readme: ---
  language:
  - id
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
  datasets:
  - common_voice
  model-index:
  - name: 'XLS-R-300M - Indonesia'
    results:
    - task: 
        name: Automatic Speech Recognition 
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: sv-SE
      metrics:
         - name: Test WER
           type: wer
           value: 38.098
         - name: Test CER
           type: cer
           value: 14.261
    
  ---
  
  <!-- 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_8_0 - ID dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3975
  - Wer: 0.2633
  
  ## 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: 8
  - 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        | 0.78  | 100  | 4.5645          | 1.0    |
  | No log        | 1.55  | 200  | 2.9016          | 1.0    |
  | No log        | 2.33  | 300  | 2.2666          | 1.0982 |
  | No log        | 3.1   | 400  | 0.6079          | 0.6376 |
  | 3.2188        | 3.88  | 500  | 0.4985          | 0.5008 |
  | 3.2188        | 4.65  | 600  | 0.4477          | 0.4469 |
  | 3.2188        | 5.43  | 700  | 0.3953          | 0.3915 |
  | 3.2188        | 6.2   | 800  | 0.4319          | 0.3921 |
  | 3.2188        | 6.98  | 900  | 0.4171          | 0.3698 |
  | 0.2193        | 7.75  | 1000 | 0.3957          | 0.3600 |
  | 0.2193        | 8.53  | 1100 | 0.3730          | 0.3493 |
  | 0.2193        | 9.3   | 1200 | 0.3780          | 0.3348 |
  | 0.2193        | 10.08 | 1300 | 0.4133          | 0.3568 |
  | 0.2193        | 10.85 | 1400 | 0.3984          | 0.3193 |
  | 0.1129        | 11.63 | 1500 | 0.3845          | 0.3174 |
  | 0.1129        | 12.4  | 1600 | 0.3882          | 0.3162 |
  | 0.1129        | 13.18 | 1700 | 0.3982          | 0.3008 |
  | 0.1129        | 13.95 | 1800 | 0.3902          | 0.3198 |
  | 0.1129        | 14.73 | 1900 | 0.4082          | 0.3237 |
  | 0.0765        | 15.5  | 2000 | 0.3732          | 0.3126 |
  | 0.0765        | 16.28 | 2100 | 0.3893          | 0.3001 |
  | 0.0765        | 17.05 | 2200 | 0.4168          | 0.3083 |
  | 0.0765        | 17.83 | 2300 | 0.4193          | 0.3044 |
  | 0.0765        | 18.6  | 2400 | 0.4006          | 0.3013 |
  | 0.0588        | 19.38 | 2500 | 0.3836          | 0.2892 |
  | 0.0588        | 20.16 | 2600 | 0.3761          | 0.2903 |
  | 0.0588        | 20.93 | 2700 | 0.3895          | 0.2930 |
  | 0.0588        | 21.71 | 2800 | 0.3885          | 0.2791 |
  | 0.0588        | 22.48 | 2900 | 0.3902          | 0.2891 |
  | 0.0448        | 23.26 | 3000 | 0.4200          | 0.2849 |
  | 0.0448        | 24.03 | 3100 | 0.4013          | 0.2799 |
  | 0.0448        | 24.81 | 3200 | 0.4039          | 0.2731 |
  | 0.0448        | 25.58 | 3300 | 0.3970          | 0.2647 |
  | 0.0448        | 26.36 | 3400 | 0.4081          | 0.2690 |
  | 0.0351        | 27.13 | 3500 | 0.4090          | 0.2674 |
  | 0.0351        | 27.91 | 3600 | 0.3953          | 0.2663 |
  | 0.0351        | 28.68 | 3700 | 0.4044          | 0.2650 |
  | 0.0351        | 29.46 | 3800 | 0.3969          | 0.2646 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  