/home/tony/Work/glockenspiel/s3prl/s3prl/upstream/byol_s/byol_a/common.py:20: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend("sox_io")
/home/tony/Work/glockenspiel/s3prl/s3prl/run_downstream.py:157: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend('sox_io')
Some weights of the model checkpoint at m-a-p/MERT-v1-330M were not used when initializing MERTModel: ['encoder.pos_conv_embed.conv.weight_g', 'encoder.pos_conv_embed.conv.weight_v']
- This IS expected if you are initializing MERTModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing MERTModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of MERTModel were not initialized from the model checkpoint at m-a-p/MERT-v1-330M and are newly initialized: ['encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'encoder.pos_conv_embed.conv.parametrizations.weight.original0']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[Featurizer] - The selected feature hidden_states's downsample rate is 320
[Runner] - Start a new experiment
m-a-p/MERT-v1-330M {'model_config': None, 'refresh': False}

overall:   0%|          | 0/4000 [00:00<?, ?it/s][LibriPhone] - Taking the first phoneme sequences for a deterministic behavior.


word -> phonemes:   0%|          | 0/3515 [00:00<?, ?it/s]

word -> phonemes:  13%|█▎        | 453/3515 [00:00<00:00, 4521.69it/s]

word -> phonemes:  27%|██▋       | 943/3515 [00:00<00:00, 4743.09it/s]

word -> phonemes:  41%|████      | 1447/3515 [00:00<00:00, 4876.42it/s]

word -> phonemes:  55%|█████▌    | 1935/3515 [00:00<00:00, 4846.42it/s]

word -> phonemes:  69%|██████▉   | 2420/3515 [00:00<00:00, 4801.89it/s]

word -> phonemes:  83%|████████▎ | 2901/3515 [00:00<00:00, 4785.93it/s]

word -> phonemes:  96%|█████████▌| 3380/3515 [00:00<00:00, 4489.74it/s]
word -> phonemes: 100%|██████████| 3515/3515 [00:00<00:00, 4599.88it/s]


train:   0%|          | 0/3515 [00:00<?, ?it/s]
overall:   0%|          | 1/4000 [00:04<4:37:55,  4.17s/it]

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overall:   0%|          | 2/4000 [00:05<2:42:25,  2.44s/it]

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overall:   0%|          | 3/4000 [00:06<2:06:07,  1.89s/it]

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overall:   0%|          | 5/4000 [00:09<1:39:16,  1.49s/it]

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overall:   0%|          | 6/4000 [00:10<1:31:13,  1.37s/it]

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overall:   0%|          | 7/4000 [00:11<1:29:29,  1.34s/it]

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overall:   0%|          | 8/4000 [00:12<1:26:43,  1.30s/it]

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overall:   0%|          | 9/4000 [00:14<1:26:17,  1.30s/it]

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overall:   0%|          | 11/4000 [00:14<53:10,  1.25it/s]  

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overall:   0%|          | 13/4000 [00:17<1:04:58,  1.02it/s]

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overall:   0%|          | 14/4000 [00:18<1:09:48,  1.05s/it]

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overall:   0%|          | 15/4000 [00:19<1:10:10,  1.06s/it]

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overall:   0%|          | 16/4000 [00:20<1:14:01,  1.11s/it]

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overall:   0%|          | 17/4000 [00:21<1:15:28,  1.14s/it]

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overall:   0%|          | 18/4000 [00:23<1:19:12,  1.19s/it]

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overall:   0%|          | 20/4000 [00:24<1:09:29,  1.05s/it]

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overall:   1%|          | 21/4000 [00:26<1:14:23,  1.12s/it]

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overall:   1%|          | 22/4000 [00:27<1:17:56,  1.18s/it]

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overall:   1%|          | 23/4000 [00:28<1:20:17,  1.21s/it]

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overall:   1%|          | 24/4000 [00:29<1:18:44,  1.19s/it]

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overall:   1%|          | 25/4000 [00:30<1:18:04,  1.18s/it]

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overall:   1%|          | 26/4000 [00:32<1:20:08,  1.21s/it]

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overall:   1%|          | 27/4000 [00:33<1:21:58,  1.24s/it]

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overall:   1%|          | 28/4000 [00:34<1:21:18,  1.23s/it]

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overall:   1%|          | 29/4000 [00:35<1:20:34,  1.22s/it]

train:   1%|          | 29/3515 [00:34<1:10:43,  1.22s/it]
overall:   1%|          | 30/4000 [00:37<1:22:06,  1.24s/it]

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overall:   1%|          | 31/4000 [00:38<1:22:09,  1.24s/it]

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overall:   1%|          | 32/4000 [00:39<1:22:12,  1.24s/it]

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overall:   1%|          | 33/4000 [00:41<1:22:58,  1.25s/it]

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overall:   1%|          | 34/4000 [00:41<1:03:38,  1.04it/s]

train:   1%|          | 34/3515 [00:39<55:51,  1.04it/s]  
overall:   1%|          | 35/4000 [00:42<1:10:14,  1.06s/it]

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overall:   1%|          | 36/4000 [00:43<1:14:38,  1.13s/it]

train:   1%|          | 36/3515 [00:42<1:05:30,  1.13s/it]
overall:   1%|          | 37/4000 [00:45<1:14:55,  1.13s/it]

train:   1%|          | 37/3515 [00:43<1:05:45,  1.13s/it]
overall:   1%|          | 38/4000 [00:46<1:16:08,  1.15s/it]

train:   1%|          | 38/3515 [00:44<1:06:49,  1.15s/it]
overall:   1%|          | 39/4000 [00:47<1:15:16,  1.14s/it]

train:   1%|          | 39/3515 [00:45<1:06:03,  1.14s/it]
overall:   1%|          | 40/4000 [00:48<1:08:20,  1.04s/it]

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overall:   1%|          | 41/4000 [00:49<1:12:06,  1.09s/it]

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overall:   1%|          | 42/4000 [00:50<1:15:49,  1.15s/it]

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overall:   1%|          | 43/4000 [00:51<1:16:13,  1.16s/it]

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overall:   1%|          | 45/4000 [00:53<1:06:05,  1.00s/it]

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overall:   1%|          | 46/4000 [00:54<54:55,  1.20it/s]  

train:   1%|▏         | 46/3515 [00:52<48:11,  1.20it/s]
overall:   1%|          | 47/4000 [00:55<1:02:30,  1.05it/s]

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overall:   1%|          | 48/4000 [00:56<1:04:02,  1.03it/s]

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overall:   1%|          | 49/4000 [00:57<1:09:37,  1.06s/it]

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overall:   1%|▏         | 50/4000 [00:58<1:12:33,  1.10s/it]

train:   1%|▏         | 50/3515 [00:56<1:03:39,  1.10s/it]
overall:   1%|▏         | 51/4000 [00:59<1:13:56,  1.12s/it]

train:   1%|▏         | 51/3515 [00:58<1:04:51,  1.12s/it]
overall:   1%|▏         | 52/4000 [01:01<1:17:26,  1.18s/it]

train:   1%|▏         | 52/3515 [00:59<1:07:55,  1.18s/it]
overall:   1%|▏         | 53/4000 [01:02<1:18:27,  1.19s/it]

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overall:   1%|▏         | 56/4000 [01:06<1:19:23,  1.21s/it]

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overall:   1%|▏         | 57/4000 [01:07<1:22:09,  1.25s/it]

train:   2%|▏         | 57/3515 [01:05<1:12:03,  1.25s/it]
overall:   1%|▏         | 58/4000 [01:08<1:23:35,  1.27s/it]

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overall:   2%|▏         | 60/4000 [01:11<1:21:54,  1.25s/it]

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overall:   2%|▏         | 61/4000 [01:12<1:20:09,  1.22s/it]

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overall:   2%|▏         | 65/4000 [01:16<1:17:42,  1.18s/it]

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overall:   2%|▏         | 66/4000 [01:18<1:19:38,  1.21s/it]

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overall:   2%|▏         | 67/4000 [01:19<1:19:39,  1.22s/it]

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overall:  11%|█         | 427/4000 [08:26<1:05:54,  1.11s/it]

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overall:  12%|█▏        | 489/4000 [09:37<57:32,  1.02it/s]

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overall:  12%|█▏        | 492/4000 [09:40<55:42,  1.05it/s]  

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train:  14%|█▍        | 495/3515 [09:42<57:37,  1.14s/it]
overall:  12%|█▏        | 496/4000 [09:45<1:07:52,  1.16s/it]

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overall:  12%|█▎        | 500/4000 [09:49<57:52,  1.01it/s]  

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train:  14%|█▍        | 508/3515 [09:58<1:02:52,  1.25s/it]
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train:  15%|█▍        | 510/3515 [10:01<1:04:04,  1.28s/it]
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train:  15%|█▍        | 515/3515 [10:07<59:35,  1.19s/it]  
overall:  13%|█▎        | 516/4000 [10:10<1:12:01,  1.24s/it]

train:  15%|█▍        | 516/3515 [10:08<1:02:00,  1.24s/it]
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train:  15%|█▍        | 517/3515 [10:09<1:01:41,  1.23s/it]
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train:  15%|█▍        | 526/3515 [10:21<1:07:24,  1.35s/it]
overall:  13%|█▎        | 527/4000 [10:24<1:16:41,  1.32s/it]

train:  15%|█▍        | 527/3515 [10:22<1:05:58,  1.32s/it]
overall:  13%|█▎        | 528/4000 [10:24<57:49,  1.00it/s]  

train:  15%|█▌        | 528/3515 [10:22<49:44,  1.00it/s]  
overall:  13%|█▎        | 529/4000 [10:25<1:04:53,  1.12s/it]

train:  15%|█▌        | 529/3515 [10:23<55:49,  1.12s/it]
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overall:  13%|█▎        | 531/4000 [10:27<58:31,  1.01s/it]  

