readme: ---
  language:
  - id
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  metrics:
  - wer
  - cer
  model-index:
  - name: wav2vec2-large-xls-r-300m-Indonesian
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_7_0
        name: Common Voice id
        args: id
      metrics:
      - type: wer
        value: 25.06
        name: Test WER With LM
      - type: cer
        value: 6.5
        name: Test CER With LM
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: id
      metrics:
      - name: Test WER
        type: wer
        value: 99.61
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: id
      metrics:
      - name: Test WER
        type: wer
        value: 106.39
  ---
  
  <!-- 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. -->
  
  # wav2vec2-large-xls-r-300m-Indonesian
  
  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: 0.4087
  - Wer: 0.2461
  - Cer: 0.0666
  
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 64
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 400
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 5.0788        | 4.26  | 200  | 2.9389          | 1.0    | 1.0    |
  | 2.8288        | 8.51  | 400  | 2.2535          | 1.0    | 0.8004 |
  | 0.907         | 12.77 | 600  | 0.4558          | 0.4243 | 0.1095 |
  | 0.4071        | 17.02 | 800  | 0.4013          | 0.3468 | 0.0913 |
  | 0.3           | 21.28 | 1000 | 0.4167          | 0.3075 | 0.0816 |
  | 0.2544        | 25.53 | 1200 | 0.4132          | 0.2835 | 0.0762 |
  | 0.2145        | 29.79 | 1400 | 0.3878          | 0.2693 | 0.0729 |
  | 0.1923        | 34.04 | 1600 | 0.4023          | 0.2623 | 0.0702 |
  | 0.1681        | 38.3  | 1800 | 0.3984          | 0.2581 | 0.0686 |
  | 0.1598        | 42.55 | 2000 | 0.3982          | 0.2493 | 0.0663 |
  | 0.1464        | 46.81 | 2200 | 0.4087          | 0.2461 | 0.0666 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  