readme: ---
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - abdusahmbzuai/arabic_speech_massive_sm
  - generated_from_trainer
  model-index:
  - name: aradia-ctc-distilhubert-ft
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # aradia-ctc-distilhubert-ft
  
  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_SM - NA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.7114
  - Wer: 0.8908
  
  ## 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  | 4.4129          | 1.0    |
  | No log        | 0.87  | 200  | 3.5927          | 1.0    |
  | No log        | 1.3   | 300  | 3.3780          | 1.0    |
  | No log        | 1.74  | 400  | 3.0830          | 1.0    |
  | 5.3551        | 2.17  | 500  | 2.6278          | 0.9999 |
  | 5.3551        | 2.61  | 600  | 1.8359          | 1.0000 |
  | 5.3551        | 3.04  | 700  | 1.7878          | 0.9914 |
  | 5.3551        | 3.48  | 800  | 1.5219          | 0.9875 |
  | 5.3551        | 3.91  | 900  | 1.4348          | 0.9879 |
  | 1.7199        | 4.35  | 1000 | 1.4354          | 0.9644 |
  | 1.7199        | 4.78  | 1100 | 1.5210          | 0.9519 |
  | 1.7199        | 5.22  | 1200 | 1.3607          | 0.9475 |
  | 1.7199        | 5.65  | 1300 | 1.3839          | 0.9343 |
  | 1.7199        | 6.09  | 1400 | 1.2806          | 0.8944 |
  | 1.2342        | 6.52  | 1500 | 1.3036          | 0.9011 |
  | 1.2342        | 6.95  | 1600 | 1.3704          | 0.9072 |
  | 1.2342        | 7.39  | 1700 | 1.2981          | 0.8891 |
  | 1.2342        | 7.82  | 1800 | 1.2786          | 0.8733 |
  | 1.2342        | 8.26  | 1900 | 1.2897          | 0.8867 |
  | 0.9831        | 8.69  | 2000 | 1.4436          | 0.8780 |
  | 0.9831        | 9.13  | 2100 | 1.3680          | 0.8873 |
  | 0.9831        | 9.56  | 2200 | 1.3471          | 0.8692 |
  | 0.9831        | 10.0  | 2300 | 1.3725          | 0.8729 |
  | 0.9831        | 10.43 | 2400 | 1.4439          | 0.8771 |
  | 0.8071        | 10.87 | 2500 | 1.5114          | 0.8928 |
  | 0.8071        | 11.3  | 2600 | 1.6156          | 0.8958 |
  | 0.8071        | 11.74 | 2700 | 1.4381          | 0.8749 |
  | 0.8071        | 12.17 | 2800 | 1.5088          | 0.8717 |
  | 0.8071        | 12.61 | 2900 | 1.5486          | 0.8813 |
  | 0.6321        | 13.04 | 3000 | 1.4536          | 0.8884 |
  | 0.6321        | 13.48 | 3100 | 1.4679          | 0.8947 |
  | 0.6321        | 13.91 | 3200 | 1.5628          | 0.9117 |
  | 0.6321        | 14.35 | 3300 | 1.5831          | 0.8716 |
  | 0.6321        | 14.78 | 3400 | 1.6733          | 0.8702 |
  | 0.4998        | 15.22 | 3500 | 1.8225          | 0.8665 |
  | 0.4998        | 15.65 | 3600 | 1.8558          | 0.8732 |
  | 0.4998        | 16.09 | 3700 | 1.7513          | 0.8766 |
  | 0.4998        | 16.52 | 3800 | 1.8562          | 0.8753 |
  | 0.4998        | 16.95 | 3900 | 1.9018          | 0.8704 |
  | 0.4421        | 17.39 | 4000 | 1.9341          | 0.8789 |
  | 0.4421        | 17.82 | 4100 | 1.9582          | 0.8781 |
  | 0.4421        | 18.26 | 4200 | 1.8863          | 0.8821 |
  | 0.4421        | 18.69 | 4300 | 1.9366          | 0.8847 |
  | 0.4421        | 19.13 | 4400 | 2.1902          | 0.8721 |
  | 0.3712        | 19.56 | 4500 | 2.1641          | 0.8670 |
  | 0.3712        | 20.0  | 4600 | 2.1639          | 0.8776 |
  | 0.3712        | 20.43 | 4700 | 2.2695          | 0.9030 |
  | 0.3712        | 20.87 | 4800 | 2.1909          | 0.8937 |
  | 0.3712        | 21.3  | 4900 | 2.1606          | 0.8959 |
  | 0.3067        | 21.74 | 5000 | 2.1756          | 0.8943 |
  | 0.3067        | 22.17 | 5100 | 2.4092          | 0.8773 |
  | 0.3067        | 22.61 | 5200 | 2.4991          | 0.8721 |
  | 0.3067        | 23.04 | 5300 | 2.3340          | 0.8910 |
  | 0.3067        | 23.48 | 5400 | 2.3567          | 0.8946 |
  | 0.2764        | 23.91 | 5500 | 2.3215          | 0.8897 |
  | 0.2764        | 24.35 | 5600 | 2.4824          | 0.9002 |
  | 0.2764        | 24.78 | 5700 | 2.4585          | 0.8963 |
  | 0.2764        | 25.22 | 5800 | 2.5804          | 0.8879 |
  | 0.2764        | 25.65 | 5900 | 2.5814          | 0.8903 |
  | 0.2593        | 26.09 | 6000 | 2.5374          | 0.8868 