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train:  15%|█▌        | 533/3515 [10:28<56:03,  1.13s/it]
overall:  13%|█▎        | 534/4000 [10:30<57:31,  1.00it/s]  

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train:  15%|█▌        | 536/3515 [10:31<55:38,  1.12s/it]
overall:  13%|█▎        | 537/4000 [10:34<1:08:39,  1.19s/it]

train:  15%|█▌        | 537/3515 [10:32<59:02,  1.19s/it]
overall:  13%|█▎        | 538/4000 [10:36<1:11:16,  1.24s/it]

train:  15%|█▌        | 538/3515 [10:34<1:01:17,  1.24s/it]
overall:  13%|█▎        | 539/4000 [10:36<1:03:40,  1.10s/it]

train:  15%|█▌        | 539/3515 [10:34<54:45,  1.10s/it]  
overall:  14%|█▎        | 540/4000 [10:37<54:35,  1.06it/s]  

train:  15%|█▌        | 540/3515 [10:35<46:56,  1.06it/s]
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train:  15%|█▌        | 541/3515 [10:36<53:13,  1.07s/it]
overall:  14%|█▎        | 542/4000 [10:40<1:05:02,  1.13s/it]

train:  15%|█▌        | 542/3515 [10:38<55:54,  1.13s/it]
overall:  14%|█▎        | 543/4000 [10:41<1:08:08,  1.18s/it]

train:  15%|█▌        | 543/3515 [10:39<58:35,  1.18s/it]
overall:  14%|█▎        | 544/4000 [10:42<1:08:21,  1.19s/it]

train:  15%|█▌        | 544/3515 [10:40<58:46,  1.19s/it]
overall:  14%|█▎        | 545/4000 [10:43<1:10:06,  1.22s/it]

train:  16%|█▌        | 545/3515 [10:41<1:00:15,  1.22s/it]
overall:  14%|█▎        | 546/4000 [10:45<1:09:46,  1.21s/it]

train:  16%|█▌        | 546/3515 [10:43<59:58,  1.21s/it]  
overall:  14%|█▎        | 547/4000 [10:46<1:11:26,  1.24s/it]

train:  16%|█▌        | 547/3515 [10:44<1:01:24,  1.24s/it]
overall:  14%|█▎        | 548/4000 [10:47<1:10:41,  1.23s/it]

train:  16%|█▌        | 548/3515 [10:45<1:00:45,  1.23s/it]
overall:  14%|█▎        | 549/4000 [10:49<1:16:31,  1.33s/it]

train:  16%|█▌        | 549/3515 [10:47<1:05:46,  1.33s/it]
overall:  14%|█▍        | 550/4000 [10:50<1:16:03,  1.32s/it]

train:  16%|█▌        | 550/3515 [10:48<1:05:21,  1.32s/it]
overall:  14%|█▍        | 551/4000 [10:51<1:14:31,  1.30s/it]

train:  16%|█▌        | 551/3515 [10:49<1:04:02,  1.30s/it]
overall:  14%|█▍        | 552/4000 [10:52<1:05:52,  1.15s/it]

train:  16%|█▌        | 552/3515 [10:50<56:36,  1.15s/it]  
overall:  14%|█▍        | 553/4000 [10:53<1:07:34,  1.18s/it]

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overall:  14%|█▍        | 554/4000 [10:54<1:09:40,  1.21s/it]

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overall:  14%|█▍        | 555/4000 [10:55<55:27,  1.04it/s]  

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overall:  14%|█▍        | 556/4000 [10:56<1:00:43,  1.06s/it]

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overall:  14%|█▍        | 557/4000 [10:57<1:04:53,  1.13s/it]

train:  16%|█▌        | 557/3515 [10:56<55:45,  1.13s/it]
overall:  14%|█▍        | 558/4000 [10:59<1:07:16,  1.17s/it]

train:  16%|█▌        | 558/3515 [10:57<57:48,  1.17s/it]
overall:  14%|█▍        | 559/4000 [11:00<1:09:14,  1.21s/it]

train:  16%|█▌        | 559/3515 [10:58<59:28,  1.21s/it]
overall:  14%|█▍        | 560/4000 [11:01<1:06:27,  1.16s/it]

train:  16%|█▌        | 560/3515 [10:59<57:05,  1.16s/it]
overall:  14%|█▍        | 561/4000 [11:02<1:07:14,  1.17s/it]

train:  16%|█▌        | 561/3515 [11:00<57:45,  1.17s/it]
overall:  14%|█▍        | 562/4000 [11:03<1:03:12,  1.10s/it]

train:  16%|█▌        | 562/3515 [11:01<54:17,  1.10s/it]
overall:  14%|█▍        | 563/4000 [11:04<1:06:19,  1.16s/it]

train:  16%|█▌        | 563/3515 [11:03<56:57,  1.16s/it]
overall:  14%|█▍        | 564/4000 [11:06<1:09:50,  1.22s/it]

train:  16%|█▌        | 564/3515 [11:04<59:59,  1.22s/it]
overall:  14%|█▍        | 565/4000 [11:06<56:35,  1.01it/s]  

train:  16%|█▌        | 565/3515 [11:04<48:36,  1.01it/s]
overall:  14%|█▍        | 566/4000 [11:08<1:02:03,  1.08s/it]

train:  16%|█▌        | 566/3515 [11:06<53:17,  1.08s/it]
overall:  14%|█▍        | 567/4000 [11:09<1:05:14,  1.14s/it]

train:  16%|█▌        | 567/3515 [11:07<56:01,  1.14s/it]
overall:  14%|█▍        | 568/4000 [11:09<50:34,  1.13it/s]  

train:  16%|█▌        | 568/3515 [11:07<43:25,  1.13it/s]
overall:  14%|█▍        | 569/4000 [11:10<57:30,  1.01s/it]

train:  16%|█▌        | 569/3515 [11:09<49:23,  1.01s/it]
overall:  14%|█▍        | 570/4000 [11:12<1:02:08,  1.09s/it]

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overall:  14%|█▍        | 571/4000 [11:13<1:06:46,  1.17s/it]

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overall:  14%|█▍        | 574/4000 [11:17<1:14:10,  1.30s/it]

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overall:  14%|█▍        | 575/4000 [11:19<1:14:54,  1.31s/it]

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overall:  14%|█▍        | 576/4000 [11:20<1:14:32,  1.31s/it]

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overall:  14%|█▍        | 578/4000 [11:22<1:13:38,  1.29s/it]

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overall:  14%|█▍        | 579/4000 [11:23<1:02:56,  1.10s/it]

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overall:  14%|█▍        | 580/4000 [11:24<1:05:11,  1.14s/it]

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overall:  15%|█▍        | 581/4000 [11:25<1:04:36,  1.13s/it]

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overall:  15%|█▍        | 597/4000 [11:45<1:03:55,  1.13s/it]

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overall:  15%|█▌        | 603/4000 [11:52<1:06:23,  1.17s/it]

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overall:  15%|█▌        | 604/4000 [11:53<1:06:53,  1.18s/it]

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overall:  15%|█▌        | 605/4000 [11:54<1:09:55,  1.24s/it]

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overall:  15%|█▌        | 606/4000 [11:55<1:07:44,  1.20s/it]

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overall:  15%|█▌        | 607/4000 [11:56<1:02:27,  1.10s/it]

train:  17%|█▋        | 607/3515 [11:54<53:31,  1.10s/it]
overall:  15%|█▌        | 608/4000 [11:57<1:05:25,  1.16s/it]

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overall:  15%|█▌        | 609/4000 [11:59<1:05:59,  1.17s/it]

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overall:  15%|█▌        | 610/4000 [12:00<1:07:59,  1.20s/it]

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overall:  15%|█▌        | 611/4000 [12:00<55:34,  1.02it/s]  

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overall:  15%|█▌        | 612/4000 [12:01<49:56,  1.13it/s]

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overall:  15%|█▌        | 613/4000 [12:02<56:37,  1.00s/it]

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overall:  15%|█▌        | 614/4000 [12:04<1:02:25,  1.11s/it]

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overall:  15%|█▌        | 617/4000 [12:07<1:08:31,  1.22s/it]

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overall:  15%|█▌        | 618/4000 [12:09<1:09:25,  1.23s/it]

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overall:  15%|█▌        | 619/4000 [12:10<1:09:22,  1.23s/it]

train:  18%|█▊        | 619/3515 [12:08<59:25,  1.23s/it]
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overall:  16%|█▌        | 621/4000 [12:13<1:14:16,  1.32s/it]

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overall:  16%|█▌        | 622/4000 [12:14<1:13:04,  1.30s/it]

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overall:  16%|█▌        | 623/4000 [12:15<1:13:02,  1.30s/it]

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overall:  16%|█▌        | 628/4000 [12:22<1:11:12,  1.27s/it]

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overall:  16%|█▌        | 629/4000 [12:23<1:11:32,  1.27s/it]

