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aradia-ctc-data2vec-ft

This model is a fine-tuned version of /l/users/abdulwahab.sahyoun/aradia/aradia-ctc-data2vec-ft on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 3.0464
  • Wer: 1.0

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: 30.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.43 100 3.3600 1.0
No log 0.87 200 3.0887 1.0
No log 1.3 300 3.0779 1.0
No log 1.74 400 3.0551 1.0
4.8553 2.17 500 3.0526 1.0
4.8553 2.61 600 3.0560 1.0
4.8553 3.04 700 3.1251 1.0
4.8553 3.48 800 3.0870 1.0
4.8553 3.91 900 3.0822 1.0
3.1133 4.35 1000 3.0484 1.0
3.1133 4.78 1100 3.0558 1.0
3.1133 5.22 1200 3.1019 1.0
3.1133 5.65 1300 3.0914 1.0
3.1133 6.09 1400 3.0691 1.0
3.109 6.52 1500 3.0589 1.0
3.109 6.95 1600 3.0508 1.0
3.109 7.39 1700 3.0540 1.0
3.109 7.82 1800 3.0546 1.0
3.109 8.26 1900 3.0524 1.0
3.1106 8.69 2000 3.0569 1.0
3.1106 9.13 2100 3.0622 1.0
3.1106 9.56 2200 3.0518 1.0
3.1106 10.0 2300 3.0749 1.0
3.1106 10.43 2400 3.0698 1.0
3.1058 10.87 2500 3.0665 1.0
3.1058 11.3 2600 3.0555 1.0
3.1058 11.74 2700 3.0589 1.0
3.1058 12.17 2800 3.0611 1.0
3.1058 12.61 2900 3.0561 1.0
3.1071 13.04 3000 3.0480 1.0
3.1071 13.48 3100 3.0492 1.0
3.1071 13.91 3200 3.0574 1.0
3.1071 14.35 3300 3.0538 1.0
3.1071 14.78 3400 3.0505 1.0
3.1061 15.22 3500 3.0600 1.0
3.1061 15.65 3600 3.0596 1.0
3.1061 16.09 3700 3.0623 1.0
3.1061 16.52 3800 3.0800 1.0
3.1061 16.95 3900 3.0583 1.0
3.1036 17.39 4000 3.0534 1.0
3.1036 17.82 4100 3.0563 1.0
3.1036 18.26 4200 3.0481 1.0
3.1036 18.69 4300 3.0477 1.0
3.1036 19.13 4400 3.0505 1.0
3.1086 19.56 4500 3.0485 1.0
3.1086 20.0 4600 3.0481 1.0
3.1086 20.43 4700 3.0615 1.0
3.1086 20.87 4800 3.0658 1.0
3.1086 21.3 4900 3.0505 1.0
3.1028 21.74 5000 3.0492 1.0
3.1028 22.17 5100 3.0485 1.0
3.1028 22.61 5200 3.0483 1.0
3.1028 23.04 5300 3.0479 1.0
3.1028 23.48 5400 3.0509 1.0
3.1087 23.91 5500 3.0530 1.0
3.1087 24.35 5600 3.0486 1.0
3.1087 24.78 5700 3.0514 1.0
3.1087 25.22 5800 3.0505 1.0
3.1087 25.65 5900 3.0508 1.0
3.1043 26.09 6000 3.0501 1.0
3.1043 26.52 6100 3.0467 1.0
3.1043 26.95 6200 3.0466 1.0
3.1043 27.39 6300 3.0465 1.0
3.1043 27.82 6400 3.0465 1.0
3.1175 28.26 6500 3.0466 1.0
3.1175 28.69 6600 3.0466 1.0
3.1175 29.13 6700 3.0465 1.0
3.1175 29.56 6800 3.0465 1.0
3.1175 30.0 6900 3.0464 1.0

Framework versions

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