readme: ---
  language:
  - gl
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - gl
  - hf-asr-leaderboard
  - model_for_talk
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: XLS-R-300M - Galician
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7.0
        type: mozilla-foundation/common_voice_7_0
        args: gl
      metrics:
      - name: Test WER
        type: wer
        value: 101.54
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: gl
      metrics:
      - name: Test WER
        type: wer
        value: 105.69
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: gl
      metrics:
      - name: Test WER
        type: wer
        value: 101.95
  ---
  
  <!-- 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-galician
  
  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 - GL dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1525
  - Wer: 0.1542
  
  ## 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: 20.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.0067        | 4.35  | 500  | 2.9632          | 1.0    |
  | 1.4939        | 8.7   | 1000 | 0.5005          | 0.4157 |
  | 0.9982        | 13.04 | 1500 | 0.1967          | 0.1857 |
  | 0.8726        | 17.39 | 2000 | 0.1587          | 0.1564 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  