/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')
2023-10-14 21:20:54 | INFO | s3prl.util.download | Requesting URL: https://huggingface.co/s3prl/converted_ckpts/resolve/main/wavlm_base_plus.pt
2023-10-14 21:20:54 | INFO | s3prl.util.download | Using URL's local file: /home/tony/.cache/s3prl/download/72cb34edf8a3724c720467cf40b77ad20b1b714b5f694e9db57f521467f9006b.wavlm_base_plus.pt
2023-10-14 21:20:54 | INFO | s3prl.upstream.wavlm.WavLM | WavLM Config: {'extractor_mode': 'default', 'encoder_layers': 12, 'encoder_embed_dim': 768, 'encoder_ffn_embed_dim': 3072, 'encoder_attention_heads': 12, 'activation_fn': 'gelu', 'layer_norm_first': False, 'conv_feature_layers': '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', 'conv_bias': False, 'feature_grad_mult': 0.1, 'normalize': False, 'dropout': 0.1, 'attention_dropout': 0.1, 'activation_dropout': 0.0, 'encoder_layerdrop': 0.05, 'dropout_input': 0.1, 'dropout_features': 0.1, 'mask_length': 10, 'mask_prob': 0.8, 'mask_selection': 'static', 'mask_other': 0.0, 'no_mask_overlap': False, 'mask_min_space': 1, 'mask_channel_length': 10, 'mask_channel_prob': 0.0, 'mask_channel_selection': 'static', 'mask_channel_other': 0.0, 'no_mask_channel_overlap': False, 'mask_channel_min_space': 1, 'conv_pos': 128, 'conv_pos_groups': 16, 'relative_position_embedding': True, 'num_buckets': 320, 'max_distance': 800, 'gru_rel_pos': True}
/home/tony/anaconda3/envs/suno_env/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
/home/tony/anaconda3/envs/suno_env/lib/python3.10/site-packages/torch/nn/functional.py:5076: UserWarning: Support for mismatched key_padding_mask and attn_mask is deprecated. Use same type for both instead.
  warnings.warn(
[Featurizer] - Take a list of 13 features and weighted sum them.
[Featurizer] - The selected feature hidden_states's downsample rate is 320
[Runner] - Start a new experiment

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]

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word -> phonemes:  94%|█████████▍| 3312/3515 [00:00<00:00, 4621.57it/s]
word -> phonemes: 100%|██████████| 3515/3515 [00:00<00:00, 4659.25it/s]


train:   0%|          | 0/3515 [00:00<?, ?it/s]/home/tony/anaconda3/envs/suno_env/lib/python3.10/site-packages/torch/nn/functional.py:5076: UserWarning: Support for mismatched key_padding_mask and attn_mask is deprecated. Use same type for both instead.
  warnings.warn(

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train:   7%|▋         | 259/3515 [02:14<23:23,  2.32it/s]
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train:   7%|▋         | 260/3515 [02:15<25:19,  2.14it/s]
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train:   8%|▊         | 264/3515 [02:17<24:18,  2.23it/s]
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train:   8%|▊         | 265/3515 [02:17<27:01,  2.00it/s]
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train:   8%|▊         | 266/3515 [02:18<29:19,  1.85it/s]
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train:   8%|▊         | 267/3515 [02:19<29:37,  1.83it/s]
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train:   8%|▊         | 268/3515 [02:19<29:57,  1.81it/s]
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train:   8%|▊         | 269/3515 [02:20<30:54,  1.75it/s]
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train:   8%|▊         | 270/3515 [02:20<31:11,  1.73it/s]
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train:   8%|▊         | 271/3515 [02:21<31:26,  1.72it/s]
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train:   8%|▊         | 272/3515 [02:22<30:29,  1.77it/s]
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train:   8%|▊         | 273/3515 [02:22<31:01,  1.74it/s]
overall:   7%|▋         | 274/4000 [02:25<32:02,  1.94it/s]

train:   8%|▊         | 274/3515 [02:23<27:52,  1.94it/s]
overall:   7%|▋         | 275/4000 [02:25<32:44,  1.90it/s]

train:   8%|▊         | 275/3515 [02:23<28:28,  1.90it/s]
overall:   7%|▋         | 276/4000 [02:26<33:46,  1.84it/s]

train:   8%|▊         | 276/3515 [02:24<29:22,  1.84it/s]
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train:   8%|▊         | 283/3515 [02:27<29:12,  1.84it/s]
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train:   8%|▊         | 284/3515 [02:28<29:02,  1.85it/s]
overall:   7%|▋         | 285/4000 [02:30<31:23,  1.97it/s]

train:   8%|▊         | 285/3515 [02:28<27:17,  1.97it/s]
overall:   7%|▋         | 286/4000 [02:31<31:52,  1.94it/s]

train:   8%|▊         | 286/3515 [02:29<27:42,  1.94it/s]
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train:   8%|▊         | 287/3515 [02:29<27:51,  1.93it/s]
overall:   7%|▋         | 288/4000 [02:32<32:57,  1.88it/s]

train:   8%|▊         | 288/3515 [02:30<28:39,  1.88it/s]
overall:   7%|▋         | 289/4000 [02:33<33:57,  1.82it/s]

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train:   8%|▊         | 290/3515 [02:31<29:07,  1.85it/s]
overall:   7%|▋         | 291/4000 [02:33<30:03,  2.06it/s]

train:   8%|▊         | 291/3515 [02:32<26:07,  2.06it/s]
overall:   7%|▋         | 292/4000 [02:34<31:35,  1.96it/s]

train:   8%|▊         | 292/3515 [02:32<27:27,  1.96it/s]
overall:   7%|▋         | 293/4000 [02:35<32:36,  1.89it/s]

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train:   8%|▊         | 294/3515 [02:33<29:03,  1.85it/s]
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train:   8%|▊         | 295/3515 [02:34<28:05,  1.91it/s]
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overall:  13%|█▎        | 504/4000 [04:22<26:12,  2.22it/s]

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overall:  13%|█▎        | 506/4000 [04:23<27:45,  2.10it/s]

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overall:  13%|█▎        | 507/4000 [04:24<32:05,  1.81it/s]

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overall:  13%|█▎        | 508/4000 [04:25<32:11,  1.81it/s]

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overall:  13%|█▎        | 511/4000 [04:26<29:45,  1.95it/s]

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overall:  13%|█▎        | 512/4000 [04:27<29:18,  1.98it/s]

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overall:  13%|█▎        | 513/4000 [04:27<29:05,  2.00it/s]

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overall:  13%|█▎        | 514/4000 [04:28<28:27,  2.04it/s]

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overall:  13%|█▎        | 515/4000 [04:28<27:42,  2.10it/s]

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overall:  13%|█▎        | 516/4000 [04:28<25:00,  2.32it/s]

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overall:  13%|█▎        | 517/4000 [04:29<25:34,  2.27it/s]

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overall:  13%|█▎        | 518/4000 [04:29<26:22,  2.20it/s]

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overall:  13%|█▎        | 519/4000 [04:30<26:38,  2.18it/s]

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overall:  13%|█▎        | 520/4000 [04:30<29:10,  1.99it/s]

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overall:  13%|█▎        | 521/4000 [04:31<31:01,  1.87it/s]

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overall:  13%|█▎        | 522/4000 [04:32<31:36,  1.83it/s]

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overall:  13%|█▎        | 523/4000 [04:32<30:04,  1.93it/s]

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overall:  13%|█▎        | 524/4000 [04:33<29:59,  1.93it/s]

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overall:  13%|█▎        | 527/4000 [04:34<30:28,  1.90it/s]

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overall:  13%|█▎        | 528/4000 [04:35<31:08,  1.86it/s]