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overall:  16%|█▌        | 631/4000 [12:25<1:06:58,  1.19s/it]

train:  18%|█▊        | 631/3515 [12:23<57:19,  1.19s/it]
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overall:  16%|█▌        | 635/4000 [12:29<56:56,  1.02s/it]  

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overall:  16%|█▌        | 636/4000 [12:30<59:11,  1.06s/it]

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overall:  16%|█▌        | 638/4000 [12:32<50:38,  1.11it/s]  

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overall:  16%|█▌        | 639/4000 [12:33<58:02,  1.04s/it]

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overall:  16%|█▋        | 653/4000 [12:51<1:11:21,  1.28s/it]

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overall:  16%|█▋        | 654/4000 [12:52<1:11:29,  1.28s/it]

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overall:  16%|█▋        | 655/4000 [12:53<1:06:17,  1.19s/it]

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overall:  16%|█▋        | 656/4000 [12:54<1:07:44,  1.22s/it]

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train:  28%|██▊       | 999/3515 [19:38<52:42,  1.26s/it]train loss: 170.8539276123047
train per: 1.3664565443986698
train loss: 95.60482788085938
train per: 1.091660237425649
train loss: 37.89525604248047
train per: 0.9590966759705658
train loss: 22.039873123168945
train per: 0.9343517547800908
train loss: 16.490604400634766
train per: 0.9456140747608602
train loss: 11.711766242980957
train per: 0.9453813358597821
train loss: 11.330503463745117
train per: 0.9481499848472326
train loss: 9.929482460021973
train per: 0.9363897742215882
train loss: 8.320924758911133
train per: 0.9006199352559084
train loss: 9.94545841217041
train per: 0.9429089966295048
train loss: 9.395139694213867
train per: 0.9422591206735267
train loss: 7.8663330078125
train per: 0.8821746969848743
train loss: 6.622476100921631
train per: 0.8820401631304527
train loss: 6.614776611328125
train per: 0.917879369238688
train loss: 5.259407997131348
train per: 0.8148321010096048
train loss: 6.917607307434082
train per: 0.8903982873906494
train loss: 7.063835144042969
train per: 0.8990719257540604
train loss: 6.7090582847595215
train per: 0.8268924554530519
train loss: 6.107632160186768
train per: 0.8633567326100829
train loss: 5.1767473220825195
train per: 0.8428602530298266
train loss: 4.331491947174072
train per: 0.7925762569071041
train loss: 4.346113204956055
train per: 0.7850457206052922
train loss: 5.103744029998779
train per: 0.8117144165691313
train loss: 5.320516109466553
train per: 0.7835269271383316
train loss: 5.670154571533203
train per: 0.7960008397186943
train loss: 5.9467573165893555
train per: 0.7607693567634602
train loss: 5.827523231506348
train per: 0.779457336834386
train loss: 5.974844932556152
train per: 0.8069153731816078
train loss: 5.13808536529541
train per: 0.7856527031139061
train loss: 4.108804702758789
train per: 0.762648548362834
train loss: 4.522386074066162
train per: 0.763751834421335
train loss: 5.067628860473633
train per: 0.7820909680962604
train loss: 4.7490949630737305
train per: 0.7739390642002176
train loss: 4.314088821411133
train per: 0.7313477410947002
train loss: 4.710111618041992
train per: 0.7333285937722167
train loss: 7.406947135925293
train per: 0.8706179978315866
train loss: 7.620482444763184
train per: 0.810299987125016
train loss: 5.9392852783203125
train per: 0.7768415611269062
train loss: 5.5472307205200195
train per: 0.7361352897289232
train loss: 4.615477561950684
train per: 0.729278517319997
train loss: 6.836825370788574
train per: 0.8627185452083282
train loss: 5.870655059814453
train per: 0.7896683546817596
train loss: 5.590880393981934
train per: 0.7712502337759491
train loss: 5.5390400886535645
train per: 0.7749467724199247
train loss: 4.596309661865234
train per: 0.7263962604080441
train loss: 7.174442291259766
train per: 0.9552154612120766
train loss: 7.389712333679199
train per: 0.7767229493004227
train loss: 6.4361701011657715
train per: 0.778037747621276
train loss: 5.20145845413208
train per: 0.7487578366847897
train loss: 5.274246692657471
train per: 0.7439426659389164
[LibriPhone] - Taking the first phoneme sequences for a deterministic behavior.



word -> phonemes:   0%|          | 0/2703 [00:00<?, ?it/s]


word -> phonemes:  23%|██▎       | 627/2703 [00:00<00:00, 6268.56it/s]


word -> phonemes:  46%|████▋     | 1254/2703 [00:00<00:00, 5951.19it/s]


word -> phonemes:  68%|██████▊   | 1851/2703 [00:00<00:00, 5956.24it/s]


word -> phonemes:  91%|█████████ | 2448/2703 [00:00<00:00, 5880.74it/s]
word -> phonemes: 100%|██████████| 2703/2703 [00:00<00:00, 5884.57it/s]



dev:   0%|          | 0/2703 [00:00<?, ?it/s]


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dev:   0%|          | 7/2703 [00:02<11:05,  4.05it/s]


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overall:  48%|████▊     | 1910/4000 [40:17<42:17,  1.21s/it]

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overall:  48%|████▊     | 1914/4000 [40:21<38:02,  1.09s/it]

train:  54%|█████▍    | 1914/3515 [40:19<29:11,  1.09s/it]
overall:  48%|████▊     | 1915/4000 [40:22<37:08,  1.07s/it]

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overall:  48%|████▊     | 1916/4000 [40:22<29:10,  1.19it/s]

train:  55%|█████▍    | 1916/3515 [40:20<22:23,  1.19it/s]
overall:  48%|████▊     | 1917/4000 [40:23<33:45,  1.03it/s]

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overall:  48%|████▊     | 1919/4000 [40:26<38:43,  1.12s/it]

train:  55%|█████▍    | 1919/3515 [40:24<29:41,  1.12s/it]
overall:  48%|████▊     | 1920/4000 [40:27<40:11,  1.16s/it]

train:  55%|█████▍    | 1920/3515 [40:25<30:49,  1.16s/it]
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train:  55%|█████▍    | 1921/3515 [40:27<33:05,  1.25s/it]
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train:  55%|█████▍    | 1922/3515 [40:28<32:08,  1.21s/it]
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train:  55%|█████▍    | 1923/3515 [40:29<33:11,  1.25s/it]
overall:  48%|████▊     | 1924/4000 [40:32<43:36,  1.26s/it]

train:  55%|█████▍    | 1924/3515 [40:31<33:25,  1.26s/it]
overall:  48%|████▊     | 1925/4000 [40:34<44:37,  1.29s/it]

train:  55%|█████▍    | 1925/3515 [40:32<34:11,  1.29s/it]
overall:  48%|████▊     | 1926/4000 [40:35<44:57,  1.30s/it]

train:  55%|█████▍    | 1926/3515 [40:33<34:26,  1.30s/it]
overall:  48%|████▊     | 1927/4000 [40:35<34:32,  1.00it/s]

train:  55%|█████▍    | 1927/3515 [40:34<26:27,  1.00it/s]
overall:  48%|████▊     | 1928/4000 [40:37<37:50,  1.10s/it]

train:  55%|█████▍    | 1928/3515 [40:35<28:59,  1.10s/it]
overall:  48%|████▊     | 1929/4000 [40:38<38:59,  1.13s/it]

train:  55%|█████▍    | 1929/3515 [40:36<29:51,  1.13s/it]
overall:  48%|████▊     | 1930/4000 [40:39<41:35,  1.21s/it]

train:  55%|█████▍    | 1930/3515 [40:38<31:50,  1.21s/it]
overall:  48%|████▊     | 1931/4000 [40:41<41:51,  1.21s/it]

train:  55%|█████▍    | 1931/3515 [40:39<32:02,  1.21s/it]
overall:  48%|████▊     | 1932/4000 [40:42<42:38,  1.24s/it]

train:  55%|█████▍    | 1932/3515 [40:40<32:38,  1.24s/it]
overall:  48%|████▊     | 1933/4000 [40:43<41:56,  1.22s/it]

train:  55%|█████▍    | 1933/3515 [40:41<32:06,  1.22s/it]
overall:  48%|████▊     | 1934/4000 [40:44<42:21,  1.23s/it]

train:  55%|█████▌    | 1934/3515 [40:42<32:24,  1.23s/it]
overall:  48%|████▊     | 1935/4000 [40:46<43:40,  1.27s/it]

train:  55%|█████▌    | 1935/3515 [40:44<33:25,  1.27s/it]
overall:  48%|████▊     | 1936/4000 [40:47<44:38,  1.30s/it]

train:  55%|█████▌    | 1936/3515 [40:45<34:08,  1.30s/it]
overall:  48%|████▊     | 1937/4000 [40:48<44:12,  1.29s/it]

train:  55%|█████▌    | 1937/3515 [40:46<33:48,  1.29s/it]
overall:  48%|████▊     | 1938/4000 [40:50<44:09,  1.29s/it]

train:  55%|█████▌    | 1938/3515 [40:48<33:46,  1.29s/it]
overall:  48%|████▊     | 1939/4000 [40:51<43:50,  1.28s/it]

train:  55%|█████▌    | 1939/3515 [40:49<33:31,  1.28s/it]
overall:  48%|████▊     | 1940/4000 [40:52<42:15,  1.23s/it]

