diff --git a/docs/source/installation/index.rst b/docs/source/installation/index.rst
index cea5e6fa43..738b24ab25 100644
--- a/docs/source/installation/index.rst
+++ b/docs/source/installation/index.rst
@@ -364,44 +364,42 @@ The log of running ``./prepare.sh`` is:
 
 .. code-block::
 
-  2021-08-23 19:27:26 (prepare.sh:24:main) dl_dir: /tmp/icefall/egs/yesno/ASR/download
-  2021-08-23 19:27:26 (prepare.sh:27:main) stage 0: Download data
-  Downloading waves_yesno.tar.gz: 4.49MB [00:03, 1.39MB/s]
-  2021-08-23 19:27:30 (prepare.sh:36:main) Stage 1: Prepare yesno manifest
-  2021-08-23 19:27:31 (prepare.sh:42:main) Stage 2: Compute fbank for yesno
-  2021-08-23 19:27:32,803 INFO [compute_fbank_yesno.py:52] Processing train
-  Extracting and storing features: 100%|_______________________________________________________________| 90/90 [00:01<00:00, 80.57it/s]
-  2021-08-23 19:27:34,085 INFO [compute_fbank_yesno.py:52] Processing test
-  Extracting and storing features: 100%|______________________________________________________________| 30/30 [00:00<00:00, 248.21it/s]
-  2021-08-23 19:27:34 (prepare.sh:48:main) Stage 3: Prepare lang
-  2021-08-23 19:27:35 (prepare.sh:63:main) Stage 4: Prepare G
-  /tmp/pip-install-fcordre9/kaldilm_6899d26f2d684ad48f21025950cd2866/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Rea
-  d(std::istream&):79
-  [I] Reading \data\ section.
-  /tmp/pip-install-fcordre9/kaldilm_6899d26f2d684ad48f21025950cd2866/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Rea
-  d(std::istream&):140
-  [I] Reading \1-grams: section.
-  2021-08-23 19:27:35 (prepare.sh:89:main) Stage 5: Compile HLG
-  2021-08-23 19:27:35,928 INFO [compile_hlg.py:120] Processing data/lang_phone
-  2021-08-23 19:27:35,929 INFO [lexicon.py:116] Converting L.pt to Linv.pt
-  2021-08-23 19:27:35,931 INFO [compile_hlg.py:48] Building ctc_topo. max_token_id: 3
-  2021-08-23 19:27:35,932 INFO [compile_hlg.py:52] Loading G.fst.txt
-  2021-08-23 19:27:35,932 INFO [compile_hlg.py:62] Intersecting L and G
-  2021-08-23 19:27:35,933 INFO [compile_hlg.py:64] LG shape: (4, None)
-  2021-08-23 19:27:35,933 INFO [compile_hlg.py:66] Connecting LG
-  2021-08-23 19:27:35,933 INFO [compile_hlg.py:68] LG shape after k2.connect: (4, None)
-  2021-08-23 19:27:35,933 INFO [compile_hlg.py:70] <class 'torch.Tensor'>
-  2021-08-23 19:27:35,933 INFO [compile_hlg.py:71] Determinizing LG
-  2021-08-23 19:27:35,934 INFO [compile_hlg.py:74] <class '_k2.RaggedInt'>
-  2021-08-23 19:27:35,934 INFO [compile_hlg.py:76] Connecting LG after k2.determinize
-  2021-08-23 19:27:35,934 INFO [compile_hlg.py:79] Removing disambiguation symbols on LG
-  2021-08-23 19:27:35,934 INFO [compile_hlg.py:87] LG shape after k2.remove_epsilon: (6, None)
-  2021-08-23 19:27:35,935 INFO [compile_hlg.py:92] Arc sorting LG
-  2021-08-23 19:27:35,935 INFO [compile_hlg.py:95] Composing H and LG
-  2021-08-23 19:27:35,935 INFO [compile_hlg.py:102] Connecting LG
-  2021-08-23 19:27:35,935 INFO [compile_hlg.py:105] Arc sorting LG
-  2021-08-23 19:27:35,936 INFO [compile_hlg.py:107] HLG.shape: (8, None)
-  2021-08-23 19:27:35,936 INFO [compile_hlg.py:123] Saving HLG.pt to data/lang_phone
+   2023-05-12 17:55:21 (prepare.sh:27:main) dl_dir: /tmp/icefall/egs/yesno/ASR/download
+   2023-05-12 17:55:21 (prepare.sh:30:main) Stage 0: Download data
+   /tmp/icefall/egs/yesno/ASR/download/waves_yesno.tar.gz: 100%|_______________________________________________________________| 4.70M/4.70M [06:54<00:00, 11.4kB/s]
+   2023-05-12 18:02:19 (prepare.sh:39:main) Stage 1: Prepare yesno manifest