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overall:  13%|█▎        | 529/4000 [04:35<30:59,  1.87it/s]

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overall:  13%|█▎        | 530/4000 [04:36<29:20,  1.97it/s]

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overall:  13%|█▎        | 531/4000 [04:36<33:45,  1.71it/s]

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overall:  13%|█▎        | 532/4000 [04:37<33:07,  1.74it/s]

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overall:  13%|█▎        | 533/4000 [04:38<32:24,  1.78it/s]

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overall:  13%|█▎        | 534/4000 [04:38<32:36,  1.77it/s]

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overall:  13%|█▎        | 535/4000 [04:39<32:01,  1.80it/s]

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overall:  13%|█▎        | 536/4000 [04:39<30:29,  1.89it/s]

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overall:  13%|█▎        | 537/4000 [04:40<30:44,  1.88it/s]

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overall:  13%|█▎        | 538/4000 [04:40<28:13,  2.04it/s]

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overall:  13%|█▎        | 539/4000 [04:41<28:43,  2.01it/s]

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overall:  14%|█▎        | 540/4000 [04:41<28:18,  2.04it/s]

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overall:  14%|█▎        | 541/4000 [04:42<29:34,  1.95it/s]

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overall:  14%|█▎        | 542/4000 [04:42<28:24,  2.03it/s]

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overall:  14%|█▎        | 543/4000 [04:43<28:57,  1.99it/s]

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overall:  14%|█▎        | 544/4000 [04:43<29:46,  1.93it/s]

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overall:  14%|█▎        | 545/4000 [04:44<29:32,  1.95it/s]

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overall:  14%|█▎        | 546/4000 [04:44<32:44,  1.76it/s]

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overall:  14%|█▎        | 549/4000 [04:46<32:02,  1.80it/s]

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overall:  14%|█▍        | 560/4000 [04:51<28:37,  2.00it/s]

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overall:  14%|█▍        | 561/4000 [04:52<27:59,  2.05it/s]

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overall:  14%|█▍        | 562/4000 [04:52<28:30,  2.01it/s]

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overall:  14%|█▍        | 563/4000 [04:53<29:56,  1.91it/s]

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overall:  14%|█▍        | 565/4000 [04:54<29:26,  1.94it/s]

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overall:  16%|█▌        | 625/4000 [05:25<25:28,  2.21it/s]

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overall:  16%|█▌        | 626/4000 [05:25<24:37,  2.28it/s]

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overall:  16%|█▌        | 630/4000 [05:27<27:19,  2.05it/s]

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overall:  16%|█▌        | 632/4000 [05:28<27:54,  2.01it/s]

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overall:  16%|█▌        | 633/4000 [05:28<28:19,  1.98it/s]

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overall:  16%|█▌        | 634/4000 [05:29<28:02,  2.00it/s]

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overall:  16%|█▌        | 636/4000 [05:30<26:25,  2.12it/s]

train:  18%|█▊        | 636/3515 [05:28<22:37,  2.12it/s]
overall:  16%|█▌        | 637/4000 [05:30<27:18,  2.05it/s]

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overall:  16%|█▌        | 638/4000 [05:31<28:10,  1.99it/s]

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overall:  16%|█▌        | 649/4000 [05:37<29:13,  1.91it/s]

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overall:  16%|█▋        | 655/4000 [05:40<31:02,  1.80it/s]

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overall:  16%|█▋        | 656/4000 [05:41<30:08,  1.85it/s]

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overall:  16%|█▋        | 657/4000 [05:41<29:14,  1.90it/s]

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overall:  17%|█▋        | 661/4000 [05:43<29:37,  1.88it/s]

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overall:  17%|█▋        | 662/4000 [05:44<25:37,  2.17it/s]

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overall:  17%|█▋        | 664/4000 [05:44<23:49,  2.33it/s]

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overall:  17%|█▋        | 665/4000 [05:45<26:20,  2.11it/s]

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overall:  17%|█▋        | 668/4000 [05:47<29:56,  1.86it/s]

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overall:  17%|█▋        | 669/4000 [05:47<29:59,  1.85it/s]

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overall:  17%|█▋        | 680/4000 [05:53<30:16,  1.83it/s]

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overall:  17%|█▋        | 681/4000 [05:54<29:30,  1.88it/s]

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overall:  17%|█▋        | 682/4000 [05:54<28:53,  1.91it/s]

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overall:  17%|█▋        | 684/4000 [05:55<25:22,  2.18it/s]

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overall:  17%|█▋        | 685/4000 [05:55<26:39,  2.07it/s]

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overall:  17%|█▋        | 686/4000 [05:56<27:46,  1.99it/s]

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overall:  17%|█▋        | 687/4000 [05:57<29:01,  1.90it/s]

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overall:  17%|█▋        | 688/4000 [05:57<29:13,  1.89it/s]

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train:  28%|██▊       | 999/3515 [08:34<21:13,  1.98it/s]train loss: 5.131654739379883
train per: 1.003725398887282
train loss: 1.1472892761230469
train per: 0.29466076965165056
train loss: 0.5441488027572632
train per: 0.13118760399334442
train loss: 0.45226675271987915
train per: 0.10866834170854271
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train per: 0.09463598523521558
train loss: 0.342909038066864
train per: 0.08400427027267471
train loss: 0.29743027687072754
train per: 0.07580491479979613
train loss: 0.30263081192970276
train per: 0.07268644368277981
train loss: 0.31574055552482605
train per: 0.0743462567192085
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train per: 0.07336380342081704
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train per: 0.06527427235047584
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train per: 0.06302659656378555
train loss: 0.2701879143714905
train per: 0.06539583642387381
train loss: 0.248855859041214
train per: 0.06049197960997054
train loss: 0.24710913002490997
train per: 0.058769963575231156
train loss: 0.2616499066352844
train per: 0.062272449247080065
train loss: 0.27166086435317993
train per: 0.0643413323619143
train loss: 0.2616215944290161
train per: 0.06143650813423354
train loss: 0.2666797935962677
train per: 0.06103621537025342
train loss: 0.23503108322620392
train per: 0.057431490942870415
train loss: 0.23546448349952698
train per: 0.05734671017386646
train loss: 0.22218795120716095
train per: 0.054004919259972195
train loss: 0.22219780087471008
train per: 0.05503812316715542
train loss: 0.2518191635608673
train per: 0.058617814627819724
train loss: 0.24341420829296112
train per: 0.05665852537905351
train loss: 0.26051849126815796
train per: 0.0638353788336706
train loss: 0.2285173386335373
train per: 0.05733709020975893
train loss: 0.2140953242778778
train per: 0.05368257261410788
train loss: 0.22192010283470154
train per: 0.05319541928333949
train loss: 0.2043338567018509
train per: 0.05182875644561698
train loss: 0.21679583191871643
train per: 0.05334730524661221
train loss: 0.22789184749126434
train per: 0.05545048594693985
train loss: 0.22212202847003937
train per: 0.052277264496959006
train loss: 0.22885599732398987
train per: 0.05530815598474597
train loss: 0.2050931453704834
train per: 0.0494903198336965
train loss: 0.2186238020658493
train per: 0.051603633441297515
train loss: 0.22966201603412628
train per: 0.05555285540704739
train loss: 0.19448669254779816
train per: 0.049087729249385516
train loss: 0.20712637901306152
train per: 0.04981958762886598
train loss: 0.20278918743133545
train per: 0.04937695086882788
train loss: 0.20866075158119202
train per: 0.0525730056980057
train loss: 0.22781772911548615
train per: 0.05525329123558763
train loss: 0.23790958523750305
train per: 0.05930226661933979
train loss: 0.19659557938575745
train per: 0.04859392575928009
train loss: 0.2301468402147293
train per: 0.05292011635712687
train loss: 0.23804083466529846
train per: 0.05864630939311155
train loss: 0.2509761452674866
train per: 0.06058324028149264
train loss: 0.23494815826416016
train per: 0.05594710622374251
train loss: 0.2172381430864334
train per: 0.05302784598989946
train loss: 0.2059580534696579
train per: 0.04925381767700139
[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, 5840.88it/s]


word -> phonemes:  44%|████▎     | 1178/2703 [00:00<00:00, 5889.58it/s]


word -> phonemes:  65%|██████▌   | 1767/2703 [00:00<00:00, 5875.15it/s]


word -> phonemes:  87%|████████▋ | 2355/2703 [00:00<00:00, 5810.70it/s]
word -> phonemes: 100%|██████████| 2703/2703 [00:00<00:00, 5784.30it/s]



