readme: ---
  language:
  - dv
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - dv
  - generated_from_trainer
  - hf-asr-leaderboard
  - model_for_talk
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R-300M - Dhivehi
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: dv
      metrics:
      - name: Test WER
        type: wer
        value: 21.31
      - name: Test CER
        type: cer
        value: 3.82
  ---
  
  <!-- 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. -->
  
  # xls-r-300m-dv
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2855
  - Wer: 0.2665
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - 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: 500
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.3386        | 0.66  | 400   | 1.1411          | 0.9432 |
  | 0.6543        | 1.33  | 800   | 0.5099          | 0.6749 |
  | 0.4646        | 1.99  | 1200  | 0.4133          | 0.5968 |
  | 0.3748        | 2.65  | 1600  | 0.3534          | 0.5515 |
  | 0.3323        | 3.32  | 2000  | 0.3635          | 0.5527 |
  | 0.3269        | 3.98  | 2400  | 0.3587          | 0.5423 |
  | 0.2984        | 4.64  | 2800  | 0.3340          | 0.5073 |
  | 0.2841        | 5.31  | 3200  | 0.3279          | 0.5004 |
  | 0.2664        | 5.97  | 3600  | 0.3114          | 0.4845 |
  | 0.2397        | 6.63  | 4000  | 0.3174          | 0.4920 |
  | 0.2332        | 7.3   | 4400  | 0.3110          | 0.4911 |
  | 0.2304        | 7.96  | 4800  | 0.3123          | 0.4785 |
  | 0.2134        | 8.62  | 5200  | 0.2984          | 0.4557 |
  | 0.2066        | 9.29  | 5600  | 0.3013          | 0.4723 |
  | 0.1951        | 9.95  | 6000  | 0.2934          | 0.4487 |
  | 0.1806        | 10.61 | 6400  | 0.2802          | 0.4547 |
  | 0.1727        | 11.28 | 6800  | 0.2842          | 0.4333 |
  | 0.1666        | 11.94 | 7200  | 0.2873          | 0.4272 |
  | 0.1562        | 12.6  | 7600  | 0.3042          | 0.4373 |
  | 0.1483        | 13.27 | 8000  | 0.3122          | 0.4313 |
  | 0.1465        | 13.93 | 8400  | 0.2760          | 0.4226 |
  | 0.1335        | 14.59 | 8800  | 0.3112          | 0.4243 |
  | 0.1293        | 15.26 | 9200  | 0.3002          | 0.4133 |
  | 0.1264        | 15.92 | 9600  | 0.2985          | 0.4145 |
  | 0.1179        | 16.58 | 10000 | 0.2925          | 0.4012 |
  | 0.1171        | 17.25 | 10400 | 0.3127          | 0.4012 |
  | 0.1141        | 17.91 | 10800 | 0.2980          | 0.3908 |
  | 0.108         | 18.57 | 11200 | 0.3108          | 0.3951 |
  | 0.1045        | 19.24 | 11600 | 0.3269          | 0.3908 |
  | 0.1047        | 19.9  | 12000 | 0.2998          | 0.3868 |
  | 0.0937        | 20.56 | 12400 | 0.2918          | 0.3875 |
  | 0.0949        | 21.23 | 12800 | 0.2906          | 0.3657 |
  | 0.0879        | 21.89 | 13200 | 0.2974          | 0.3731 |
  | 0.0854        | 22.55 | 13600 | 0.2943          | 0.3711 |
  | 0.0851        | 23.22 | 14000 | 0.2919          | 0.3580 |
  | 0.0789        | 23.88 | 14400 | 0.2983          | 0.3560 |
  | 0.0796        | 24.54 | 14800 | 0.3131          | 0.3544 |
  | 0.0761        | 25.21 | 15200 | 0.2996          | 0.3616 |
  | 0.0755        | 25.87 | 15600 | 0.2972          | 0.3506 |
  | 0.0726        | 26.53 | 16000 | 0.2902          | 0.3474 |
  | 0.0707        | 27.2  | 16400 | 0.3083          | 0.3480 |
  | 0.0669        | 27.86 | 16800 | 0.3035          | 0.3330 |
  | 0.0637        | 28.52 | 17200 | 0.2963          | 0.3370 |
  | 0.0596        | 29.19 | 17600 | 0.2830          | 0.3326 |
  | 0.0583        | 29.85 | 18000 | 0.2969          | 0.3287 |
  | 0.0566        | 30.51 | 18400 | 0.3002          | 0.3480 |
  | 0.0574        | 31.18 | 18800 | 0.2916          | 0.3296 |
  | 0.0536        | 31.84 | 19200 | 0.2933          | 0.3225 |
  | 0.0548        | 32.5  | 19600 | 0.2900          | 0.3179 |
  | 0.0506        | 33.17 | 20000 | 0.3073          | 0.3225 |
  | 0.0511        | 33.83 | 20400 | 0.2925          | 0.3275 |
  | 0.0483        | 34.49 | 20800 | 0.2919          | 0.3245 |
  | 0.0456        | 35.1