readme: ---
  license: apache-2.0
  tags:
  - google/fleurs
  - generated_from_trainer
  - automatic-speech-recognition
  - pashto
  - ps
  datasets:
  - fleurs
  metrics:
  - wer
  model-index:
  - name: facebook/wav2vec2-xls-r-300m
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: google/fleurs
        type: google/fleurs
        args: 'config: ps_af, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 51.59447476125512
  ---
  
  <!-- 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. -->
  
  # facebook/wav2vec2-xls-r-300m
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/FLEURS - PS_AF dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9162
  - Wer: 51.59
  - Cer: 19.72
  
  ## 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-07
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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: 1000
  - training_steps: 6000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Cer    | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
  | 5.0767        | 6.33  | 500  | 1.0    | 4.8783          | 1.0    |
  | 3.1156        | 12.66 | 1000 | 1.0    | 3.0990          | 1.0    |
  | 1.3506        | 18.99 | 1500 | 0.2889 | 1.1056          | 0.7031 |
  | 0.9997        | 25.32 | 2000 | 0.2301 | 0.9191          | 0.5944 |
  | 0.7838        | 31.65 | 2500 | 0.2152 | 0.8952          | 0.5556 |
  | 0.6665        | 37.97 | 3000 | 0.2017 | 0.8908          | 0.5252 |
  | 0.6265        | 44.3  | 3500 | 0.1954 | 0.9063          | 0.5133 |
  | 0.5935        | 50.63 | 4000 | 0.1969 | 0.9162          | 0.5156 |
  | 0.5174        | 56.96 | 4500 | 0.1972 | 0.9287          | 0.5140 |
  | 0.5462        | 63.29 | 5000 | 0.1974 | 0.9370          | 0.5138 |
  | 0.5564        | 69.62 | 5500 | 0.1977 | 0.9461          | 0.5148 |
  | 0.5252        | 75.95 | 6000 | 0.9505 | 0.5118          | 0.1969 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  