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train:  55%|█████▌    | 1938/3515 [21:14<13:39,  1.92it/s]
overall:  48%|████▊     | 1939/4000 [21:16<17:29,  1.96it/s]

train:  55%|█████▌    | 1939/3515 [21:14<13:22,  1.96it/s]
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train:  55%|█████▌    | 1940/3515 [21:15<14:08,  1.86it/s]
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train:  55%|█████▌    | 1942/3515 [21:16<12:59,  2.02it/s]
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train:  55%|█████▌    | 1943/3515 [21:16<13:41,  1.91it/s]
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train:  55%|█████▌    | 1944/3515 [21:17<14:04,  1.86it/s]
overall:  49%|████▊     | 1946/4000 [21:20<15:56,  2.15it/s]

train:  55%|█████▌    | 1946/3515 [21:18<12:10,  2.15it/s]
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train:  55%|█████▌    | 1947/3515 [21:18<12:52,  2.03it/s]
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train:  56%|█████▌    | 1958/3515 [21:23<10:56,  2.37it/s]
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train:  56%|█████▌    | 1960/3515 [21:24<10:16,  2.52it/s]
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train:  56%|█████▌    | 1961/3515 [21:24<10:58,  2.36it/s]
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train:  56%|█████▌    | 1962/3515 [21:25<13:34,  1.91it/s]
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train:  56%|█████▌    | 1963/3515 [21:26<13:23,  1.93it/s]
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train:  56%|█████▌    | 1966/3515 [21:27<13:26,  1.92it/s]
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train:  56%|█████▌    | 1967/3515 [21:28<13:17,  1.94it/s]
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train:  56%|█████▌    | 1968/3515 [21:28<13:07,  1.96it/s]
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train:  56%|█████▌    | 1969/3515 [21:28<10:44,  2.40it/s]
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train:  56%|█████▌    | 1970/3515 [21:29<12:23,  2.08it/s]
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train:  56%|█████▌    | 1971/3515 [21:30<13:01,  1.97it/s]
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train:  56%|█████▌    | 1972/3515 [21:30<13:20,  1.93it/s]
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train:  57%|█████▋    | 1989/3515 [21:39<14:10,  1.80it/s]
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train:  57%|█████▋    | 1990/3515 [21:39<12:08,  2.09it/s]
overall:  50%|████▉     | 1991/4000 [21:42<16:58,  1.97it/s]

train:  57%|█████▋    | 1991/3515 [21:40<12:52,  1.97it/s]
overall:  50%|████▉     | 1992/4000 [21:43<16:47,  1.99it/s]

train:  57%|█████▋    | 1992/3515 [21:41<12:44,  1.99it/s]
overall:  50%|████▉     | 1993/4000 [21:43<14:35,  2.29it/s]

train:  57%|█████▋    | 1993/3515 [21:41<11:04,  2.29it/s]
overall:  50%|████▉     | 1994/4000 [21:43<15:27,  2.16it/s]

train:  57%|█████▋    | 1994/3515 [21:41<11:43,  2.16it/s]
overall:  50%|████▉     | 1995/4000 [21:44<16:45,  1.99it/s]

train:  57%|█████▋    | 1995/3515 [21:42<12:42,  1.99it/s]
overall:  50%|████▉     | 1996/4000 [21:44<17:17,  1.93it/s]

train:  57%|█████▋    | 1996/3515 [21:42<13:06,  1.93it/s]
overall:  50%|████▉     | 1997/4000 [21:45<17:22,  1.92it/s]

train:  57%|█████▋    | 1997/3515 [21:43<13:09,  1.92it/s]
overall:  50%|████▉     | 1998/4000 [21:45<15:02,  2.22it/s]

train:  57%|█████▋    | 1998/3515 [21:43<11:23,  2.22it/s]
overall:  50%|████▉     | 1999/4000 [21:46<15:51,  2.10it/s]

train:  57%|█████▋    | 1999/3515 [21:44<12:00,  2.10it/s]dev loss: 0.30985331535339355
dev per: 0.06360041623309053
[Runner] - Save the checkpoint to:
1. result/downstream/grid/dev-best.ckpt
train loss: 0.2236747443675995
train per: 0.052516118676262075
train loss: 0.22747361660003662
train per: 0.05631922079962297
train loss: 0.20161926746368408
train per: 0.05042926958129706
train loss: 0.2164197713136673
train per: 0.053468242175523005
train loss: 0.1999080926179886
train per: 0.048336423568191185
train loss: 0.21332044899463654
train per: 0.052796559674619825
train loss: 0.20144908130168915
train per: 0.049542953496627615
train loss: 0.20215734839439392
train per: 0.04898579661478423
train loss: 0.2549412250518799
train per: 0.06205751007515521
train loss: 0.22516611218452454
train per: 0.05564163577001826
train loss: 0.20999684929847717
train per: 0.0506053086698248
train loss: 0.19001898169517517
train per: 0.04598192042138231
train loss: 0.23004886507987976
train per: 0.05479093316262694
train loss: 0.21113114058971405
train per: 0.05001745374756894
train loss: 0.21682485938072205
train per: 0.054753637328894884
train loss: 0.1981768012046814
train per: 0.04864585535433256
train loss: 0.19016222655773163
train per: 0.04987487921508387
train loss: 0.1957218199968338
train per: 0.04627424293977543
train loss: 0.2035934031009674
train per: 0.051114592119532475
train loss: 0.19228234887123108
train per: 0.04711604598604697
train loss: 0.1863463670015335
train per: 0.04610024779803234
train loss: 0.19111251831054688
train per: 0.05050965665236051
train loss: 0.20141275227069855
train per: 0.04939190866219906
train loss: 0.20336170494556427
train per: 0.05229193807374193
train loss: 0.20707638561725616
train per: 0.05242762138106905
train loss: 0.19938428699970245
train per: 0.04900745814650413
train loss: 0.19271284341812134
train per: 0.048673915252514986
train loss: 0.22580596804618835
train per: 0.05483389530705876
train loss: 0.1937205046415329
train per: 0.04970344327849494
train loss: 0.22039704024791718
train per: 0.053719559360913285
train loss: 0.1955549120903015
train per: 0.04900164865359956
train loss: 0.1695961356163025
train per: 0.04527712724434036
train loss: 0.1995117962360382
train per: 0.04771386430678466
train loss: 0.18565820157527924
train per: 0.04519536361936811
train loss: 0.19657614827156067
train per: 0.04741573566735454
train loss: 0.17175701260566711
train per: 0.044311034877072615
train loss: 0.18966415524482727
train per: 0.04700272479564033
train loss: 0.18544897437095642
train per: 0.04779331040035131
train loss: 0.18116125464439392
train per: 0.04642482496157693
train loss: 0.18313658237457275
train per: 0.04437040963125615
train loss: 0.19804902374744415
train per: 0.050153197046561855
train loss: 0.1755964457988739
train per: 0.04341271463614064
train loss: 0.19430404901504517
train per: 0.0468463132958247
train loss: 0.19474419951438904
train per: 0.04653538681582509
train loss: 0.2155943363904953
train per: 0.053082872928176796
train loss: 0.1761283129453659
train per: 0.04402351093448826
train loss: 0.1994813084602356
train per: 0.05129943502824859
train loss: 0.20631703734397888
train per: 0.05190331451729628
train loss: 0.19842925667762756
train per: 0.05005128683327117
train loss: 0.17177633941173553
train per: 0.04380730982823195
[LibriPhone] - Taking the first phoneme sequences for a deterministic behavior.



