Delete egs/ami/ASR/xlsr_transducer/inference_results/log-inference-ihm-2026-01-26-04-44-36

egs/ami/ASR/xlsr_transducer/inference_results/log-inference-ihm-2026-01-26-04-44-36

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-2026-01-26 04:44:36,166 INFO [inference.py:419] ================================================================================
-2026-01-26 04:44:36,166 INFO [inference.py:420] XLSR-Transducer Inference on AMI
-2026-01-26 04:44:36,166 INFO [inference.py:421] ================================================================================
-2026-01-26 04:44:36,166 INFO [inference.py:422] Experiment dir: /workspace/icefall/egs/ami/ASR/xlsr_transducer/exp_16gb_scd
-2026-01-26 04:44:36,166 INFO [inference.py:423] Output dir: /workspace/icefall/egs/ami/ASR/xlsr_transducer/exp_16gb_scd/inference_results
-2026-01-26 04:44:36,166 INFO [inference.py:424] Test set: ihm
-2026-01-26 04:44:36,166 INFO [inference.py:425] Decoding method: greedy_search
-2026-01-26 04:44:36,166 INFO [inference.py:431] Device: cpu
-2026-01-26 04:44:36,166 INFO [inference.py:434] Loading BPE model from data/lang_bpe_500_scd
-2026-01-26 04:44:36,168 INFO [inference.py:442] Vocabulary size: 500
-2026-01-26 04:44:36,168 INFO [inference.py:443] Blank ID: 0
-2026-01-26 04:44:36,168 INFO [inference.py:446] Creating model
-2026-01-26 04:44:37,655 INFO [inference.py:453] Loading checkpoint: /workspace/icefall/egs/ami/ASR/xlsr_transducer/exp_16gb_scd/checkpoint-30000.pt
-2026-01-26 04:44:37,655 INFO [checkpoint.py:111] Loading checkpoint from /workspace/icefall/egs/ami/ASR/xlsr_transducer/exp_16gb_scd/checkpoint-30000.pt
-2026-01-26 04:44:42,489 INFO [inference.py:482] Number of model parameters: 317,511,772
-2026-01-26 04:44:42,489 INFO [inference.py:485] Loading test data
-2026-01-26 04:44:42,489 INFO [asr_datamodule.py:448] About to get AMI IHM test cuts with [SCD] tokens
-2026-01-26 04:44:43,623 INFO [inference.py:496] Number of test utterances: 6676
-2026-01-26 04:44:43,623 INFO [inference.py:499] Starting inference...
-2026-01-26 04:44:44,773 INFO [inference.py:318] Audio shape: torch.Size([6, 246400]), dtype: torch.float32
-2026-01-26 04:44:44,776 INFO [inference.py:319] Audio range: [-0.090, 0.104]
-2026-01-26 04:44:44,779 INFO [inference.py:320] Audio lengths: tensor([246400, 244799, 238079, 228000, 224000, 222880], dtype=torch.int32)
-2026-01-26 04:44:52,532 INFO [inference.py:341] Encoder out shape: torch.Size([6, 769, 1024])
-2026-01-26 04:44:52,532 INFO [inference.py:342] Encoder out lens: tensor([769, 764, 743, 712, 699, 696])
-2026-01-26 04:44:52,533 INFO [inference.py:343] Encoder out range: [-4.808, 7.175]
-2026-01-26 04:44:52,980 INFO [inference.py:353] Number of hypotheses: 6
-2026-01-26 04:44:52,980 INFO [inference.py:355] First hypothesis: [11, 4, 2, 11]
-2026-01-26 04:44:52,988 INFO [inference.py:318] Audio shape: torch.Size([23, 67200]), dtype: torch.float32
-2026-01-26 04:44:52,989 INFO [inference.py:319] Audio range: [-0.401, 0.443]
-2026-01-26 04:44:52,990 INFO [inference.py:320] Audio lengths: tensor([67200, 66559, 66400, 64159, 63680, 63040, 61120, 60960, 59519, 59040,
-        58560, 55840, 54559, 52480, 52160, 50559, 50400, 50240, 48480, 47840,
-        47520, 44639, 44000], dtype=torch.int32)
-2026-01-26 04:45:00,200 INFO [inference.py:341] Encoder out shape: torch.Size([23, 209, 1024])
-2026-01-26 04:45:00,201 INFO [inference.py:342] Encoder out lens: tensor([209, 207, 207, 200, 198, 196, 190, 190, 185, 184, 182, 174, 170, 163,
-        162, 157, 157, 156, 151, 149, 148, 139, 137])
-2026-01-26 04:45:00,201 INFO [inference.py:343] Encoder out range: [-4.808, 7.173]
-2026-01-26 04:45:01,019 INFO [inference.py:353] Number of hypotheses: 23
-2026-01-26 04:45:01,019 INFO [inference.py:355] First hypothesis: [11, 4, 2, 11]
-2026-01-26 04:45:01,031 INFO [inference.py:318] Audio shape: torch.Size([39, 40640]), dtype: torch.float32
-2026-01-26 04:45:01,032 INFO [inference.py:319] Audio range: [-0.439, 0.480]
-2026-01-26 04:45:01,033 INFO [inference.py:320] Audio lengths: tensor([40640, 37279, 36799, 36480, 36480, 33280, 33279, 32320, 32159, 30400,
-        28800, 28480, 28160, 23520, 23039, 22880, 22400, 21920, 21920, 20960,
-        20160, 20000, 19200, 19040, 18880, 18240, 16480, 14720, 13600, 12960,
-        12320, 11680, 11520, 10880,  9440,  9120,  7840,  5920,  5760],
-       dtype=torch.int32)