+   2023-05-12 18:02:21 (prepare.sh:45:main) Stage 2: Compute fbank for yesno
+   2023-05-12 18:02:23,199 INFO [compute_fbank_yesno.py:65] Processing train
+   Extracting and storing features: 100%|_______________________________________________________________| 90/90 [00:00<00:00, 212.60it/s]
+   2023-05-12 18:02:23,640 INFO [compute_fbank_yesno.py:65] Processing test
+   Extracting and storing features: 100%|_______________________________________________________________| 30/30 [00:00<00:00, 304.53it/s]
+   2023-05-12 18:02:24 (prepare.sh:51:main) Stage 3: Prepare lang
+   2023-05-12 18:02:26 (prepare.sh:66:main) Stage 4: Prepare G
+   /project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):79
+   [I] Reading \data\ section.
+   /project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):140
+   [I] Reading \1-grams: section.
+   2023-05-12 18:02:26 (prepare.sh:92:main) Stage 5: Compile HLG
+   2023-05-12 18:02:28,581 INFO [compile_hlg.py:124] Processing data/lang_phone
+   2023-05-12 18:02:28,582 INFO [lexicon.py:171] Converting L.pt to Linv.pt
+   2023-05-12 18:02:28,609 INFO [compile_hlg.py:48] Building ctc_topo. max_token_id: 3
+   2023-05-12 18:02:28,610 INFO [compile_hlg.py:52] Loading G.fst.txt
+   2023-05-12 18:02:28,611 INFO [compile_hlg.py:62] Intersecting L and G
+   2023-05-12 18:02:28,613 INFO [compile_hlg.py:64] LG shape: (4, None)
+   2023-05-12 18:02:28,613 INFO [compile_hlg.py:66] Connecting LG
+   2023-05-12 18:02:28,614 INFO [compile_hlg.py:68] LG shape after k2.connect: (4, None)
+   2023-05-12 18:02:28,614 INFO [compile_hlg.py:70] <class 'torch.Tensor'>
+   2023-05-12 18:02:28,614 INFO [compile_hlg.py:71] Determinizing LG
+   2023-05-12 18:02:28,615 INFO [compile_hlg.py:74] <class '_k2.ragged.RaggedTensor'>
+   2023-05-12 18:02:28,615 INFO [compile_hlg.py:76] Connecting LG after k2.determinize
+   2023-05-12 18:02:28,615 INFO [compile_hlg.py:79] Removing disambiguation symbols on LG
+   2023-05-12 18:02:28,616 INFO [compile_hlg.py:91] LG shape after k2.remove_epsilon: (6, None)
+   2023-05-12 18:02:28,617 INFO [compile_hlg.py:96] Arc sorting LG
+   2023-05-12 18:02:28,617 INFO [compile_hlg.py:99] Composing H and LG
+   2023-05-12 18:02:28,619 INFO [compile_hlg.py:106] Connecting LG
+   2023-05-12 18:02:28,619 INFO [compile_hlg.py:109] Arc sorting LG
+   2023-05-12 18:02:28,619 INFO [compile_hlg.py:111] HLG.shape: (8, None)
+   2023-05-12 18:02:28,619 INFO [compile_hlg.py:127] Saving HLG.pt to data/lang_phone
 
 
 Training
@@ -434,49 +432,53 @@ The training log is given below:
 
 .. code-block::
 
-  2021-08-23 19:30:31,072 INFO [train.py:465] Training started
-  2021-08-23 19:30:31,072 INFO [train.py:466] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01,
-  'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, '
-  best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_doub
-  le_scores': True, 'world_size': 1, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 15, 'feature_dir': PosixPath('data/fbank'
-  ), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0
-  , 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2}
-  2021-08-23 19:30:31,074 INFO [lexicon.py:113] Loading pre-compiled data/lang_phone/Linv.pt
-  2021-08-23 19:30:31,098 INFO [asr_datamodule.py:146] About to get train cuts
-  2021-08-23 19:30:31,098 INFO [asr_datamodule.py:240] About to get train cuts
-  2021-08-23 19:30:31,102 INFO [asr_datamodule.py:149] About to create train dataset
-  2021-08-23 19:30:31,102 INFO [asr_datamodule.py:200] Using SingleCutSampler.