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


word -> phonemes:  14%|█▍        | 389/2703 [00:00<00:02, 1032.95it/s]


word -> phonemes:  32%|███▏      | 871/2703 [00:00<00:00, 2073.54it/s]


word -> phonemes:  50%|█████     | 1364/2703 [00:00<00:00, 2878.50it/s]


word -> phonemes:  68%|██████▊   | 1829/2703 [00:00<00:00, 3386.80it/s]


word -> phonemes:  84%|████████▎ | 2259/2703 [00:00<00:00, 3651.01it/s]


word -> phonemes: 100%|█████████▉| 2695/2703 [00:00<00:00, 3857.38it/s]
word -> phonemes: 100%|██████████| 2703/2703 [00:00<00:00, 3058.87it/s]



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


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overall:  74%|███████▎  | 2943/4000 [31:29<07:59,  2.21it/s]

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overall:  74%|███████▍  | 2960/4000 [31:37<06:07,  2.83it/s]

train:  84%|████████▍ | 2960/3515 [31:35<03:15,  2.83it/s]
overall:  74%|███████▍  | 2961/4000 [31:37<06:42,  2.58it/s]

train:  84%|████████▍ | 2961/3515 [31:35<03:34,  2.58it/s]
overall:  74%|███████▍  | 2962/4000 [31:38<06:41,  2.58it/s]

train:  84%|████████▍ | 2962/3515 [31:36<03:34,  2.58it/s]
overall:  74%|███████▍  | 2963/4000 [31:38<07:22,  2.35it/s]

train:  84%|████████▍ | 2963/3515 [31:36<03:55,  2.35it/s]
overall:  74%|███████▍  | 2964/4000 [31:39<07:48,  2.21it/s]

train:  84%|████████▍ | 2964/3515 [31:37<04:09,  2.21it/s]
overall:  74%|███████▍  | 2965/4000 [31:39<06:02,  2.85it/s]

train:  84%|████████▍ | 2965/3515 [31:37<03:12,  2.85it/s]
overall:  74%|███████▍  | 2966/4000 [31:39<06:54,  2.49it/s]

train:  84%|████████▍ | 2966/3515 [31:37<03:40,  2.49it/s]
overall:  74%|███████▍  | 2967/4000 [31:40<06:27,  2.66it/s]

train:  84%|████████▍ | 2967/3515 [31:38<03:25,  2.66it/s]
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train:  85%|████████▌ | 2993/3515 [31:50<04:10,  2.09it/s]
overall:  75%|███████▍  | 2994/4000 [31:53<08:57,  1.87it/s]

train:  85%|████████▌ | 2994/3515 [31:51<04:38,  1.87it/s]
overall:  75%|███████▍  | 2995/4000 [31:53<09:01,  1.86it/s]

train:  85%|████████▌ | 2995/3515 [31:51<04:40,  1.86it/s]
overall:  75%|███████▍  | 2996/4000 [31:54<09:05,  1.84it/s]

train:  85%|████████▌ | 2996/3515 [31:52<04:41,  1.84it/s]
overall:  75%|███████▍  | 2997/4000 [31:54<08:17,  2.02it/s]

train:  85%|████████▌ | 2997/3515 [31:52<04:16,  2.02it/s]
overall:  75%|███████▍  | 2998/4000 [31:55<08:11,  2.04it/s]

train:  85%|████████▌ | 2998/3515 [31:53<04:13,  2.04it/s]
overall:  75%|███████▍  | 2999/4000 [31:55<08:26,  1.98it/s]

train:  85%|████████▌ | 2999/3515 [31:53<04:21,  1.98it/s]dev loss: 0.2944258749485016
dev per: 0.06311134235171696
[Runner] - Save the checkpoint to:
1. result/downstream/grid/dev-best.ckpt
train loss: 0.1723829060792923
train per: 0.0442749681147349
train loss: 0.205099418759346
train per: 0.05167275648500868
train loss: 0.2070222795009613
train per: 0.0495643259866735
train loss: 0.19542181491851807
train per: 0.048782257479381815
train loss: 0.17272932827472687
train per: 0.04336830896869939
train loss: 0.18489326536655426
train per: 0.0466502363947289
train loss: 0.19888190925121307
train per: 0.04947923861093461
train loss: 0.17844882607460022
train per: 0.046931767282138055
train loss: 0.18803644180297852
train per: 0.04664065666801784
train loss: 0.19516949355602264
train per: 0.0471603239294977
train loss: 0.2053821086883545
train per: 0.0515348174922643
train loss: 0.18323151767253876
train per: 0.04657852373789354
train loss: 0.18103262782096863
train per: 0.04464969587744987
train loss: 0.17817047238349915
train per: 0.043707645426843134
train loss: 0.17751438915729523
train per: 0.04381772173800203
train loss: 0.18932653963565826
train per: 0.0467789234698704
train loss: 0.17366740107536316
train per: 0.04304660666382531
train loss: 0.16978254914283752
train per: 0.04308554557509946
train loss: 0.17424039542675018
train per: 0.043721273991269606
train loss: 0.16209638118743896
train per: 0.04122233828066169
train loss: 0.17498579621315002
train per: 0.04303990107956437
train loss: 0.1979718655347824
train per: 0.0499987120372994
train loss: 0.18679413199424744
train per: 0.04659480646741793
train loss: 0.18653587996959686
train per: 0.045515964367931136
train loss: 0.18889851868152618
train per: 0.04616677953436238
train loss: 0.197768896818161
train per: 0.04665641251007105
train loss: 0.2152591496706009
train per: 0.054542189384027294
train loss: 0.1815996915102005
train per: 0.0452472898152887
train loss: 0.1795482337474823
train per: 0.04505716207128446
train loss: 0.18630197644233704
train per: 0.04626387543768159
train loss: 0.18354707956314087
train per: 0.04708788923950001
train loss: 0.1819714903831482
train per: 0.04521963824289406
train loss: 0.19588956236839294
train per: 0.046972158422497336
train loss: 0.19584348797798157
train per: 0.04638880216463732
train loss: 0.1867656707763672
train per: 0.04709711386588809
train loss: 0.17839641869068146
train per: 0.043628986698166505
train loss: 0.16934429109096527
train per: 0.04358548361974975
train loss: 0.1892479509115219
train per: 0.046112843206275775
train loss: 0.1853315383195877
train per: 0.04704454055694666
train loss: 0.19694022834300995
train per: 0.04818027770049824
train loss: 0.17710645496845245
train per: 0.04624263078032885
train loss: 0.16970883309841156
train per: 0.04225906543787338
train loss: 0.1791798323392868
train per: 0.04439433796818239
train loss: 0.16564208269119263
train per: 0.04207650273224044
train loss: 0.1932278871536255
train per: 0.04885368493555053
train loss: 0.1765490472316742
train per: 0.04592956671120443
train loss: 0.18286462128162384
train per: 0.04335869710319049
train loss: 0.17720279097557068
train per: 0.04424472009705613
train loss: 0.1831202507019043
train per: 0.04808303325566617
train loss: 0.17732460796833038
train per: 0.044889948824961494
[LibriPhone] - Taking the first phoneme sequences for a deterministic behavior.