train:  55%|█████▌    | 1940/3515 [40:50<32:18,  1.23s/it]
overall:  49%|████▊     | 1941/4000 [40:53<42:23,  1.24s/it]

train:  55%|█████▌    | 1941/3515 [40:51<32:24,  1.24s/it]
overall:  49%|████▊     | 1942/4000 [40:55<43:03,  1.26s/it]

train:  55%|█████▌    | 1942/3515 [40:53<32:54,  1.26s/it]
overall:  49%|████▊     | 1943/4000 [40:56<43:05,  1.26s/it]

train:  55%|█████▌    | 1943/3515 [40:54<32:56,  1.26s/it]
overall:  49%|████▊     | 1944/4000 [40:57<42:30,  1.24s/it]

train:  55%|█████▌    | 1944/3515 [40:55<32:29,  1.24s/it]
overall:  49%|████▊     | 1945/4000 [40:58<44:39,  1.30s/it]

train:  55%|█████▌    | 1945/3515 [40:57<34:06,  1.30s/it]
overall:  49%|████▊     | 1946/4000 [41:00<44:45,  1.31s/it]

train:  55%|█████▌    | 1946/3515 [40:58<34:11,  1.31s/it]
overall:  49%|████▊     | 1947/4000 [41:01<44:49,  1.31s/it]

train:  55%|█████▌    | 1947/3515 [40:59<34:14,  1.31s/it]
overall:  49%|████▊     | 1948/4000 [41:02<41:00,  1.20s/it]

train:  55%|█████▌    | 1948/3515 [41:00<31:19,  1.20s/it]
overall:  49%|████▊     | 1949/4000 [41:03<40:56,  1.20s/it]

train:  55%|█████▌    | 1949/3515 [41:01<31:15,  1.20s/it]
overall:  49%|████▉     | 1950/4000 [41:05<42:23,  1.24s/it]

train:  55%|█████▌    | 1950/3515 [41:03<32:21,  1.24s/it]
overall:  49%|████▉     | 1951/4000 [41:06<42:49,  1.25s/it]

train:  56%|█████▌    | 1951/3515 [41:04<32:41,  1.25s/it]
overall:  49%|████▉     | 1952/4000 [41:07<40:14,  1.18s/it]

train:  56%|█████▌    | 1952/3515 [41:05<30:42,  1.18s/it]
overall:  49%|████▉     | 1953/4000 [41:08<42:00,  1.23s/it]

train:  56%|█████▌    | 1953/3515 [41:06<32:03,  1.23s/it]
overall:  49%|████▉     | 1954/4000 [41:10<43:55,  1.29s/it]

train:  56%|█████▌    | 1954/3515 [41:08<33:30,  1.29s/it]
overall:  49%|████▉     | 1955/4000 [41:11<42:43,  1.25s/it]

train:  56%|█████▌    | 1955/3515 [41:09<32:35,  1.25s/it]
overall:  49%|████▉     | 1956/4000 [41:12<42:05,  1.24s/it]

train:  56%|█████▌    | 1956/3515 [41:10<32:06,  1.24s/it]
overall:  49%|████▉     | 1957/4000 [41:13<42:07,  1.24s/it]

train:  56%|█████▌    | 1957/3515 [41:11<32:07,  1.24s/it]
overall:  49%|████▉     | 1958/4000 [41:14<42:19,  1.24s/it]

train:  56%|█████▌    | 1958/3515 [41:13<32:16,  1.24s/it]
overall:  49%|████▉     | 1959/4000 [41:16<42:47,  1.26s/it]

train:  56%|█████▌    | 1959/3515 [41:14<32:37,  1.26s/it]
overall:  49%|████▉     | 1960/4000 [41:17<44:14,  1.30s/it]

train:  56%|█████▌    | 1960/3515 [41:15<33:43,  1.30s/it]
overall:  49%|████▉     | 1961/4000 [41:19<44:45,  1.32s/it]

train:  56%|█████▌    | 1961/3515 [41:17<34:06,  1.32s/it]
overall:  49%|████▉     | 1962/4000 [41:20<44:33,  1.31s/it]

train:  56%|█████▌    | 1962/3515 [41:18<33:57,  1.31s/it]
overall:  49%|████▉     | 1963/4000 [41:21<43:21,  1.28s/it]

train:  56%|█████▌    | 1963/3515 [41:19<33:02,  1.28s/it]
overall:  49%|████▉     | 1964/4000 [41:22<43:30,  1.28s/it]

train:  56%|█████▌    | 1964/3515 [41:20<33:08,  1.28s/it]
overall:  49%|████▉     | 1965/4000 [41:23<40:26,  1.19s/it]

train:  56%|█████▌    | 1965/3515 [41:21<30:47,  1.19s/it]
overall:  49%|████▉     | 1966/4000 [41:25<41:38,  1.23s/it]

train:  56%|█████▌    | 1966/3515 [41:23<31:42,  1.23s/it]
overall:  49%|████▉     | 1967/4000 [41:26<41:12,  1.22s/it]

train:  56%|█████▌    | 1967/3515 [41:24<31:23,  1.22s/it]
overall:  49%|████▉     | 1968/4000 [41:26<34:08,  1.01s/it]

train:  56%|█████▌    | 1968/3515 [41:24<25:59,  1.01s/it]
overall:  49%|████▉     | 1969/4000 [41:28<37:44,  1.11s/it]

train:  56%|█████▌    | 1969/3515 [41:26<28:43,  1.11s/it]
overall:  49%|████▉     | 1970/4000 [41:29<39:33,  1.17s/it]

train:  56%|█████▌    | 1970/3515 [41:27<30:06,  1.17s/it]
overall:  49%|████▉     | 1971/4000 [41:30<40:20,  1.19s/it]

train:  56%|█████▌    | 1971/3515 [41:28<30:41,  1.19s/it]
overall:  49%|████▉     | 1972/4000 [41:32<41:22,  1.22s/it]

train:  56%|█████▌    | 1972/3515 [41:30<31:28,  1.22s/it]
overall:  49%|████▉     | 1973/4000 [41:33<42:23,  1.25s/it]

train:  56%|█████▌    | 1973/3515 [41:31<32:14,  1.25s/it]
overall:  49%|████▉     | 1974/4000 [41:34<42:41,  1.26s/it]

train:  56%|█████▌    | 1974/3515 [41:32<32:28,  1.26s/it]
overall:  49%|████▉     | 1975/4000 [41:35<34:25,  1.02s/it]

train:  56%|█████▌    | 1975/3515 [41:33<26:10,  1.02s/it]
overall:  49%|████▉     | 1976/4000 [41:36<37:08,  1.10s/it]

train:  56%|█████▌    | 1976/3515 [41:34<28:14,  1.10s/it]
overall:  49%|████▉     | 1977/4000 [41:37<39:05,  1.16s/it]

train:  56%|█████▌    | 1977/3515 [41:35<29:43,  1.16s/it]
overall:  49%|████▉     | 1978/4000 [41:38<40:46,  1.21s/it]

train:  56%|█████▋    | 1978/3515 [41:37<30:59,  1.21s/it]
overall:  49%|████▉     | 1979/4000 [41:40<41:29,  1.23s/it]

train:  56%|█████▋    | 1979/3515 [41:38<31:31,  1.23s/it]
overall:  50%|████▉     | 1980/4000 [41:41<41:13,  1.22s/it]

train:  56%|█████▋    | 1980/3515 [41:39<31:19,  1.22s/it]
overall:  50%|████▉     | 1981/4000 [41:42<40:09,  1.19s/it]

train:  56%|█████▋    | 1981/3515 [41:40<30:30,  1.19s/it]
overall:  50%|████▉     | 1982/4000 [41:43<40:56,  1.22s/it]

train:  56%|█████▋    | 1982/3515 [41:42<31:06,  1.22s/it]
overall:  50%|████▉     | 1983/4000 [41:45<42:29,  1.26s/it]

train:  56%|█████▋    | 1983/3515 [41:43<32:16,  1.26s/it]
overall:  50%|████▉     | 1984/4000 [41:46<43:59,  1.31s/it]

train:  56%|█████▋    | 1984/3515 [41:44<33:24,  1.31s/it]
overall:  50%|████▉     | 1985/4000 [41:47<43:46,  1.30s/it]

train:  56%|█████▋    | 1985/3515 [41:46<33:14,  1.30s/it]
overall:  50%|████▉     | 1986/4000 [41:49<43:44,  1.30s/it]

train:  57%|█████▋    | 1986/3515 [41:47<33:12,  1.30s/it]
overall:  50%|████▉     | 1987/4000 [41:50<43:31,  1.30s/it]

train:  57%|█████▋    | 1987/3515 [41:48<33:02,  1.30s/it]
overall:  50%|████▉     | 1988/4000 [41:51<42:31,  1.27s/it]

train:  57%|█████▋    | 1988/3515 [41:49<32:16,  1.27s/it]
overall:  50%|████▉     | 1989/4000 [41:52<42:24,  1.27s/it]

train:  57%|█████▋    | 1989/3515 [41:51<32:10,  1.27s/it]
overall:  50%|████▉     | 1990/4000 [41:54<42:09,  1.26s/it]

train:  57%|█████▋    | 1990/3515 [41:52<31:59,  1.26s/it]
overall:  50%|████▉     | 1991/4000 [41:55<42:04,  1.26s/it]

train:  57%|█████▋    | 1991/3515 [41:53<31:55,  1.26s/it]
overall:  50%|████▉     | 1992/4000 [41:56<42:38,  1.27s/it]

