readme: ---
  license: apache-2.0
  language: ab
  tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-300m-ab-CV8
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: ab
      metrics:
      - name: Test WER
        type: wer
        value: 44.9
  ---
  
  
  # wav2vec2-xls-r-300m-ab-CV8
  
  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: 0.2105
  - Wer: 0.5474
  
  ## 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: 0.0001
  - 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: 300
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.7729        | 0.63  | 500   | 3.0624          | 1.0021 |
  | 2.7348        | 1.26  | 1000  | 1.0460          | 0.9815 |
  | 1.2756        | 1.9   | 1500  | 0.4618          | 0.8309 |
  | 1.0419        | 2.53  | 2000  | 0.3725          | 0.7449 |
  | 0.9491        | 3.16  | 2500  | 0.3368          | 0.7345 |
  | 0.9006        | 3.79  | 3000  | 0.3014          | 0.6936 |
  | 0.8519        | 4.42  | 3500  | 0.2852          | 0.6767 |
  | 0.8243        | 5.06  | 4000  | 0.2701          | 0.6504 |
  | 0.7902        | 5.69  | 4500  | 0.2641          | 0.6221 |
  | 0.7767        | 6.32  | 5000  | 0.2549          | 0.6192 |
  | 0.7516        | 6.95  | 5500  | 0.2515          | 0.6179 |
  | 0.737         | 7.59  | 6000  | 0.2408          | 0.5963 |
  | 0.7217        | 8.22  | 6500  | 0.2429          | 0.6261 |
  | 0.7101        | 8.85  | 7000  | 0.2366          | 0.5687 |
  | 0.6922        | 9.48  | 7500  | 0.2277          | 0.5680 |
  | 0.6866        | 10.11 | 8000  | 0.2242          | 0.5847 |
  | 0.6703        | 10.75 | 8500  | 0.2222          | 0.5803 |
  | 0.6649        | 11.38 | 9000  | 0.2247          | 0.5765 |
  | 0.6513        | 12.01 | 9500  | 0.2182          | 0.5644 |
  | 0.6369        | 12.64 | 10000 | 0.2128          | 0.5508 |
  | 0.6425        | 13.27 | 10500 | 0.2132          | 0.5514 |
  | 0.6399        | 13.91 | 11000 | 0.2116          | 0.5495 |
  | 0.6208        | 14.54 | 11500 | 0.2105          | 0.5474 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.1
  - Tokenizers 0.10.3
  