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


word -> phonemes:  20%|█▉        | 534/2703 [00:00<00:00, 5337.80it/s]


word -> phonemes:  41%|████      | 1097/2703 [00:00<00:00, 5507.97it/s]


word -> phonemes:  61%|██████    | 1652/2703 [00:00<00:00, 5523.58it/s]


word -> phonemes:  82%|████████▏ | 2221/2703 [00:00<00:00, 5586.13it/s]
word -> phonemes: 100%|██████████| 2703/2703 [00:00<00:00, 5511.78it/s]



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


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overall:  75%|███████▍  | 2999/4000 [33:30<08:26,  1.98it/s]

train:  85%|████████▌ | 2999/3515 [33:28<04:21,  1.98it/s]
                                                            

                                                          

overall:  75%|███████▍  | 2999/4000 [33:30<08:26,  1.98it/s]

train:  85%|████████▌ | 2999/3515 [33:28<04:21,  1.98it/s]
overall:  75%|███████▌  | 3000/4000 [33:30<7:59:08, 28.75s/it]

train:  85%|████████▌ | 3000/3515 [33:28<4:06:45, 28.75s/it]
overall:  75%|███████▌  | 3001/4000 [33:30<5:37:33, 20.27s/it]

train:  85%|████████▌ | 3001/3515 [33:28<2:53:40, 20.27s/it]
overall:  75%|███████▌  | 3002/4000 [33:31<3:58:59, 14.37s/it]

train:  85%|████████▌ | 3002/3515 [33:29<2:02:50, 14.37s/it]
overall:  75%|███████▌  | 3003/4000 [33:31<2:49:26, 10.20s/it]

train:  85%|████████▌ | 3003/3515 [33:29<1:27:00, 10.20s/it]
overall:  75%|███████▌  | 3004/4000 [33:32<2:00:58,  7.29s/it]

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overall:  76%|███████▋  | 3058/4000 [33:59<07:31,  2.09it/s]

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overall:  76%|███████▋  | 3060/4000 [33:59<06:36,  2.37it/s]

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overall:  77%|███████▋  | 3061/4000 [34:00<05:44,  2.72it/s]

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overall:  77%|███████▋  | 3063/4000 [34:01<07:02,  2.22it/s]

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overall:  77%|███████▋  | 3064/4000 [34:01<07:19,  2.13it/s]

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overall:  77%|███████▋  | 3068/4000 [34:03<08:10,  1.90it/s]

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overall:  77%|███████▋  | 3085/4000 [34:11<06:09,  2.47it/s]

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overall:  77%|███████▋  | 3087/4000 [34:12<06:30,  2.34it/s]

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overall:  77%|███████▋  | 3088/4000 [34:12<06:51,  2.22it/s]

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overall:  77%|███████▋  | 3089/4000 [34:13<07:09,  2.12it/s]

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overall:  77%|███████▋  | 3090/4000 [34:14<07:54,  1.92it/s]

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overall:  77%|███████▋  | 3091/4000 [34:14<07:53,  1.92it/s]

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overall:  77%|███████▋  | 3093/4000 [34:15<06:16,  2.41it/s]

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overall:  88%|████████▊ | 3515/4000 [37:36<04:03,  1.99it/s]

train: 100%|██████████| 3515/3515 [37:34<00:00,  1.99it/s]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 [37:34<00:00,  1.56it/s]
dev loss: 0.2835083603858948
dev per: 0.05761706555671176
[Runner] - Save the checkpoint to:
1. result/downstream/grid/dev-best.ckpt
train loss: 0.18157067894935608
train per: 0.044367623744935705
train loss: 0.17555367946624756
train per: 0.04475212148280482
train loss: 0.1607193648815155
train per: 0.03949044585987261
train loss: 0.20322194695472717
train per: 0.051844178370955694
train loss: 0.18599259853363037
train per: 0.0479032170563663
train loss: 0.1783680021762848
train per: 0.04705190413174533
train loss: 0.19431832432746887
train per: 0.04759551084491481
train loss: 0.1762281060218811
train per: 0.044577104690137745
train loss: 0.19093140959739685
train per: 0.04918611918802141
train loss: 0.18413183093070984
train per: 0.04754616619831393
train loss: 0.17384681105613708
train per: 0.041987005254847144
train loss: 0.1710144728422165
train per: 0.04382291748966317
train loss: 0.19230009615421295
train per: 0.04791511630698322
train loss: 0.1927177459001541
train per: 0.047849108166800866
train loss: 0.17809513211250305
train per: 0.04387702173810534
train loss: 0.18512411415576935
train per: 0.04467767560541139
train loss: 0.1614159345626831
train per: 0.0403516057585825
train loss: 0.1664193570613861
train per: 0.04239390042256109
train loss: 0.15340201556682587
train per: 0.0407776829528483
train loss: 0.17316889762878418
train per: 0.04340327680367922
train loss: 0.18645556271076202
train per: 0.04565011820330969
train loss: 0.1761084794998169
train per: 0.04311038501865353
train loss: 0.188185453414917
train per: 0.04891139240506329
train loss: 0.18498855829238892
train per: 0.04746587164600659
train loss: 0.1603468656539917
train per: 0.04161669465099544
[LibriPhone] - Taking the first phoneme sequences for a deterministic behavior.


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

word -> phonemes:  12%|█▏        | 408/3515 [00:00<00:00, 4075.63it/s]

word -> phonemes:  25%|██▍       | 865/3515 [00:00<00:00, 4365.69it/s]

word -> phonemes:  38%|███▊      | 1341/3515 [00:00<00:00, 4542.16it/s]

word -> phonemes:  51%|█████▏    | 1803/3515 [00:00<00:00, 4570.46it/s]

word -> phonemes:  65%|██████▍   | 2277/3515 [00:00<00:00, 4629.27it/s]

word -> phonemes:  78%|███████▊  | 2740/3515 [00:00<00:00, 4514.62it/s]

word -> phonemes:  91%|█████████ | 3192/3515 [00:00<00:00, 4469.01it/s]
word -> phonemes: 100%|██████████| 3515/3515 [00:00<00:00, 4465.55it/s]


train:   0%|          | 0/3515 [00:00<?, ?it/s]
overall:  88%|████████▊ | 3516/4000 [37:39<10:48,  1.34s/it]

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overall:  88%|████████▊ | 3518/4000 [37:40<07:52,  1.02it/s]

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overall:  88%|████████▊ | 3520/4000 [37:41<05:02,  1.59it/s]

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overall:  88%|████████▊ | 3523/4000 [37:43<04:22,  1.81it/s]

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overall:  88%|████████▊ | 3524/4000 [37:43<04:06,  1.93it/s]

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overall:  88%|████████▊ | 3525/4000 [37:44<04:09,  1.90it/s]

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overall:  88%|████████▊ | 3526/4000 [37:44<04:07,  1.92it/s]

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overall:  88%|████████▊ | 3527/4000 [37:45<04:09,  1.90it/s]

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overall:  88%|████████▊ | 3528/4000 [37:45<04:08,  1.90it/s]

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overall:  88%|████████▊ | 3529/4000 [37:46<03:37,  2.17it/s]

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overall:  88%|████████▊ | 3530/4000 [37:46<03:05,  2.53it/s]

train:   0%|          | 15/3515 [00:07<22:46,  2.56it/s]
overall:  88%|████████▊ | 3531/4000 [37:46<03:13,  2.42it/s]

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overall:  88%|████████▊ | 3532/4000 [37:47<03:28,  2.25it/s]

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train:   1%|          | 18/3515 [00:09<26:50,  2.17it/s]
overall:  88%|████████▊ | 3534/4000 [37:48<03:33,  2.18it/s]

