDG0/SI1705.WAV +MJDG0_SX142 TRAIN/DR7/MJDG0/SX142.WAV +MJDG0_SX232 TRAIN/DR7/MJDG0/SX232.WAV +MJDG0_SX322 TRAIN/DR7/MJDG0/SX322.WAV +MJDG0_SX412 TRAIN/DR7/MJDG0/SX412.WAV +MJDG0_SX52 TRAIN/DR7/MJDG0/SX52.WAV +MJDM0_SI1340 TRAIN/DR5/MJDM0/SI1340.WAV +MJDM0_SI1937 TRAIN/DR5/MJDM0/SI1937.WAV +MJDM0_SI974 TRAIN/DR5/MJDM0/SI974.WAV +MJDM0_SX170 TRAIN/DR5/MJDM0/SX170.WAV +MJDM0_SX260 TRAIN/DR5/MJDM0/SX260.WAV +MJDM0_SX350 TRAIN/DR5/MJDM0/SX350.WAV +MJDM0_SX440 TRAIN/DR5/MJDM0/SX440.WAV +MJDM0_SX80 TRAIN/DR5/MJDM0/SX80.WAV +MJEB0_SI1286 TRAIN/DR2/MJEB0/SI1286.WAV +MJEB0_SI1916 TRAIN/DR2/MJEB0/SI1916.WAV +MJEB0_SI656 TRAIN/DR2/MJEB0/SI656.WAV +MJEB0_SX170 TRAIN/DR2/MJEB0/SX170.WAV +MJEB0_SX206 TRAIN/DR2/MJEB0/SX206.WAV +MJEB0_SX26 TRAIN/DR2/MJEB0/SX26.WAV +MJEB0_SX296 TRAIN/DR2/MJEB0/SX296.WAV +MJEB0_SX386 TRAIN/DR2/MJEB0/SX386.WAV +MJEB1_SI1467 TRAIN/DR1/MJEB1/SI1467.WAV +MJEB1_SI2097 TRAIN/DR1/MJEB1/SI2097.WAV +MJEB1_SI837 TRAIN/DR1/MJEB1/SI837.WAV +MJEB1_SX117 TRAIN/DR1/MJEB1/SX117.WAV +MJEB1_SX207 TRAIN/DR1/MJEB1/SX207.WAV +MJEB1_SX27 TRAIN/DR1/MJEB1/SX27.WAV +MJEB1_SX297 TRAIN/DR1/MJEB1/SX297.WAV +MJEB1_SX387 TRAIN/DR1/MJEB1/SX387.WAV +MJEE0_SI1237 TRAIN/DR4/MJEE0/SI1237.WAV +MJEE0_SI1867 TRAIN/DR4/MJEE0/SI1867.WAV +MJEE0_SI607 TRAIN/DR4/MJEE0/SI607.WAV +MJEE0_SX157 TRAIN/DR4/MJEE0/SX157.WAV +MJEE0_SX247 TRAIN/DR4/MJEE0/SX247.WAV +MJEE0_SX337 TRAIN/DR4/MJEE0/SX337.WAV +MJEE0_SX427 TRAIN/DR4/MJEE0/SX427.WAV +MJEE0_SX67 TRAIN/DR4/MJEE0/SX67.WAV +MJFH0_SI1107 TRAIN/DR5/MJFH0/SI1107.WAV +MJFH0_SI1737 TRAIN/DR5/MJFH0/SI1737.WAV +MJFH0_SI477 TRAIN/DR5/MJFH0/SI477.WAV +MJFH0_SX117 TRAIN/DR5/MJFH0/SX117.WAV +MJFH0_SX207 TRAIN/DR5/MJFH0/SX207.WAV +MJFH0_SX27 TRAIN/DR5/MJFH0/SX27.WAV +MJFH0_SX297 TRAIN/DR5/MJFH0/SX297.WAV +MJFH0_SX387 TRAIN/DR5/MJFH0/SX387.WAV +MJFR0_SI1605 TRAIN/DR7/MJFR0/SI1605.WAV +MJFR0_SI2235 TRAIN/DR7/MJFR0/SI2235.WAV +MJFR0_SI975 TRAIN/DR7/MJFR0/SI975.WAV +MJFR0_SX165 TRAIN/DR7/MJFR0/SX165.WAV +MJFR0_SX255 TRAIN/DR7/MJFR0/SX255.WAV +MJFR0_SX345 TRAIN/DR7/MJFR0/SX345.WAV +MJFR0_SX435 TRAIN/DR7/MJFR0/SX435.WAV +MJFR0_SX75 TRAIN/DR7/MJFR0/SX75.WAV +MJHI0_SI1328 TRAIN/DR2/MJHI0/SI1328.WAV +MJHI0_SI555 TRAIN/DR2/MJHI0/SI555.WAV +MJHI0_SI698 TRAIN/DR2/MJHI0/SI698.WAV +MJHI0_SX158 TRAIN/DR2/MJHI0/SX158.WAV +MJHI0_SX248 TRAIN/DR2/MJHI0/SX248.WAV +MJHI0_SX338 TRAIN/DR2/MJHI0/SX338.WAV +MJHI0_SX428 TRAIN/DR2/MJHI0/SX428.WAV +MJHI0_SX68 TRAIN/DR2/MJHI0/SX68.WAV +MJJB0_SI1139 TRAIN/DR3/MJJB0/SI1139.WAV +MJJB0_SI1277 TRAIN/DR3/MJJB0/SI1277.WAV +MJJB0_SI1769 TRAIN/DR3/MJJB0/SI1769.WAV +MJJB0_SX149 TRAIN/DR3/MJJB0/SX149.WAV +MJJB0_SX239 TRAIN/DR3/MJJB0/SX239.WAV +MJJB0_SX329 TRAIN/DR3/MJJB0/SX329.WAV +MJJB0_SX419 TRAIN/DR3/MJJB0/SX419.WAV +MJJB0_SX59 TRAIN/DR3/MJJB0/SX59.WAV +MJJJ0_SI1163 TRAIN/DR4/MJJJ0/SI1163.WAV +MJJJ0_SI1793 TRAIN/DR4/MJJJ0/SI1793.WAV +MJJJ0_SI533 TRAIN/DR4/MJJJ0/SI533.WAV +MJJJ0_SX173 TRAIN/DR4/MJJJ0/SX173.WAV +MJJJ0_SX263 TRAIN/DR4/MJJJ0/SX263.WAV +MJJJ0_SX353 TRAIN/DR4/MJJJ0/SX353.WAV +MJJJ0_SX443 TRAIN/DR4/MJJJ0/SX443.WAV +MJJJ0_SX83 TRAIN/DR4/MJJJ0/SX83.WAV +MJJM0_SI1251 TRAIN/DR7/MJJM0/SI1251.WAV +MJJM0_SI1457 TRAIN/DR7/MJJM0/SI1457.WAV +MJJM0_SI827 TRAIN/DR7/MJJM0/SI827.WAV +MJJM0_SX107 TRAIN/DR7/MJJM0/SX107.WAV +MJJM0_SX17 TRAIN/DR7/MJJM0/SX17.WAV +MJJM0_SX197 TRAIN/DR7/MJJM0/SX197.WAV +MJJM0_SX287 TRAIN/DR7/MJJM0/SX287.WAV +MJJM0_SX377 TRAIN/DR7/MJJM0/SX377.WAV +MJKR0_SI1201 TRAIN/DR3/MJKR0/SI1201.WAV +MJKR0_SI1831 TRAIN/DR3/MJKR0/SI1831.WAV +MJKR0_SI571 TRAIN/DR3/MJKR0/SI571.WAV +MJKR0_SX121 TRAIN/DR3/MJKR0/SX121.WAV +MJKR0_SX211 TRAIN/DR3/MJKR0/SX211.WAV +MJKR0_SX301 TRAIN/DR3/MJKR0/SX301.WAV +MJKR0_SX31 TRAIN/DR3/MJKR0/SX31.WAV +MJKR0_SX391 TRAIN/DR3/MJKR0/SX391.WAV +MJLB0_SI1616 TRAIN/DR4/MJLB0/SI1616.WAV +MJLB0_SI2246 TRAIN/DR4/MJLB0/SI2246.WAV +MJLB0_SI986 TRAIN/DR4/MJLB0/SI986.WAV +MJLB0_SX176 TRAIN/DR4/MJLB0/SX176.WAV +MJLB0_SX266 TRAIN/DR4/MJLB0/SX266.WAV +MJLB0_SX356 TRAIN/DR4/MJLB0/SX356.WAV +MJLB0_SX446 TRAIN/DR4/MJLB0/SX446.WAV +MJLB0_SX86 TRAIN/DR4/MJLB0/SX86.WAV +MJLG1_SI1012 TRAIN/DR3/MJLG1/SI1012.WAV +MJLG1_SI1642 TRAIN/DR3/MJLG1/SI1642.WAV +MJLG1_SI2272 TRAIN/DR3/MJLG1/SI2272.WAV +MJLG1_SX112 TRAIN/DR3/MJLG1/SX112.WAV +MJLG1_SX202 TRAIN/DR3/MJLG1/SX202.WAV +MJLG1_SX22 TRAIN/DR3/MJLG1/SX22.WAV +MJLG1_SX292 TRAIN/DR3/MJLG1/SX292.WAV +MJLG1_SX382 TRAIN/DR3/MJLG1/SX382.WAV +MJLS0_SI1096 TRAIN/DR4/MJLS0/SI1096.WAV +MJLS0_SI1726 TRAIN/DR4/MJLS0/SI1726.WAV +MJLS0_SI466 TRAIN/DR4/MJLS0/SI466.WAV +MJLS0_SX106 TRAIN/DR4/MJLS0/SX106.WAV +MJLS0_SX16 TRAIN/DR4/MJLS0/SX16.WAV +MJLS0_SX196 TRAIN/DR4/MJLS0/SX196.WAV +MJLS0_SX286 TRAIN/DR4/MJLS0/SX286.WAV +MJLS0_SX376 TRAIN/DR4/MJLS0/SX376.WAV +MJMA0_SI1495 TRAIN/DR2/MJMA0/SI1495.WAV +MJMA0_SI2125 TRAIN/DR2/MJMA0/SI2125.WAV +MJMA0_SI865 TRAIN/DR2/MJMA0/SI865.WAV +MJMA0_SX145 TRAIN/DR2/MJMA0/SX145.WAV +MJMA0_SX235 TRAIN/DR2/MJMA0/SX235.WAV +MJMA0_SX325 TRAIN/DR2/MJMA0/SX325.WAV +MJMA0_SX415 TRAIN/DR2/MJMA0/SX415.WAV +MJMA0_SX55 TRAIN/DR2/MJMA0/SX55.WAV +MJMD0_SI1028 TRAIN/DR2/MJMD0/SI1028.WAV +MJMD0_SI1658 TRAIN/DR2/MJMD0/SI1658.WAV +MJMD0_SI2288 TRAIN/DR2/MJMD0/SI2288.WAV +MJMD0_SX128 TRAIN/DR2/MJMD0/SX128.WAV +MJMD0_SX218 TRAIN/DR2/MJMD0/SX218.WAV +MJMD0_SX308 TRAIN/DR2/MJMD0/SX308.WAV +MJMD0_SX38 TRAIN/DR2/MJMD0/SX38.WAV +MJMD0_SX398 TRAIN/DR2/MJMD0/SX398.WAV +MJMM0_SI1255 TRAIN/DR4/MJMM0/SI1255.WAV +MJMM0_SI1885 TRAIN/DR4/MJMM0/SI1885.WAV +MJMM0_SI625 TRAIN/DR4/MJMM0/SI625.WAV +MJMM0_SX175 TRAIN/DR4/MJMM0/SX175.WAV +MJMM0_SX265 TRAIN/DR4/MJMM0/SX265.WAV +MJMM0_SX355 TRAIN/DR4/MJMM0/SX355.WAV +MJMM0_SX445 TRAIN/DR4/MJMM0/SX445.WAV +MJMM0_SX85 TRAIN/DR4/MJMM0/SX85.WAV +MJPG0_SI1191 TRAIN/DR5/MJPG0/SI1191.WAV +MJPG0_SI1821 TRAIN/DR5/MJPG0/SI1821.WAV +MJPG0_SI561 TRAIN/DR5/MJPG0/SI561.WAV +MJPG0_SX111 TRAIN/DR5/MJPG0/SX111.WAV +MJPG0_SX201 TRAIN/DR5/MJPG0/SX201.WAV +MJPG0_SX21 TRAIN/DR5/MJPG0/SX21.WAV +MJPG0_SX291 TRAIN/DR5/MJPG0/SX291.WAV +MJPG0_SX381 TRAIN/DR5/MJPG0/SX381.WAV +MJPM0_SI1368 TRAIN/DR2/MJPM0/SI1368.WAV +MJPM0_SI1998 TRAIN/DR2/MJPM0/SI1998.WAV +MJPM0_SI738 TRAIN/DR2/MJPM0/SI738.WAV +MJPM0_SX108 TRAIN/DR2/MJPM0/SX108.WAV +MJPM0_SX18 TRAIN/DR2/MJPM0/SX18.WAV +MJPM0_SX198 TRAIN/DR2/MJPM0/SX198.WAV +MJPM0_SX288 TRAIN/DR2/MJPM0/SX288.WAV +MJPM0_SX378 TRAIN/DR2/MJPM0/SX378.WAV +MJPM1_SI1897 TRAIN/DR4/MJPM1/SI1897.WAV +MJPM1_SI2280 TRAIN/DR4/MJPM1/SI2280.WAV +MJPM1_SI761 TRAIN/DR4/MJPM1/SI761.WAV +MJPM1_SX131 TRAIN/DR4/MJPM1/SX131.WAV +MJPM1_SX221 TRAIN/DR4/MJPM1/SX221.WAV +MJPM1_SX311 TRAIN/DR4/MJPM1/SX311.WAV +MJPM1_SX401 TRAIN/DR4/MJPM1/SX401.WAV +MJPM1_SX41 TRAIN/DR4/MJPM1/SX41.WAV +MJRA0_SI1236 TRAIN/DR7/MJRA0/SI1236.WAV +MJRA0_SI1866 TRAIN/DR7/MJRA0/SI1866.WAV +MJRA0_SI606 TRAIN/DR7/MJRA0/SI606.WAV +MJRA0_SX156 TRAIN/DR7/MJRA0/SX156.WAV +MJRA0_SX246 TRAIN/DR7/MJRA0/SX246.WAV +MJRA0_SX336 TRAIN/DR7/MJRA0/SX336.WAV +MJRA0_SX426 TRAIN/DR7/MJRA0/SX426.WAV +MJRA0_SX66 TRAIN/DR7/MJRA0/SX66.WAV +MJRG0_SI1366 TRAIN/DR5/MJRG0/SI1366.WAV +MJRG0_SI1996 TRAIN/DR5/MJRG0/SI1996.WAV +MJRG0_SI736 TRAIN/DR5/MJRG0/SI736.WAV +MJRG0_SX106 TRAIN/DR5/MJRG0/SX106.WAV +MJRG0_SX16 TRAIN/DR5/MJRG0/SX16.WAV +MJRG0_SX286 TRAIN/DR5/MJRG0/SX286.WAV +MJRG0_SX352 TRAIN/DR5/MJRG0/SX352.WAV +MJRG0_SX376 TRAIN/DR5/MJRG0/SX376.WAV +MJRH0_SI1125 TRAIN/DR4/MJRH0/SI1125.WAV +MJRH0_SI1755 TRAIN/DR4/MJRH0/SI1755.WAV +MJRH0_SI1840 TRAIN/DR4/MJRH0/SI1840.WAV +MJRH0_SX135 TRAIN/DR4/MJRH0/SX135.WAV +MJRH0_SX225 TRAIN/DR4/MJRH0/SX225.WAV +MJRH0_SX315 TRAIN/DR4/MJRH0/SX315.WAV +MJRH0_SX405 TRAIN/DR4/MJRH0/SX405.WAV +MJRH0_SX45 TRAIN/DR4/MJRH0/SX45.WAV +MJRH1_SI1558 TRAIN/DR3/MJRH1/SI1558.WAV +MJRH1_SI1774 TRAIN/DR3/MJRH1/SI1774.WAV +MJRH1_SI514 TRAIN/DR3/MJRH1/SI514.WAV +MJRH1_SX154 TRAIN/DR3/MJRH1/SX154.WAV +MJRH1_SX244 TRAIN/DR3/MJRH1/SX244.WAV +MJRH1_SX334 TRAIN/DR3/MJRH1/SX334.WAV +MJRH1_SX424 TRAIN/DR3/MJRH1/SX424.WAV +MJRH1_SX64 TRAIN/DR3/MJRH1/SX64.WAV +MJRK0_SI1662 TRAIN/DR6/MJRK0/SI1662.WAV +MJRK0_SI2103 TRAIN/DR6/MJRK0/SI2103.WAV +MJRK0_SI880 TRAIN/DR6/MJRK0/SI880.WAV +MJRK0_SX160 TRAIN/DR6/MJRK0/SX160.WAV +MJRK0_SX250 TRAIN/DR6/MJRK0/SX250.WAV +MJRK0_SX340 TRAIN/DR6/MJRK0/SX340.WAV +MJRK0_SX430 TRAIN/DR6/MJRK0/SX430.WAV +MJRK0_SX70 TRAIN/DR6/MJRK0/SX70.WAV +MJRP0_SI1835 TRAIN/DR2/MJRP0/SI1835.WAV +MJRP0_SI1845 TRAIN/DR2/MJRP0/SI1845.WAV +MJRP0_SI585 TRAIN/DR2/MJRP0/SI585.WAV +MJRP0_SX135 TRAIN/DR2/MJRP0/SX135.WAV +MJRP0_SX225 TRAIN/DR2/MJRP0/SX225.WAV +MJRP0_SX315 TRAIN/DR2/MJRP0/SX315.WAV +MJRP0_SX405 TRAIN/DR2/MJRP0/SX405.WAV +MJRP0_SX45 TRAIN/DR2/MJRP0/SX45.WAV +MJSR0_SI1424 TRAIN/DR4/MJSR0/SI1424.WAV +MJSR0_SI2054 TRAIN/DR4/MJSR0/SI2054.WAV +MJSR0_SI794 TRAIN/DR4/MJSR0/SI794.WAV +MJSR0_SX164 TRAIN/DR4/MJSR0/SX164.WAV +MJSR0_SX254 TRAIN/DR4/MJSR0/SX254.WAV +MJSR0_SX344 TRAIN/DR4/MJSR0/SX344.WAV +MJSR0_SX434 TRAIN/DR4/MJSR0/SX434.WAV +MJSR0_SX74 TRAIN/DR4/MJSR0/SX74.WAV +MJWG0_SI2155 TRAIN/DR5/MJWG0/SI2155.WAV +MJWG0_SI813 TRAIN/DR5/MJWG0/SI813.WAV +MJWG0_SI895 TRAIN/DR5/MJWG0/SI895.WAV +MJWG0_SX175 TRAIN/DR5/MJWG0/SX175.WAV +MJWG0_SX265 TRAIN/DR5/MJWG0/SX265.WAV +MJWG0_SX355 TRAIN/DR5/MJWG0/SX355.WAV +MJWG0_SX445 TRAIN/DR5/MJWG0/SX445.WAV +MJWG0_SX85 TRAIN/DR5/MJWG0/SX85.WAV +MJWS0_SI1143 TRAIN/DR4/MJWS0/SI1143.WAV +MJWS0_SI1773 TRAIN/DR4/MJWS0/SI1773.WAV +MJWS0_SI513 TRAIN/DR4/MJWS0/SI513.WAV +MJWS0_SX153 TRAIN/DR4/MJWS0/SX153.WAV +MJWS0_SX243 TRAIN/DR4/MJWS0/SX243.WAV +MJWS0_SX333 TRAIN/DR4/MJWS0/SX333.WAV +MJWS0_SX423 TRAIN/DR4/MJWS0/SX423.WAV +MJWS0_SX63 TRAIN/DR4/MJWS0/SX63.WAV +MJWT0_SI1291 TRAIN/DR1/MJWT0/SI1291.WAV +MJWT0_SI1381 TRAIN/DR1/MJWT0/SI1381.WAV +MJWT0_SI751 TRAIN/DR1/MJWT0/SI751.WAV +MJWT0_SX121 TRAIN/DR1/MJWT0/SX121.WAV +MJWT0_SX211 TRAIN/DR1/MJWT0/SX211.WAV +MJWT0_SX301 TRAIN/DR1/MJWT0/SX301.WAV +MJWT0_SX31 TRAIN/DR1/MJWT0/SX31.WAV +MJWT0_SX391 TRAIN/DR1/MJWT0/SX391.WAV +MJXA0_SI1507 TRAIN/DR5/MJXA0/SI1507.WAV +MJXA0_SI2137 TRAIN/DR5/MJXA0/SI2137.WAV +MJXA0_SI877 TRAIN/DR5/MJXA0/SI877.WAV +MJXA0_SX157 TRAIN/DR5/MJXA0/SX157.WAV +MJXA0_SX247 TRAIN/DR5/MJXA0/SX247.WAV +MJXA0_SX337 TRAIN/DR5/MJXA0/SX337.WAV +MJXA0_SX427 TRAIN/DR5/MJXA0/SX427.WAV +MJXA0_SX67 TRAIN/DR5/MJXA0/SX67.WAV +MJXL0_SI1172 TRAIN/DR4/MJXL0/SI1172.WAV +MJXL0_SI1795 TRAIN/DR4/MJXL0/SI1795.WAV +MJXL0_SI542 TRAIN/DR4/MJXL0/SI542.WAV +MJXL0_SX182 TRAIN/DR4/MJXL0/SX182.WAV +MJXL0_SX272 TRAIN/DR4/MJXL0/SX272.WAV +MJXL0_SX362 TRAIN/DR4/MJXL0/SX362.WAV +MJXL0_SX452 TRAIN/DR4/MJXL0/SX452.WAV +MJXL0_SX92 TRAIN/DR4/MJXL0/SX92.WAV +MKAG0_SI1609 TRAIN/DR7/MKAG0/SI1609.WAV +MKAG0_SI2239 TRAIN/DR7/MKAG0/SI2239.WAV +MKAG0_SI979 TRAIN/DR7/MKAG0/SI979.WAV +MKAG0_SX169 TRAIN/DR7/MKAG0/SX169.WAV +MKAG0_SX259 TRAIN/DR7/MKAG0/SX259.WAV +MKAG0_SX30 TRAIN/DR7/MKAG0/SX30.WAV +MKAG0_SX439 TRAIN/DR7/MKAG0/SX439.WAV +MKAG0_SX79 TRAIN/DR7/MKAG0/SX79.WAV +MKAH0_SI1528 TRAIN/DR2/MKAH0/SI1528.WAV +MKAH0_SI2158 TRAIN/DR2/MKAH0/SI2158.WAV +MKAH0_SI898 TRAIN/DR2/MKAH0/SI898.WAV +MKAH0_SX178 TRAIN/DR2/MKAH0/SX178.WAV +MKAH0_SX268 TRAIN/DR2/MKAH0/SX268.WAV +MKAH0_SX358 TRAIN/DR2/MKAH0/SX358.WAV +MKAH0_SX448 TRAIN/DR2/MKAH0/SX448.WAV +MKAH0_SX88 TRAIN/DR2/MKAH0/SX88.WAV +MKAJ0_SI1414 TRAIN/DR2/MKAJ0/SI1414.WAV +MKAJ0_SI2044 TRAIN/DR2/MKAJ0/SI2044.WAV +MKAJ0_SI784 TRAIN/DR2/MKAJ0/SI784.WAV +MKAJ0_SX154 TRAIN/DR2/MKAJ0/SX154.WAV +MKAJ0_SX244 TRAIN/DR2/MKAJ0/SX244.WAV +MKAJ0_SX334 TRAIN/DR2/MKAJ0/SX334.WAV +MKAJ0_SX424 TRAIN/DR2/MKAJ0/SX424.WAV +MKAJ0_SX64 TRAIN/DR2/MKAJ0/SX64.WAV +MKAM0_SI1250 TRAIN/DR4/MKAM0/SI1250.WAV +MKAM0_SI1316 TRAIN/DR4/MKAM0/SI1316.WAV +MKAM0_SI1465 TRAIN/DR4/MKAM0/SI1465.WAV +MKAM0_SX146 TRAIN/DR4/MKAM0/SX146.WAV +MKAM0_SX236 TRAIN/DR4/MKAM0/SX236.WAV +MKAM0_SX326 TRAIN/DR4/MKAM0/SX326.WAV +MKAM0_SX416 TRAIN/DR4/MKAM0/SX416.WAV +MKAM0_SX56 TRAIN/DR4/MKAM0/SX56.WAV +MKDB0_SI2132 TRAIN/DR7/MKDB0/SI2132.WAV +MKDB0_SI588 TRAIN/DR7/MKDB0/SI588.WAV +MKDB0_SI872 TRAIN/DR7/MKDB0/SI872.WAV +MKDB0_SX152 TRAIN/DR7/MKDB0/SX152.WAV +MKDB0_SX242 TRAIN/DR7/MKDB0/SX242.WAV +MKDB0_SX332 TRAIN/DR7/MKDB0/SX332.WAV +MKDB0_SX422 TRAIN/DR7/MKDB0/SX422.WAV +MKDB0_SX62 TRAIN/DR7/MKDB0/SX62.WAV +MKDD0_SI1567 TRAIN/DR8/MKDD0/SI1567.WAV +MKDD0_SI2197 TRAIN/DR8/MKDD0/SI2197.WAV +MKDD0_SI937 TRAIN/DR8/MKDD0/SI937.WAV +MKDD0_SX127 TRAIN/DR8/MKDD0/SX127.WAV +MKDD0_SX217 TRAIN/DR8/MKDD0/SX217.WAV +MKDD0_SX307 TRAIN/DR8/MKDD0/SX307.WAV +MKDD0_SX37 