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aradia-ctc-v1

This model is a fine-tuned version of /l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1 on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7171
  • Wer: 0.3336

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 20.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.22 100 5.1889 1.0
No log 0.43 200 3.1129 1.0
No log 0.65 300 3.0503 1.0
No log 0.87 400 3.0279 1.0
6.2756 1.09 500 2.9965 1.0
6.2756 1.3 600 2.3618 0.9993
6.2756 1.52 700 1.2715 0.8758
6.2756 1.74 800 0.9971 0.7156
6.2756 1.96 900 0.8927 0.6382
1.712 2.17 1000 0.8252 0.5926
1.712 2.39 1100 0.7794 0.5434
1.712 2.61 1200 0.7557 0.5092
1.712 2.83 1300 0.7347 0.5203
1.712 3.04 1400 0.7189 0.4929
0.9305 3.26 1500 0.6820 0.4595
0.9305 3.48 1600 0.6792 0.4504
0.9305 3.69 1700 0.6596 0.4442
0.9305 3.91 1800 0.6756 0.4432
0.9305 4.13 1900 0.6663 0.4392
0.737 4.35 2000 0.6479 0.4372
0.737 4.56 2100 0.6353 0.4203
0.737 4.78 2200 0.6251 0.4088
0.737 5.0 2300 0.6209 0.4177
0.737 5.22 2400 0.6639 0.4094
0.6247 5.43 2500 0.6408 0.3970
0.6247 5.65 2600 0.6373 0.3932
0.6247 5.87 2700 0.6411 0.3928
0.6247 6.09 2800 0.6378 0.3897
0.6247 6.3 2900 0.6396 0.3929
0.5443 6.52 3000 0.6544 0.3864
0.5443 6.74 3100 0.6218 0.3786
0.5443 6.96 3200 0.6200 0.3784
0.5443 7.17 3300 0.6157 0.3791
0.5443 7.39 3400 0.6317 0.3798
0.4845 7.61 3500 0.6540 0.3771
0.4845 7.83 3600 0.6436 0.3670
0.4845 8.04 3700 0.6335 0.3695
0.4845 8.26 3800 0.6579 0.3610
0.4845 8.48 3900 0.6170 0.3613
0.4279 8.69 4000 0.6523 0.3617
0.4279 8.91 4100 0.6349 0.3577
0.4279 9.13 4200 0.6344 0.3673
0.4279 9.35 4300 0.6215 0.3641
0.4279 9.56 4400 0.6513 0.3608
0.3825 9.78 4500 0.6386 0.3605
0.3825 10.0 4600 0.6724 0.3549
0.3825 10.22 4700 0.6776 0.3602
0.3825 10.43 4800 0.6739 0.3544
0.3825 10.65 4900 0.6688 0.3557
0.3477 10.87 5000 0.6674 0.3564
0.3477 11.09 5100 0.6786 0.3476
0.3477 11.3 5200 0.6818 0.3478
0.3477 11.52 5300 0.6874 0.3470
0.3477 11.74 5400 0.6993 0.3424
0.3101 11.96 5500 0.6950 0.3404
0.3101 12.17 5600 0.6872 0.3406
0.3101 12.39 5700 0.6846 0.3424
0.3101 12.61 5800 0.7051 0.3405
0.3101 12.83 5900 0.7051 0.3378
0.2859 13.04 6000 0.6955 0.3403
0.2859 13.26 6100 0.7115 0.3390
0.2859 13.48 6200 0.7074 0.3384
0.2859 13.69 6300 0.7002 0.3376
0.2859 13.91 6400 0.7171 0.3360
0.2714 14.13 6500 0.7193 0.3341
0.2714 14.35 6600 0.7132 0.3347
0.2714 14.56 6700 0.7184 0.3353
0.2714 14.78 6800 0.7171 0.3331

Framework versions

  • Transformers 4.18.0.dev0
  • Pytorch 1.10.2+cu113
  • Datasets 1.18.4
  • Tokenizers 0.11.6
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