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overall:  88%|████████▊ | 3535/4000 [37:48<03:34,  2.16it/s]

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overall:  88%|████████▊ | 3536/4000 [37:49<03:47,  2.04it/s]

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overall:  88%|████████▊ | 3537/4000 [37:49<03:52,  1.99it/s]

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train:   1%|          | 29/3515 [00:15<29:50,  1.95it/s]
overall:  89%|████████▊ | 3545/4000 [37:53<03:51,  1.97it/s]

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train:   1%|          | 31/3515 [00:16<29:24,  1.97it/s]
overall:  89%|████████▊ | 3547/4000 [37:55<03:54,  1.93it/s]

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overall:  89%|████████▊ | 3548/4000 [37:55<03:15,  2.31it/s]

train:   1%|          | 33/3515 [00:16<25:07,  2.31it/s]
overall:  89%|████████▊ | 3549/4000 [37:55<03:26,  2.18it/s]

train:   1%|          | 34/3515 [00:17<26:35,  2.18it/s]
overall:  89%|████████▉ | 3550/4000 [37:56<03:35,  2.09it/s]

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overall:  89%|████████▉ | 3551/4000 [37:56<03:40,  2.03it/s]

train:   1%|          | 36/3515 [00:18<28:31,  2.03it/s]
overall:  89%|████████▉ | 3552/4000 [37:57<03:39,  2.04it/s]

train:   1%|          | 37/3515 [00:18<28:21,  2.04it/s]
overall:  89%|████████▉ | 3553/4000 [37:57<03:52,  1.92it/s]

train:   1%|          | 38/3515 [00:19<30:07,  1.92it/s]
overall:  89%|████████▉ | 3554/4000 [37:58<03:55,  1.89it/s]

train:   1%|          | 39/3515 [00:19<30:36,  1.89it/s]
overall:  89%|████████▉ | 3555/4000 [37:58<03:37,  2.04it/s]

train:   1%|          | 40/3515 [00:20<28:20,  2.04it/s]
overall:  89%|████████▉ | 3556/4000 [37:59<03:40,  2.01it/s]

train:   1%|          | 41/3515 [00:20<28:47,  2.01it/s]
overall:  89%|████████▉ | 3557/4000 [37:59<03:42,  1.99it/s]

train:   1%|          | 42/3515 [00:21<29:03,  1.99it/s]
overall:  89%|████████▉ | 3558/4000 [38:00<03:40,  2.01it/s]

train:   1%|          | 43/3515 [00:21<28:51,  2.01it/s]
overall:  89%|████████▉ | 3559/4000 [38:00<03:43,  1.97it/s]

train:   1%|▏         | 44/3515 [00:22<29:22,  1.97it/s]
overall:  89%|████████▉ | 3560/4000 [38:01<03:45,  1.95it/s]

train:   1%|▏         | 45/3515 [00:22<29:38,  1.95it/s]
overall:  89%|████████▉ | 3561/4000 [38:01<03:45,  1.95it/s]

train:   1%|▏         | 46/3515 [00:23<29:43,  1.95it/s]
overall:  89%|████████▉ | 3562/4000 [38:02<03:45,  1.94it/s]

train:   1%|▏         | 47/3515 [00:23<29:45,  1.94it/s]
overall:  89%|████████▉ | 3563/4000 [38:02<03:01,  2.41it/s]

train:   1%|▏         | 48/3515 [00:24<24:00,  2.41it/s]
overall:  89%|████████▉ | 3564/4000 [38:03<03:13,  2.25it/s]

train:   1%|▏         | 49/3515 [00:24<25:37,  2.25it/s]
overall:  89%|████████▉ | 3565/4000 [38:03<03:35,  2.01it/s]

train:   1%|▏         | 50/3515 [00:25<28:40,  2.01it/s]
overall:  89%|████████▉ | 3566/4000 [38:04<03:35,  2.01it/s]

train:   1%|▏         | 51/3515 [00:25<28:42,  2.01it/s]
overall:  89%|████████▉ | 3567/4000 [38:04<03:08,  2.29it/s]

train:   1%|▏         | 52/3515 [00:26<25:09,  2.29it/s]
overall:  89%|████████▉ | 3568/4000 [38:05<03:14,  2.22it/s]

train:   2%|▏         | 53/3515 [00:26<25:56,  2.22it/s]
overall:  89%|████████▉ | 3569/4000 [38:05<03:20,  2.15it/s]

train:   2%|▏         | 54/3515 [00:27<26:46,  2.15it/s]
overall:  89%|████████▉ | 3570/4000 [38:06<03:26,  2.08it/s]

train:   2%|▏         | 55/3515 [00:27<27:39,  2.08it/s]
overall:  89%|████████▉ | 3571/4000 [38:06<03:34,  2.00it/s]

train:   2%|▏         | 56/3515 [00:28<28:48,  2.00it/s]
overall:  89%|████████▉ | 3572/4000 [38:07<03:37,  1.97it/s]

train:   2%|▏         | 57/3515 [00:28<29:13,  1.97it/s]
overall:  89%|████████▉ | 3573/4000 [38:07<03:41,  1.93it/s]

train:   2%|▏         | 58/3515 [00:29<29:50,  1.93it/s]
overall:  89%|████████▉ | 3574/4000 [38:07<03:01,  2.35it/s]

train:   2%|▏         | 59/3515 [00:29<24:32,  2.35it/s]
overall:  89%|████████▉ | 3575/4000 [38:08<03:13,  2.19it/s]

train:   2%|▏         | 60/3515 [00:29<26:15,  2.19it/s]
overall:  89%|████████▉ | 3576/4000 [38:08<03:02,  2.32it/s]

train:   2%|▏         | 61/3515 [00:30<24:48,  2.32it/s]
overall:  89%|████████▉ | 3577/4000 [38:08<02:35,  2.72it/s]

train:   2%|▏         | 62/3515 [00:30<21:07,  2.72it/s]
overall:  89%|████████▉ | 3578/4000 [38:09<02:56,  2.39it/s]

train:   2%|▏         | 63/3515 [00:31<24:04,  2.39it/s]
overall:  89%|████████▉ | 3579/4000 [38:10<03:08,  2.24it/s]

train:   2%|▏         | 64/3515 [00:31<25:43,  2.24it/s]
overall:  90%|████████▉ | 3580/4000 [38:10<03:18,  2.12it/s]

train:   2%|▏         | 65/3515 [00:32<27:07,  2.12it/s]
overall:  90%|████████▉ | 3581/4000 [38:11<03:23,  2.06it/s]

train:   2%|▏         | 66/3515 [00:32<27:54,  2.06it/s]
overall:  90%|████████▉ | 3582/4000 [38:11<03:26,  2.03it/s]

train:   2%|▏         | 67/3515 [00:33<28:22,  2.03it/s]
overall:  90%|████████▉ | 3583/4000 [38:12<03:25,  2.03it/s]

train:   2%|▏         | 68/3515 [00:33<28:22,  2.03it/s]
overall:  90%|████████▉ | 3584/4000 [38:12<03:26,  2.01it/s]

train:   2%|▏         | 69/3515 [00:34<28:30,  2.01it/s]
overall:  90%|████████▉ | 3585/4000 [38:13<03:24,  2.03it/s]

train:   2%|▏         | 70/3515 [00:34<28:15,  2.03it/s]
overall:  90%|████████▉ | 3586/4000 [38:13<03:29,  1.98it/s]

train:   2%|▏         | 71/3515 [00:35<29:01,  1.98it/s]
overall:  90%|████████▉ | 3587/4000 [38:14<03:26,  2.00it/s]