TRAIN/DR8/MKDD0/SX37.WAV +MKDD0_SX397 TRAIN/DR8/MKDD0/SX397.WAV +MKDT0_SI2153 TRAIN/DR2/MKDT0/SI2153.WAV +MKDT0_SI814 TRAIN/DR2/MKDT0/SI814.WAV +MKDT0_SI893 TRAIN/DR2/MKDT0/SI893.WAV +MKDT0_SX173 TRAIN/DR2/MKDT0/SX173.WAV +MKDT0_SX263 TRAIN/DR2/MKDT0/SX263.WAV +MKDT0_SX353 TRAIN/DR2/MKDT0/SX353.WAV +MKDT0_SX443 TRAIN/DR2/MKDT0/SX443.WAV +MKDT0_SX83 TRAIN/DR2/MKDT0/SX83.WAV +MKES0_SI1253 TRAIN/DR6/MKES0/SI1253.WAV +MKES0_SI1883 TRAIN/DR6/MKES0/SI1883.WAV +MKES0_SI623 TRAIN/DR6/MKES0/SI623.WAV +MKES0_SX173 TRAIN/DR6/MKES0/SX173.WAV +MKES0_SX263 TRAIN/DR6/MKES0/SX263.WAV +MKES0_SX353 TRAIN/DR6/MKES0/SX353.WAV +MKES0_SX443 TRAIN/DR6/MKES0/SX443.WAV +MKES0_SX83 TRAIN/DR6/MKES0/SX83.WAV +MKJO0_SI1517 TRAIN/DR2/MKJO0/SI1517.WAV +MKJO0_SI2147 TRAIN/DR2/MKJO0/SI2147.WAV +MKJO0_SI887 TRAIN/DR2/MKJO0/SI887.WAV +MKJO0_SX167 TRAIN/DR2/MKJO0/SX167.WAV +MKJO0_SX257 TRAIN/DR2/MKJO0/SX257.WAV +MKJO0_SX424 TRAIN/DR2/MKJO0/SX424.WAV +MKJO0_SX437 TRAIN/DR2/MKJO0/SX437.WAV +MKJO0_SX77 TRAIN/DR2/MKJO0/SX77.WAV +MKLN0_SI1598 TRAIN/DR6/MKLN0/SI1598.WAV +MKLN0_SI2228 TRAIN/DR6/MKLN0/SI2228.WAV +MKLN0_SI968 TRAIN/DR6/MKLN0/SI968.WAV +MKLN0_SX158 TRAIN/DR6/MKLN0/SX158.WAV +MKLN0_SX248 TRAIN/DR6/MKLN0/SX248.WAV +MKLN0_SX338 TRAIN/DR6/MKLN0/SX338.WAV +MKLN0_SX428 TRAIN/DR6/MKLN0/SX428.WAV +MKLN0_SX68 TRAIN/DR6/MKLN0/SX68.WAV +MKLR0_SI1059 TRAIN/DR7/MKLR0/SI1059.WAV +MKLR0_SI1689 TRAIN/DR7/MKLR0/SI1689.WAV +MKLR0_SI2319 TRAIN/DR7/MKLR0/SI2319.WAV +MKLR0_SX159 TRAIN/DR7/MKLR0/SX159.WAV +MKLR0_SX249 TRAIN/DR7/MKLR0/SX249.WAV +MKLR0_SX339 TRAIN/DR7/MKLR0/SX339.WAV +MKLR0_SX429 TRAIN/DR7/MKLR0/SX429.WAV +MKLR0_SX69 TRAIN/DR7/MKLR0/SX69.WAV +MKLS0_SI1437 TRAIN/DR1/MKLS0/SI1437.WAV +MKLS0_SI1533 TRAIN/DR1/MKLS0/SI1533.WAV +MKLS0_SI2067 TRAIN/DR1/MKLS0/SI2067.WAV +MKLS0_SX177 TRAIN/DR1/MKLS0/SX177.WAV +MKLS0_SX267 TRAIN/DR1/MKLS0/SX267.WAV +MKLS0_SX357 TRAIN/DR1/MKLS0/SX357.WAV +MKLS0_SX447 TRAIN/DR1/MKLS0/SX447.WAV +MKLS0_SX87 TRAIN/DR1/MKLS0/SX87.WAV +MKLS1_SI1545 TRAIN/DR3/MKLS1/SI1545.WAV +MKLS1_SI2175 TRAIN/DR3/MKLS1/SI2175.WAV +MKLS1_SI915 TRAIN/DR3/MKLS1/SI915.WAV +MKLS1_SX105 TRAIN/DR3/MKLS1/SX105.WAV +MKLS1_SX15 TRAIN/DR3/MKLS1/SX15.WAV +MKLS1_SX195 TRAIN/DR3/MKLS1/SX195.WAV +MKLS1_SX285 TRAIN/DR3/MKLS1/SX285.WAV +MKLS1_SX375 TRAIN/DR3/MKLS1/SX375.WAV +MKLW0_SI1571 TRAIN/DR1/MKLW0/SI1571.WAV +MKLW0_SI1844 TRAIN/DR1/MKLW0/SI1844.WAV +MKLW0_SI2201 TRAIN/DR1/MKLW0/SI2201.WAV +MKLW0_SX131 TRAIN/DR1/MKLW0/SX131.WAV +MKLW0_SX221 TRAIN/DR1/MKLW0/SX221.WAV +MKLW0_SX311 TRAIN/DR1/MKLW0/SX311.WAV +MKLW0_SX401 TRAIN/DR1/MKLW0/SX401.WAV +MKLW0_SX41 TRAIN/DR1/MKLW0/SX41.WAV +MKRG0_SI1491 TRAIN/DR8/MKRG0/SI1491.WAV +MKRG0_SI2121 TRAIN/DR8/MKRG0/SI2121.WAV +MKRG0_SI861 TRAIN/DR8/MKRG0/SI861.WAV +MKRG0_SX141 TRAIN/DR8/MKRG0/SX141.WAV +MKRG0_SX231 TRAIN/DR8/MKRG0/SX231.WAV +MKRG0_SX31 TRAIN/DR8/MKRG0/SX31.WAV +MKRG0_SX411 TRAIN/DR8/MKRG0/SX411.WAV +MKRG0_SX51 TRAIN/DR8/MKRG0/SX51.WAV +MKXL0_SI1185 TRAIN/DR3/MKXL0/SI1185.WAV +MKXL0_SI1815 TRAIN/DR3/MKXL0/SI1815.WAV +MKXL0_SI1958 TRAIN/DR3/MKXL0/SI1958.WAV +MKXL0_SX105 TRAIN/DR3/MKXL0/SX105.WAV +MKXL0_SX15 TRAIN/DR3/MKXL0/SX15.WAV +MKXL0_SX195 TRAIN/DR3/MKXL0/SX195.WAV +MKXL0_SX285 TRAIN/DR3/MKXL0/SX285.WAV +MKXL0_SX375 TRAIN/DR3/MKXL0/SX375.WAV +MLBC0_SI1239 TRAIN/DR4/MLBC0/SI1239.WAV +MLBC0_SI1869 TRAIN/DR4/MLBC0/SI1869.WAV +MLBC0_SI609 TRAIN/DR4/MLBC0/SI609.WAV +MLBC0_SX159 TRAIN/DR4/MLBC0/SX159.WAV +MLBC0_SX249 TRAIN/DR4/MLBC0/SX249.WAV +MLBC0_SX339 TRAIN/DR4/MLBC0/SX339.WAV +MLBC0_SX429 TRAIN/DR4/MLBC0/SX429.WAV +MLBC0_SX69 TRAIN/DR4/MLBC0/SX69.WAV +MLEL0_SI1246 TRAIN/DR4/MLEL0/SI1246.WAV +MLEL0_SI1876 TRAIN/DR4/MLEL0/SI1876.WAV +MLEL0_SI616 TRAIN/DR4/MLEL0/SI616.WAV +MLEL0_SX166 TRAIN/DR4/MLEL0/SX166.WAV +MLEL0_SX256 TRAIN/DR4/MLEL0/SX256.WAV +MLEL0_SX346 TRAIN/DR4/MLEL0/SX346.WAV +MLEL0_SX436 TRAIN/DR4/MLEL0/SX436.WAV +MLEL0_SX76 TRAIN/DR4/MLEL0/SX76.WAV +MLJC0_SI1225 TRAIN/DR4/MLJC0/SI1225.WAV +MLJC0_SI1855 TRAIN/DR4/MLJC0/SI1855.WAV +MLJC0_SI595 TRAIN/DR4/MLJC0/SI595.WAV +MLJC0_SX145 TRAIN/DR4/MLJC0/SX145.WAV +MLJC0_SX235 TRAIN/DR4/MLJC0/SX235.WAV +MLJC0_SX325 TRAIN/DR4/MLJC0/SX325.WAV +MLJC0_SX415 TRAIN/DR4/MLJC0/SX415.WAV +MLJC0_SX55 TRAIN/DR4/MLJC0/SX55.WAV +MLJH0_SI1324 TRAIN/DR4/MLJH0/SI1324.WAV +MLJH0_SI1422 TRAIN/DR4/MLJH0/SI1422.WAV +MLJH0_SI694 TRAIN/DR4/MLJH0/SI694.WAV +MLJH0_SX154 TRAIN/DR4/MLJH0/SX154.WAV +MLJH0_SX244 TRAIN/DR4/MLJH0/SX244.WAV +MLJH0_SX334 TRAIN/DR4/MLJH0/SX334.WAV +MLJH0_SX424 TRAIN/DR4/MLJH0/SX424.WAV +MLJH0_SX64 TRAIN/DR4/MLJH0/SX64.WAV +MLNS0_SI1407 TRAIN/DR3/MLNS0/SI1407.WAV +MLNS0_SI2037 TRAIN/DR3/MLNS0/SI2037.WAV +MLNS0_SI777 TRAIN/DR3/MLNS0/SI777.WAV +MLNS0_SX147 TRAIN/DR3/MLNS0/SX147.WAV +MLNS0_SX237 TRAIN/DR3/MLNS0/SX237.WAV +MLNS0_SX327 TRAIN/DR3/MLNS0/SX327.WAV +MLNS0_SX417 TRAIN/DR3/MLNS0/SX417.WAV +MLNS0_SX57 TRAIN/DR3/MLNS0/SX57.WAV +MLSH0_SI1417 TRAIN/DR4/MLSH0/SI1417.WAV +MLSH0_SI2047 TRAIN/DR4/MLSH0/SI2047.WAV +MLSH0_SI787 TRAIN/DR4/MLSH0/SI787.WAV +MLSH0_SX157 TRAIN/DR4/MLSH0/SX157.WAV +MLSH0_SX247 TRAIN/DR4/MLSH0/SX247.WAV +MLSH0_SX337 TRAIN/DR4/MLSH0/SX337.WAV +MLSH0_SX427 TRAIN/DR4/MLSH0/SX427.WAV +MLSH0_SX67 TRAIN/DR4/MLSH0/SX67.WAV +MMAA0_SI1588 TRAIN/DR2/MMAA0/SI1588.WAV +MMAA0_SI2105 TRAIN/DR2/MMAA0/SI2105.WAV +MMAA0_SI845 TRAIN/DR2/MMAA0/SI845.WAV +MMAA0_SX125 TRAIN/DR2/MMAA0/SX125.WAV +MMAA0_SX215 TRAIN/DR2/MMAA0/SX215.WAV +MMAA0_SX305 TRAIN/DR2/MMAA0/SX305.WAV +MMAA0_SX35 TRAIN/DR2/MMAA0/SX35.WAV +MMAA0_SX395 TRAIN/DR2/MMAA0/SX395.WAV +MMAB1_SI1494 TRAIN/DR5/MMAB1/SI1494.WAV +MMAB1_SI2124 TRAIN/DR5/MMAB1/SI2124.WAV +MMAB1_SI864 TRAIN/DR5/MMAB1/SI864.WAV +MMAB1_SX144 TRAIN/DR5/MMAB1/SX144.WAV +MMAB1_SX234 TRAIN/DR5/MMAB1/SX234.WAV +MMAB1_SX324 TRAIN/DR5/MMAB1/SX324.WAV +MMAB1_SX414 TRAIN/DR5/MMAB1/SX414.WAV +MMAB1_SX54 TRAIN/DR5/MMAB1/SX54.WAV +MMAG0_SI1126 TRAIN/DR2/MMAG0/SI1126.WAV +MMAG0_SI1756 TRAIN/DR2/MMAG0/SI1756.WAV +MMAG0_SI496 TRAIN/DR2/MMAG0/SI496.WAV +MMAG0_SX136 TRAIN/DR2/MMAG0/SX136.WAV +MMAG0_SX226 TRAIN/DR2/MMAG0/SX226.WAV +MMAG0_SX316 TRAIN/DR2/MMAG0/SX316.WAV +MMAG0_SX406 TRAIN/DR2/MMAG0/SX406.WAV +MMAG0_SX46 TRAIN/DR2/MMAG0/SX46.WAV +MMAM0_SI1597 TRAIN/DR3/MMAM0/SI1597.WAV +MMAM0_SI1668 TRAIN/DR3/MMAM0/SI1668.WAV +MMAM0_SI2227 TRAIN/DR3/MMAM0/SI2227.WAV +MMAM0_SX157 TRAIN/DR3/MMAM0/SX157.WAV +MMAM0_SX247 TRAIN/DR3/MMAM0/SX247.WAV +MMAM0_SX337 TRAIN/DR3/MMAM0/SX337.WAV +MMAM0_SX427 TRAIN/DR3/MMAM0/SX427.WAV +MMAM0_SX67 TRAIN/DR3/MMAM0/SX67.WAV +MMAR0_SI1336 TRAIN/DR3/MMAR0/SI1336.WAV +MMAR0_SI1966 TRAIN/DR3/MMAR0/SI1966.WAV +MMAR0_SI706 TRAIN/DR3/MMAR0/SI706.WAV +MMAR0_SX166 TRAIN/DR3/MMAR0/SX166.WAV +MMAR0_SX256 TRAIN/DR3/MMAR0/SX256.WAV +MMAR0_SX346 TRAIN/DR3/MMAR0/SX346.WAV +MMAR0_SX436 TRAIN/DR3/MMAR0/SX436.WAV +MMAR0_SX76 TRAIN/DR3/MMAR0/SX76.WAV +MMBS0_SI1151 TRAIN/DR4/MMBS0/SI1151.WAV +MMBS0_SI1781 TRAIN/DR4/MMBS0/SI1781.WAV +MMBS0_SI521 TRAIN/DR4/MMBS0/SI521.WAV +MMBS0_SX161 TRAIN/DR4/MMBS0/SX161.WAV +MMBS0_SX251 TRAIN/DR4/MMBS0/SX251.WAV +MMBS0_SX341 TRAIN/DR4/MMBS0/SX341.WAV +MMBS0_SX431 TRAIN/DR4/MMBS0/SX431.WAV +MMBS0_SX71 TRAIN/DR4/MMBS0/SX71.WAV +MMCC0_SI1338 TRAIN/DR5/MMCC0/SI1338.WAV +MMCC0_SI1968 TRAIN/DR5/MMCC0/SI1968.WAV +MMCC0_SI708 TRAIN/DR5/MMCC0/SI708.WAV +MMCC0_SX168 TRAIN/DR5/MMCC0/SX168.WAV +MMCC0_SX258 TRAIN/DR5/MMCC0/SX258.WAV +MMCC0_SX348 TRAIN/DR5/MMCC0/SX348.WAV +MMCC0_SX438 TRAIN/DR5/MMCC0/SX438.WAV +MMCC0_SX78 TRAIN/DR5/MMCC0/SX78.WAV +MMDB0_SI1358 TRAIN/DR6/MMDB0/SI1358.WAV +MMDB0_SI1617 TRAIN/DR6/MMDB0/SI1617.WAV +MMDB0_SI987 TRAIN/DR6/MMDB0/SI987.WAV +MMDB0_SX177 TRAIN/DR6/MMDB0/SX177.WAV +MMDB0_SX267 TRAIN/DR6/MMDB0/SX267.WAV +MMDB0_SX357 TRAIN/DR6/MMDB0/SX357.WAV +MMDB0_SX447 TRAIN/DR6/MMDB0/SX447.WAV +MMDB0_SX87 TRAIN/DR6/MMDB0/SX87.WAV +MMDG0_SI1780 TRAIN/DR7/MMDG0/SI1780.WAV +MMDG0_SI2035 TRAIN/DR7/MMDG0/SI2035.WAV +MMDG0_SI520 TRAIN/DR7/MMDG0/SI520.WAV +MMDG0_SX160 TRAIN/DR7/MMDG0/SX160.WAV +MMDG0_SX250 TRAIN/DR7/MMDG0/SX250.WAV +MMDG0_SX340 TRAIN/DR7/MMDG0/SX340.WAV +MMDG0_SX430 TRAIN/DR7/MMDG0/SX430.WAV +MMDG0_SX70 TRAIN/DR7/MMDG0/SX70.WAV +MMDM0_SI1311 TRAIN/DR4/MMDM0/SI1311.WAV +MMDM0_SI1941 TRAIN/DR4/MMDM0/SI1941.WAV +MMDM0_SI681 TRAIN/DR4/MMDM0/SI681.WAV +MMDM0_SX141 TRAIN/DR4/MMDM0/SX141.WAV +MMDM0_SX231 TRAIN/DR4/MMDM0/SX231.WAV +MMDM0_SX321 TRAIN/DR4/MMDM0/SX321.WAV +MMDM0_SX411 TRAIN/DR4/MMDM0/SX411.WAV +MMDM0_SX51 TRAIN/DR4/MMDM0/SX51.WAV +MMDM1_SI1650 TRAIN/DR5/MMDM1/SI1650.WAV +MMDM1_SI2043 TRAIN/DR5/MMDM1/SI2043.WAV +MMDM1_SI783 TRAIN/DR5/MMDM1/SI783.WAV +MMDM1_SX153 TRAIN/DR5/MMDM1/SX153.WAV +MMDM1_SX243 TRAIN/DR5/MMDM1/SX243.WAV +MMDM1_SX333 TRAIN/DR5/MMDM1/SX333.WAV +MMDM1_SX423 TRAIN/DR5/MMDM1/SX423.WAV +MMDM1_SX63 TRAIN/DR5/MMDM1/SX63.WAV +MMDS0_SI1343 TRAIN/DR2/MMDS0/SI1343.WAV +MMDS0_SI1973 TRAIN/DR2/MMDS0/SI1973.WAV +MMDS0_SI713 TRAIN/DR2/MMDS0/SI713.WAV +MMDS0_SX173 TRAIN/DR2/MMDS0/SX173.WAV +MMDS0_SX263 TRAIN/DR2/MMDS0/SX263.WAV +MMDS0_SX353 TRAIN/DR2/MMDS0/SX353.WAV +MMDS0_SX443 TRAIN/DR2/MMDS0/SX443.WAV +MMDS0_SX83 TRAIN/DR2/MMDS0/SX83.WAV +MMEA0_SI1388 TRAIN/DR8/MMEA0/SI1388.WAV +MMEA0_SI2018 TRAIN/DR8/MMEA0/SI2018.WAV +MMEA0_SI758 TRAIN/DR8/MMEA0/SI758.WAV +MMEA0_SX128 TRAIN/DR8/MMEA0/SX128.WAV +MMEA0_SX218 TRAIN/DR8/MMEA0/SX218.WAV +MMEA0_SX308 TRAIN/DR8/MMEA0/SX308.WAV +MMEA0_SX38 TRAIN/DR8/MMEA0/SX38.WAV +MMEA0_SX398 TRAIN/DR8/MMEA0/SX398.WAV +MMEB0_SI1357 TRAIN/DR3/MMEB0/SI1357.WAV +MMEB0_SI1987 TRAIN/DR3/MMEB0/SI1987.WAV +MMEB0_SI727 TRAIN/DR3/MMEB0/SI727.WAV +MMEB0_SX187 TRAIN/DR3/MMEB0/SX187.WAV +MMEB0_SX327 TRAIN/DR3/MMEB0/SX327.WAV +MMEB0_SX367 TRAIN/DR3/MMEB0/SX367.WAV +MMEB0_SX7 TRAIN/DR3/MMEB0/SX7.WAV +MMEB0_SX97 TRAIN/DR3/MMEB0/SX97.WAV +MMGC0_SI1305 TRAIN/DR4/MMGC0/SI1305.WAV +MMGC0_SI1935 TRAIN/DR4/MMGC0/SI1935.WAV +MMGC0_SI2184 TRAIN/DR4/MMGC0/SI2184.WAV +MMGC0_SX135 TRAIN/DR4/MMGC0/SX135.WAV +MMGC0_SX225 TRAIN/DR4/MMGC0/SX225.WAV +MMGC0_SX315 TRAIN/DR4/MMGC0/SX315.WAV +MMGC0_SX405 TRAIN/DR4/MMGC0/SX405.WAV +MMGC0_SX45 TRAIN/DR4/MMGC0/SX45.WAV +MMGG0_SI1079 TRAIN/DR1/MMGG0/SI1079.WAV +MMGG0_SI1709 TRAIN/DR1/MMGG0/SI1709.WAV +MMGG0_SI2339 TRAIN/DR1/MMGG0/SI2339.WAV +MMGG0_SX179 TRAIN/DR1/MMGG0/SX179.WAV +MMGG0_SX269 TRAIN/DR1/MMGG0/SX269.WAV +MMGG0_SX359 TRAIN/DR1/MMGG0/SX359.WAV +MMGG0_SX449 TRAIN/DR1/MMGG0/SX449.WAV +MMGG0_SX89 TRAIN/DR1/MMGG0/SX89.WAV +MMGK0_SI1322 TRAIN/DR2/MMGK0/SI1322.WAV +MMGK0_SI1952 TRAIN/DR2/MMGK0/SI1952.WAV +MMGK0_SI692 TRAIN/DR2/MMGK0/SI692.WAV +MMGK0_SX152 TRAIN/DR2/MMGK0/SX152.WAV +MMGK0_SX242 TRAIN/DR2/MMGK0/SX242.WAV +MMGK0_SX332 TRAIN/DR2/MMGK0/SX332.WAV +MMGK0_SX422 TRAIN/DR2/MMGK0/SX422.WAV +MMGK0_SX62 TRAIN/DR2/MMGK0/SX62.WAV +MMJB1_SI1408 TRAIN/DR3/MMJB1/SI1408.WAV +MMJB1_SI2038 TRAIN/DR3/MMJB1/SI2038.WAV +MMJB1_SI778 TRAIN/DR3/MMJB1/SI778.WAV +MMJB1_SX148 TRAIN/DR3/MMJB1/SX148.WAV +MMJB1_SX238 TRAIN/DR3/MMJB1/SX238.WAV +MMJB1_SX328 TRAIN/DR3/MMJB1/SX328.WAV +MMJB1_SX418 TRAIN/DR3/MMJB1/SX418.WAV +MMJB1_SX58 TRAIN/DR3/MMJB1/SX58.WAV +MMLM0_SI1527 TRAIN/DR8/MMLM0/SI1527.WAV +MMLM0_SI2150 TRAIN/DR8/MMLM0/SI2150.WAV +MMLM0_SI897 TRAIN/DR8/MMLM0/SI897.WAV +MMLM0_SX177 TRAIN/DR8/MMLM0/SX177.WAV +MMLM0_SX267 TRAIN/DR8/MMLM0/SX267.WAV +MMLM0_SX357 TRAIN/DR8/MMLM0/SX357.WAV +MMLM0_SX447 TRAIN/DR8/MMLM0/SX447.WAV +MMLM0_SX87 TRAIN/DR8/MMLM0/SX87.WAV +MMPM0_SI1061 TRAIN/DR8/MMPM0/SI1061.WAV +MMPM0_SI1691 TRAIN/DR8/MMPM0/SI1691.WAV +MMPM0_SI2321 TRAIN/DR8/MMPM0/SI2321.WAV +MMPM0_SX161 TRAIN/DR8/MMPM0/SX161.WAV +MMPM0_SX251 TRAIN/DR8/MMPM0/SX251.WAV +MMPM0_SX341 TRAIN/DR8/MMPM0/SX341.WAV +MMPM0_SX431 TRAIN/DR8/MMPM0/SX431.WAV +MMPM0_SX71 TRAIN/DR8/MMPM0/SX71.WAV +MMRP0_SI2034 TRAIN/DR1/MMRP0/SI2034.WAV +MMRP0_SI717 TRAIN/DR1/MMRP0/SI717.WAV +MMRP0_SI774 TRAIN/DR1/MMRP0/SI774.WAV +MMRP0_SX144 TRAIN/DR1/MMRP0/SX144.WAV +MMRP0_SX234 TRAIN/DR1/MMRP0/SX234.WAV +MMRP0_SX324 TRAIN/DR1/MMRP0/SX324.WAV +MMRP0_SX414 TRAIN/DR1/MMRP0/SX414.WAV +MMRP0_SX54 TRAIN/DR1/MMRP0/SX54.WAV +MMSM0_SI1106 TRAIN/DR3/MMSM0/SI1106.WAV +MMSM0_SI1736 TRAIN/DR3/MMSM0/SI1736.WAV +MMSM0_SI476 TRAIN/DR3/MMSM0/SI476.WAV +MMSM0_SX116 TRAIN/DR3/MMSM0/SX116.WAV +MMSM0_SX206 TRAIN/DR3/MMSM0/SX206.WAV +MMSM0_SX26 TRAIN/DR3/MMSM0/SX26.WAV +MMSM0_SX296 TRAIN/DR3/MMSM0/SX296.WAV +MMSM0_SX386 TRAIN/DR3/MMSM0/SX386.WAV +MMVP0_SI1284 TRAIN/DR5/MMVP0/SI1284.WAV +MMVP0_SI1914 TRAIN/DR5/MMVP0/SI1914.WAV +MMVP0_SI654 