train:  57%|█████▋    | 1992/3515 [41:54<32:20,  1.27s/it]
overall:  50%|████▉     | 1993/4000 [41:58<44:50,  1.34s/it]

train:  57%|█████▋    | 1993/3515 [41:56<34:00,  1.34s/it]
overall:  50%|████▉     | 1994/4000 [41:59<42:10,  1.26s/it]

train:  57%|█████▋    | 1994/3515 [41:57<31:58,  1.26s/it]
overall:  50%|████▉     | 1995/4000 [42:00<41:30,  1.24s/it]

train:  57%|█████▋    | 1995/3515 [41:58<31:28,  1.24s/it]
overall:  50%|████▉     | 1996/4000 [42:01<41:33,  1.24s/it]

train:  57%|█████▋    | 1996/3515 [41:59<31:30,  1.24s/it]
overall:  50%|████▉     | 1997/4000 [42:03<42:34,  1.28s/it]

train:  57%|█████▋    | 1997/3515 [42:01<32:15,  1.28s/it]
overall:  50%|████▉     | 1998/4000 [42:04<42:49,  1.28s/it]

train:  57%|█████▋    | 1998/3515 [42:02<32:27,  1.28s/it]
overall:  50%|████▉     | 1999/4000 [42:05<42:24,  1.27s/it]

train:  57%|█████▋    | 1999/3515 [42:03<32:07,  1.27s/it]dev loss: 6.321500301361084
dev per: 0.7417273673257024
[Runner] - Save the checkpoint to:
1. result/downstream/grid/dev-best.ckpt
train loss: 6.510401248931885
train per: 0.7782850241545893
train loss: 5.633842468261719
train per: 0.7421272847390442
train loss: 5.531514644622803
train per: 0.7822916666666667
train loss: 4.682122230529785
train per: 0.695669159504409
train loss: 4.85817289352417
train per: 0.7548594028842908
train loss: 4.636421203613281
train per: 0.7261119623387293
train loss: 4.371976375579834
train per: 0.7152041617589321
train loss: 5.1809515953063965
train per: 0.8001963802345947
train loss: 5.818780422210693
train per: 0.7835390264778155
train loss: 5.546392440795898
train per: 0.7763554012638659
train loss: 5.180281162261963
train per: 0.7391639474833638
train loss: 4.8770341873168945
train per: 0.7417780860580258
train loss: 5.555272579193115
train per: 0.7344369922539202
train loss: 6.441160678863525
train per: 0.7937304828248859
train loss: 5.776013374328613
train per: 0.7491248267274393
train loss: 5.484251976013184
train per: 0.7771168108776267
train loss: 6.884018898010254
train per: 0.7691384220224469
train loss: 11.477361679077148
train per: 0.8852143526373097
train loss: 8.8572359085083
train per: 0.8331796992283178
train loss: 5.6338677406311035
train per: 0.7578614835535835
train loss: 4.515310764312744
train per: 0.7040850533978258
train loss: 4.815310001373291
train per: 0.7804163417688618
train loss: 4.1221394538879395
train per: 0.706321772416193
train loss: 3.7071712017059326
train per: 0.700288995166364
train loss: 6.7604546546936035
train per: 0.7808101967957986
train loss: 6.751893520355225
train per: 0.7726141476021825
train loss: 8.186959266662598
train per: 0.79170692431562
train loss: 6.421639442443848
train per: 0.7441182384878343
train loss: 6.495104789733887
train per: 0.7495030280615783
train loss: 6.346510887145996
train per: 0.7358399184297731
train loss: 5.597454071044922
train per: 0.7145822403686047
train loss: 5.84044885635376
train per: 0.7413560844185002
train loss: 7.136773109436035
train per: 0.7964308945388727
train loss: 6.0322065353393555
train per: 0.7428103548835748
train loss: 4.877264976501465
train per: 0.7308301524239827
train loss: 4.813778877258301
train per: 0.7418602939372377
train loss: 6.486254692077637
train per: 0.7626576322228497
train loss: 7.224073886871338
train per: 0.7789653269105324
train loss: 7.1253509521484375
train per: 0.7892293183082243
train loss: 6.191193580627441
train per: 0.7696482362020464
train loss: 4.673836708068848
train per: 0.7191319161543589
train loss: 4.691823959350586
train per: 0.7550475134222268
train loss: 4.5606818199157715
train per: 0.7328095285757181
train loss: 6.06382417678833
train per: 0.7898021124195702
train loss: 5.242193222045898
train per: 0.7988375283757827
train loss: 9.678924560546875
train per: 0.854032362459547
train loss: 15.080388069152832
train per: 0.9151361024771593
train loss: 7.2732062339782715
train per: 0.7601543268320394
train loss: 6.987454414367676
train per: 0.8169161938563824
train loss: 5.957434177398682
train per: 0.7301774343402297
[LibriPhone] - Taking the first phoneme sequences for a deterministic behavior.



word -> phonemes:   0%|          | 0/2703 [00:00<?, ?it/s]


word -> phonemes:  22%|██▏       | 595/2703 [00:00<00:00, 5946.89it/s]


word -> phonemes:  45%|████▍     | 1206/2703 [00:00<00:00, 6041.79it/s]


word -> phonemes:  67%|██████▋   | 1824/2703 [00:00<00:00, 6102.02it/s]


word -> phonemes:  90%|█████████ | 2435/2703 [00:00<00:00, 6041.31it/s]
word -> phonemes: 100%|██████████| 2703/2703 [00:00<00:00, 6014.87it/s]



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train:  85%|████████▌ | 2994/3515 [1:03:55<09:18,  1.07s/it]
overall:  75%|███████▍  | 2995/4000 [1:03:58<18:55,  1.13s/it]

train:  85%|████████▌ | 2995/3515 [1:03:56<09:47,  1.13s/it]
overall:  75%|███████▍  | 2996/4000 [1:03:59<20:02,  1.20s/it]

train:  85%|████████▌ | 2996/3515 [1:03:57<10:21,  1.20s/it]
overall:  75%|███████▍  | 2997/4000 [1:04:00<20:17,  1.21s/it]

train:  85%|████████▌ | 2997/3515 [1:03:58<10:28,  1.21s/it]
overall:  75%|███████▍  | 2998/4000 [1:04:02<20:46,  1.24s/it]

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overall:  75%|███████▍  | 2999/4000 [1:04:03<19:30,  1.17s/it]

train:  85%|████████▌ | 2999/3515 [1:04:01<10:03,  1.17s/it]dev loss: 4.946700572967529
dev per: 0.7219667013527575
[Runner] - Save the checkpoint to:
1. result/downstream/grid/dev-best.ckpt
train loss: 5.568317890167236
train per: 0.7544210136884942
train loss: 6.802553653717041
train per: 0.7793278997479012
train loss: 4.7165021896362305
train per: 0.6892534073091304
train loss: 5.014016628265381
train per: 0.7269814842843448
train loss: 5.174878120422363
train per: 0.7552348893619127
train loss: 6.503209114074707
train per: 0.7769634082736282
train loss: 7.708383083343506
train per: 0.779737501843386
train loss: 5.938698768615723
train per: 0.742479773183044
train loss: 5.223974704742432
train per: 0.7249970255800119
train loss: 4.405364036560059
train per: 0.7126391873412776
train loss: 5.661632537841797
train per: 0.7889945138100644
train loss: 6.185935020446777
train per: 0.7659516861706929
train loss: 6.521889686584473
train per: 0.7863907839168354
train loss: 6.358872890472412
train per: 0.7742476542369585
train loss: 5.984816074371338
train per: 0.7124364308830329
train loss: 4.87791109085083
train per: 0.7373781891190441
train loss: 5.867334842681885
train per: 0.7644228371977063
train loss: 8.379741668701172
train per: 0.8467254729557251
train loss: 7.3708930015563965
train per: 0.8038506444449531
train loss: 5.360440254211426
train per: 0.7153155035520268
train loss: 6.232646465301514
train per: 0.7864892375679634
train loss: 5.1810383796691895
train per: 0.7093473005640613
train loss: 5.476273536682129
train per: 0.7333349187852267
train loss: 5.686248779296875
train per: 0.763733346815987
train loss: 4.731559753417969
train per: 0.7193933770225599
train loss: 7.930749416351318
train per: 0.8604494550170853
train loss: 7.1206464767456055
train per: 0.7648724496245544
train loss: 8.610038757324219
train per: 0.7660789792537277
train loss: 6.419788360595703
train per: 0.7248284553984036
train loss: 8.522356033325195
train per: 0.8111223060477232
train loss: 6.475420951843262
train per: 0.7291597633136094
train loss: 6.029609680175781
train per: 0.7314829293719052
train loss: 5.252496719360352
train per: 0.723473065718659
train loss: 4.979009628295898
train per: 0.7235873065921938
train loss: 5.1342854499816895
train per: 0.7052251770680208
train loss: 6.648953914642334
train per: 0.7641620665630526
train loss: 5.579315185546875
train per: 0.732702167622722
train loss: 6.801863193511963
train per: 0.7614410651350999
train loss: 5.7054948806762695
train per: 0.713515179534597
train loss: 5.213549613952637
train per: 0.7259915734418132
train loss: 6.642926216125488
train per: 0.7401473120336713
train loss: 6.39078426361084
train per: 0.7673971249577582
train loss: 5.178160667419434
train per: 0.7318251078226286
train loss: 4.764087200164795
train per: 0.7178202165186717
train loss: 5.7994794845581055
train per: 0.8502341331781264
train loss: 5.029609203338623
train per: 0.7023260427515746
train loss: 5.843174934387207
train per: 0.7803573691547332
train loss: 5.489323616027832
train per: 0.7441927226621373
train loss: 6.233389854431152
train per: 0.7701094208700293
train loss: 8.856123924255371
train per: 0.8203612634764517
[LibriPhone] - Taking the first phoneme sequences for a deterministic behavior.