-  2021-08-23 19:30:31,102 INFO [asr_datamodule.py:206] About to create train dataloader
-  2021-08-23 19:30:31,102 INFO [asr_datamodule.py:219] About to get test cuts
-  2021-08-23 19:30:31,102 INFO [asr_datamodule.py:246] About to get test cuts
-  2021-08-23 19:30:31,357 INFO [train.py:416] Epoch 0, batch 0, batch avg loss 1.0789, total avg loss: 1.0789, batch size: 4
-  2021-08-23 19:30:31,848 INFO [train.py:416] Epoch 0, batch 10, batch avg loss 0.5356, total avg loss: 0.7556, batch size: 4
-  2021-08-23 19:30:32,301 INFO [train.py:432] Epoch 0, valid loss 0.9972, best valid loss: 0.9972 best valid epoch: 0
-  2021-08-23 19:30:32,805 INFO [train.py:416] Epoch 0, batch 20, batch avg loss 0.2436, total avg loss: 0.5717, batch size: 3
-  2021-08-23 19:30:33,109 INFO [train.py:432] Epoch 0, valid loss 0.4167, best valid loss: 0.4167 best valid epoch: 0
-  2021-08-23 19:30:33,121 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-0.pt
-  2021-08-23 19:30:33,325 INFO [train.py:416] Epoch 1, batch 0, batch avg loss 0.2214, total avg loss: 0.2214, batch size: 5
-  2021-08-23 19:30:33,798 INFO [train.py:416] Epoch 1, batch 10, batch avg loss 0.0781, total avg loss: 0.1343, batch size: 5
-  2021-08-23 19:30:34,065 INFO [train.py:432] Epoch 1, valid loss 0.0859, best valid loss: 0.0859 best valid epoch: 1
-  2021-08-23 19:30:34,556 INFO [train.py:416] Epoch 1, batch 20, batch avg loss 0.0421, total avg loss: 0.0975, batch size: 3
-  2021-08-23 19:30:34,810 INFO [train.py:432] Epoch 1, valid loss 0.0431, best valid loss: 0.0431 best valid epoch: 1
-  2021-08-23 19:30:34,824 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-1.pt
+   2023-05-12 18:04:59,759 INFO [train.py:481] Training started
+   2023-05-12 18:04:59,759 INFO [train.py:482] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01, 'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 
+   'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'reset_interval': 20, 'valid_interval': 10, 'beam_size': 10, 
+   'reduction': 'sum', 'use_double_scores': True, 'world_size': 1, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 15, 'seed': 42, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0,
+   'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': False, 'return_cuts': True, 'num_workers': 2, 
+   'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '3b7f09fa35e72589914f67089c0da9f196a92ca4', 'k2-git-date': 'Mon May 8 22:58:45 2023', 
+   'lhotse-version': '1.15.0.dev+git.6fcfced.clean', 'torch-version': '2.0.0+cu118', 'torch-cuda-available': False, 'torch-cuda-version': '11.8', 'python-version': '3.1', 'icefall-git-branch': 'master', 