train:   2%|▏         | 72/3515 [00:35<28:43,  2.00it/s]
overall:  90%|████████▉ | 3588/4000 [38:14<03:31,  1.95it/s]

train:   2%|▏         | 73/3515 [00:36<29:23,  1.95it/s]
overall:  90%|████████▉ | 3589/4000 [38:15<03:49,  1.79it/s]

train:   2%|▏         | 74/3515 [00:36<31:58,  1.79it/s]
overall:  90%|████████▉ | 3590/4000 [38:15<03:42,  1.84it/s]

train:   2%|▏         | 75/3515 [00:37<31:08,  1.84it/s]
overall:  90%|████████▉ | 3591/4000 [38:16<03:35,  1.90it/s]

train:   2%|▏         | 76/3515 [00:37<30:07,  1.90it/s]
overall:  90%|████████▉ | 3592/4000 [38:16<03:37,  1.88it/s]

train:   2%|▏         | 77/3515 [00:38<30:29,  1.88it/s]
overall:  90%|████████▉ | 3593/4000 [38:17<03:08,  2.16it/s]

train:   2%|▏         | 78/3515 [00:38<26:30,  2.16it/s]
overall:  90%|████████▉ | 3594/4000 [38:17<03:16,  2.06it/s]

train:   2%|▏         | 79/3515 [00:39<27:46,  2.06it/s]
overall:  90%|████████▉ | 3595/4000 [38:18<03:17,  2.05it/s]

train:   2%|▏         | 80/3515 [00:39<27:55,  2.05it/s]
overall:  90%|████████▉ | 3596/4000 [38:18<03:01,  2.22it/s]

train:   2%|▏         | 81/3515 [00:40<25:44,  2.22it/s]
overall:  90%|████████▉ | 3597/4000 [38:19<03:10,  2.11it/s]

train:   2%|▏         | 82/3515 [00:40<27:06,  2.11it/s]
overall:  90%|████████▉ | 3598/4000 [38:19<03:15,  2.05it/s]

train:   2%|▏         | 83/3515 [00:41<27:52,  2.05it/s]
overall:  90%|████████▉ | 3599/4000 [38:20<03:18,  2.02it/s]

train:   2%|▏         | 84/3515 [00:41<28:22,  2.02it/s]
overall:  90%|█████████ | 3600/4000 [38:20<03:24,  1.95it/s]

train:   2%|▏         | 85/3515 [00:42<29:16,  1.95it/s]
overall:  90%|█████████ | 3601/4000 [38:21<03:41,  1.80it/s]

train:   2%|▏         | 86/3515 [00:42<31:46,  1.80it/s]
overall:  90%|█████████ | 3602/4000 [38:21<03:38,  1.82it/s]

train:   2%|▏         | 87/3515 [00:43<31:19,  1.82it/s]
overall:  90%|█████████ | 3603/4000 [38:22<03:37,  1.82it/s]

train:   3%|▎         | 88/3515 [00:43<31:18,  1.82it/s]
overall:  90%|█████████ | 3604/4000 [38:22<03:31,  1.87it/s]

train:   3%|▎         | 89/3515 [00:44<30:29,  1.87it/s]
overall:  90%|█████████ | 3605/4000 [38:23<03:25,  1.92it/s]

train:   3%|▎         | 90/3515 [00:44<29:41,  1.92it/s]
overall:  90%|█████████ | 3606/4000 [38:23<02:44,  2.40it/s]

train:   3%|▎         | 91/3515 [00:45<23:47,  2.40it/s]
overall:  90%|█████████ | 3607/4000 [38:24<02:58,  2.20it/s]

train:   3%|▎         | 92/3515 [00:45<25:53,  2.20it/s]
overall:  90%|█████████ | 3608/4000 [38:24<03:08,  2.08it/s]

train:   3%|▎         | 93/3515 [00:46<27:22,  2.08it/s]
overall:  90%|█████████ | 3609/4000 [38:25<03:11,  2.04it/s]

train:   3%|▎         | 94/3515 [00:46<27:58,  2.04it/s]
overall:  90%|█████████ | 3610/4000 [38:25<03:19,  1.96it/s]

train:   3%|▎         | 95/3515 [00:47<29:09,  1.95it/s]
overall:  90%|█████████ | 3611/4000 [38:26<03:22,  1.92it/s]

train:   3%|▎         | 96/3515 [00:47<29:37,  1.92it/s]
overall:  90%|█████████ | 3612/4000 [38:26<03:17,  1.97it/s]

train:   3%|▎         | 97/3515 [00:48<28:58,  1.97it/s]
overall:  90%|█████████ | 3613/4000 [38:27<03:35,  1.79it/s]

train:   3%|▎         | 98/3515 [00:48<31:46,  1.79it/s]
overall:  90%|█████████ | 3614/4000 [38:27<03:19,  1.93it/s]

train:   3%|▎         | 99/3515 [00:49<29:28,  1.93it/s]
overall:  90%|█████████ | 3615/4000 [38:28<03:21,  1.91it/s]

train:   3%|▎         | 100/3515 [00:49<29:49,  1.91it/s]
overall:  90%|█████████ | 3616/4000 [38:28<03:18,  1.93it/s]

train:   3%|▎         | 101/3515 [00:50<29:24,  1.93it/s]
overall:  90%|█████████ | 3617/4000 [38:29<03:17,  1.94it/s]

train:   3%|▎         | 102/3515 [00:50<29:22,  1.94it/s]
overall:  90%|█████████ | 3618/4000 [38:29<03:05,  2.06it/s]

train:   3%|▎         | 103/3515 [00:51<27:34,  2.06it/s]
overall:  90%|█████████ | 3619/4000 [38:30<03:08,  2.02it/s]

train:   3%|▎         | 104/3515 [00:51<28:04,  2.02it/s]
overall:  90%|█████████ | 3620/4000 [38:30<03:09,  2.00it/s]

train:   3%|▎         | 105/3515 [00:52<28:22,  2.00it/s]
overall:  91%|█████████ | 3621/4000 [38:31<03:11,  1.98it/s]

train:   3%|▎         | 106/3515 [00:52<28:46,  1.98it/s]
overall:  91%|█████████ | 3622/4000 [38:31<03:15,  1.93it/s]

train:   3%|▎         | 107/3515 [00:53<29:26,  1.93it/s]
overall:  91%|█████████ | 3623/4000 [38:32<03:19,  1.89it/s]

train:   3%|▎         | 108/3515 [00:53<30:04,  1.89it/s]
overall:  91%|█████████ | 3624/4000 [38:32<03:18,  1.90it/s]

train:   3%|▎         | 109/3515 [00:54<29:54,  1.90it/s]
overall:  91%|█████████ | 3625/4000 [38:33<03:32,  1.76it/s]

train:   3%|▎         | 110/3515 [00:55<32:12,  1.76it/s]
overall:  91%|█████████ | 3626/4000 [38:34<03:30,  1.78it/s]

train:   3%|▎         | 111/3515 [00:55<31:52,  1.78it/s]
overall:  91%|█████████ | 3627/4000 [38:34<03:24,  1.83it/s]

train:   3%|▎         | 112/3515 [00:56<31:01,  1.83it/s]
overall:  91%|█████████ | 3628/4000 [38:35<03:21,  1.85it/s]

train:   3%|▎         | 113/3515 [00:56<30:38,  1.85it/s]
overall:  91%|█████████ | 3629/4000 [38:35<03:12,  1.93it/s]

train:   3%|▎         | 114/3515 [00:57<29:23,  1.93it/s]
overall:  91%|█████████ | 3630/4000 [38:36<03:10,  1.94it/s]

train:   3%|▎         | 115/3515 [00:57<29:10,  1.94it/s]
overall:  91%|█████████ | 3631/4000 [38:36<03:10,  1.93it/s]