word -> phonemes:   0%|          | 0/2703 [00:00<?, ?it/s]


word -> phonemes:  22%|██▏       | 585/2703 [00:00<00:00, 5843.65it/s]


word -> phonemes:  44%|████▍     | 1192/2703 [00:00<00:00, 5974.37it/s]


word -> phonemes:  67%|██████▋   | 1812/2703 [00:00<00:00, 6075.12it/s]


word -> phonemes:  90%|████████▉ | 2420/2703 [00:00<00:00, 6027.43it/s]
word -> phonemes: 100%|██████████| 2703/2703 [00:00<00:00, 5969.92it/s]



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overall:  83%|████████▎ | 3329/4000 [1:12:58<13:01,  1.16s/it]

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overall:  87%|████████▋ | 3482/4000 [1:15:58<09:54,  1.15s/it]

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overall:  87%|████████▋ | 3483/4000 [1:15:59<10:10,  1.18s/it]

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overall:  87%|████████▋ | 3486/4000 [1:16:03<10:29,  1.23s/it]

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overall:  87%|████████▋ | 3487/4000 [1:16:04<09:06,  1.07s/it]

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overall:  87%|████████▋ | 3488/4000 [1:16:05<09:26,  1.11s/it]

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train:  99%|█████████▉| 3497/3515 [1:16:14<00:23,  1.30s/it]
overall:  87%|████████▋ | 3498/4000 [1:16:17<10:59,  1.31s/it]

train: 100%|█████████▉| 3498/3515 [1:16:15<00:22,  1.31s/it]
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overall:  88%|████████▊ | 3511/4000 [1:16:32<07:47,  1.05it/s]

train: 100%|█████████▉| 3511/3515 [1:16:30<00:03,  1.05it/s]
overall:  88%|████████▊ | 3512/4000 [1:16:33<08:34,  1.06s/it]

train: 100%|█████████▉| 3512/3515 [1:16:31<00:03,  1.06s/it]
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overall:  88%|████████▊ | 3515/4000 [1:16:37<09:44,  1.20s/it]

train: 100%|██████████| 3515/3515 [1:16:35<00:00,  1.20s/it]403-128339-0009 Failed!!!
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/home/victor/glockenspiel/s3prl/data/LibriSpeech/train-clean-100/403/128339/403-128339-0009.flac WTF Failed!!!
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train: 100%|██████████| 3515/3515 [1:16:35<00:00,  1.31s/it]
dev loss: 6.780801773071289
dev per: 0.7064047866805411
[Runner] - Save the checkpoint to:
1. result/downstream/grid/dev-best.ckpt
train loss: 6.088210105895996
train per: 0.7314035994549128
train loss: 6.344150543212891
train per: 0.7604708972374654
train loss: 4.2174177169799805
train per: 0.6907150782596378
train loss: 4.436736583709717
train per: 0.7234856958218377
train loss: 5.71452522277832
train per: 0.7798385025822467
train loss: 7.885630130767822
train per: 0.8532793138828234
train loss: 9.243884086608887
train per: 0.7811228202571221
train loss: 8.110095977783203
train per: 0.7486009262832883
train loss: 6.718605041503906
train per: 0.7784779570575531
train loss: 6.082343101501465
train per: 0.7273272696728983
train loss: 8.658651351928711
train per: 0.8052217810848473
train loss: 7.59554386138916
train per: 0.7414731065802546
train loss: 6.940751075744629
train per: 0.7624910557872141
train loss: 6.336518287658691
train per: 0.7286959872826244
train loss: 6.076478481292725
train per: 0.7498861593768724
train loss: 5.540098190307617
train per: 0.7120437312643273
train loss: 5.59691858291626
train per: 0.7305888246277152
train loss: 6.200398921966553
train per: 0.7411900777635594
train loss: 5.466437339782715
train per: 0.6995268924302789
train loss: 5.3231940269470215
train per: 0.727283304246655
train loss: 5.368950843811035
train per: 0.7338821030270845
train loss: 7.539132595062256
train per: 0.8189156786998801
train loss: 6.789988040924072
train per: 0.7172557688963959
train loss: 5.17861270904541
train per: 0.7065389070232705
train loss: 4.967621803283691
train per: 0.7177667321696913
[LibriPhone] - Taking the first phoneme sequences for a deterministic behavior.


word -> phonemes:   0%|          | 0/3515 [00:00<?, ?it/s]

word -> phonemes:  13%|█▎        | 448/3515 [00:00<00:00, 4471.75it/s]

word -> phonemes:  27%|██▋       | 938/3515 [00:00<00:00, 4723.28it/s]

word -> phonemes:  41%|████      | 1435/3515 [00:00<00:00, 4832.65it/s]

word -> phonemes:  55%|█████▍    | 1919/3515 [00:00<00:00, 4779.49it/s]

word -> phonemes:  68%|██████▊   | 2398/3515 [00:00<00:00, 4762.72it/s]

word -> phonemes:  82%|████████▏ | 2875/3515 [00:00<00:00, 4755.21it/s]

word -> phonemes:  95%|█████████▌| 3351/3515 [00:00<00:00, 4665.07it/s]
word -> phonemes: 100%|██████████| 3515/3515 [00:00<00:00, 4711.76it/s]


train:   0%|          | 0/3515 [00:00<?, ?it/s]
overall:  88%|████████▊ | 3516/4000 [1:16:41<17:28,  2.17s/it]

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overall:  88%|████████▊ | 3521/4000 [1:16:48<12:02,  1.51s/it]

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overall:  88%|████████▊ | 3522/4000 [1:16:49<11:18,  1.42s/it]

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overall:  88%|████████▊ | 3523/4000 [1:16:50<08:57,  1.13s/it]

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overall:  88%|████████▊ | 3526/4000 [1:16:54<09:45,  1.23s/it]

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overall:  88%|████████▊ | 3527/4000 [1:16:55<09:36,  1.22s/it]

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overall:  88%|████████▊ | 3528/4000 [1:16:56<09:48,  1.25s/it]

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overall:  88%|████████▊ | 3529/4000 [1:16:57<09:39,  1.23s/it]

train:   0%|          | 14/3515 [00:18<1:11:31,  1.23s/it]
overall:  88%|████████▊ | 3530/4000 [1:16:59<09:45,  1.25s/it]

train:   0%|          | 15/3515 [00:19<1:12:27,  1.24s/it]
overall:  88%|████████▊ | 3531/4000 [1:17:00<09:50,  1.26s/it]

train:   0%|          | 16/3515 [00:21<1:13:17,  1.26s/it]
overall:  88%|████████▊ | 3532/4000 [1:17:01<10:01,  1.28s/it]

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overall:  88%|████████▊ | 3533/4000 [1:17:02<10:01,  1.29s/it]

train:   1%|          | 18/3515 [00:23<1:14:57,  1.29s/it]
overall:  88%|████████▊ | 3534/4000 [1:17:04<10:01,  1.29s/it]

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overall:  88%|████████▊ | 3535/4000 [1:17:05<10:10,  1.31s/it]

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overall:  88%|████████▊ | 3536/4000 [1:17:06<10:13,  1.32s/it]

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overall:  88%|████████▊ | 3537/4000 [1:17:08<10:16,  1.33s/it]

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overall:  88%|████████▊ | 3538/4000 [1:17:09<10:06,  1.31s/it]

train:   1%|          | 23/3515 [00:30<1:16:27,  1.31s/it]
overall:  88%|████████▊ | 3539/4000 [1:17:10<10:11,  1.33s/it]

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overall:  88%|████████▊ | 3540/4000 [1:17:12<10:14,  1.34s/it]

train:   1%|          | 25/3515 [00:33<1:17:45,  1.34s/it]
overall:  89%|████████▊ | 3541/4000 [1:17:13<10:02,  1.31s/it]

train:   1%|          | 26/3515 [00:34<1:16:20,  1.31s/it]
overall:  89%|████████▊ | 3542/4000 [1:17:14<09:42,  1.27s/it]

train:   1%|          | 27/3515 [00:35<1:13:55,  1.27s/it]
overall:  89%|████████▊ | 3543/4000 [1:17:16<09:42,  1.27s/it]

train:   1%|          | 28/3515 [00:36<1:14:00,  1.27s/it]
overall:  89%|████████▊ | 3544/4000 [1:17:17<09:27,  1.24s/it]

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overall:  89%|████████▊ | 3545/4000 [1:17:18<09:45,  1.29s/it]

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overall:  89%|████████▊ | 3546/4000 [1:17:19<09:30,  1.26s/it]

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overall:  89%|████████▊ | 3547/4000 [1:17:20<07:37,  1.01s/it]

train:   1%|          | 32/3515 [00:40<58:36,  1.01s/it]  
overall:  89%|████████▊ | 3548/4000 [1:17:21<08:23,  1.11s/it]

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overall:  89%|████████▊ | 3549/4000 [1:17:22<08:53,  1.18s/it]

