readme: ---
  language:
  - br
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: XLS-R-300M - Breton
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: br
      metrics:
      - name: Test WER
        type: wer
        value: 107.955
      - name: Test CER
        type: cer
        value: 379.33
  ---
  
  <!-- 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-breton
  
  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_7_0 - BR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6102
  - Wer: 0.4455
  
  ## 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: 32
  - 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: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 2.9205        | 3.33  | 500  | 2.8659          | 1.0    |
  | 1.6403        | 6.67  | 1000 | 0.9440          | 0.7593 |
  | 1.3483        | 10.0  | 1500 | 0.7580          | 0.6215 |
  | 1.2255        | 13.33 | 2000 | 0.6851          | 0.5722 |
  | 1.1139        | 16.67 | 2500 | 0.6409          | 0.5220 |
  | 1.0688        | 20.0  | 3000 | 0.6245          | 0.5055 |
  | 0.99          | 23.33 | 3500 | 0.6142          | 0.4874 |
  | 0.9345        | 26.67 | 4000 | 0.5946          | 0.4829 |
  | 0.9058        | 30.0  | 4500 | 0.6229          | 0.4704 |
  | 0.8683        | 33.33 | 5000 | 0.6153          | 0.4666 |
  | 0.8367        | 36.67 | 5500 | 0.5952          | 0.4542 |
  | 0.8162        | 40.0  | 6000 | 0.6030          | 0.4541 |
  | 0.8042        | 43.33 | 6500 | 0.5972          | 0.4485 |
  | 0.7836        | 46.67 | 7000 | 0.6070          | 0.4497 |
  | 0.7556        | 50.0  | 7500 | 0.6102          | 0.4455 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  