+   'icefall-git-sha1': '30bde4b-clean', 'icefall-git-date': 'Thu May 11 17:37:47 2023', 'icefall-path': '/tmp/icefall', 
+   'k2-path': 'tmp/lib/python3.10/site-packages/k2-1.24.3.dev20230512+cuda11.8.torch2.0.0-py3.10-linux-x86_64.egg/k2/__init__.py', 
+   'lhotse-path': 'tmp/lib/python3.10/site-packages/lhotse/__init__.py', 'hostname': 'host', 'IP address': '0.0.0.0'}}
+   2023-05-12 18:04:59,761 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
+   2023-05-12 18:04:59,764 INFO [train.py:495] device: cpu
+   2023-05-12 18:04:59,791 INFO [asr_datamodule.py:146] About to get train cuts
+   2023-05-12 18:04:59,791 INFO [asr_datamodule.py:244] About to get train cuts
+   2023-05-12 18:04:59,852 INFO [asr_datamodule.py:149] About to create train dataset
+   2023-05-12 18:04:59,852 INFO [asr_datamodule.py:199] Using SingleCutSampler.
+   2023-05-12 18:04:59,852 INFO [asr_datamodule.py:205] About to create train dataloader
+   2023-05-12 18:04:59,853 INFO [asr_datamodule.py:218] About to get test cuts
+   2023-05-12 18:04:59,853 INFO [asr_datamodule.py:252] About to get test cuts
+   2023-05-12 18:04:59,986 INFO [train.py:422] Epoch 0, batch 0, loss[loss=1.065, over 2436.00 frames. ], tot_loss[loss=1.065, over 2436.00 frames. ], batch size: 4
+   2023-05-12 18:05:00,352 INFO [train.py:422] Epoch 0, batch 10, loss[loss=0.4561, over 2828.00 frames. ], tot_loss[loss=0.7076, over 22192.90 frames. ], batch size: 4
+   2023-05-12 18:05:00,691 INFO [train.py:444] Epoch 0, validation loss=0.9002, over 18067.00 frames.
+   2023-05-12 18:05:00,996 INFO [train.py:422] Epoch 0, batch 20, loss[loss=0.2555, over 2695.00 frames. ], tot_loss[loss=0.484, over 34971.47 frames. ], batch size: 5
+   2023-05-12 18:05:01,217 INFO [train.py:444] Epoch 0, validation loss=0.4688, over 18067.00 frames.
+   2023-05-12 18:05:01,251 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-0.pt
+   2023-05-12 18:05:01,389 INFO [train.py:422] Epoch 1, batch 0, loss[loss=0.2532, over 2436.00 frames. ], tot_loss[loss=0.2532, over 2436.00 frames. ], batch size: 4
+   2023-05-12 18:05:01,637 INFO [train.py:422] Epoch 1, batch 10, loss[loss=0.1139, over 2828.00 frames. ], tot_loss[loss=0.1592, over 22192.90 frames. ], batch size: 4
+   2023-05-12 18:05:01,859 INFO [train.py:444] Epoch 1, validation loss=0.1629, over 18067.00 frames.
+   2023-05-12 18:05:02,094 INFO [train.py:422] Epoch 1, batch 20, loss[loss=0.0767, over 2695.00 frames. ], tot_loss[loss=0.118, over 34971.47 frames. ], batch size: 5
+   2023-05-12 18:05:02,350 INFO [train.py:444] Epoch 1, validation loss=0.06778, over 18067.00 frames.
+   2023-05-12 18:05:02,395 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-1.pt
 
   ... ...