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overall:  89%|████████▉ | 3550/4000 [1:17:24<09:01,  1.20s/it]

train:   1%|          | 35/3515 [00:44<1:09:48,  1.20s/it]
overall:  89%|████████▉ | 3551/4000 [1:17:25<09:00,  1.20s/it]

train:   1%|          | 36/3515 [00:46<1:09:48,  1.20s/it]
overall:  89%|████████▉ | 3552/4000 [1:17:26<09:12,  1.23s/it]

train:   1%|          | 37/3515 [00:47<1:11:30,  1.23s/it]
overall:  89%|████████▉ | 3553/4000 [1:17:27<09:11,  1.23s/it]

train:   1%|          | 38/3515 [00:48<1:11:26,  1.23s/it]
overall:  89%|████████▉ | 3554/4000 [1:17:29<09:33,  1.29s/it]

train:   1%|          | 39/3515 [00:50<1:14:32,  1.29s/it]
overall:  89%|████████▉ | 3555/4000 [1:17:30<09:33,  1.29s/it]

train:   1%|          | 40/3515 [00:51<1:14:40,  1.29s/it]
overall:  89%|████████▉ | 3556/4000 [1:17:31<09:37,  1.30s/it]

train:   1%|          | 41/3515 [00:52<1:15:18,  1.30s/it]
overall:  89%|████████▉ | 3557/4000 [1:17:33<09:36,  1.30s/it]

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overall:  89%|████████▉ | 3558/4000 [1:17:34<09:03,  1.23s/it]

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overall:  89%|████████▉ | 3559/4000 [1:17:35<08:59,  1.22s/it]

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overall:  89%|████████▉ | 3560/4000 [1:17:36<09:17,  1.27s/it]

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overall:  89%|████████▉ | 3561/4000 [1:17:38<09:17,  1.27s/it]

train:   1%|▏         | 46/3515 [00:58<1:13:21,  1.27s/it]
overall:  89%|████████▉ | 3562/4000 [1:17:38<07:54,  1.08s/it]

train:   1%|▏         | 47/3515 [00:59<1:02:34,  1.08s/it]
overall:  89%|████████▉ | 3563/4000 [1:17:39<07:29,  1.03s/it]

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overall:  89%|████████▉ | 3564/4000 [1:17:41<08:14,  1.13s/it]

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overall:  89%|████████▉ | 3565/4000 [1:17:42<08:35,  1.19s/it]

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overall:  89%|████████▉ | 3566/4000 [1:17:43<08:40,  1.20s/it]

train:   1%|▏         | 51/3515 [01:04<1:09:13,  1.20s/it]
overall:  89%|████████▉ | 3567/4000 [1:17:44<08:58,  1.24s/it]

train:   1%|▏         | 52/3515 [01:05<1:11:44,  1.24s/it]
overall:  89%|████████▉ | 3568/4000 [1:17:46<08:53,  1.24s/it]

train:   2%|▏         | 53/3515 [01:06<1:11:15,  1.24s/it]
overall:  89%|████████▉ | 3569/4000 [1:17:47<09:04,  1.26s/it]

train:   2%|▏         | 54/3515 [01:08<1:12:53,  1.26s/it]
overall:  89%|████████▉ | 3570/4000 [1:17:48<08:50,  1.23s/it]

train:   2%|▏         | 55/3515 [01:09<1:11:12,  1.23s/it]
overall:  89%|████████▉ | 3571/4000 [1:17:50<09:04,  1.27s/it]

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train:   2%|▏         | 87/3515 [01:50<1:14:52,  1.31s/it]
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train:   3%|▎         | 88/3515 [01:52<1:14:55,  1.31s/it]
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train:   3%|▎         | 89/3515 [01:53<1:13:10,  1.28s/it]
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train:   3%|▎         | 90/3515 [01:54<1:12:47,  1.28s/it]
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train:   3%|▎         | 91/3515 [01:55<1:11:37,  1.26s/it]
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train:   3%|▎         | 92/3515 [01:57<1:12:07,  1.26s/it]
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train:   3%|▎         | 93/3515 [01:58<1:13:34,  1.29s/it]
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train:   3%|▎         | 94/3515 [01:58<58:17,  1.02s/it]  
overall:  90%|█████████ | 3610/4000 [1:18:39<06:35,  1.02s/it]

train:   3%|▎         | 95/3515 [01:59<57:52,  1.02s/it]
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train:   3%|▎         | 97/3515 [02:02<1:02:44,  1.10s/it]
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overall:  90%|█████████ | 3615/4000 [1:18:45<07:36,  1.19s/it]

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overall:  90%|█████████ | 3616/4000 [1:18:46<07:52,  1.23s/it]

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train:   3%|▎         | 103/3515 [02:09<1:13:20,  1.29s/it]
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train:   3%|▎         | 104/3515 [02:10<1:02:17,  1.10s/it]
overall:  90%|█████████ | 3620/4000 [1:18:50<05:57,  1.06it/s]

train:   3%|▎         | 105/3515 [02:11<53:31,  1.06it/s]  
overall:  91%|█████████ | 3621/4000 [1:18:50<04:39,  1.36it/s]

train:   3%|▎         | 106/3515 [02:11<41:55,  1.36it/s]
overall:  91%|█████████ | 3622/4000 [1:18:51<05:37,  1.12it/s]

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overall:  91%|█████████ | 3623/4000 [1:18:53<06:31,  1.04s/it]

train:   3%|▎         | 108/3515 [02:14<58:58,  1.04s/it]
overall:  91%|█████████ | 3624/4000 [1:18:54<06:49,  1.09s/it]

train:   3%|▎         | 109/3515 [02:15<1:01:47,  1.09s/it]
overall:  91%|█████████ | 3625/4000 [1:18:54<05:35,  1.12it/s]

train:   3%|▎         | 110/3515 [02:15<50:46,  1.12it/s]  
overall:  91%|█████████ | 3626/4000 [1:18:56<06:32,  1.05s/it]

train:   3%|▎         | 111/3515 [02:17<59:35,  1.05s/it]
overall:  91%|█████████ | 3627/4000 [1:18:57<06:49,  1.10s/it]

train:   3%|▎         | 112/3515 [02:18<1:02:19,  1.10s/it]
overall:  91%|█████████ | 3628/4000 [1:18:58<07:21,  1.19s/it]

train:   3%|▎         | 113/3515 [02:19<1:07:18,  1.19s/it]
overall:  91%|█████████ | 3629/4000 [1:19:00<07:34,  1.23s/it]

train:   3%|▎         | 114/3515 [02:21<1:09:29,  1.23s/it]
overall:  91%|█████████ | 3630/4000 [1:19:00<05:50,  1.06it/s]

train:   3%|▎         | 115/3515 [02:21<53:41,  1.06it/s]  
overall:  91%|█████████ | 3631/4000 [1:19:01<06:26,  1.05s/it]

train:   3%|▎         | 116/3515 [02:22<59:20,  1.05s/it]
overall:  91%|█████████ | 3632/4000 [1:19:03<06:49,  1.11s/it]

train:   3%|▎         | 117/3515 [02:23<1:02:59,  1.11s/it]
overall:  91%|█████████ | 3633/4000 [1:19:04<06:32,  1.07s/it]

train:   3%|▎         | 118/3515 [02:24<1:00:36,  1.07s/it]
overall:  91%|█████████ | 3634/4000 [1:19:05<06:51,  1.12s/it]

train:   3%|▎         | 119/3515 [02:26<1:03:37,  1.12s/it]
overall:  91%|█████████ | 3635/4000 [1:19:06<06:38,  1.09s/it]

train:   3%|▎         | 120/3515 [02:27<1:01:50,  1.09s/it]
overall:  91%|█████████ | 3636/4000 [1:19:07<07:03,  1.16s/it]

train:   3%|▎         | 121/3515 [02:28<1:05:48,  1.16s/it]
overall:  91%|█████████ | 3637/4000 [1:19:08<06:50,  1.13s/it]

train:   3%|▎         | 122/3515 [02:29<1:03:55,  1.13s/it]
overall:  91%|█████████ | 3638/4000 [1:19:10<07:09,  1.19s/it]

train:   3%|▎         | 123/3515 [02:30<1:07:00,  1.19s/it]
overall:  91%|█████████ | 3639/4000 [1:19:11<07:25,  1.23s/it]

train:   4%|▎         | 124/3515 [02:32<1:09:46,  1.23s/it]
overall:  91%|█████████ | 3640/4000 [1:19:12<07:16,  1.21s/it]

train:   4%|▎         | 125/3515 [02:33<1:08:32,  1.21s/it]
overall:  91%|█████████ | 3641/4000 [1:19:13<05:54,  1.01it/s]

train:   4%|▎         | 126/3515 [02:33<55:49,  1.01it/s]  
overall:  91%|█████████ | 3642/4000 [1:19:14<06:29,  1.09s/it]

train:   4%|▎         | 127/3515 [02:35<1:01:25,  1.09s/it]
overall:  91%|█████████ | 3643/4000 [1:19:15<06:29,  1.09s/it]

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overall:  91%|█████████ | 3644/4000 [1:19:16<06:51,  1.16s/it]

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overall:  91%|█████████ | 3645/4000 [1:19:17<06:38,  1.12s/it]

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overall:  91%|█████████ | 3646/4000 [1:19:19<07:01,  1.19s/it]

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overall:  91%|█████████ | 3647/4000 [1:19:20<06:59,  1.19s/it]