train:   3%|▎         | 116/3515 [00:58<29:17,  1.93it/s]
overall:  91%|█████████ | 3632/4000 [38:37<03:07,  1.96it/s]

train:   3%|▎         | 117/3515 [00:58<28:54,  1.96it/s]
overall:  91%|█████████ | 3633/4000 [38:37<02:53,  2.12it/s]

train:   3%|▎         | 118/3515 [00:59<26:44,  2.12it/s]
overall:  91%|█████████ | 3634/4000 [38:38<02:46,  2.20it/s]

train:   3%|▎         | 119/3515 [00:59<25:47,  2.20it/s]
overall:  91%|█████████ | 3635/4000 [38:38<02:31,  2.41it/s]

train:   3%|▎         | 120/3515 [00:59<23:29,  2.41it/s]
overall:  91%|█████████ | 3636/4000 [38:38<02:42,  2.24it/s]

train:   3%|▎         | 121/3515 [01:00<25:18,  2.24it/s]
overall:  91%|█████████ | 3637/4000 [38:39<03:09,  1.91it/s]

train:   3%|▎         | 122/3515 [01:01<29:32,  1.91it/s]
overall:  91%|█████████ | 3638/4000 [38:40<03:12,  1.88it/s]

train:   3%|▎         | 123/3515 [01:01<30:02,  1.88it/s]
overall:  91%|█████████ | 3639/4000 [38:40<03:09,  1.91it/s]

train:   4%|▎         | 124/3515 [01:02<29:35,  1.91it/s]
overall:  91%|█████████ | 3640/4000 [38:41<03:09,  1.90it/s]

train:   4%|▎         | 125/3515 [01:02<29:42,  1.90it/s]
overall:  91%|█████████ | 3641/4000 [38:41<03:06,  1.93it/s]

train:   4%|▎         | 126/3515 [01:03<29:19,  1.93it/s]
overall:  91%|█████████ | 3642/4000 [38:42<03:08,  1.90it/s]

train:   4%|▎         | 127/3515 [01:03<29:39,  1.90it/s]
overall:  91%|█████████ | 3643/4000 [38:42<03:04,  1.93it/s]

train:   4%|▎         | 128/3515 [01:04<29:14,  1.93it/s]
overall:  91%|█████████ | 3644/4000 [38:43<03:04,  1.93it/s]

train:   4%|▎         | 129/3515 [01:04<29:16,  1.93it/s]
overall:  91%|█████████ | 3645/4000 [38:43<03:04,  1.93it/s]

train:   4%|▎         | 130/3515 [01:05<29:16,  1.93it/s]
overall:  91%|█████████ | 3646/4000 [38:44<03:06,  1.90it/s]

train:   4%|▎         | 131/3515 [01:05<29:44,  1.90it/s]
overall:  91%|█████████ | 3647/4000 [38:44<03:04,  1.92it/s]

train:   4%|▍         | 132/3515 [01:06<29:24,  1.92it/s]
overall:  91%|█████████ | 3648/4000 [38:45<03:07,  1.88it/s]

train:   4%|▍         | 133/3515 [01:06<30:03,  1.88it/s]
overall:  91%|█████████ | 3649/4000 [38:45<03:16,  1.78it/s]

train:   4%|▍         | 134/3515 [01:07<31:36,  1.78it/s]
overall:  91%|█████████▏| 3650/4000 [38:46<02:34,  2.26it/s]

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overall: 100%|█████████▉| 3996/4000 [41:33<00:01,  2.02it/s]

train:  14%|█▎        | 481/3515 [03:54<25:01,  2.02it/s]
overall: 100%|█████████▉| 3997/4000 [41:34<00:01,  1.86it/s]

train:  14%|█▎        | 482/3515 [03:55<27:06,  1.86it/s]
overall: 100%|█████████▉| 3998/4000 [41:34<00:01,  1.90it/s]

train:  14%|█▎        | 483/3515 [03:56<26:38,  1.90it/s]
overall: 100%|█████████▉| 3999/4000 [41:35<00:00,  1.91it/s]

train:  14%|█▍        | 484/3515 [03:56<26:23,  1.91it/s]train loss: 0.1778881847858429
train per: 0.04218817436340095
train loss: 0.17427434027194977
train per: 0.042218176450327216
train loss: 0.16104023158550262
train per: 0.04144046770984303
train loss: 0.17389202117919922
train per: 0.04399235813497186
train loss: 0.1839785873889923
train per: 0.04698797877817824
train loss: 0.18613499402999878
train per: 0.046278030972949684
train loss: 0.16860568523406982
train per: 0.04299095908285954
train loss: 0.18583892285823822
train per: 0.04475544864786063
train loss: 0.18026816844940186
train per: 0.046862087404598705
train loss: 0.15378229320049286
train per: 0.04225485104574855
train loss: 0.16122527420520782
train per: 0.039523833094094935
train loss: 0.17664194107055664
train per: 0.04370730723665969
train loss: 0.1757233440876007
train per: 0.04236173600055375
train loss: 0.1804244965314865
train per: 0.04487179487179487
train loss: 0.1886744648218155
train per: 0.04861983238075711
train loss: 0.18231041729450226
train per: 0.046117980944658424
train loss: 0.18112114071846008
train per: 0.04526748971193416
train loss: 0.17697599530220032
train per: 0.044086668672885944
train loss: 0.17442025244235992
train per: 0.04526955855597051
train loss: 0.15605442225933075
train per: 0.03859151121924845
train loss: 0.1543087661266327
train per: 0.04071234353183255
train loss: 0.1648152470588684
train per: 0.04247104247104247
train loss: 0.16841453313827515
train per: 0.04411958382720927
train loss: 0.17506378889083862
train per: 0.04536875903821172
train loss: 0.17615827918052673
train per: 0.04395681435782389
[LibriPhone] - Taking the first phoneme sequences for a deterministic behavior.



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


word -> phonemes:  22%|██▏       | 592/2703 [00:00<00:00, 5912.37it/s]


word -> phonemes:  44%|████▍     | 1184/2703 [00:00<00:00, 5828.27it/s]


word -> phonemes:  66%|██████▌   | 1789/2703 [00:00<00:00, 5925.25it/s]


word -> phonemes:  88%|████████▊ | 2382/2703 [00:00<00:00, 5887.03it/s]
word -> phonemes: 100%|██████████| 2703/2703 [00:00<00:00, 5821.92it/s]



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overall: 100%|█████████▉| 3999/4000 [43:10<00:00,  1.91it/s]

train:  14%|█▍        | 484/3515 [05:31<26:23,  1.91it/s]
                                                            

                                                         

overall: 100%|█████████▉| 3999/4000 [43:10<00:00,  1.91it/s]

train:  14%|█▍        | 484/3515 [05:31<26:23,  1.91it/s]
                                                            

                                                         

overall: 100%|█████████▉| 3999/4000 [43:10<00:00,  1.91it/s]

train:  14%|█▍        | 484/3515 [05:32<26:23,  1.91it/s]
overall: 100%|██████████| 4000/4000 [43:10<00:00, 29.03s/it]

train:  14%|█▍        | 485/3515 [05:32<24:26:10, 29.03s/it]403-128339-0009 Failed!!!
403-128339-0009 Failed!!!

train:  14%|█▍        | 485/3515 [05:32<34:37,  1.46it/s]   

overall: 100%|██████████| 4000/4000 [43:10<00:00,  1.54it/s]
dev loss: 0.2945002019405365
dev per: 0.056581685744016647
[Runner] - Save the checkpoint to:
1. result/downstream/grid/dev-best.ckpt
2. result/downstream/grid/states-4000.ckpt