TRAIN/DR5/MMVP0/SI654.WAV +MMVP0_SX114 TRAIN/DR5/MMVP0/SX114.WAV +MMVP0_SX204 TRAIN/DR5/MMVP0/SX204.WAV +MMVP0_SX294 TRAIN/DR5/MMVP0/SX294.WAV +MMVP0_SX347 TRAIN/DR5/MMVP0/SX347.WAV +MMVP0_SX384 TRAIN/DR5/MMVP0/SX384.WAV +MMWB0_SI1619 TRAIN/DR5/MMWB0/SI1619.WAV +MMWB0_SI2249 TRAIN/DR5/MMWB0/SI2249.WAV +MMWB0_SI989 TRAIN/DR5/MMWB0/SI989.WAV +MMWB0_SX179 TRAIN/DR5/MMWB0/SX179.WAV +MMWB0_SX269 TRAIN/DR5/MMWB0/SX269.WAV +MMWB0_SX359 TRAIN/DR5/MMWB0/SX359.WAV +MMWB0_SX449 TRAIN/DR5/MMWB0/SX449.WAV +MMWB0_SX89 TRAIN/DR5/MMWB0/SX89.WAV +MMWS0_SI1518 TRAIN/DR8/MMWS0/SI1518.WAV +MMWS0_SI559 TRAIN/DR8/MMWS0/SI559.WAV +MMWS0_SI888 TRAIN/DR8/MMWS0/SI888.WAV +MMWS0_SX168 TRAIN/DR8/MMWS0/SX168.WAV +MMWS0_SX258 TRAIN/DR8/MMWS0/SX258.WAV +MMWS0_SX348 TRAIN/DR8/MMWS0/SX348.WAV +MMWS0_SX438 TRAIN/DR8/MMWS0/SX438.WAV +MMWS0_SX78 TRAIN/DR8/MMWS0/SX78.WAV +MMWS1_SI1071 TRAIN/DR7/MMWS1/SI1071.WAV +MMWS1_SI1701 TRAIN/DR7/MMWS1/SI1701.WAV +MMWS1_SI2331 TRAIN/DR7/MMWS1/SI2331.WAV +MMWS1_SX261 TRAIN/DR7/MMWS1/SX261.WAV +MMWS1_SX27 TRAIN/DR7/MMWS1/SX27.WAV +MMWS1_SX351 TRAIN/DR7/MMWS1/SX351.WAV +MMWS1_SX441 TRAIN/DR7/MMWS1/SX441.WAV +MMWS1_SX81 TRAIN/DR7/MMWS1/SX81.WAV +MMXS0_SI2136 TRAIN/DR2/MMXS0/SI2136.WAV +MMXS0_SI629 TRAIN/DR2/MMXS0/SI629.WAV +MMXS0_SI876 TRAIN/DR2/MMXS0/SI876.WAV +MMXS0_SX156 TRAIN/DR2/MMXS0/SX156.WAV +MMXS0_SX246 TRAIN/DR2/MMXS0/SX246.WAV +MMXS0_SX336 TRAIN/DR2/MMXS0/SX336.WAV +MMXS0_SX426 TRAIN/DR2/MMXS0/SX426.WAV +MMXS0_SX66 TRAIN/DR2/MMXS0/SX66.WAV +MNET0_SI1446 TRAIN/DR4/MNET0/SI1446.WAV +MNET0_SI2076 TRAIN/DR4/MNET0/SI2076.WAV +MNET0_SI816 TRAIN/DR4/MNET0/SI816.WAV +MNET0_SX186 TRAIN/DR4/MNET0/SX186.WAV +MNET0_SX276 TRAIN/DR4/MNET0/SX276.WAV +MNET0_SX366 TRAIN/DR4/MNET0/SX366.WAV +MNET0_SX6 TRAIN/DR4/MNET0/SX6.WAV +MNET0_SX96 TRAIN/DR4/MNET0/SX96.WAV +MNTW0_SI1068 TRAIN/DR7/MNTW0/SI1068.WAV +MNTW0_SI1698 TRAIN/DR7/MNTW0/SI1698.WAV +MNTW0_SI2328 TRAIN/DR7/MNTW0/SI2328.WAV +MNTW0_SX168 TRAIN/DR7/MNTW0/SX168.WAV +MNTW0_SX202 TRAIN/DR7/MNTW0/SX202.WAV +MNTW0_SX258 TRAIN/DR7/MNTW0/SX258.WAV +MNTW0_SX348 TRAIN/DR7/MNTW0/SX348.WAV +MNTW0_SX78 TRAIN/DR7/MNTW0/SX78.WAV +MPAR0_SI1576 TRAIN/DR7/MPAR0/SI1576.WAV +MPAR0_SI2206 TRAIN/DR7/MPAR0/SI2206.WAV +MPAR0_SI946 TRAIN/DR7/MPAR0/SI946.WAV +MPAR0_SX136 TRAIN/DR7/MPAR0/SX136.WAV +MPAR0_SX226 TRAIN/DR7/MPAR0/SX226.WAV +MPAR0_SX316 TRAIN/DR7/MPAR0/SX316.WAV +MPAR0_SX406 TRAIN/DR7/MPAR0/SX406.WAV +MPAR0_SX46 TRAIN/DR7/MPAR0/SX46.WAV +MPEB0_SI1034 TRAIN/DR4/MPEB0/SI1034.WAV +MPEB0_SI1860 TRAIN/DR4/MPEB0/SI1860.WAV +MPEB0_SI600 TRAIN/DR4/MPEB0/SI600.WAV +MPEB0_SX150 TRAIN/DR4/MPEB0/SX150.WAV +MPEB0_SX240 TRAIN/DR4/MPEB0/SX240.WAV +MPEB0_SX330 TRAIN/DR4/MPEB0/SX330.WAV +MPEB0_SX420 TRAIN/DR4/MPEB0/SX420.WAV +MPEB0_SX60 TRAIN/DR4/MPEB0/SX60.WAV +MPFU0_SI1258 TRAIN/DR7/MPFU0/SI1258.WAV +MPFU0_SI1888 TRAIN/DR7/MPFU0/SI1888.WAV +MPFU0_SI628 TRAIN/DR7/MPFU0/SI628.WAV +MPFU0_SX178 TRAIN/DR7/MPFU0/SX178.WAV +MPFU0_SX268 TRAIN/DR7/MPFU0/SX268.WAV +MPFU0_SX358 TRAIN/DR7/MPFU0/SX358.WAV +MPFU0_SX448 TRAIN/DR7/MPFU0/SX448.WAV +MPFU0_SX88 TRAIN/DR7/MPFU0/SX88.WAV +MPGH0_SI1554 TRAIN/DR1/MPGH0/SI1554.WAV +MPGH0_SI675 TRAIN/DR1/MPGH0/SI675.WAV +MPGH0_SI924 TRAIN/DR1/MPGH0/SI924.WAV +MPGH0_SX114 TRAIN/DR1/MPGH0/SX114.WAV +MPGH0_SX204 TRAIN/DR1/MPGH0/SX204.WAV +MPGH0_SX24 TRAIN/DR1/MPGH0/SX24.WAV +MPGH0_SX294 TRAIN/DR1/MPGH0/SX294.WAV +MPGH0_SX384 TRAIN/DR1/MPGH0/SX384.WAV +MPGR0_SI1410 TRAIN/DR1/MPGR0/SI1410.WAV +MPGR0_SI2040 TRAIN/DR1/MPGR0/SI2040.WAV +MPGR0_SI780 TRAIN/DR1/MPGR0/SI780.WAV +MPGR0_SX150 TRAIN/DR1/MPGR0/SX150.WAV +MPGR0_SX240 TRAIN/DR1/MPGR0/SX240.WAV +MPGR0_SX330 TRAIN/DR1/MPGR0/SX330.WAV +MPGR0_SX420 TRAIN/DR1/MPGR0/SX420.WAV +MPGR0_SX60 TRAIN/DR1/MPGR0/SX60.WAV +MPGR1_SI1269 TRAIN/DR6/MPGR1/SI1269.WAV +MPGR1_SI1499 TRAIN/DR6/MPGR1/SI1499.WAV +MPGR1_SI2129 TRAIN/DR6/MPGR1/SI2129.WAV +MPGR1_SX149 TRAIN/DR6/MPGR1/SX149.WAV +MPGR1_SX239 TRAIN/DR6/MPGR1/SX239.WAV +MPGR1_SX329 TRAIN/DR6/MPGR1/SX329.WAV +MPGR1_SX419 TRAIN/DR6/MPGR1/SX419.WAV +MPGR1_SX59 TRAIN/DR6/MPGR1/SX59.WAV +MPMB0_SI1501 TRAIN/DR5/MPMB0/SI1501.WAV +MPMB0_SI2131 TRAIN/DR5/MPMB0/SI2131.WAV +MPMB0_SI871 TRAIN/DR5/MPMB0/SI871.WAV +MPMB0_SX151 TRAIN/DR5/MPMB0/SX151.WAV +MPMB0_SX241 TRAIN/DR5/MPMB0/SX241.WAV +MPMB0_SX331 TRAIN/DR5/MPMB0/SX331.WAV +MPMB0_SX421 TRAIN/DR5/MPMB0/SX421.WAV +MPMB0_SX61 TRAIN/DR5/MPMB0/SX61.WAV +MPPC0_SI1412 TRAIN/DR2/MPPC0/SI1412.WAV +MPPC0_SI2042 TRAIN/DR2/MPPC0/SI2042.WAV +MPPC0_SI782 TRAIN/DR2/MPPC0/SI782.WAV +MPPC0_SX152 TRAIN/DR2/MPPC0/SX152.WAV +MPPC0_SX242 TRAIN/DR2/MPPC0/SX242.WAV +MPPC0_SX332 TRAIN/DR2/MPPC0/SX332.WAV +MPPC0_SX422 TRAIN/DR2/MPPC0/SX422.WAV +MPPC0_SX62 TRAIN/DR2/MPPC0/SX62.WAV +MPRB0_SI1205 TRAIN/DR2/MPRB0/SI1205.WAV +MPRB0_SI1215 TRAIN/DR2/MPRB0/SI1215.WAV +MPRB0_SI575 TRAIN/DR2/MPRB0/SI575.WAV +MPRB0_SX125 TRAIN/DR2/MPRB0/SX125.WAV +MPRB0_SX215 TRAIN/DR2/MPRB0/SX215.WAV +MPRB0_SX305 TRAIN/DR2/MPRB0/SX305.WAV +MPRB0_SX35 TRAIN/DR2/MPRB0/SX35.WAV +MPRB0_SX395 TRAIN/DR2/MPRB0/SX395.WAV +MPRD0_SI1431 TRAIN/DR3/MPRD0/SI1431.WAV +MPRD0_SI2061 TRAIN/DR3/MPRD0/SI2061.WAV +MPRD0_SI801 TRAIN/DR3/MPRD0/SI801.WAV +MPRD0_SX171 TRAIN/DR3/MPRD0/SX171.WAV +MPRD0_SX261 TRAIN/DR3/MPRD0/SX261.WAV +MPRD0_SX351 TRAIN/DR3/MPRD0/SX351.WAV +MPRD0_SX441 TRAIN/DR3/MPRD0/SX441.WAV +MPRD0_SX81 TRAIN/DR3/MPRD0/SX81.WAV +MPRK0_SI1097 TRAIN/DR4/MPRK0/SI1097.WAV +MPRK0_SI1727 TRAIN/DR4/MPRK0/SI1727.WAV +MPRK0_SI467 TRAIN/DR4/MPRK0/SI467.WAV +MPRK0_SX107 TRAIN/DR4/MPRK0/SX107.WAV +MPRK0_SX17 TRAIN/DR4/MPRK0/SX17.WAV +MPRK0_SX197 TRAIN/DR4/MPRK0/SX197.WAV +MPRK0_SX287 TRAIN/DR4/MPRK0/SX287.WAV +MPRK0_SX377 TRAIN/DR4/MPRK0/SX377.WAV +MPRT0_SI1210 TRAIN/DR4/MPRT0/SI1210.WAV +MPRT0_SI495 TRAIN/DR4/MPRT0/SI495.WAV +MPRT0_SI580 TRAIN/DR4/MPRT0/SI580.WAV +MPRT0_SX130 TRAIN/DR4/MPRT0/SX130.WAV +MPRT0_SX220 TRAIN/DR4/MPRT0/SX220.WAV +MPRT0_SX310 TRAIN/DR4/MPRT0/SX310.WAV +MPRT0_SX40 TRAIN/DR4/MPRT0/SX40.WAV +MPRT0_SX400 TRAIN/DR4/MPRT0/SX400.WAV +MPSW0_SI1067 TRAIN/DR1/MPSW0/SI1067.WAV +MPSW0_SI1697 TRAIN/DR1/MPSW0/SI1697.WAV +MPSW0_SI2327 TRAIN/DR1/MPSW0/SI2327.WAV +MPSW0_SX167 TRAIN/DR1/MPSW0/SX167.WAV +MPSW0_SX24 TRAIN/DR1/MPSW0/SX24.WAV +MPSW0_SX257 TRAIN/DR1/MPSW0/SX257.WAV +MPSW0_SX437 TRAIN/DR1/MPSW0/SX437.WAV +MPSW0_SX77 TRAIN/DR1/MPSW0/SX77.WAV +MRAB0_SI1224 TRAIN/DR2/MRAB0/SI1224.WAV +MRAB0_SI1854 TRAIN/DR2/MRAB0/SI1854.WAV +MRAB0_SI594 TRAIN/DR2/MRAB0/SI594.WAV +MRAB0_SX144 TRAIN/DR2/MRAB0/SX144.WAV +MRAB0_SX234 TRAIN/DR2/MRAB0/SX234.WAV +MRAB0_SX324 TRAIN/DR2/MRAB0/SX324.WAV +MRAB0_SX414 TRAIN/DR2/MRAB0/SX414.WAV +MRAB0_SX54 TRAIN/DR2/MRAB0/SX54.WAV +MRAB1_SI1478 TRAIN/DR4/MRAB1/SI1478.WAV +MRAB1_SI2108 TRAIN/DR4/MRAB1/SI2108.WAV +MRAB1_SI848 TRAIN/DR4/MRAB1/SI848.WAV +MRAB1_SX128 TRAIN/DR4/MRAB1/SX128.WAV +MRAB1_SX218 TRAIN/DR4/MRAB1/SX218.WAV +MRAB1_SX308 TRAIN/DR4/MRAB1/SX308.WAV +MRAB1_SX38 TRAIN/DR4/MRAB1/SX38.WAV +MRAB1_SX398 TRAIN/DR4/MRAB1/SX398.WAV +MRAI0_SI1954 TRAIN/DR1/MRAI0/SI1954.WAV +MRAI0_SI2052 TRAIN/DR1/MRAI0/SI2052.WAV +MRAI0_SI792 TRAIN/DR1/MRAI0/SI792.WAV +MRAI0_SX162 TRAIN/DR1/MRAI0/SX162.WAV +MRAI0_SX252 TRAIN/DR1/MRAI0/SX252.WAV +MRAI0_SX342 TRAIN/DR1/MRAI0/SX342.WAV +MRAI0_SX432 TRAIN/DR1/MRAI0/SX432.WAV +MRAI0_SX72 TRAIN/DR1/MRAI0/SX72.WAV +MRAM0_SI1275 TRAIN/DR5/MRAM0/SI1275.WAV +MRAM0_SI1905 TRAIN/DR5/MRAM0/SI1905.WAV +MRAM0_SI1951 TRAIN/DR5/MRAM0/SI1951.WAV +MRAM0_SX105 TRAIN/DR5/MRAM0/SX105.WAV +MRAM0_SX15 TRAIN/DR5/MRAM0/SX15.WAV +MRAM0_SX195 TRAIN/DR5/MRAM0/SX195.WAV +MRAM0_SX285 TRAIN/DR5/MRAM0/SX285.WAV +MRAM0_SX375 TRAIN/DR5/MRAM0/SX375.WAV +MRAV0_SI1008 TRAIN/DR5/MRAV0/SI1008.WAV +MRAV0_SI1638 TRAIN/DR5/MRAV0/SI1638.WAV +MRAV0_SI2268 TRAIN/DR5/MRAV0/SI2268.WAV +MRAV0_SX108 TRAIN/DR5/MRAV0/SX108.WAV +MRAV0_SX18 TRAIN/DR5/MRAV0/SX18.WAV +MRAV0_SX198 TRAIN/DR5/MRAV0/SX198.WAV +MRAV0_SX288 TRAIN/DR5/MRAV0/SX288.WAV +MRAV0_SX378 TRAIN/DR5/MRAV0/SX378.WAV +MRBC0_SI1665 TRAIN/DR3/MRBC0/SI1665.WAV +MRBC0_SI1859 TRAIN/DR3/MRBC0/SI1859.WAV +MRBC0_SI599 TRAIN/DR3/MRBC0/SI599.WAV +MRBC0_SX149 TRAIN/DR3/MRBC0/SX149.WAV +MRBC0_SX239 TRAIN/DR3/MRBC0/SX239.WAV +MRBC0_SX329 TRAIN/DR3/MRBC0/SX329.WAV +MRBC0_SX419 TRAIN/DR3/MRBC0/SX419.WAV +MRBC0_SX59 TRAIN/DR3/MRBC0/SX59.WAV +MRCG0_SI1428 TRAIN/DR1/MRCG0/SI1428.WAV +MRCG0_SI2058 TRAIN/DR1/MRCG0/SI2058.WAV +MRCG0_SI798 TRAIN/DR1/MRCG0/SI798.WAV +MRCG0_SX168 TRAIN/DR1/MRCG0/SX168.WAV +MRCG0_SX258 TRAIN/DR1/MRCG0/SX258.WAV +MRCG0_SX348 TRAIN/DR1/MRCG0/SX348.WAV +MRCG0_SX438 TRAIN/DR1/MRCG0/SX438.WAV +MRCG0_SX78 TRAIN/DR1/MRCG0/SX78.WAV +MRCW0_SI1371 TRAIN/DR2/MRCW0/SI1371.WAV +MRCW0_SI2001 TRAIN/DR2/MRCW0/SI2001.WAV +MRCW0_SI741 TRAIN/DR2/MRCW0/SI741.WAV +MRCW0_SX111 TRAIN/DR2/MRCW0/SX111.WAV +MRCW0_SX201 TRAIN/DR2/MRCW0/SX201.WAV +MRCW0_SX21 TRAIN/DR2/MRCW0/SX21.WAV +MRCW0_SX291 TRAIN/DR2/MRCW0/SX291.WAV +MRCW0_SX381 TRAIN/DR2/MRCW0/SX381.WAV +MRDD0_SI1050 TRAIN/DR1/MRDD0/SI1050.WAV +MRDD0_SI1680 TRAIN/DR1/MRDD0/SI1680.WAV +MRDD0_SI2310 TRAIN/DR1/MRDD0/SI2310.WAV +MRDD0_SX150 TRAIN/DR1/MRDD0/SX150.WAV +MRDD0_SX240 TRAIN/DR1/MRDD0/SX240.WAV +MRDD0_SX277 TRAIN/DR1/MRDD0/SX277.WAV +MRDD0_SX330 TRAIN/DR1/MRDD0/SX330.WAV +MRDD0_SX60 TRAIN/DR1/MRDD0/SX60.WAV +MRDM0_SI1044 TRAIN/DR8/MRDM0/SI1044.WAV +MRDM0_SI1595 TRAIN/DR8/MRDM0/SI1595.WAV +MRDM0_SI965 TRAIN/DR8/MRDM0/SI965.WAV +MRDM0_SX155 TRAIN/DR8/MRDM0/SX155.WAV +MRDM0_SX245 TRAIN/DR8/MRDM0/SX245.WAV +MRDM0_SX335 TRAIN/DR8/MRDM0/SX335.WAV +MRDM0_SX425 TRAIN/DR8/MRDM0/SX425.WAV +MRDM0_SX65 TRAIN/DR8/MRDM0/SX65.WAV +MRDS0_SI1167 TRAIN/DR3/MRDS0/SI1167.WAV +MRDS0_SI1797 TRAIN/DR3/MRDS0/SI1797.WAV +MRDS0_SI537 TRAIN/DR3/MRDS0/SI537.WAV +MRDS0_SX177 TRAIN/DR3/MRDS0/SX177.WAV +MRDS0_SX267 TRAIN/DR3/MRDS0/SX267.WAV +MRDS0_SX357 TRAIN/DR3/MRDS0/SX357.WAV +MRDS0_SX447 TRAIN/DR3/MRDS0/SX447.WAV +MRDS0_SX87 TRAIN/DR3/MRDS0/SX87.WAV +MREE0_SI1104 TRAIN/DR3/MREE0/SI1104.WAV +MREE0_SI1734 TRAIN/DR3/MREE0/SI1734.WAV +MREE0_SI1959 TRAIN/DR3/MREE0/SI1959.WAV +MREE0_SX114 TRAIN/DR3/MREE0/SX114.WAV +MREE0_SX204 TRAIN/DR3/MREE0/SX204.WAV +MREE0_SX24 TRAIN/DR3/MREE0/SX24.WAV +MREE0_SX294 TRAIN/DR3/MREE0/SX294.WAV +MREE0_SX384 TRAIN/DR3/MREE0/SX384.WAV +MREH1_SI1599 TRAIN/DR3/MREH1/SI1599.WAV +MREH1_SI2229 TRAIN/DR3/MREH1/SI2229.WAV +MREH1_SI969 TRAIN/DR3/MREH1/SI969.WAV +MREH1_SX159 TRAIN/DR3/MREH1/SX159.WAV +MREH1_SX249 TRAIN/DR3/MREH1/SX249.WAV +MREH1_SX339 TRAIN/DR3/MREH1/SX339.WAV +MREH1_SX429 TRAIN/DR3/MREH1/SX429.WAV +MREH1_SX69 TRAIN/DR3/MREH1/SX69.WAV +MREM0_SI1591 TRAIN/DR7/MREM0/SI1591.WAV +MREM0_SI511 TRAIN/DR7/MREM0/SI511.WAV +MREM0_SI961 TRAIN/DR7/MREM0/SI961.WAV +MREM0_SX151 TRAIN/DR7/MREM0/SX151.WAV +MREM0_SX241 TRAIN/DR7/MREM0/SX241.WAV +MREM0_SX331 TRAIN/DR7/MREM0/SX331.WAV +MREM0_SX421 TRAIN/DR7/MREM0/SX421.WAV +MREM0_SX61 TRAIN/DR7/MREM0/SX61.WAV +MREW1_SI1500 TRAIN/DR5/MREW1/SI1500.WAV +MREW1_SI2130 TRAIN/DR5/MREW1/SI2130.WAV +MREW1_SI870 TRAIN/DR5/MREW1/SI870.WAV +MREW1_SX150 TRAIN/DR5/MREW1/SX150.WAV +MREW1_SX240 TRAIN/DR5/MREW1/SX240.WAV +MREW1_SX330 TRAIN/DR5/MREW1/SX330.WAV +MREW1_SX420 TRAIN/DR5/MREW1/SX420.WAV +MREW1_SX60 TRAIN/DR5/MREW1/SX60.WAV +MRFK0_SI1076 TRAIN/DR2/MRFK0/SI1076.WAV +MRFK0_SI1706 TRAIN/DR2/MRFK0/SI1706.WAV +MRFK0_SI2336 TRAIN/DR2/MRFK0/SI2336.WAV +MRFK0_SX176 TRAIN/DR2/MRFK0/SX176.WAV +MRFK0_SX266 TRAIN/DR2/MRFK0/SX266.WAV +MRFK0_SX356 TRAIN/DR2/MRFK0/SX356.WAV +MRFK0_SX446 TRAIN/DR2/MRFK0/SX446.WAV +MRFK0_SX86 TRAIN/DR2/MRFK0/SX86.WAV +MRFL0_SI1156 TRAIN/DR4/MRFL0/SI1156.WAV +MRFL0_SI1786 TRAIN/DR4/MRFL0/SI1786.WAV +MRFL0_SI526 TRAIN/DR4/MRFL0/SI526.WAV +MRFL0_SX166 TRAIN/DR4/MRFL0/SX166.WAV +MRFL0_SX256 TRAIN/DR4/MRFL0/SX256.WAV +MRFL0_SX346 TRAIN/DR4/MRFL0/SX346.WAV +MRFL0_SX436 TRAIN/DR4/MRFL0/SX436.WAV +MRFL0_SX76 TRAIN/DR4/MRFL0/SX76.WAV +MRGM0_SI1162 TRAIN/DR4/MRGM0/SI1162.WAV +MRGM0_SI1792 TRAIN/DR4/MRGM0/SI1792.WAV +MRGM0_SI532 TRAIN/DR4/MRGM0/SI532.WAV +MRGM0_SX172 TRAIN/DR4/MRGM0/SX172.WAV +MRGM0_SX262 TRAIN/DR4/MRGM0/SX262.WAV +MRGM0_SX416 TRAIN/DR4/MRGM0/SX416.WAV +MRGM0_SX442 TRAIN/DR4/MRGM0/SX442.WAV +MRGM0_SX82 TRAIN/DR4/MRGM0/SX82.WAV +MRGS0_SI1356 TRAIN/DR2/MRGS0/SI1356.WAV +MRGS0_SI1986 TRAIN/DR2/MRGS0/SI1986.WAV +MRGS0_SI726 TRAIN/DR2/MRGS0/SI726.WAV +MRGS0_SX186 TRAIN/DR2/MRGS0/SX186.WAV +MRGS0_SX276 TRAIN/DR2/MRGS0/SX276.WAV +MRGS0_SX366 TRAIN/DR2/MRGS0/SX366.WAV +MRGS0_SX6 