 
-  2021-08-23 19:30:49,657 INFO [train.py:416] Epoch 13, batch 0, batch avg loss 0.0109, total avg loss: 0.0109, batch size: 5
-  2021-08-23 19:30:49,984 INFO [train.py:416] Epoch 13, batch 10, batch avg loss 0.0093, total avg loss: 0.0096, batch size: 4
-  2021-08-23 19:30:50,239 INFO [train.py:432] Epoch 13, valid loss 0.0104, best valid loss: 0.0101 best valid epoch: 12
-  2021-08-23 19:30:50,569 INFO [train.py:416] Epoch 13, batch 20, batch avg loss 0.0092, total avg loss: 0.0096, batch size: 2
-  2021-08-23 19:30:50,819 INFO [train.py:432] Epoch 13, valid loss 0.0101, best valid loss: 0.0101 best valid epoch: 13
-  2021-08-23 19:30:50,835 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-13.pt
-  2021-08-23 19:30:51,024 INFO [train.py:416] Epoch 14, batch 0, batch avg loss 0.0105, total avg loss: 0.0105, batch size: 5
-  2021-08-23 19:30:51,317 INFO [train.py:416] Epoch 14, batch 10, batch avg loss 0.0099, total avg loss: 0.0097, batch size: 4
-  2021-08-23 19:30:51,552 INFO [train.py:432] Epoch 14, valid loss 0.0108, best valid loss: 0.0101 best valid epoch: 13
-  2021-08-23 19:30:51,869 INFO [train.py:416] Epoch 14, batch 20, batch avg loss 0.0096, total avg loss: 0.0097, batch size: 5
-  2021-08-23 19:30:52,107 INFO [train.py:432] Epoch 14, valid loss 0.0102, best valid loss: 0.0101 best valid epoch: 13
-  2021-08-23 19:30:52,126 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-14.pt
-  2021-08-23 19:30:52,128 INFO [train.py:537] Done!
+   2023-05-12 18:05:14,789 INFO [train.py:422] Epoch 13, batch 0, loss[loss=0.01056, over 2436.00 frames. ], tot_loss[loss=0.01056, over 2436.00 frames. ], batch size: 4
+   2023-05-12 18:05:15,016 INFO [train.py:422] Epoch 13, batch 10, loss[loss=0.009022, over 2828.00 frames. ], tot_loss[loss=0.009985, over 22192.90 frames. ], batch size: 4
+   2023-05-12 18:05:15,271 INFO [train.py:444] Epoch 13, validation loss=0.01088, over 18067.00 frames.
+   2023-05-12 18:05:15,497 INFO [train.py:422] Epoch 13, batch 20, loss[loss=0.01174, over 2695.00 frames. ], tot_loss[loss=0.01077, over 34971.47 frames. ], batch size: 5
+   2023-05-12 18:05:15,747 INFO [train.py:444] Epoch 13, validation loss=0.01087, over 18067.00 frames.
+   2023-05-12 18:05:15,783 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-13.pt
+   2023-05-12 18:05:15,921 INFO [train.py:422] Epoch 14, batch 0, loss[loss=0.01045, over 2436.00 frames. ], tot_loss[loss=0.01045, over 2436.00 frames. ], batch size: 4
+   2023-05-12 18:05:16,146 INFO [train.py:422] Epoch 14, batch 10, loss[loss=0.008957, over 2828.00 frames. ], tot_loss[loss=0.009903, over 22192.90 frames. ], batch size: 4
+   2023-05-12 18:05:16,374 INFO [train.py:444] Epoch 14, validation loss=0.01092, over 18067.00 frames.
+   2023-05-12 18:05:16,598 INFO [train.py:422] Epoch 14, batch 20, loss[loss=0.01169, over 2695.00 frames. ], tot_loss[loss=0.01065, over 34971.47 frames. ], batch size: 5
+   2023-05-12 18:05:16,824 INFO [train.py:444] Epoch 14, validation loss=0.01077, over 18067.00 frames.
+   2023-05-12 18:05:16,862 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-14.pt
+   2023-05-12 18:05:16,865 INFO [train.py:555] Done!
