readme: ---
  language:
  - ar
  license: apache-2.0
  tags:
  - ar
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: XLS-R-300M - Arabic
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: ar
      metrics:
      - name: Test WER
        type: wer
        value: 47.54
      - name: Test CER
        type: cer
        value: 17.64
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: ar
      metrics:
      - name: Test WER
        type: wer
        value: 93.72
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: ar
      metrics:
      - name: Test WER
        type: wer
        value: 92.49
  ---
  
  <!-- 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. -->
  
  # 
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4502
  - Wer: 0.4783
  
  ## 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: 7.5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.7972        | 0.43  | 500   | 5.1401          | 1.0    |
  | 3.3241        | 0.86  | 1000  | 3.3220          | 1.0    |
  | 3.1432        | 1.29  | 1500  | 3.0806          | 0.9999 |
  | 2.9297        | 1.72  | 2000  | 2.5678          | 1.0057 |
  | 2.2593        | 2.14  | 2500  | 1.1068          | 0.8218 |
  | 2.0504        | 2.57  | 3000  | 0.7878          | 0.7114 |
  | 1.937         | 3.0   | 3500  | 0.6955          | 0.6450 |
  | 1.8491        | 3.43  | 4000  | 0.6452          | 0.6304 |
  | 1.803         | 3.86  | 4500  | 0.5961          | 0.6042 |
  | 1.7545        | 4.29  | 5000  | 0.5550          | 0.5748 |
  | 1.7045        | 4.72  | 5500  | 0.5374          | 0.5743 |
  | 1.6733        | 5.15  | 6000  | 0.5337          | 0.5404 |
  | 1.6761        | 5.57  | 6500  | 0.5054          | 0.5266 |
  | 1.655         | 6.0   | 7000  | 0.4926          | 0.5243 |
  | 1.6252        | 6.43  | 7500  | 0.4946          | 0.5183 |
  | 1.6209        | 6.86  | 8000  | 0.4915          | 0.5194 |
  | 1.5772        | 7.29  | 8500  | 0.4725          | 0.5104 |
  | 1.5602        | 7.72  | 9000  | 0.4726          | 0.5097 |
  | 1.5783        | 8.15  | 9500  | 0.4667          | 0.4956 |
  | 1.5442        | 8.58  | 10000 | 0.4685          | 0.4937 |
  | 1.5597        | 9.01  | 10500 | 0.4708          | 0.4957 |
  | 1.5406        | 9.43  | 11000 | 0.4539          | 0.4810 |
  | 1.5274        | 9.86  | 11500 | 0.4502          | 0.4783 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  