TRAIN/DR2/MRGS0/SX6.WAV +MRGS0_SX96 TRAIN/DR2/MRGS0/SX96.WAV +MRHL0_SI1515 TRAIN/DR2/MRHL0/SI1515.WAV +MRHL0_SI2145 TRAIN/DR2/MRHL0/SI2145.WAV +MRHL0_SI885 TRAIN/DR2/MRHL0/SI885.WAV +MRHL0_SX165 TRAIN/DR2/MRHL0/SX165.WAV +MRHL0_SX255 TRAIN/DR2/MRHL0/SX255.WAV +MRHL0_SX345 TRAIN/DR2/MRHL0/SX345.WAV +MRHL0_SX435 TRAIN/DR2/MRHL0/SX435.WAV +MRHL0_SX75 TRAIN/DR2/MRHL0/SX75.WAV +MRJB1_SI1020 TRAIN/DR3/MRJB1/SI1020.WAV +MRJB1_SI1413 TRAIN/DR3/MRJB1/SI1413.WAV +MRJB1_SI2021 TRAIN/DR3/MRJB1/SI2021.WAV +MRJB1_SX120 TRAIN/DR3/MRJB1/SX120.WAV +MRJB1_SX210 TRAIN/DR3/MRJB1/SX210.WAV +MRJB1_SX30 TRAIN/DR3/MRJB1/SX30.WAV +MRJB1_SX300 TRAIN/DR3/MRJB1/SX300.WAV +MRJB1_SX390 TRAIN/DR3/MRJB1/SX390.WAV +MRJH0_SI1519 TRAIN/DR2/MRJH0/SI1519.WAV +MRJH0_SI889 TRAIN/DR2/MRJH0/SI889.WAV +MRJH0_SI914 TRAIN/DR2/MRJH0/SI914.WAV +MRJH0_SX169 TRAIN/DR2/MRJH0/SX169.WAV +MRJH0_SX259 TRAIN/DR2/MRJH0/SX259.WAV +MRJH0_SX307 TRAIN/DR2/MRJH0/SX307.WAV +MRJH0_SX439 TRAIN/DR2/MRJH0/SX439.WAV +MRJH0_SX79 TRAIN/DR2/MRJH0/SX79.WAV +MRJM0_SI1095 TRAIN/DR2/MRJM0/SI1095.WAV +MRJM0_SI1228 TRAIN/DR2/MRJM0/SI1228.WAV +MRJM0_SI1858 TRAIN/DR2/MRJM0/SI1858.WAV +MRJM0_SX148 TRAIN/DR2/MRJM0/SX148.WAV +MRJM0_SX238 TRAIN/DR2/MRJM0/SX238.WAV +MRJM0_SX328 TRAIN/DR2/MRJM0/SX328.WAV +MRJM0_SX418 TRAIN/DR2/MRJM0/SX418.WAV +MRJM0_SX58 TRAIN/DR2/MRJM0/SX58.WAV +MRJM1_SI1298 TRAIN/DR2/MRJM1/SI1298.WAV +MRJM1_SI1928 TRAIN/DR2/MRJM1/SI1928.WAV +MRJM1_SI668 TRAIN/DR2/MRJM1/SI668.WAV +MRJM1_SX128 TRAIN/DR2/MRJM1/SX128.WAV +MRJM1_SX218 TRAIN/DR2/MRJM1/SX218.WAV +MRJM1_SX308 TRAIN/DR2/MRJM1/SX308.WAV +MRJM1_SX38 TRAIN/DR2/MRJM1/SX38.WAV +MRJM1_SX398 TRAIN/DR2/MRJM1/SX398.WAV +MRJT0_SI1498 TRAIN/DR2/MRJT0/SI1498.WAV +MRJT0_SI1805 TRAIN/DR2/MRJT0/SI1805.WAV +MRJT0_SI868 TRAIN/DR2/MRJT0/SI868.WAV +MRJT0_SX148 TRAIN/DR2/MRJT0/SX148.WAV +MRJT0_SX238 TRAIN/DR2/MRJT0/SX238.WAV +MRJT0_SX328 TRAIN/DR2/MRJT0/SX328.WAV +MRJT0_SX418 TRAIN/DR2/MRJT0/SX418.WAV +MRJT0_SX58 TRAIN/DR2/MRJT0/SX58.WAV +MRKM0_SI1267 TRAIN/DR5/MRKM0/SI1267.WAV +MRKM0_SI1391 TRAIN/DR5/MRKM0/SI1391.WAV +MRKM0_SI637 TRAIN/DR5/MRKM0/SI637.WAV +MRKM0_SX187 TRAIN/DR5/MRKM0/SX187.WAV +MRKM0_SX277 TRAIN/DR5/MRKM0/SX277.WAV +MRKM0_SX367 TRAIN/DR5/MRKM0/SX367.WAV +MRKM0_SX7 TRAIN/DR5/MRKM0/SX7.WAV +MRKM0_SX97 TRAIN/DR5/MRKM0/SX97.WAV +MRLD0_SI1594 TRAIN/DR5/MRLD0/SI1594.WAV +MRLD0_SI2224 TRAIN/DR5/MRLD0/SI2224.WAV +MRLD0_SI964 TRAIN/DR5/MRLD0/SI964.WAV +MRLD0_SX154 TRAIN/DR5/MRLD0/SX154.WAV +MRLD0_SX244 TRAIN/DR5/MRLD0/SX244.WAV +MRLD0_SX334 TRAIN/DR5/MRLD0/SX334.WAV +MRLD0_SX424 TRAIN/DR5/MRLD0/SX424.WAV +MRLD0_SX64 TRAIN/DR5/MRLD0/SX64.WAV +MRLJ0_SI1420 TRAIN/DR2/MRLJ0/SI1420.WAV +MRLJ0_SI2050 TRAIN/DR2/MRLJ0/SI2050.WAV +MRLJ0_SI790 TRAIN/DR2/MRLJ0/SI790.WAV +MRLJ0_SX160 TRAIN/DR2/MRLJ0/SX160.WAV +MRLJ0_SX250 TRAIN/DR2/MRLJ0/SX250.WAV +MRLJ0_SX340 TRAIN/DR2/MRLJ0/SX340.WAV +MRLJ0_SX430 TRAIN/DR2/MRLJ0/SX430.WAV +MRLJ0_SX70 TRAIN/DR2/MRLJ0/SX70.WAV +MRLJ1_SI1671 TRAIN/DR7/MRLJ1/SI1671.WAV +MRLJ1_SI2301 TRAIN/DR7/MRLJ1/SI2301.WAV +MRLJ1_SI2332 TRAIN/DR7/MRLJ1/SI2332.WAV +MRLJ1_SX141 TRAIN/DR7/MRLJ1/SX141.WAV +MRLJ1_SX231 TRAIN/DR7/MRLJ1/SX231.WAV +MRLJ1_SX321 TRAIN/DR7/MRLJ1/SX321.WAV +MRLJ1_SX411 TRAIN/DR7/MRLJ1/SX411.WAV +MRLJ1_SX51 TRAIN/DR7/MRLJ1/SX51.WAV +MRLK0_SI1468 TRAIN/DR8/MRLK0/SI1468.WAV +MRLK0_SI2140 TRAIN/DR8/MRLK0/SI2140.WAV +MRLK0_SI843 TRAIN/DR8/MRLK0/SI843.WAV +MRLK0_SX123 TRAIN/DR8/MRLK0/SX123.WAV +MRLK0_SX213 TRAIN/DR8/MRLK0/SX213.WAV +MRLK0_SX303 TRAIN/DR8/MRLK0/SX303.WAV +MRLK0_SX33 TRAIN/DR8/MRLK0/SX33.WAV +MRLK0_SX393 TRAIN/DR8/MRLK0/SX393.WAV +MRLR0_SI1196 TRAIN/DR2/MRLR0/SI1196.WAV +MRLR0_SI1826 TRAIN/DR2/MRLR0/SI1826.WAV +MRLR0_SI566 TRAIN/DR2/MRLR0/SI566.WAV +MRLR0_SX116 TRAIN/DR2/MRLR0/SX116.WAV +MRLR0_SX206 TRAIN/DR2/MRLR0/SX206.WAV +MRLR0_SX26 TRAIN/DR2/MRLR0/SX26.WAV +MRLR0_SX296 TRAIN/DR2/MRLR0/SX296.WAV +MRLR0_SX386 TRAIN/DR2/MRLR0/SX386.WAV +MRMB0_SI1581 TRAIN/DR6/MRMB0/SI1581.WAV +MRMB0_SI2211 TRAIN/DR6/MRMB0/SI2211.WAV +MRMB0_SI951 TRAIN/DR6/MRMB0/SI951.WAV +MRMB0_SX141 TRAIN/DR6/MRMB0/SX141.WAV +MRMB0_SX231 TRAIN/DR6/MRMB0/SX231.WAV +MRMB0_SX321 TRAIN/DR6/MRMB0/SX321.WAV +MRMB0_SX411 TRAIN/DR6/MRMB0/SX411.WAV +MRMB0_SX51 TRAIN/DR6/MRMB0/SX51.WAV +MRMG0_SI1080 TRAIN/DR7/MRMG0/SI1080.WAV +MRMG0_SI1710 TRAIN/DR7/MRMG0/SI1710.WAV +MRMG0_SI2340 TRAIN/DR7/MRMG0/SI2340.WAV +MRMG0_SX180 TRAIN/DR7/MRMG0/SX180.WAV +MRMG0_SX270 TRAIN/DR7/MRMG0/SX270.WAV +MRMG0_SX360 TRAIN/DR7/MRMG0/SX360.WAV +MRMG0_SX450 TRAIN/DR7/MRMG0/SX450.WAV +MRMG0_SX90 TRAIN/DR7/MRMG0/SX90.WAV +MRMH0_SI1021 TRAIN/DR7/MRMH0/SI1021.WAV +MRMH0_SI1349 TRAIN/DR7/MRMH0/SI1349.WAV +MRMH0_SI2281 TRAIN/DR7/MRMH0/SI2281.WAV +MRMH0_SX121 TRAIN/DR7/MRMH0/SX121.WAV +MRMH0_SX211 TRAIN/DR7/MRMH0/SX211.WAV +MRMH0_SX301 TRAIN/DR7/MRMH0/SX301.WAV +MRMH0_SX31 TRAIN/DR7/MRMH0/SX31.WAV +MRMH0_SX391 TRAIN/DR7/MRMH0/SX391.WAV +MRML0_SI1421 TRAIN/DR5/MRML0/SI1421.WAV +MRML0_SI2051 TRAIN/DR5/MRML0/SI2051.WAV +MRML0_SI791 TRAIN/DR5/MRML0/SI791.WAV +MRML0_SX161 TRAIN/DR5/MRML0/SX161.WAV +MRML0_SX251 TRAIN/DR5/MRML0/SX251.WAV +MRML0_SX341 TRAIN/DR5/MRML0/SX341.WAV +MRML0_SX431 TRAIN/DR5/MRML0/SX431.WAV +MRML0_SX71 TRAIN/DR5/MRML0/SX71.WAV +MRMS0_SI1113 TRAIN/DR2/MRMS0/SI1113.WAV +MRMS0_SI2057 TRAIN/DR2/MRMS0/SI2057.WAV +MRMS0_SI2100 TRAIN/DR2/MRMS0/SI2100.WAV +MRMS0_SX120 TRAIN/DR2/MRMS0/SX120.WAV +MRMS0_SX210 TRAIN/DR2/MRMS0/SX210.WAV +MRMS0_SX30 TRAIN/DR2/MRMS0/SX30.WAV +MRMS0_SX300 TRAIN/DR2/MRMS0/SX300.WAV +MRMS0_SX390 TRAIN/DR2/MRMS0/SX390.WAV +MRPC1_SI1482 TRAIN/DR7/MRPC1/SI1482.WAV +MRPC1_SI2026 TRAIN/DR7/MRPC1/SI2026.WAV +MRPC1_SI2112 TRAIN/DR7/MRPC1/SI2112.WAV +MRPC1_SX132 TRAIN/DR7/MRPC1/SX132.WAV +MRPC1_SX222 TRAIN/DR7/MRPC1/SX222.WAV +MRPC1_SX312 TRAIN/DR7/MRPC1/SX312.WAV +MRPC1_SX402 TRAIN/DR7/MRPC1/SX402.WAV +MRPC1_SX42 TRAIN/DR7/MRPC1/SX42.WAV +MRRE0_SI1334 TRAIN/DR8/MRRE0/SI1334.WAV +MRRE0_SI704 TRAIN/DR8/MRRE0/SI704.WAV +MRRE0_SI952 TRAIN/DR8/MRRE0/SI952.WAV +MRRE0_SX164 TRAIN/DR8/MRRE0/SX164.WAV +MRRE0_SX254 TRAIN/DR8/MRRE0/SX254.WAV +MRRE0_SX344 TRAIN/DR8/MRRE0/SX344.WAV +MRRE0_SX434 TRAIN/DR8/MRRE0/SX434.WAV +MRRE0_SX74 TRAIN/DR8/MRRE0/SX74.WAV +MRSO0_SI1206 TRAIN/DR1/MRSO0/SI1206.WAV +MRSO0_SI1659 TRAIN/DR1/MRSO0/SI1659.WAV +MRSO0_SI2289 TRAIN/DR1/MRSO0/SI2289.WAV +MRSO0_SX129 TRAIN/DR1/MRSO0/SX129.WAV +MRSO0_SX219 TRAIN/DR1/MRSO0/SX219.WAV +MRSO0_SX309 TRAIN/DR1/MRSO0/SX309.WAV +MRSO0_SX39 TRAIN/DR1/MRSO0/SX39.WAV +MRSO0_SX399 TRAIN/DR1/MRSO0/SX399.WAV +MRSP0_SI1429 TRAIN/DR4/MRSP0/SI1429.WAV +MRSP0_SI2059 TRAIN/DR4/MRSP0/SI2059.WAV +MRSP0_SI799 TRAIN/DR4/MRSP0/SI799.WAV +MRSP0_SX169 TRAIN/DR4/MRSP0/SX169.WAV +MRSP0_SX196 TRAIN/DR4/MRSP0/SX196.WAV +MRSP0_SX259 TRAIN/DR4/MRSP0/SX259.WAV +MRSP0_SX439 TRAIN/DR4/MRSP0/SX439.WAV +MRSP0_SX79 TRAIN/DR4/MRSP0/SX79.WAV +MRTC0_SI1458 TRAIN/DR3/MRTC0/SI1458.WAV +MRTC0_SI2088 TRAIN/DR3/MRTC0/SI2088.WAV +MRTC0_SI828 TRAIN/DR3/MRTC0/SI828.WAV +MRTC0_SX108 TRAIN/DR3/MRTC0/SX108.WAV +MRTC0_SX18 TRAIN/DR3/MRTC0/SX18.WAV +MRTC0_SX198 TRAIN/DR3/MRTC0/SX198.WAV +MRTC0_SX288 TRAIN/DR3/MRTC0/SX288.WAV +MRTC0_SX378 TRAIN/DR3/MRTC0/SX378.WAV +MRTJ0_SI1551 TRAIN/DR3/MRTJ0/SI1551.WAV +MRTJ0_SI2032 TRAIN/DR3/MRTJ0/SI2032.WAV +MRTJ0_SI772 TRAIN/DR3/MRTJ0/SI772.WAV +MRTJ0_SX142 TRAIN/DR3/MRTJ0/SX142.WAV +MRTJ0_SX232 TRAIN/DR3/MRTJ0/SX232.WAV +MRTJ0_SX322 TRAIN/DR3/MRTJ0/SX322.WAV +MRTJ0_SX412 TRAIN/DR3/MRTJ0/SX412.WAV +MRTJ0_SX52 TRAIN/DR3/MRTJ0/SX52.WAV +MRVG0_SI1140 TRAIN/DR5/MRVG0/SI1140.WAV +MRVG0_SI1770 TRAIN/DR5/MRVG0/SI1770.WAV +MRVG0_SI510 TRAIN/DR5/MRVG0/SI510.WAV +MRVG0_SX150 TRAIN/DR5/MRVG0/SX150.WAV +MRVG0_SX240 TRAIN/DR5/MRVG0/SX240.WAV +MRVG0_SX330 TRAIN/DR5/MRVG0/SX330.WAV +MRVG0_SX420 TRAIN/DR5/MRVG0/SX420.WAV +MRVG0_SX60 TRAIN/DR5/MRVG0/SX60.WAV +MRWA0_SI1603 TRAIN/DR3/MRWA0/SI1603.WAV +MRWA0_SI2233 TRAIN/DR3/MRWA0/SI2233.WAV +MRWA0_SI973 TRAIN/DR3/MRWA0/SI973.WAV +MRWA0_SX163 TRAIN/DR3/MRWA0/SX163.WAV +MRWA0_SX253 TRAIN/DR3/MRWA0/SX253.WAV +MRWA0_SX343 TRAIN/DR3/MRWA0/SX343.WAV +MRWA0_SX433 TRAIN/DR3/MRWA0/SX433.WAV +MRWA0_SX73 TRAIN/DR3/MRWA0/SX73.WAV +MRWS0_SI1102 TRAIN/DR1/MRWS0/SI1102.WAV +MRWS0_SI1732 TRAIN/DR1/MRWS0/SI1732.WAV +MRWS0_SI472 TRAIN/DR1/MRWS0/SI472.WAV +MRWS0_SX112 TRAIN/DR1/MRWS0/SX112.WAV +MRWS0_SX202 TRAIN/DR1/MRWS0/SX202.WAV +MRWS0_SX22 TRAIN/DR1/MRWS0/SX22.WAV +MRWS0_SX292 TRAIN/DR1/MRWS0/SX292.WAV +MRWS0_SX382 TRAIN/DR1/MRWS0/SX382.WAV +MRXB0_SI1585 TRAIN/DR6/MRXB0/SI1585.WAV +MRXB0_SI2215 TRAIN/DR6/MRXB0/SI2215.WAV +MRXB0_SI955 TRAIN/DR6/MRXB0/SI955.WAV +MRXB0_SX145 TRAIN/DR6/MRXB0/SX145.WAV +MRXB0_SX235 TRAIN/DR6/MRXB0/SX235.WAV +MRXB0_SX325 TRAIN/DR6/MRXB0/SX325.WAV +MRXB0_SX415 TRAIN/DR6/MRXB0/SX415.WAV +MRXB0_SX55 TRAIN/DR6/MRXB0/SX55.WAV +MSAH1_SI1049 TRAIN/DR7/MSAH1/SI1049.WAV +MSAH1_SI1679 TRAIN/DR7/MSAH1/SI1679.WAV +MSAH1_SI2309 TRAIN/DR7/MSAH1/SI2309.WAV +MSAH1_SX149 TRAIN/DR7/MSAH1/SX149.WAV +MSAH1_SX239 TRAIN/DR7/MSAH1/SX239.WAV +MSAH1_SX329 TRAIN/DR7/MSAH1/SX329.WAV +MSAH1_SX419 TRAIN/DR7/MSAH1/SX419.WAV +MSAH1_SX59 TRAIN/DR7/MSAH1/SX59.WAV +MSAS0_SI1376 TRAIN/DR5/MSAS0/SI1376.WAV +MSAS0_SI2006 TRAIN/DR5/MSAS0/SI2006.WAV +MSAS0_SI746 TRAIN/DR5/MSAS0/SI746.WAV +MSAS0_SX116 TRAIN/DR5/MSAS0/SX116.WAV +MSAS0_SX206 TRAIN/DR5/MSAS0/SX206.WAV +MSAS0_SX26 TRAIN/DR5/MSAS0/SX26.WAV +MSAS0_SX296 TRAIN/DR5/MSAS0/SX296.WAV +MSAS0_SX386 TRAIN/DR5/MSAS0/SX386.WAV +MSAT0_SI1526 TRAIN/DR2/MSAT0/SI1526.WAV +MSAT0_SI2156 TRAIN/DR2/MSAT0/SI2156.WAV +MSAT0_SI896 TRAIN/DR2/MSAT0/SI896.WAV +MSAT0_SX176 TRAIN/DR2/MSAT0/SX176.WAV +MSAT0_SX266 TRAIN/DR2/MSAT0/SX266.WAV +MSAT0_SX356 TRAIN/DR2/MSAT0/SX356.WAV +MSAT0_SX446 TRAIN/DR2/MSAT0/SX446.WAV +MSAT0_SX86 TRAIN/DR2/MSAT0/SX86.WAV +MSAT1_SI1073 TRAIN/DR6/MSAT1/SI1073.WAV +MSAT1_SI1703 TRAIN/DR6/MSAT1/SI1703.WAV +MSAT1_SI2333 TRAIN/DR6/MSAT1/SI2333.WAV +MSAT1_SX173 TRAIN/DR6/MSAT1/SX173.WAV +MSAT1_SX263 TRAIN/DR6/MSAT1/SX263.WAV +MSAT1_SX353 TRAIN/DR6/MSAT1/SX353.WAV +MSAT1_SX443 TRAIN/DR6/MSAT1/SX443.WAV +MSAT1_SX83 TRAIN/DR6/MSAT1/SX83.WAV +MSDB0_SI1007 TRAIN/DR7/MSDB0/SI1007.WAV +MSDB0_SI1637 TRAIN/DR7/MSDB0/SI1637.WAV +MSDB0_SI2267 TRAIN/DR7/MSDB0/SI2267.WAV +MSDB0_SX107 TRAIN/DR7/MSDB0/SX107.WAV +MSDB0_SX17 TRAIN/DR7/MSDB0/SX17.WAV +MSDB0_SX197 TRAIN/DR7/MSDB0/SX197.WAV +MSDB0_SX287 TRAIN/DR7/MSDB0/SX287.WAV +MSDB0_SX377 TRAIN/DR7/MSDB0/SX377.WAV +MSDH0_SI2113 TRAIN/DR5/MSDH0/SI2113.WAV +MSDH0_SI2240 TRAIN/DR5/MSDH0/SI2240.WAV +MSDH0_SI980 TRAIN/DR5/MSDH0/SI980.WAV +MSDH0_SX170 TRAIN/DR5/MSDH0/SX170.WAV +MSDH0_SX260 TRAIN/DR5/MSDH0/SX260.WAV +MSDH0_SX350 TRAIN/DR5/MSDH0/SX350.WAV +MSDH0_SX440 TRAIN/DR5/MSDH0/SX440.WAV +MSDH0_SX80 TRAIN/DR5/MSDH0/SX80.WAV +MSDS0_SI1077 TRAIN/DR6/MSDS0/SI1077.WAV +MSDS0_SI1707 TRAIN/DR6/MSDS0/SI1707.WAV +MSDS0_SI2337 TRAIN/DR6/MSDS0/SI2337.WAV +MSDS0_SX177 TRAIN/DR6/MSDS0/SX177.WAV +MSDS0_SX267 TRAIN/DR6/MSDS0/SX267.WAV +MSDS0_SX357 TRAIN/DR6/MSDS0/SX357.WAV +MSDS0_SX447 TRAIN/DR6/MSDS0/SX447.WAV +MSDS0_SX87 TRAIN/DR6/MSDS0/SX87.WAV +MSEM1_SI1440 TRAIN/DR5/MSEM1/SI1440.WAV +MSEM1_SI2070 TRAIN/DR5/MSEM1/SI2070.WAV +MSEM1_SI810 TRAIN/DR5/MSEM1/SI810.WAV +MSEM1_SX180 TRAIN/DR5/MSEM1/SX180.WAV +MSEM1_SX270 TRAIN/DR5/MSEM1/SX270.WAV +MSEM1_SX360 TRAIN/DR5/MSEM1/SX360.WAV +MSEM1_SX450 TRAIN/DR5/MSEM1/SX450.WAV +MSEM1_SX90 TRAIN/DR5/MSEM1/SX90.WAV +MSES0_SI1589 TRAIN/DR7/MSES0/SI1589.WAV +MSES0_SI2216 TRAIN/DR7/MSES0/SI2216.WAV +MSES0_SI2219 TRAIN/DR7/MSES0/SI2219.WAV +MSES0_SX149 TRAIN/DR7/MSES0/SX149.WAV +MSES0_SX239 TRAIN/DR7/MSES0/SX239.WAV +MSES0_SX329 TRAIN/DR7/MSES0/SX329.WAV +MSES0_SX419 TRAIN/DR7/MSES0/SX419.WAV +MSES0_SX59 TRAIN/DR7/MSES0/SX59.WAV +MSFH0_SI1216 TRAIN/DR4/MSFH0/SI1216.WAV +MSFH0_SI1738 TRAIN/DR4/MSFH0/SI1738.WAV +MSFH0_SI586 TRAIN/DR4/MSFH0/SI586.WAV +MSFH0_SX136 TRAIN/DR4/MSFH0/SX136.WAV +MSFH0_SX226 TRAIN/DR4/MSFH0/SX226.WAV +MSFH0_SX316 TRAIN/DR4/MSFH0/SX316.WAV +MSFH0_SX406 TRAIN/DR4/MSFH0/SX406.WAV +MSFH0_SX46 TRAIN/DR4/MSFH0/SX46.WAV +MSFV0_SI1262 TRAIN/DR3/MSFV0/SI1262.WAV +MSFV0_SI1892 TRAIN/DR3/MSFV0/SI1892.WAV +MSFV0_SI632 TRAIN/DR3/MSFV0/SI632.WAV +MSFV0_SX182 TRAIN/DR3/MSFV0/SX182.WAV +MSFV0_SX272 TRAIN/DR3/MSFV0/SX272.WAV +MSFV0_SX362 TRAIN/DR3/MSFV0/SX362.WAV +MSFV0_SX452 TRAIN/DR3/MSFV0/SX452.WAV +MSFV0_SX92 TRAIN/DR3/MSFV0/SX92.WAV +MSJK0_SI1596 TRAIN/DR6/MSJK0/SI1596.WAV +MSJK0_SI2226 TRAIN/DR6/MSJK0/SI2226.WAV +MSJK0_SI966 TRAIN/DR6/MSJK0/SI966.WAV +MSJK0_SX156 TRAIN/DR6/MSJK0/SX156.WAV +MSJK0_SX246 TRAIN/DR6/MSJK0/SX246.WAV +MSJK0_SX336 TRAIN/DR6/MSJK0/SX336.WAV +MSJK0_SX426 TRAIN/DR6/MSJK0/SX426.WAV +MSJK0_SX66 TRAIN/DR6/MSJK0/SX66.WAV +MSMC0_SI1907 TRAIN/DR4/MSMC0/SI1907.WAV +MSMC0_SI509 TRAIN/DR4/MSMC0/SI509.WAV +MSMC0_SI647 TRAIN/DR4/MSMC0/SI647.WAV +MSMC0_SX107 TRAIN/DR4/MSMC0/SX107.WAV +MSMC0_SX17 TRAIN/DR4/MSMC0/SX17.WAV +MSMC0_SX197 TRAIN/DR4/MSMC0/SX197.WAV +MSMC0_SX287 TRAIN/DR4/MSMC0/SX287.WAV +MSMC0_SX377 TRAIN/DR4/MSMC0/SX377.WAV +MSMR0_SI1150 TRAIN/DR6/MSMR0/SI1150.WAV +MSMR0_SI1405 TRAIN/DR6/MSMR0/SI1405.WAV +MSMR0_SI775 TRAIN/DR6/MSMR0/SI775.WAV +MSMR0_SX145 TRAIN/DR6/MSMR0/SX145.WAV +MSMR0_SX235 TRAIN/DR6/MSMR0/SX235.WAV +MSMR0_SX325 TRAIN/DR6/MSMR0/SX325.WAV +MSMR0_SX415 TRAIN/DR6/MSMR0/SX415.WAV +MSMR0_SX55 TRAIN/DR6/MSMR0/SX55.WAV +MSMS0_SI1433 TRAIN/DR4/MSMS0/SI1433.WAV +MSMS0_SI2063 TRAIN/DR4/MSMS0/SI2063.WAV +MSMS0_SI803 TRAIN/DR4/MSMS0/SI803.WAV +MSMS0_SX173 TRAIN/DR4/MSMS0/SX173.WAV +MSMS0_SX263 TRAIN/DR4/MSMS0/SX263.WAV +MSMS0_SX353 TRAIN/DR4/MSMS0/SX353.WAV +MSMS0_SX443 TRAIN/DR4/MSMS0/SX443.WAV +MSMS0_SX83 TRAIN/DR4/MSMS0/SX83.WAV +MSRG0_SI1221 TRAIN/DR4/MSRG0/SI1221.WAV +MSRG0_SI1851 TRAIN/DR4/MSRG0/SI1851.WAV +MSRG0_SI591 TRAIN/DR4/MSRG0/SI591.WAV +MSRG0_SX141 TRAIN/DR4/MSRG0/SX141.WAV +MSRG0_SX231 TRAIN/DR4/MSRG0/SX231.WAV +MSRG0_SX321 TRAIN/DR4/MSRG0/SX321.WAV +MSRG0_SX411 TRAIN/DR4/MSRG0/SX411.WAV +MSRG0_SX51 TRAIN/DR4/MSRG0/SX51.WAV +MSRR0_SI1131 TRAIN/DR5/MSRR0/SI1131.WAV +MSRR0_SI1761 TRAIN/DR5/MSRR0/SI1761.WAV +MSRR0_SI501 TRAIN/DR5/MSRR0/SI501.WAV +MSRR0_SX141 TRAIN/DR5/MSRR0/SX141.WAV +MSRR0_SX231 TRAIN/DR5/MSRR0/SX231.WAV +MSRR0_SX30 TRAIN/DR5/MSRR0/SX30.WAV +MSRR0_SX411 TRAIN/DR5/MSRR0/SX411.WAV +MSRR0_SX51 TRAIN/DR5/MSRR0/SX51.WAV +MSTF0_SI1396 TRAIN/DR4/MSTF0/SI1396.WAV +MSTF0_SI766 TRAIN/DR4/MSTF0/SI766.WAV +MSTF0_SI852 TRAIN/DR4/MSTF0/SI852.WAV +MSTF0_SX136 TRAIN/DR4/MSTF0/SX136.WAV +MSTF0_SX226 TRAIN/DR4/MSTF0/SX226.WAV +MSTF0_SX316 TRAIN/DR4/MSTF0/SX316.WAV +MSTF0_SX406 TRAIN/DR4/MSTF0/SX406.WAV +MSTF0_SX46 TRAIN/DR4/MSTF0/SX46.WAV +MSVS0_SI1568 TRAIN/DR6/MSVS0/SI1568.WAV +MSVS0_SI2198 TRAIN/DR6/MSVS0/SI2198.WAV +MSVS0_SI938 TRAIN/DR6/MSVS0/SI938.WAV +MSVS0_SX128 TRAIN/DR6/MSVS0/SX128.WAV +MSVS0_SX218 TRAIN/DR6/MSVS0/SX218.WAV +MSVS0_SX308 TRAIN/DR6/MSVS0/SX308.WAV +MSVS0_SX38 TRAIN/DR6/MSVS0/SX38.WAV +MSVS0_SX398 TRAIN/DR6/MSVS0/SX398.WAV +MTAB0_SI1572 TRAIN/DR7/MTAB0/SI1572.WAV +MTAB0_SI2202 TRAIN/DR7/MTAB0/SI2202.WAV +MTAB0_SI942 TRAIN/DR7/MTAB0/SI942.WAV +MTAB0_SX132 TRAIN/DR7/MTAB0/SX132.WAV +MTAB0_SX222 TRAIN/DR7/MTAB0/SX222.WAV +MTAB0_SX312 TRAIN/DR7/MTAB0/SX312.WAV +MTAB0_SX402 TRAIN/DR7/MTAB0/SX402.WAV +MTAB0_SX42 TRAIN/DR7/MTAB0/SX42.WAV +MTAS0_SI1385 TRAIN/DR4/MTAS0/SI1385.WAV +MTAS0_SI2015 TRAIN/DR4/MTAS0/SI2015.WAV +MTAS0_SI755 TRAIN/DR4/MTAS0/SI755.WAV +MTAS0_SX125 TRAIN/DR4/MTAS0/SX125.WAV +MTAS0_SX215 TRAIN/DR4/MTAS0/SX215.WAV +MTAS0_SX305 TRAIN/DR4/MTAS0/SX305.WAV +MTAS0_SX35 TRAIN/DR4/MTAS0/SX35.WAV +MTAS0_SX395 TRAIN/DR4/MTAS0/SX395.WAV +MTAT0_SI1110 TRAIN/DR5/MTAT0/SI1110.WAV +MTAT0_SI1740 TRAIN/DR5/MTAT0/SI1740.WAV +MTAT0_SI811 TRAIN/DR5/MTAT0/SI811.WAV +MTAT0_SX120 TRAIN/DR5/MTAT0/SX120.WAV +MTAT0_SX210 TRAIN/DR5/MTAT0/SX210.WAV +MTAT0_SX30 TRAIN/DR5/MTAT0/SX30.WAV +MTAT0_SX300 TRAIN/DR5/MTAT0/SX300.WAV +MTAT0_SX390 TRAIN/DR5/MTAT0/SX390.WAV +MTAT1_SI1409 TRAIN/DR2/MTAT1/SI1409.WAV +MTAT1_SI1627 TRAIN/DR2/MTAT1/SI1627.WAV +MTAT1_SI779 TRAIN/DR2/MTAT1/SI779.WAV +MTAT1_SX149 TRAIN/DR2/MTAT1/SX149.WAV +MTAT1_SX239 TRAIN/DR2/MTAT1/SX239.WAV +MTAT1_SX329 TRAIN/DR2/MTAT1/SX329.WAV +MTAT1_SX419 TRAIN/DR2/MTAT1/SX419.WAV +MTAT1_SX59 TRAIN/DR2/MTAT1/SX59.WAV +MTBC0_SI1173 TRAIN/DR2/MTBC0/SI1173.WAV +MTBC0_SI1803 TRAIN/DR2/MTBC0/SI1803.WAV +MTBC0_SI543 TRAIN/DR2/MTBC0/SI543.WAV +MTBC0_SX183 TRAIN/DR2/MTBC0/SX183.WAV +MTBC0_SX273 TRAIN/DR2/MTBC0/SX273.WAV +MTBC0_SX347 TRAIN/DR2/MTBC0/SX347.WAV +MTBC0_SX363 TRAIN/DR2/MTBC0/SX363.WAV +MTBC0_SX93 TRAIN/DR2/MTBC0/SX93.WAV +MTCS0_SI1972 TRAIN/DR8/MTCS0/SI1972.WAV +MTCS0_SI2265 TRAIN/DR8/MTCS0/SI2265.WAV +MTCS0_SI712 TRAIN/DR8/MTCS0/SI712.WAV +MTCS0_SX172 TRAIN/DR8/MTCS0/SX172.WAV +MTCS0_SX262 TRAIN/DR8/MTCS0/SX262.WAV +MTCS0_SX352 TRAIN/DR8/MTCS0/SX352.WAV +MTCS0_SX442 TRAIN/DR8/MTCS0/SX442.WAV +MTCS0_SX82 TRAIN/DR8/MTCS0/SX82.WAV +MTDB0_SI1401 TRAIN/DR2/MTDB0/SI1401.WAV +MTDB0_SI2031 TRAIN/DR2/MTDB0/SI2031.WAV +MTDB0_SI771 TRAIN/DR2/MTDB0/SI771.WAV +MTDB0_SX141 TRAIN/DR2/MTDB0/SX141.WAV +MTDB0_SX231 TRAIN/DR2/MTDB0/SX231.WAV +MTDB0_SX321 TRAIN/DR2/MTDB0/SX321.WAV +MTDB0_SX411 TRAIN/DR2/MTDB0/SX411.WAV +MTDB0_SX51 TRAIN/DR2/MTDB0/SX51.WAV +MTDP0_SI1274 TRAIN/DR5/MTDP0/SI1274.WAV +MTDP0_SI1521 TRAIN/DR5/MTDP0/SI1521.WAV +MTDP0_SI2151 TRAIN/DR5/MTDP0/SI2151.WAV +MTDP0_SX171 TRAIN/DR5/MTDP0/SX171.WAV +MTDP0_SX261 TRAIN/DR5/MTDP0/SX261.WAV +MTDP0_SX351 TRAIN/DR5/MTDP0/SX351.WAV +MTDP0_SX441 TRAIN/DR5/MTDP0/SX441.WAV +MTDP0_SX81 TRAIN/DR5/MTDP0/SX81.WAV +MTER0_SI1157 TRAIN/DR7/MTER0/SI1157.WAV +MTER0_SI1787 TRAIN/DR7/MTER0/SI1787.WAV +MTER0_SI527 TRAIN/DR7/MTER0/SI527.WAV +MTER0_SX167 TRAIN/DR7/MTER0/SX167.WAV +MTER0_SX17 TRAIN/DR7/MTER0/SX17.WAV +MTER0_SX257 TRAIN/DR7/MTER0/SX257.WAV +MTER0_SX437 TRAIN/DR7/MTER0/SX437.WAV +MTER0_SX77 TRAIN/DR7/MTER0/SX77.WAV +MTJG0_SI1520 TRAIN/DR2/MTJG0/SI1520.WAV +MTJG0_SI2157 TRAIN/DR2/MTJG0/SI2157.WAV +MTJG0_SI890 TRAIN/DR2/MTJG0/SI890.WAV +MTJG0_SX170 TRAIN/DR2/MTJG0/SX170.WAV +MTJG0_SX260 TRAIN/DR2/MTJG0/SX260.WAV +MTJG0_SX350 TRAIN/DR2/MTJG0/SX350.WAV +MTJG0_SX440 TRAIN/DR2/MTJG0/SX440.WAV +MTJG0_SX80 TRAIN/DR2/MTJG0/SX80.WAV +MTJM0_SI1226 TRAIN/DR3/MTJM0/SI1226.WAV +MTJM0_SI1856 TRAIN/DR3/MTJM0/SI1856.WAV +MTJM0_SI655 TRAIN/DR3/MTJM0/SI655.WAV +MTJM0_SX146 TRAIN/DR3/MTJM0/SX146.WAV +MTJM0_SX236 TRAIN/DR3/MTJM0/SX236.WAV +MTJM0_SX326 TRAIN/DR3/MTJM0/SX326.WAV +MTJM0_SX416 TRAIN/DR3/MTJM0/SX416.WAV +MTJM0_SX56 TRAIN/DR3/MTJM0/SX56.WAV +MTJS0_SI1192 TRAIN/DR1/MTJS0/SI1192.WAV +MTJS0_SI1822 TRAIN/DR1/MTJS0/SI1822.WAV +MTJS0_SI562 TRAIN/DR1/MTJS0/SI562.WAV +MTJS0_SX112 TRAIN/DR1/MTJS0/SX112.WAV +MTJS0_SX202 TRAIN/DR1/MTJS0/SX202.WAV +MTJS0_SX22 TRAIN/DR1/MTJS0/SX22.WAV +MTJS0_SX292 TRAIN/DR1/MTJS0/SX292.WAV +MTJS0_SX382 TRAIN/DR1/MTJS0/SX382.WAV +MTJU0_SI2020 TRAIN/DR6/MTJU0/SI2020.WAV +MTJU0_SI2269 TRAIN/DR6/MTJU0/SI2269.WAV +MTJU0_SI760 TRAIN/DR6/MTJU0/SI760.WAV +MTJU0_SX130 TRAIN/DR6/MTJU0/SX130.WAV +MTJU0_SX220 TRAIN/DR6/MTJU0/SX220.WAV +MTJU0_SX310 TRAIN/DR6/MTJU0/SX310.WAV +MTJU0_SX40 TRAIN/DR6/MTJU0/SX40.WAV +MTJU0_SX400 TRAIN/DR6/MTJU0/SX400.WAV +MTKD0_SI1187 TRAIN/DR7/MTKD0/SI1187.WAV +MTKD0_SI1817 TRAIN/DR7/MTKD0/SI1817.WAV +MTKD0_SI630 TRAIN/DR7/MTKD0/SI630.WAV +MTKD0_SX107 TRAIN/DR7/MTKD0/SX107.WAV +MTKD0_SX17 TRAIN/DR7/MTKD0/SX17.WAV +MTKD0_SX197 TRAIN/DR7/MTKD0/SX197.WAV +MTKD0_SX287 TRAIN/DR7/MTKD0/SX287.WAV +MTKD0_SX377 TRAIN/DR7/MTKD0/SX377.WAV +MTKP0_SI1023 TRAIN/DR3/MTKP0/SI1023.WAV +MTKP0_SI2283 TRAIN/DR3/MTKP0/SI2283.WAV +MTKP0_SI454 TRAIN/DR3/MTKP0/SI454.WAV +MTKP0_SX123 TRAIN/DR3/MTKP0/SX123.WAV +MTKP0_SX213 TRAIN/DR3/MTKP0/SX213.WAV +MTKP0_SX303 TRAIN/DR3/MTKP0/SX303.WAV +MTKP0_SX33 TRAIN/DR3/MTKP0/SX33.WAV +MTKP0_SX393 TRAIN/DR3/MTKP0/SX393.WAV +MTLB0_SI1134 TRAIN/DR3/MTLB0/SI1134.WAV +MTLB0_SI1764 TRAIN/DR3/MTLB0/SI1764.WAV +MTLB0_SI504 TRAIN/DR3/MTLB0/SI504.WAV +MTLB0_SX144 TRAIN/DR3/MTLB0/SX144.WAV +MTLB0_SX234 TRAIN/DR3/MTLB0/SX234.WAV +MTLB0_SX324 TRAIN/DR3/MTLB0/SX324.WAV +MTLB0_SX414 TRAIN/DR3/MTLB0/SX414.WAV +MTLB0_SX54 TRAIN/DR3/MTLB0/SX54.WAV +MTLC0_SI1313 TRAIN/DR7/MTLC0/SI1313.WAV +MTLC0_SI1477 TRAIN/DR7/MTLC0/SI1477.WAV +MTLC0_SI847 TRAIN/DR7/MTLC0/SI847.WAV +MTLC0_SX127 TRAIN/DR7/MTLC0/SX127.WAV +MTLC0_SX217 TRAIN/DR7/MTLC0/SX217.WAV +MTLC0_SX307 TRAIN/DR7/MTLC0/SX307.WAV +MTLC0_SX37 TRAIN/DR7/MTLC0/SX37.WAV +MTLC0_SX397 TRAIN/DR7/MTLC0/SX397.WAV +MTML0_SI1065 TRAIN/DR7/MTML0/SI1065.WAV +MTML0_SI1695 TRAIN/DR7/MTML0/SI1695.WAV +MTML0_SI2325 TRAIN/DR7/MTML0/SI2325.WAV +MTML0_SX165 TRAIN/DR7/MTML0/SX165.WAV +MTML0_SX255 TRAIN/DR7/MTML0/SX255.WAV +MTML0_SX345 TRAIN/DR7/MTML0/SX345.WAV +MTML0_SX435 TRAIN/DR7/MTML0/SX435.WAV +MTML0_SX75 TRAIN/DR7/MTML0/SX75.WAV +MTMN0_SI1064 TRAIN/DR7/MTMN0/SI1064.WAV +MTMN0_SI2324 TRAIN/DR7/MTMN0/SI2324.WAV +MTMN0_SI582 TRAIN/DR7/MTMN0/SI582.WAV +MTMN0_SX164 TRAIN/DR7/MTMN0/SX164.WAV +MTMN0_SX254 TRAIN/DR7/MTMN0/SX254.WAV +MTMN0_SX344 TRAIN/DR7/MTMN0/SX344.WAV +MTMN0_SX434 TRAIN/DR7/MTMN0/SX434.WAV +MTMN0_SX74 TRAIN/DR7/MTMN0/SX74.WAV +MTMT0_SI1118 TRAIN/DR5/MTMT0/SI1118.WAV +MTMT0_SI1748 TRAIN/DR5/MTMT0/SI1748.WAV +MTMT0_SI488 TRAIN/DR5/MTMT0/SI488.WAV +MTMT0_SX128 TRAIN/DR5/MTMT0/SX128.WAV +MTMT0_SX218 TRAIN/DR5/MTMT0/SX218.WAV +MTMT0_SX308 TRAIN/DR5/MTMT0/SX308.WAV +MTMT0_SX38 TRAIN/DR5/MTMT0/SX38.WAV +MTMT0_SX398 TRAIN/DR5/MTMT0/SX398.WAV +MTPF0_SI1235 TRAIN/DR1/MTPF0/SI1235.WAV +MTPF0_SI1865 TRAIN/DR1/MTPF0/SI1865.WAV +MTPF0_SI605 TRAIN/DR1/MTPF0/SI605.WAV +MTPF0_SX155 TRAIN/DR1/MTPF0/SX155.WAV +MTPF0_SX245 TRAIN/DR1/MTPF0/SX245.WAV +MTPF0_SX335 TRAIN/DR1/MTPF0/SX335.WAV +MTPF0_SX425 TRAIN/DR1/MTPF0/SX425.WAV +MTPF0_SX65 TRAIN/DR1/MTPF0/SX65.WAV +MTPG0_SI1383 TRAIN/DR3/MTPG0/SI1383.WAV +MTPG0_SI2013 TRAIN/DR3/MTPG0/SI2013.WAV +MTPG0_SI753 TRAIN/DR3/MTPG0/SI753.WAV +MTPG0_SX123 TRAIN/DR3/MTPG0/SX123.WAV +MTPG0_SX213 TRAIN/DR3/MTPG0/SX213.WAV +MTPG0_SX303 TRAIN/DR3/MTPG0/SX303.WAV +MTPG0_SX33 TRAIN/DR3/MTPG0/SX33.WAV +MTPG0_SX393 TRAIN/DR3/MTPG0/SX393.WAV +MTPP0_SI1508 TRAIN/DR3/MTPP0/SI1508.WAV +MTPP0_SI2138 TRAIN/DR3/MTPP0/SI2138.WAV +MTPP0_SI878 TRAIN/DR3/MTPP0/SI878.WAV +MTPP0_SX158 TRAIN/DR3/MTPP0/SX158.WAV +MTPP0_SX248 TRAIN/DR3/MTPP0/SX248.WAV +MTPP0_SX338 TRAIN/DR3/MTPP0/SX338.WAV +MTPP0_SX428 TRAIN/DR3/MTPP0/SX428.WAV +MTPP0_SX68 TRAIN/DR3/MTPP0/SX68.WAV +MTPR0_SI1600 TRAIN/DR7/MTPR0/SI1600.WAV +MTPR0_SI2230 TRAIN/DR7/MTPR0/SI2230.WAV +MTPR0_SI506 TRAIN/DR7/MTPR0/SI506.WAV +MTPR0_SX160 TRAIN/DR7/MTPR0/SX160.WAV +MTPR0_SX250 TRAIN/DR7/MTPR0/SX250.WAV +MTPR0_SX340 TRAIN/DR7/MTPR0/SX340.WAV +MTPR0_SX430 TRAIN/DR7/MTPR0/SX430.WAV +MTPR0_SX70 TRAIN/DR7/MTPR0/SX70.WAV +MTQC0_SI1441 TRAIN/DR4/MTQC0/SI1441.WAV +MTQC0_SI2071 TRAIN/DR4/MTQC0/SI2071.WAV +MTQC0_SI480 TRAIN/DR4/MTQC0/SI480.WAV +MTQC0_SX181 TRAIN/DR4/MTQC0/SX181.WAV +MTQC0_SX271 TRAIN/DR4/MTQC0/SX271.WAV +MTQC0_SX361 TRAIN/DR4/MTQC0/SX361.WAV +MTQC0_SX451 TRAIN/DR4/MTQC0/SX451.WAV +MTQC0_SX91 TRAIN/DR4/MTQC0/SX91.WAV +MTRC0_SI1623 TRAIN/DR4/MTRC0/SI1623.WAV +MTRC0_SI589 TRAIN/DR4/MTRC0/SI589.WAV +MTRC0_SI993 TRAIN/DR4/MTRC0/SI993.WAV +MTRC0_SX170 TRAIN/DR4/MTRC0/SX170.WAV +MTRC0_SX183 TRAIN/DR4/MTRC0/SX183.WAV +MTRC0_SX273 TRAIN/DR4/MTRC0/SX273.WAV +MTRC0_SX363 TRAIN/DR4/MTRC0/SX363.WAV +MTRC0_SX93 TRAIN/DR4/MTRC0/SX93.WAV +MTRR0_SI1548 TRAIN/DR1/MTRR0/SI1548.WAV +MTRR0_SI2178 TRAIN/DR1/MTRR0/SI2178.WAV +MTRR0_SI918 TRAIN/DR1/MTRR0/SI918.WAV +MTRR0_SX108 TRAIN/DR1/MTRR0/SX108.WAV +MTRR0_SX18 TRAIN/DR1/MTRR0/SX18.WAV +MTRR0_SX198 TRAIN/DR1/MTRR0/SX198.WAV +MTRR0_SX288 TRAIN/DR1/MTRR0/SX288.WAV +MTRR0_SX378 TRAIN/DR1/MTRR0/SX378.WAV +MTRT0_SI1227 TRAIN/DR4/MTRT0/SI1227.WAV +MTRT0_SI1857 TRAIN/DR4/MTRT0/SI1857.WAV +MTRT0_SI597 TRAIN/DR4/MTRT0/SI597.WAV +MTRT0_SX147 TRAIN/DR4/MTRT0/SX147.WAV +MTRT0_SX237 TRAIN/DR4/MTRT0/SX237.WAV +MTRT0_SX254 TRAIN/DR4/MTRT0/SX254.WAV +MTRT0_SX417 TRAIN/DR4/MTRT0/SX417.WAV +MTRT0_SX57 TRAIN/DR4/MTRT0/SX57.WAV +MTWH1_SI1512 TRAIN/DR7/MTWH1/SI1512.WAV +MTWH1_SI2142 TRAIN/DR7/MTWH1/SI2142.WAV +MTWH1_SI882 TRAIN/DR7/MTWH1/SI882.WAV +MTWH1_SX162 TRAIN/DR7/MTWH1/SX162.WAV +MTWH1_SX252 TRAIN/DR7/MTWH1/SX252.WAV +MTWH1_SX342 TRAIN/DR7/MTWH1/SX342.WAV +MTWH1_SX432 TRAIN/DR7/MTWH1/SX432.WAV +MTWH1_SX72 TRAIN/DR7/MTWH1/SX72.WAV +MTXS0_SI1060 TRAIN/DR6/MTXS0/SI1060.WAV +MTXS0_SI1690 TRAIN/DR6/MTXS0/SI1690.WAV +MTXS0_SI2320 TRAIN/DR6/MTXS0/SI2320.WAV +MTXS0_SX160 TRAIN/DR6/MTXS0/SX160.WAV +MTXS0_SX250 TRAIN/DR6/MTXS0/SX250.WAV +MTXS0_SX340 TRAIN/DR6/MTXS0/SX340.WAV +MTXS0_SX430 TRAIN/DR6/MTXS0/SX430.WAV +MTXS0_SX70 TRAIN/DR6/MTXS0/SX70.WAV +MVJH0_SI1556 TRAIN/DR3/MVJH0/SI1556.WAV +MVJH0_SI2186 TRAIN/DR3/MVJH0/SI2186.WAV +MVJH0_SI926 TRAIN/DR3/MVJH0/SI926.WAV +MVJH0_SX116 TRAIN/DR3/MVJH0/SX116.WAV +MVJH0_SX206 TRAIN/DR3/MVJH0/SX206.WAV +MVJH0_SX26 TRAIN/DR3/MVJH0/SX26.WAV +MVJH0_SX296 TRAIN/DR3/MVJH0/SX296.WAV +MVJH0_SX386 TRAIN/DR3/MVJH0/SX386.WAV +MVLO0_SI1147 TRAIN/DR5/MVLO0/SI1147.WAV +MVLO0_SI1777 TRAIN/DR5/MVLO0/SI1777.WAV +MVLO0_SI517 TRAIN/DR5/MVLO0/SI517.WAV +MVLO0_SX157 TRAIN/DR5/MVLO0/SX157.WAV +MVLO0_SX247 TRAIN/DR5/MVLO0/SX247.WAV +MVLO0_SX337 TRAIN/DR5/MVLO0/SX337.WAV +MVLO0_SX427 TRAIN/DR5/MVLO0/SX427.WAV +MVLO0_SX67 TRAIN/DR5/MVLO0/SX67.WAV +MVRW0_SI1485 TRAIN/DR7/MVRW0/SI1485.WAV +MVRW0_SI2115 TRAIN/DR7/MVRW0/SI2115.WAV +MVRW0_SI855 TRAIN/DR7/MVRW0/SI855.WAV +MVRW0_SX135 TRAIN/DR7/MVRW0/SX135.WAV +MVRW0_SX225 TRAIN/DR7/MVRW0/SX225.WAV +MVRW0_SX315 TRAIN/DR7/MVRW0/SX315.WAV +MVRW0_SX405 TRAIN/DR7/MVRW0/SX405.WAV +MVRW0_SX45 TRAIN/DR7/MVRW0/SX45.WAV +MWAC0_SI1601 TRAIN/DR5/MWAC0/SI1601.WAV +MWAC0_SI2231 TRAIN/DR5/MWAC0/SI2231.WAV +MWAC0_SI971 TRAIN/DR5/MWAC0/SI971.WAV +MWAC0_SX161 TRAIN/DR5/MWAC0/SX161.WAV +MWAC0_SX251 TRAIN/DR5/MWAC0/SX251.WAV +MWAC0_SX341 TRAIN/DR5/MWAC0/SX341.WAV +MWAC0_SX431 TRAIN/DR5/MWAC0/SX431.WAV +MWAC0_SX71 TRAIN/DR5/MWAC0/SX71.WAV +MWAD0_SI1062 TRAIN/DR1/MWAD0/SI1062.WAV +MWAD0_SI1749 TRAIN/DR1/MWAD0/SI1749.WAV +MWAD0_SI2322 TRAIN/DR1/MWAD0/SI2322.WAV +MWAD0_SX162 TRAIN/DR1/MWAD0/SX162.WAV +MWAD0_SX252 TRAIN/DR1/MWAD0/SX252.WAV +MWAD0_SX342 TRAIN/DR1/MWAD0/SX342.WAV +MWAD0_SX432 TRAIN/DR1/MWAD0/SX432.WAV +MWAD0_SX72 TRAIN/DR1/MWAD0/SX72.WAV +MWAR0_SI1045 TRAIN/DR1/MWAR0/SI1045.WAV +MWAR0_SI1675 TRAIN/DR1/MWAR0/SI1675.WAV +MWAR0_SI2305 TRAIN/DR1/MWAR0/SI2305.WAV +MWAR0_SX145 TRAIN/DR1/MWAR0/SX145.WAV +MWAR0_SX235 TRAIN/DR1/MWAR0/SX235.WAV +MWAR0_SX325 TRAIN/DR1/MWAR0/SX325.WAV +MWAR0_SX415 TRAIN/DR1/MWAR0/SX415.WAV +MWAR0_SX55 TRAIN/DR1/MWAR0/SX55.WAV +MWCH0_SI1622 TRAIN/DR5/MWCH0/SI1622.WAV +MWCH0_SI1895 TRAIN/DR5/MWCH0/SI1895.WAV +MWCH0_SI2252 TRAIN/DR5/MWCH0/SI2252.WAV +MWCH0_SX182 TRAIN/DR5/MWCH0/SX182.WAV +MWCH0_SX272 TRAIN/DR5/MWCH0/SX272.WAV +MWCH0_SX362 TRAIN/DR5/MWCH0/SX362.WAV +MWCH0_SX452 TRAIN/DR5/MWCH0/SX452.WAV +MWCH0_SX92 TRAIN/DR5/MWCH0/SX92.WAV +MWDK0_SI1436 TRAIN/DR3/MWDK0/SI1436.WAV +MWDK0_SI2017 TRAIN/DR3/MWDK0/SI2017.WAV +MWDK0_SI806 TRAIN/DR3/MWDK0/SI806.WAV +MWDK0_SX176 TRAIN/DR3/MWDK0/SX176.WAV +MWDK0_SX266 TRAIN/DR3/MWDK0/SX266.WAV +MWDK0_SX356 TRAIN/DR3/MWDK0/SX356.WAV +MWDK0_SX446 TRAIN/DR3/MWDK0/SX446.WAV +MWDK0_SX86 TRAIN/DR3/MWDK0/SX86.WAV +MWEM0_SI1320 TRAIN/DR5/MWEM0/SI1320.WAV +MWEM0_SI1393 TRAIN/DR5/MWEM0/SI1393.WAV +MWEM0_SI1950 TRAIN/DR5/MWEM0/SI1950.WAV +MWEM0_SX150 TRAIN/DR5/MWEM0/SX150.WAV +MWEM0_SX240 TRAIN/DR5/MWEM0/SX240.WAV +MWEM0_SX330 TRAIN/DR5/MWEM0/SX330.WAV +MWEM0_SX420 TRAIN/DR5/MWEM0/SX420.WAV +MWEM0_SX60 TRAIN/DR5/MWEM0/SX60.WAV +MWGR0_SI1606 TRAIN/DR3/MWGR0/SI1606.WAV +MWGR0_SI2236 TRAIN/DR3/MWGR0/SI2236.WAV +MWGR0_SI976 TRAIN/DR3/MWGR0/SI976.WAV +MWGR0_SX166 TRAIN/DR3/MWGR0/SX166.WAV +MWGR0_SX256 TRAIN/DR3/MWGR0/SX256.WAV +MWGR0_SX346 TRAIN/DR3/MWGR0/SX346.WAV +MWGR0_SX436 TRAIN/DR3/MWGR0/SX436.WAV +MWGR0_SX76 TRAIN/DR3/MWGR0/SX76.WAV +MWRE0_SI1057 TRAIN/DR7/MWRE0/SI1057.WAV +MWRE0_SI1687 TRAIN/DR7/MWRE0/SI1687.WAV +MWRE0_SI2317 TRAIN/DR7/MWRE0/SI2317.WAV +MWRE0_SX157 TRAIN/DR7/MWRE0/SX157.WAV +MWRE0_SX247 TRAIN/DR7/MWRE0/SX247.WAV +MWRE0_SX337 TRAIN/DR7/MWRE0/SX337.WAV +MWRE0_SX427 TRAIN/DR7/MWRE0/SX427.WAV +MWRE0_SX67 TRAIN/DR7/MWRE0/SX67.WAV +MWRP0_SI1443 TRAIN/DR7/MWRP0/SI1443.WAV +MWRP0_SI1525 TRAIN/DR7/MWRP0/SI1525.WAV +MWRP0_SI2073 TRAIN/DR7/MWRP0/SI2073.WAV +MWRP0_SX183 TRAIN/DR7/MWRP0/SX183.WAV +MWRP0_SX273 TRAIN/DR7/MWRP0/SX273.WAV +MWRP0_SX3 TRAIN/DR7/MWRP0/SX3.WAV +MWRP0_SX363 TRAIN/DR7/MWRP0/SX363.WAV +MWRP0_SX93 TRAIN/DR7/MWRP0/SX93.WAV +MWSB0_SI1626 TRAIN/DR2/MWSB0/SI1626.WAV +MWSB0_SI2256 TRAIN/DR2/MWSB0/SI2256.WAV +MWSB0_SI996 TRAIN/DR2/MWSB0/SI996.WAV +MWSB0_SX186 TRAIN/DR2/MWSB0/SX186.WAV +MWSB0_SX276 TRAIN/DR2/MWSB0/SX276.WAV +MWSB0_SX366 TRAIN/DR2/MWSB0/SX366.WAV +MWSB0_SX6 TRAIN/DR2/MWSB0/SX6.WAV +MWSB0_SX96 TRAIN/DR2/MWSB0/SX96.WAV +MWSH0_SI1426 TRAIN/DR5/MWSH0/SI1426.WAV +MWSH0_SI2266 TRAIN/DR5/MWSH0/SI2266.WAV +MWSH0_SI796 TRAIN/DR5/MWSH0/SI796.WAV +MWSH0_SX166 TRAIN/DR5/MWSH0/SX166.WAV +MWSH0_SX256 TRAIN/DR5/MWSH0/SX256.WAV +MWSH0_SX346 TRAIN/DR5/MWSH0/SX346.WAV +MWSH0_SX436 TRAIN/DR5/MWSH0/SX436.WAV +MWSH0_SX76 TRAIN/DR5/MWSH0/SX76.WAV +MZMB0_SI1166 TRAIN/DR2/MZMB0/SI1166.WAV +MZMB0_SI1796 TRAIN/DR2/MZMB0/SI1796.WAV +MZMB0_SI536 TRAIN/DR2/MZMB0/SI536.WAV +MZMB0_SX176 TRAIN/DR2/MZMB0/SX176.WAV +MZMB0_SX266 TRAIN/DR2/MZMB0/SX266.WAV +MZMB0_SX356 TRAIN/DR2/MZMB0/SX356.WAV +MZMB0_SX446 TRAIN/DR2/MZMB0/SX446.WAV +MZMB0_SX86 TRAIN/DR2/MZMB0/SX86.WAV diff --git a/egs/timit/ASR/splits_dir/tst_samples.txt b/egs/timit/ASR/splits_dir/tst_samples.txt new file mode 100644 index 000000000..689531b8f --- /dev/null +++ b/egs/timit/ASR/splits_dir/tst_samples.txt @@ -0,0 +1,192 @@ +FDHC0_SI1559 TEST/DR7/FDHC0/SI1559.WAV +FDHC0_SI2189 TEST/DR7/FDHC0/SI2189.WAV +FDHC0_SI929 TEST/DR7/FDHC0/SI929.WAV +FDHC0_SX119 TEST/DR7/FDHC0/SX119.WAV +FDHC0_SX209 TEST/DR7/FDHC0/SX209.WAV +FDHC0_SX29 TEST/DR7/FDHC0/SX29.WAV +FDHC0_SX299 TEST/DR7/FDHC0/SX299.WAV +FDHC0_SX389 TEST/DR7/FDHC0/SX389.WAV +FELC0_SI1386 TEST/DR1/FELC0/SI1386.WAV +FELC0_SI2016 TEST/DR1/FELC0/SI2016.WAV +FELC0_SI756 TEST/DR1/FELC0/SI756.WAV +FELC0_SX126 TEST/DR1/FELC0/SX126.WAV +FELC0_SX216 TEST/DR1/FELC0/SX216.WAV +FELC0_SX306 TEST/DR1/FELC0/SX306.WAV +FELC0_SX36 TEST/DR1/FELC0/SX36.WAV +FELC0_SX396 TEST/DR1/FELC0/SX396.WAV +FJLM0_SI1043 TEST/DR4/FJLM0/SI1043.WAV +FJLM0_SI1673 TEST/DR4/FJLM0/SI1673.WAV +FJLM0_SI2303 TEST/DR4/FJLM0/SI2303.WAV +FJLM0_SX143 TEST/DR4/FJLM0/SX143.WAV +FJLM0_SX233 TEST/DR4/FJLM0/SX233.WAV +FJLM0_SX323 TEST/DR4/FJLM0/SX323.WAV +FJLM0_SX413 TEST/DR4/FJLM0/SX413.WAV +FJLM0_SX53 TEST/DR4/FJLM0/SX53.WAV +FMGD0_SI1564 TEST/DR6/FMGD0/SI1564.WAV +FMGD0_SI2194 TEST/DR6/FMGD0/SI2194.WAV +FMGD0_SI934 TEST/DR6/FMGD0/SI934.WAV +FMGD0_SX124 TEST/DR6/FMGD0/SX124.WAV +FMGD0_SX214 TEST/DR6/FMGD0/SX214.WAV +FMGD0_SX304 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TEST/DR2/MWEW0/SI1991.WAV +MWEW0_SI731 TEST/DR2/MWEW0/SI731.WAV +MWEW0_SX101 TEST/DR2/MWEW0/SX101.WAV +MWEW0_SX11 TEST/DR2/MWEW0/SX11.WAV +MWEW0_SX191 TEST/DR2/MWEW0/SX191.WAV +MWEW0_SX281 TEST/DR2/MWEW0/SX281.WAV +MWEW0_SX371 TEST/DR2/MWEW0/SX371.WAV diff --git a/egs/timit/ASR/tdnn_lstm_ctc/__init__.py b/egs/timit/ASR/tdnn_lstm_ctc/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/timit/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/timit/ASR/tdnn_lstm_ctc/asr_datamodule.py new file mode 100644 index 000000000..078a4be89 --- /dev/null +++ b/egs/timit/ASR/tdnn_lstm_ctc/asr_datamodule.py @@ -0,0 +1,336 @@ +# Copyright 2021 Piotr Żelasko +# Mingshuang Luo +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import logging +from functools import lru_cache +from pathlib import Path +from typing import List, Union + +from lhotse import CutSet, Fbank, FbankConfig, load_manifest +from lhotse.dataset import ( + BucketingSampler, + CutConcatenate, + CutMix, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SingleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import OnTheFlyFeatures +from torch.utils.data import DataLoader + +from icefall.dataset.datamodule import DataModule +from icefall.utils import str2bool + + +class TimitAsrDataModule(DataModule): + """ + DataModule for k2 ASR experiments. + It assumes there is always one train and valid dataloader, + but there can be multiple test dataloaders (e.g. LibriSpeech test-clean + and test-other). + + It contains all the common data pipeline modules used in ASR + experiments, e.g.: + - dynamic batch size, + - bucketing samplers, + - cut concatenation, + - augmentation, + - on-the-fly feature extraction + + This class should be derived for specific corpora used in ASR tasks. + """ + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + super().add_arguments(parser) + group = parser.add_argument_group( + title="ASR data related options", + description="These options are used for the preparation of " + "PyTorch DataLoaders from Lhotse CutSet's -- they control the " + "effective batch sizes, sampling strategies, applied data " + "augmentations, etc.", + ) + group.add_argument( + "--feature-dir", + type=Path, + default=Path("data/fbank"), + help="Path to directory with train/valid/test cuts.", + ) + group.add_argument( + "--max-duration", + type=int, + default=200.0, + help="Maximum pooled recordings duration (seconds) in a " + "single batch. You can reduce it if it causes CUDA OOM.", + ) + group.add_argument( + "--bucketing-sampler", + type=str2bool, + default=True, + help="When enabled, the batches will come from buckets of " + "similar duration (saves padding frames).", + ) + group.add_argument( + "--num-buckets", + type=int, + default=30, + help="The number of buckets for the BucketingSampler" + "(you might want to increase it for larger datasets).", + ) + group.add_argument( + "--concatenate-cuts", + type=str2bool, + default=False, + help="When enabled, utterances (cuts) will be concatenated " + "to minimize the amount of padding.", + ) + group.add_argument( + "--duration-factor", + type=float, + default=1.0, + help="Determines the maximum duration of a concatenated cut " + "relative to the duration of the longest cut in a batch.", + ) + group.add_argument( + "--gap", + type=float, + default=1.0, + help="The amount of padding (in seconds) inserted between " + "concatenated cuts. This padding is filled with noise when " + "noise augmentation is used.", + ) + group.add_argument( + "--on-the-fly-feats", + type=str2bool, + default=False, + help="When enabled, use on-the-fly cut mixing and feature " + "extraction. Will drop existing precomputed feature manifests " + "if available.", + ) + group.add_argument( + "--shuffle", + type=str2bool, + default=True, + help="When enabled (=default), the examples will be " + "shuffled for each epoch.", + ) + group.add_argument( + "--return-cuts", + type=str2bool, + default=True, + help="When enabled, each batch will have the " + "field: batch['supervisions']['cut'] with the cuts that " + "were used to construct it.", + ) + + group.add_argument( + "--num-workers", + type=int, + default=2, + help="The number of training dataloader workers that " + "collect the batches.", + ) + + def train_dataloaders(self) -> DataLoader: + logging.info("About to get train cuts") + cuts_train = self.train_cuts() + + logging.info("About to get Musan cuts") + cuts_musan = load_manifest(self.args.feature_dir / "cuts_musan.json.gz") + + logging.info("About to create train dataset") + transforms = [CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20))] + if self.args.concatenate_cuts: + logging.info( + f"Using cut concatenation with duration factor " + f"{self.args.duration_factor} and gap {self.args.gap}." + ) + # Cut concatenation should be the first transform in the list, + # so that if we e.g. mix noise in, it will fill the gaps between + # different utterances. + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + input_transforms = [ + SpecAugment( + num_frame_masks=2, + features_mask_size=27, + num_feature_masks=2, + frames_mask_size=100, + ) + ] + + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.on_the_fly_feats: + # NOTE: the PerturbSpeed transform should be added only if we + # remove it from data prep stage. + # Add on-the-fly speed perturbation; since originally it would + # have increased epoch size by 3, we will apply prob 2/3 and use + # 3x more epochs. + # Speed perturbation probably should come first before + # concatenation, but in principle the transforms order doesn't have + # to be strict (e.g. could be randomized) + # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa + # Drop feats to be on the safe side. + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.bucketing_sampler: + logging.info("Using BucketingSampler.") + train_sampler = BucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + num_buckets=self.args.num_buckets, + bucket_method="equal_duration", + drop_last=True, + ) + else: + logging.info("Using SingleCutSampler.") + train_sampler = SingleCutSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + ) + logging.info("About to create train dataloader") + + train_dl = DataLoader( + train, + sampler=train_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + ) + + return train_dl + + def valid_dataloaders(self) -> DataLoader: + logging.info("About to get dev cuts") + cuts_valid = self.valid_cuts() + + transforms = [] + if self.args.concatenate_cuts: + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + logging.info("About to create dev dataset") + if self.args.on_the_fly_feats: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + return_cuts=self.args.return_cuts, + ) + else: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + return_cuts=self.args.return_cuts, + ) + valid_sampler = SingleCutSampler( + cuts_valid, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.info("About to create dev dataloader") + valid_dl = DataLoader( + validate, + sampler=valid_sampler, + batch_size=None, + num_workers=2, + persistent_workers=False, + ) + + return valid_dl + + def test_dataloaders(self) -> Union[DataLoader, List[DataLoader]]: + cuts = self.test_cuts() + is_list = isinstance(cuts, list) + test_loaders = [] + if not is_list: + cuts = [cuts] + + for cuts_test in cuts: + logging.debug("About to create test dataset") + test = K2SpeechRecognitionDataset( + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ) + if self.args.on_the_fly_feats + else PrecomputedFeatures(), + return_cuts=self.args.return_cuts, + ) + sampler = SingleCutSampler( + cuts_test, max_duration=self.args.max_duration + ) + logging.debug("About to create test dataloader") + test_dl = DataLoader( + test, batch_size=None, sampler=sampler, num_workers=1 + ) + test_loaders.append(test_dl) + + if is_list: + return test_loaders + else: + return test_loaders[0] + + @lru_cache() + def train_cuts(self) -> CutSet: + logging.info("About to get train cuts") + cuts_train = load_manifest( + self.args.feature_dir / "cuts_TRAIN.json.gz" + ) + + return cuts_train + + @lru_cache() + def valid_cuts(self) -> CutSet: + logging.info("About to get dev cuts") + cuts_valid = load_manifest( + self.args.feature_dir / "cuts_DEV.json.gz" + ) + + return cuts_valid + + @lru_cache() + def test_cuts(self) -> CutSet: + logging.debug("About to get test cuts") + cuts_test = load_manifest( + self.args.feature_dir / "cuts_TEST.json.gz" + ) + + return cuts_test diff --git a/egs/timit/ASR/tdnn_lstm_ctc/decode.py b/egs/timit/ASR/tdnn_lstm_ctc/decode.py new file mode 100644 index 000000000..0f90a6e9f --- /dev/null +++ b/egs/timit/ASR/tdnn_lstm_ctc/decode.py @@ -0,0 +1,503 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import logging +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import torch +import torch.nn as nn +from asr_datamodule import TimitAsrDataModule +from model import TdnnLstm + +from icefall.checkpoint import average_checkpoints, load_checkpoint +from icefall.decode import ( + get_lattice, + nbest_decoding, + one_best_decoding, + rescore_with_n_best_list, + rescore_with_whole_lattice, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + get_texts, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=19, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + parser.add_argument( + "--avg", + type=int, + default=5, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + parser.add_argument( + "--method", + type=str, + default="whole-lattice-rescoring", + help="""Decoding method. + Supported values are: + - (1) 1best. Extract the best path from the decoding lattice as the + decoding result. + - (2) nbest. Extract n paths from the decoding lattice; the path + with the highest score is the decoding result. + - (3) nbest-rescoring. Extract n paths from the decoding lattice, + rescore them with an n-gram LM (e.g., a 4-gram LM), the path with + the highest score is the decoding result. + - (4) whole-lattice-rescoring. Rescore the decoding lattice with an + n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice + is the decoding result. + """, + ) + + parser.add_argument( + "--num-paths", + type=int, + default=100, + help="""Number of paths for n-best based decoding method. + Used only when "method" is one of the following values: + nbest, nbest-rescoring + """, + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""The scale to be applied to `lattice.scores`. + It's needed if you use any kinds of n-best based rescoring. + Used only when "method" is one of the following values: + nbest, nbest-rescoring + A smaller value results in more unique paths. + """, + ) + + parser.add_argument( + "--export", + type=str2bool, + default=False, + help="""When enabled, the averaged model is saved to + tdnn/exp/pretrained.pt. Note: only model.state_dict() is saved. + pretrained.pt contains a dict {"model": model.state_dict()}, + which can be loaded by `icefall.checkpoint.load_checkpoint()`. + """, + ) + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "exp_dir": Path("tdnn_lstm_ctc/exp/"), + "lang_dir": Path("data/lang_phone"), + "lm_dir": Path("data/lm"), + "feature_dim": 80, + "subsampling_factor": 3, + "search_beam": 20, + "output_beam": 5, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + } + ) + return params + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + HLG: k2.Fsa, + batch: dict, + lexicon: Lexicon, + G: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if no rescoring is used, the key is the string `no_rescore`. + If LM rescoring is used, the key is the string `lm_scale_xxx`, + where `xxx` is the value of `lm_scale`. An example key is + `lm_scale_0.7` + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + + - params.method is "1best", it uses 1best decoding without LM rescoring. + - params.method is "nbest", it uses nbest decoding without LM rescoring. + - params.method is "nbest-rescoring", it uses nbest LM rescoring. + - params.method is "whole-lattice-rescoring", it uses whole lattice LM + rescoring. + + model: + The neural model. + HLG: + The decoding graph. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + lexicon: + It contains word symbol table. + G: + An LM. It is not None when params.method is "nbest-rescoring" + or "whole-lattice-rescoring". In general, the G in HLG + is a 3-gram LM, while this G is a 4-gram LM. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = HLG.device + feature = batch["inputs"] + assert feature.ndim == 3 + feature = feature.to(device) + # at entry, feature is (N, T, C) + + feature = feature.permute(0, 2, 1) # now feature is (N, C, T) + + nnet_output = model(feature) + # nnet_output is (N, T, C) + + supervisions = batch["supervisions"] + + supervision_segments = torch.stack( + ( + supervisions["sequence_idx"], + supervisions["start_frame"] // params.subsampling_factor, + supervisions["num_frames"] // params.subsampling_factor, + ), + 1, + ).to(torch.int32) + + lattice = get_lattice( + nnet_output=nnet_output, + decoding_graph=HLG, + supervision_segments=supervision_segments, + search_beam=params.search_beam, + output_beam=params.output_beam, + min_active_states=params.min_active_states, + max_active_states=params.max_active_states, + ) + + if params.method in ["1best", "nbest"]: + if params.method == "1best": + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + key = "no_rescore" + else: + best_path = nbest_decoding( + lattice=lattice, + num_paths=params.num_paths, + use_double_scores=params.use_double_scores, + nbest_scale=params.nbest_scale, + ) + key = f"no_rescore-{params.num_paths}" + hyps = get_texts(best_path) + hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps] + return {key: hyps} + + assert params.method in ["nbest-rescoring", "whole-lattice-rescoring"] + + lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7] + lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3] + lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0] + + if params.method == "nbest-rescoring": + best_path_dict = rescore_with_n_best_list( + lattice=lattice, + G=G, + num_paths=params.num_paths, + lm_scale_list=lm_scale_list, + nbest_scale=params.nbest_scale, + ) + else: + best_path_dict = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=lm_scale_list, + ) + + ans = dict() + for lm_scale_str, best_path in best_path_dict.items(): + hyps = get_texts(best_path) + hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps] + ans[lm_scale_str] = hyps + return ans + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + HLG: k2.Fsa, + lexicon: Lexicon, + G: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + HLG: + The decoding graph. + lexicon: + It contains word symbol table. + G: + An LM. It is not None when params.method is "nbest-rescoring" + or "whole-lattice-rescoring". In general, the G in HLG + is a 3-gram LM, while this G is a 4-gram LM. + Returns: + Return a dict, whose key may be "no-rescore" if no LM rescoring + is used, or it may be "lm_scale_0.7" if LM rescoring is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + results = [] + + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + + hyps_dict = decode_one_batch( + params=params, + model=model, + HLG=HLG, + batch=batch, + lexicon=lexicon, + G=G, + ) + + for lm_scale, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + ref_words = ref_text.split() + this_batch.append((ref_words, hyp_words)) + + results[lm_scale].extend(this_batch) + + num_cuts += len(batch["supervisions"]["text"]) + + if batch_idx % 100 == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt" + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out PERs, per-phone error statistics and aligned + # ref/hyp pairs. + errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}.txt" + with open(errs_filename, "w") as f: + wer = write_error_stats(f, f"{test_set_name}-{key}", results) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = params.exp_dir / f"per-summary-{test_set_name}.txt" + with open(errs_info, "w") as f: + print("settings\tPER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, PER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + TimitAsrDataModule.add_arguments(parser) + args = parser.parse_args() + + params = get_params() + params.update(vars(args)) + + setup_logger(f"{params.exp_dir}/log/log-decode") + logging.info("Decoding started") + logging.info(params) + + lexicon = Lexicon(params.lang_dir) + max_phone_id = max(lexicon.tokens) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + HLG = k2.Fsa.from_dict( + torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu") + ) + HLG = HLG.to(device) + assert HLG.requires_grad is False + + if not hasattr(HLG, "lm_scores"): + HLG.lm_scores = HLG.scores.clone() + + if params.method in ["nbest-rescoring", "whole-lattice-rescoring"]: + if not (params.lm_dir / "G_4_gram.pt").is_file(): + logging.info("Loading G_4_gram.fst.txt") + logging.warning("It may take 8 minutes.") + with open(params.lm_dir / "G_4_gram.fst.txt") as f: + first_word_disambig_id = lexicon.word_table["#0"] + + G = k2.Fsa.from_openfst(f.read(), acceptor=False) + # G.aux_labels is not needed in later computations, so + # remove it here. + del G.aux_labels + # CAUTION: The following line is crucial. + # Arcs entering the back-off state have label equal to #0. + # We have to change it to 0 here. + G.labels[G.labels >= first_word_disambig_id] = 0 + G = k2.Fsa.from_fsas([G]).to(device) + G = k2.arc_sort(G) + torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt") + else: + logging.info("Loading pre-compiled G_4_gram.pt") + d = torch.load(params.lm_dir / "G_4_gram.pt", map_location="cpu") + G = k2.Fsa.from_dict(d).to(device) + + if params.method == "whole-lattice-rescoring": + # Add epsilon self-loops to G as we will compose + # it with the whole lattice later + G = k2.add_epsilon_self_loops(G) + G = k2.arc_sort(G) + G = G.to(device) + + # G.lm_scores is used to replace HLG.lm_scores during + # LM rescoring. + G.lm_scores = G.scores.clone() + else: + G = None + + model = TdnnLstm( + num_features=params.feature_dim, + num_classes=max_phone_id + 1, # +1 for the blank symbol + subsampling_factor=params.subsampling_factor, + ) + if params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + #load_checkpoint(f"tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretrained.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.load_state_dict(average_checkpoints(filenames)) + + if params.export: + logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt") + torch.save( + {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt" + ) + return + + model.to(device) + model.eval() + + timit = TimitAsrDataModule(args) + # CAUTION: `test_sets` is for displaying only. + # If you want to skip test-clean, you have to skip + # it inside the for loop. That is, use + # + # if test_set == 'test-clean': continue + # + #test_sets = ["test-clean", "test-other"] + #test_sets = ["test-other"] + #for test_set, test_dl in zip(test_sets, librispeech.test_dataloaders()): + #if test_set == "test-clean": continue + #if test_set == "test-other": break + test_set = "TEST" + test_dl = timit.test_dataloaders() + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + HLG=HLG, + lexicon=lexicon, + G=G, + ) + + save_results( + params=params, test_set_name=test_set, results_dict=results_dict + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/timit/ASR/tdnn_lstm_ctc/model.py b/egs/timit/ASR/tdnn_lstm_ctc/model.py new file mode 100644 index 000000000..5e04c11b4 --- /dev/null +++ b/egs/timit/ASR/tdnn_lstm_ctc/model.py @@ -0,0 +1,103 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn + + +class TdnnLstm(nn.Module): + def __init__( + self, num_features: int, num_classes: int, subsampling_factor: int = 3 + ) -> None: + """ + Args: + num_features: + The input dimension of the model. + num_classes: + The output dimension of the model. + subsampling_factor: + It reduces the number of output frames by this factor. + """ + super().__init__() + self.num_features = num_features + self.num_classes = num_classes + self.subsampling_factor = subsampling_factor + self.tdnn = nn.Sequential( + nn.Conv1d( + in_channels=num_features, + out_channels=500, + kernel_size=3, + stride=1, + padding=1, + ), + nn.ReLU(inplace=True), + nn.BatchNorm1d(num_features=500, affine=False), + nn.Conv1d( + in_channels=500, + out_channels=500, + kernel_size=3, + stride=1, + padding=1, + ), + nn.ReLU(inplace=True), + nn.BatchNorm1d(num_features=500, affine=False), + nn.Conv1d( + in_channels=500, + out_channels=500, + kernel_size=3, + stride=self.subsampling_factor, # stride: subsampling_factor! + padding=1, + ), + nn.ReLU(inplace=True), + nn.BatchNorm1d(num_features=500, affine=False), + ) + self.lstms = nn.ModuleList( + [ + nn.LSTM(input_size=500, hidden_size=500, num_layers=1) + for _ in range(5) + ] + ) + self.lstm_bnorms = nn.ModuleList( + [nn.BatchNorm1d(num_features=500, affine=False) for _ in range(5)] + ) + self.dropout = nn.Dropout(0.2) + self.linear = nn.Linear(in_features=500, out_features=self.num_classes) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: + Its shape is [N, C, T] + + Returns: + The output tensor has shape [N, T, C] + """ + x = self.tdnn(x) + x = x.permute(2, 0, 1) # (N, C, T) -> (T, N, C) -> how LSTM expects it + for lstm, bnorm in zip(self.lstms, self.lstm_bnorms): + x_new, _ = lstm(x) + x_new = bnorm(x_new.permute(1, 2, 0)).permute( + 2, 0, 1 + ) # (T, N, C) -> (N, C, T) -> (T, N, C) + x_new = self.dropout(x_new) + x = x_new + x # skip connections + x = x.transpose( + 1, 0 + ) # (T, N, C) -> (N, T, C) -> linear expects "features" in the last dim + x = self.linear(x) + x = nn.functional.log_softmax(x, dim=-1) + return x diff --git a/egs/timit/ASR/tdnn_lstm_ctc/train.py b/egs/timit/ASR/tdnn_lstm_ctc/train.py new file mode 100644 index 000000000..e86199bbb --- /dev/null +++ b/egs/timit/ASR/tdnn_lstm_ctc/train.py @@ -0,0 +1,595 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import logging +from pathlib import Path +from shutil import copyfile +from typing import Optional, Tuple + +import k2 +import torch +import torch.multiprocessing as mp +import torch.nn as nn +import torch.optim as optim +from asr_datamodule import TimitAsrDataModule +from lhotse.utils import fix_random_seed +from model import TdnnLstm +from torch import Tensor +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.nn.utils import clip_grad_norm_ +from torch.optim.lr_scheduler import StepLR +from torch.utils.tensorboard import SummaryWriter + +from icefall.checkpoint import load_checkpoint +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.dist import cleanup_dist, setup_dist +from icefall.graph_compiler import CtcTrainingGraphCompiler +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + MetricsTracker, + encode_supervisions, + get_env_info, + setup_logger, + str2bool, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=20, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=0, + help="""Resume training from from this epoch. + If it is positive, it will load checkpoint from + tdnn_lstm_ctc/exp/epoch-{start_epoch-1}.pt + """, + ) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + is saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - exp_dir: It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + + - lang_dir: It contains language related input files such as + "lexicon.txt" + + - lr: It specifies the initial learning rate + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - weight_decay: The weight_decay for the optimizer. + + - subsampling_factor: The subsampling factor for the model. + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval` is 0 + + - beam_size: It is used in k2.ctc_loss + + - reduction: It is used in k2.ctc_loss + + - use_double_scores: It is used in k2.ctc_loss + """ + params = AttributeDict( + { + "exp_dir": Path("tdnn_lstm_ctc/exp"), + "lang_dir": Path("data/lang_phone"), + "lr": 1e-3, + "feature_dim": 80, + "weight_decay": 5e-4, + "subsampling_factor": 3, + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 10, + "reset_interval": 200, + "valid_interval": 1000, + "beam_size": 10, + "reduction": "sum", + "use_double_scores": True, + "env_info": get_env_info(), + } + ) + + return params + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, +) -> None: + """Load checkpoint from file. + + If params.start_epoch is positive, it will load the checkpoint from + `params.start_epoch - 1`. Otherwise, this function does nothing. + + Apart from loading state dict for `model`, `optimizer` and `scheduler`, + it also updates `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + optimizer: + The optimizer that we are using. + scheduler: + The learning rate scheduler we are using. + Returns: + Return None. + """ + if params.start_epoch <= 0: + return + + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + saved_params = load_checkpoint( + filename, + model=model, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: torch.optim.lr_scheduler._LRScheduler, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + params=params, + optimizer=optimizer, + scheduler=scheduler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: nn.Module, + batch: dict, + graph_compiler: CtcTrainingGraphCompiler, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute CTC loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of TdnnLstm in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + graph_compiler: + It is used to build a decoding graph from a ctc topo and training + transcript. The training transcript is contained in the given `batch`, + while the ctc topo is built when this compiler is instantiated. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + """ + device = graph_compiler.device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + feature = feature.permute(0, 2, 1) # now feature is (N, C, T) + assert feature.ndim == 3 + feature = feature.to(device) + + with torch.set_grad_enabled(is_training): + nnet_output = model(feature) + # nnet_output is (N, T, C) + + # NOTE: We need `encode_supervisions` to sort sequences with + # different duration in decreasing order, required by + # `k2.intersect_dense` called in `k2.ctc_loss` + supervisions = batch["supervisions"] + supervision_segments, texts = encode_supervisions( + supervisions, subsampling_factor=params.subsampling_factor + ) + decoding_graph = graph_compiler.compile(texts) + + dense_fsa_vec = k2.DenseFsaVec( + nnet_output, + supervision_segments, + allow_truncate=params.subsampling_factor - 1, + ) + + loss = k2.ctc_loss( + decoding_graph=decoding_graph, + dense_fsa_vec=dense_fsa_vec, + output_beam=params.beam_size, + reduction=params.reduction, + use_double_scores=params.use_double_scores, + ) + + assert loss.requires_grad == is_training + + info = MetricsTracker() + info["frames"] = supervision_segments[:, 2].sum().item() + info["loss"] = loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: nn.Module, + graph_compiler: CtcTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process. The validation loss + is saved in `params.valid_loss`. + """ + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + batch=batch, + graph_compiler=graph_compiler, + is_training=False, + ) + assert loss.requires_grad is False + + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + graph_compiler: CtcTrainingGraphCompiler, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + graph_compiler: + It is used to convert transcripts to FSAs. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + """ + model.train() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(train_dl): + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + loss, loss_info = compute_loss( + params=params, + model=model, + batch=batch, + graph_compiler=graph_compiler, + is_training=True, + ) + # summary stats. + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + optimizer.zero_grad() + loss.backward() + clip_grad_norm_(model.parameters(), 5.0, 2.0) + optimizer.step() + + if batch_idx % params.log_interval == 0: + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}" + ) + if batch_idx % params.log_interval == 0: + + if tb_writer is not None: + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + valid_info = compute_validation_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, + "train/valid_", + params.batch_idx_train, + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(42) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + logging.info(params) + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + lexicon = Lexicon(params.lang_dir) + max_phone_id = max(lexicon.tokens) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + + graph_compiler = CtcTrainingGraphCompiler(lexicon=lexicon, device=device) + + model = TdnnLstm( + num_features=params.feature_dim, + num_classes=max_phone_id + 1, # +1 for the blank symbol + subsampling_factor=params.subsampling_factor, + ) + + checkpoints = load_checkpoint_if_available(params=params, model=model) + + model.to(device) + if world_size > 1: + model = DDP(model, device_ids=[rank]) + + optimizer = optim.AdamW( + model.parameters(), + lr=params.lr, + weight_decay=params.weight_decay, + ) + scheduler = StepLR(optimizer, step_size=8, gamma=0.8) + + if checkpoints: + optimizer.load_state_dict(checkpoints["optimizer"]) + scheduler.load_state_dict(checkpoints["scheduler"]) + + timit = TimitAsrDataModule(args) + train_dl = timit.train_dataloaders() + valid_dl = timit.valid_dataloaders() + + for epoch in range(params.start_epoch, params.num_epochs): + train_dl.sampler.set_epoch(epoch) + + if epoch > params.start_epoch: + logging.info(f"epoch {epoch}, lr: {scheduler.get_last_lr()[0]}") + + if tb_writer is not None: + tb_writer.add_scalar( + "train/lr", + scheduler.get_last_lr()[0], + params.batch_idx_train, + ) + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + optimizer=optimizer, + graph_compiler=graph_compiler, + train_dl=train_dl, + valid_dl=valid_dl, + tb_writer=tb_writer, + world_size=world_size, + ) + + scheduler.step() + + save_checkpoint( + params=params, + model=model, + optimizer=optimizer, + scheduler=scheduler, + rank=rank, + ) + + logging.info("Done!") + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def main(): + parser = get_parser() + TimitAsrDataModule.add_arguments(parser) + args = parser.parse_args() + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +if __name__ == "__main__": + main()