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overall:  91%|█████████ | 3648/4000 [1:19:21<07:22,  1.26s/it]

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overall:  91%|█████████ | 3649/4000 [1:19:22<07:15,  1.24s/it]

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overall:  91%|█████████▏| 3650/4000 [1:19:24<07:22,  1.27s/it]

train:   4%|▍         | 135/3515 [02:45<1:11:16,  1.27s/it]
overall:  91%|█████████▏| 3651/4000 [1:19:25<07:25,  1.28s/it]

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overall:  91%|█████████▏| 3652/4000 [1:19:26<07:11,  1.24s/it]

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overall:  91%|█████████▏| 3653/4000 [1:19:28<07:20,  1.27s/it]

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overall:  91%|█████████▏| 3654/4000 [1:19:29<07:23,  1.28s/it]

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overall:  91%|█████████▏| 3655/4000 [1:19:30<07:22,  1.28s/it]

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overall:  91%|█████████▏| 3656/4000 [1:19:31<07:13,  1.26s/it]

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overall:  91%|█████████▏| 3657/4000 [1:19:32<06:57,  1.22s/it]

train:   4%|▍         | 142/3515 [02:53<1:08:28,  1.22s/it]
overall:  91%|█████████▏| 3658/4000 [1:19:34<07:11,  1.26s/it]

train:   4%|▍         | 143/3515 [02:55<1:10:57,  1.26s/it]
overall:  91%|█████████▏| 3659/4000 [1:19:35<07:10,  1.26s/it]

train:   4%|▍         | 144/3515 [02:56<1:10:58,  1.26s/it]
overall:  92%|█████████▏| 3660/4000 [1:19:37<07:21,  1.30s/it]

train:   4%|▍         | 145/3515 [02:57<1:12:51,  1.30s/it]
overall:  92%|█████████▏| 3661/4000 [1:19:38<07:24,  1.31s/it]

train:   4%|▍         | 146/3515 [02:59<1:13:33,  1.31s/it]
overall:  92%|█████████▏| 3662/4000 [1:19:38<05:41,  1.01s/it]

train:   4%|▍         | 147/3515 [02:59<56:46,  1.01s/it]  
overall:  92%|█████████▏| 3663/4000 [1:19:39<04:38,  1.21it/s]

train:   4%|▍         | 148/3515 [02:59<46:20,  1.21it/s]
overall:  92%|█████████▏| 3664/4000 [1:19:40<05:24,  1.03it/s]

train:   4%|▍         | 149/3515 [03:01<54:14,  1.03it/s]
overall:  92%|█████████▏| 3665/4000 [1:19:41<05:57,  1.07s/it]

train:   4%|▍         | 150/3515 [03:02<59:49,  1.07s/it]
overall:  92%|█████████▏| 3666/4000 [1:19:43<06:25,  1.16s/it]

train:   4%|▍         | 151/3515 [03:03<1:04:45,  1.16s/it]
overall:  92%|█████████▏| 3667/4000 [1:19:44<06:44,  1.21s/it]

train:   4%|▍         | 152/3515 [03:05<1:08:00,  1.21s/it]
overall:  92%|█████████▏| 3668/4000 [1:19:45<06:44,  1.22s/it]

train:   4%|▍         | 153/3515 [03:06<1:08:11,  1.22s/it]
overall:  92%|█████████▏| 3669/4000 [1:19:46<06:57,  1.26s/it]

train:   4%|▍         | 154/3515 [03:07<1:10:40,  1.26s/it]
overall:  92%|█████████▏| 3670/4000 [1:19:48<07:04,  1.29s/it]

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overall:  92%|█████████▏| 3671/4000 [1:19:49<07:08,  1.30s/it]

train:   4%|▍         | 156/3515 [03:10<1:12:49,  1.30s/it]
overall:  92%|█████████▏| 3672/4000 [1:19:50<07:11,  1.32s/it]

train:   4%|▍         | 157/3515 [03:11<1:13:36,  1.32s/it]
overall:  92%|█████████▏| 3673/4000 [1:19:52<07:07,  1.31s/it]

train:   4%|▍         | 158/3515 [03:13<1:13:06,  1.31s/it]
overall:  92%|█████████▏| 3674/4000 [1:19:53<07:14,  1.33s/it]

train:   5%|▍         | 159/3515 [03:14<1:14:34,  1.33s/it]
overall:  92%|█████████▏| 3675/4000 [1:19:54<06:57,  1.28s/it]

train:   5%|▍         | 160/3515 [03:15<1:11:46,  1.28s/it]
overall:  92%|█████████▏| 3676/4000 [1:19:56<07:07,  1.32s/it]

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overall:  92%|█████████▏| 3677/4000 [1:19:57<06:27,  1.20s/it]

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overall:  92%|█████████▏| 3678/4000 [1:19:58<06:24,  1.19s/it]

train:   5%|▍         | 163/3515 [03:19<1:06:43,  1.19s/it]
overall:  92%|█████████▏| 3679/4000 [1:19:59<05:33,  1.04s/it]

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overall: 100%|█████████▉| 3991/4000 [1:26:13<00:10,  1.16s/it]

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overall: 100%|█████████▉| 3992/4000 [1:26:14<00:09,  1.22s/it]

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overall: 100%|█████████▉| 3993/4000 [1:26:15<00:08,  1.19s/it]

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overall: 100%|█████████▉| 3994/4000 [1:26:17<00:07,  1.21s/it]

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overall: 100%|█████████▉| 3995/4000 [1:26:18<00:06,  1.22s/it]

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overall: 100%|█████████▉| 3996/4000 [1:26:19<00:05,  1.25s/it]

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overall: 100%|█████████▉| 3997/4000 [1:26:21<00:03,  1.27s/it]

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overall: 100%|█████████▉| 3998/4000 [1:26:21<00:02,  1.10s/it]

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overall: 100%|█████████▉| 3999/4000 [1:26:23<00:01,  1.16s/it]

train:  14%|█▍        | 484/3515 [09:43<58:44,  1.16s/it]train loss: 5.937865257263184
train per: 0.774685126430151
train loss: 6.020648002624512
train per: 0.7625233242428592
train loss: 4.7896833419799805
train per: 0.7173529716834354
train loss: 5.174662113189697
train per: 0.7418463371054199
train loss: 5.476626396179199
train per: 0.71703512757777
train loss: 7.068173408508301
train per: 0.753828942812242
train loss: 9.780097961425781
train per: 0.8614715168882168
train loss: 6.581614017486572
train per: 0.7242942460365049
train loss: 6.612222194671631
train per: 0.7165432098765432
train loss: 6.885746955871582
train per: 0.7253098188751191
train loss: 11.242056846618652
train per: 0.823896906265849
train loss: 9.73503303527832
train per: 0.8879571630632451
train loss: 6.924781799316406
train per: 0.700928886858535
train loss: 5.667749881744385
train per: 0.7013686767854136
train loss: 6.009960174560547
train per: 0.7763890943154366
train loss: 5.187079429626465
train per: 0.7061167040412233
train loss: 4.400243282318115
train per: 0.6803869407496977
train loss: 4.803403854370117
train per: 0.7162130510141941
train loss: 5.647146701812744
train per: 0.7726690469052356
train loss: 5.9762749671936035
train per: 0.7142130300948892
train loss: 6.290423393249512
train per: 0.7528331623846096
train loss: 6.790640830993652
train per: 0.7576051449571254
train loss: 6.4233527183532715
train per: 0.7229466465790431
train loss: 5.887401580810547
train per: 0.7056468657532884
train loss: 5.2746758460998535
train per: 0.7106114357262103
[LibriPhone] - Taking the first phoneme sequences for a deterministic behavior.



word -> phonemes:   0%|          | 0/2703 [00:00<?, ?it/s]


word -> phonemes:  22%|██▏       | 586/2703 [00:00<00:00, 5855.29it/s]


word -> phonemes:  44%|████▍     | 1193/2703 [00:00<00:00, 5981.29it/s]


word -> phonemes:  67%|██████▋   | 1812/2703 [00:00<00:00, 6072.41it/s]


word -> phonemes:  90%|████████▉ | 2420/2703 [00:00<00:00, 6003.17it/s]
word -> phonemes: 100%|██████████| 2703/2703 [00:00<00:00, 5958.38it/s]



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overall: 100%|█████████▉| 3999/4000 [1:28:52<00:01,  1.16s/it]

train:  14%|█▍        | 484/3515 [12:12<58:44,  1.16s/it]
                                                              

                                                         

overall: 100%|█████████▉| 3999/4000 [1:28:52<00:01,  1.16s/it]

train:  14%|█▍        | 484/3515 [12:12<58:44,  1.16s/it]
overall: 100%|██████████| 4000/4000 [1:28:52<00:00, 45.54s/it]

train:  14%|█▍        | 485/3515 [12:13<38:19:51, 45.54s/it]/home/victor/glockenspiel/s3prl/data/LibriSpeech/train-clean-100/403/128339/403-128339-0009.flac WTF Failed!!!
403-128339-0009 Failed!!!

train:  14%|█▍        | 485/3515 [12:13<1:16:21,  1.51s/it] 

overall: 100%|██████████| 4000/4000 [1:28:52<00:00,  1.33s/it]
dev loss: 5.288796424865723
dev per: 0.7588657648283038
[Runner] - Save the checkpoint to:
1. result/downstream/grid/states-4000.ckpt