readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - fleurs
  metrics:
  - wer
  model-index:
  - name: wav2vec2-large-xls-r-300m-he
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: fleurs
        type: fleurs
        config: he_il
        split: train
        args: he_il
      metrics:
      - name: Wer
        type: wer
        value: 0.5953778429933969
  ---
  
  <!-- 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-he
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset.
  It achieves the following results on the evaluation set:
  - Loss: inf
  - Wer: 0.5954
  
  ## 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: 2
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 6
  - total_train_batch_size: 12
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 20
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 8.8899        | 0.99  | 200  | inf             | 1.0    |
  | 3.0802        | 1.98  | 400  | inf             | 1.0    |
  | 1.4275        | 2.97  | 600  | inf             | 0.8155 |
  | 0.8737        | 3.96  | 800  | inf             | 0.7276 |
  | 0.6503        | 4.95  | 1000 | inf             | 0.6858 |
  | 0.5176        | 5.94  | 1200 | inf             | 0.6660 |
  | 0.4084        | 6.93  | 1400 | inf             | 0.6682 |
  | 0.3469        | 7.92  | 1600 | inf             | 0.6473 |
  | 3.2485        | 6.67  | 1800 | inf             | 1.0    |
  | 0.6476        | 7.41  | 2000 | inf             | 0.6574 |
  | 0.3229        | 8.15  | 2200 | inf             | 0.6499 |
  | 0.2899        | 8.89  | 2400 | inf             | 0.6376 |
  | 0.26          | 9.63  | 2600 | inf             | 0.6405 |
  | 0.2038        | 10.37 | 2800 | inf             | 0.6409 |
  | 0.2158        | 11.11 | 3000 | inf             | 0.6313 |
  | 0.1892        | 11.85 | 3200 | inf             | 0.6185 |
  | 0.1611        | 12.59 | 3400 | inf             | 0.6271 |
  | 0.1584        | 13.33 | 3600 | inf             | 0.6101 |
  | 0.1443        | 14.07 | 3800 | inf             | 0.6121 |
  | 0.1353        | 14.81 | 4000 | inf             | 0.6194 |
  | 0.1109        | 15.56 | 4200 | inf             | 0.6321 |
  | 0.1116        | 16.3  | 4400 | inf             | 0.6025 |
  | 0.1054        | 17.04 | 4600 | inf             | 0.6029 |
  | 0.0966        | 17.78 | 4800 | inf             | 0.6069 |
  | 0.0824        | 18.52 | 5000 | inf             | 0.5998 |
  | 0.0812        | 19.26 | 5200 | inf             | 0.5972 |
  | 0.0749        | 20.0  | 5400 | inf             | 0.5954 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  