readme: ---
  language:
  - bas
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - bas
  - generated_from_trainer
  - hf-asr-leaderboard
  - model_for_talk
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R-300M - Basaa
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: bas
      metrics:
      - name: Test WER
        type: wer
        value: 38.057
      - name: Test CER
        type: cer
        value: 11.233
  ---
  
  <!-- 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-basaa-cv8
  
  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 - BAS dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4648
  - Wer: 0.5472
  
  ## 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: 7e-05
  - train_batch_size: 32
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 2.9421        | 12.82 | 500  | 2.8894          | 1.0    |
  | 1.1872        | 25.64 | 1000 | 0.6688          | 0.7460 |
  | 0.8894        | 38.46 | 1500 | 0.4868          | 0.6516 |
  | 0.769         | 51.28 | 2000 | 0.4960          | 0.6507 |
  | 0.6936        | 64.1  | 2500 | 0.4781          | 0.5384 |
  | 0.624         | 76.92 | 3000 | 0.4643          | 0.5430 |
  | 0.5966        | 89.74 | 3500 | 0.4530          | 0.5591 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  