 
 Decoding
 ~~~~~~~~
@@ -491,22 +493,25 @@ The decoding log is:
 
 .. code-block::
 
-  2021-08-23 19:35:30,192 INFO [decode.py:249] Decoding started
-  2021-08-23 19:35:30,192 INFO [decode.py:250] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 23, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2}
-  2021-08-23 19:35:30,193 INFO [lexicon.py:113] Loading pre-compiled data/lang_phone/Linv.pt
-  2021-08-23 19:35:30,213 INFO [decode.py:259] device: cpu
-  2021-08-23 19:35:30,217 INFO [decode.py:279] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
-  /tmp/icefall/icefall/checkpoint.py:146: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch.
-  It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.
-  To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at  /pytorch/aten/src/ATen/native/BinaryOps.cpp:450.)
-    avg[k] //= n
-  2021-08-23 19:35:30,220 INFO [asr_datamodule.py:219] About to get test cuts
-  2021-08-23 19:35:30,220 INFO [asr_datamodule.py:246] About to get test cuts
-  2021-08-23 19:35:30,409 INFO [decode.py:190] batch 0/8, cuts processed until now is 4
-  2021-08-23 19:35:30,571 INFO [decode.py:228] The transcripts are stored in tdnn/exp/recogs-test_set.txt
-  2021-08-23 19:35:30,572 INFO [utils.py:317] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
-  2021-08-23 19:35:30,573 INFO [decode.py:236] Wrote detailed error stats to tdnn/exp/errs-test_set.txt
-  2021-08-23 19:35:30,573 INFO [decode.py:299] Done!
+   2023-05-12 18:08:30,482 INFO [decode.py:263] Decoding started
+   2023-05-12 18:08:30,483 INFO [decode.py:264] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 23, 
+   'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'export': False, 'feature_dir': PosixPath('data/fbank'), 
+   'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': False, 'return_cuts': True, 
+   'num_workers': 2, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '3b7f09fa35e72589914f67089c0da9f196a92ca4', 'k2-git-date': 'Mon May 8 22:58:45 2023', 
+   'lhotse-version': '1.15.0.dev+git.6fcfced.clean', 'torch-version': '2.0.0+cu118', 'torch-cuda-available': False, 'torch-cuda-version': '11.8', 'python-version': '3.1', 'icefall-git-branch': 'master', 
+   'icefall-git-sha1': '30bde4b-clean', 'icefall-git-date': 'Thu May 11 17:37:47 2023', 'icefall-path': '/tmp/icefall', 
+   'k2-path': '/tmp/lib/python3.10/site-packages/k2-1.24.3.dev20230512+cuda11.8.torch2.0.0-py3.10-linux-x86_64.egg/k2/__init__.py', 
+   'lhotse-path': '/tmp/lib/python3.10/site-packages/lhotse/__init__.py', 'hostname': 'host', 'IP address': '0.0.0.0'}}
+   2023-05-12 18:08:30,483 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
+   2023-05-12 18:08:30,487 INFO [decode.py:273] device: cpu
+   2023-05-12 18:08:30,513 INFO [decode.py:291] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
+   2023-05-12 18:08:30,521 INFO [asr_datamodule.py:218] About to get test cuts
+   2023-05-12 18:08:30,521 INFO [asr_datamodule.py:252] About to get test cuts
+   2023-05-12 18:08:30,675 INFO [decode.py:204] batch 0/?, cuts processed until now is 4
+   2023-05-12 18:08:30,923 INFO [decode.py:241] The transcripts are stored in tdnn/exp/recogs-test_set.txt
+   2023-05-12 18:08:30,924 INFO [utils.py:558] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
+   2023-05-12 18:08:30,925 INFO [decode.py:249] Wrote detailed error stats to tdnn/exp/errs-test_set.txt
+   2023-05-12 18:08:30,925 INFO [decode.py:316] Done!
 
 **Congratulations!** You have successfully setup the environment and have run the first recipe in ``icefall``.