readme: ---
  language:
  - pt
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - pt
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: wav2vec2-xls-r-1b-cv8
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: pt
      metrics:
      - name: Test WER
        type: wer
        value: 17.7
      - name: Test CER
        type: cer
        value: 5.21
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: sv
      metrics:
      - name: Test WER
        type: wer
        value: 45.68
      - name: Test CER
        type: cer
        value: 18.67
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: pt
      metrics:
      - name: Test WER
        type: wer
        value: 45.29
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: pt
      metrics:
      - name: Test WER
        type: wer
        value: 48.03
  ---
  <!-- 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-xls-r-1b-cv8
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2007
  - Wer: 0.1838
  
  ## 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-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 30.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 2.1172        | 0.32  | 500   | 1.2852          | 0.9783 |
  | 1.4152        | 0.64  | 1000  | 0.6434          | 0.6105 |
  | 1.4342        | 0.96  | 1500  | 0.4844          | 0.3989 |
  | 1.4657        | 1.29  | 2000  | 0.5080          | 0.4490 |
  | 1.4961        | 1.61  | 2500  | 0.4764          | 0.4264 |
  | 1.4515        | 1.93  | 3000  | 0.4519          | 0.4068 |
  | 1.3924        | 2.25  | 3500  | 0.4472          | 0.4132 |
  | 1.4524        | 2.57  | 4000  | 0.4455          | 0.3939 |
  | 1.4328        | 2.89  | 4500  | 0.4369          | 0.4069 |
  | 1.3456        | 3.22  | 5000  | 0.4234          | 0.3774 |
  | 1.3725        | 3.54  | 5500  | 0.4387          | 0.3789 |
  | 1.3812        | 3.86  | 6000  | 0.4298          | 0.3825 |
  | 1.3282        | 4.18  | 6500  | 0.4025          | 0.3703 |
  | 1.3326        | 4.5   | 7000  | 0.3917          | 0.3502 |
  | 1.3028        | 4.82  | 7500  | 0.3889          | 0.3582 |
  | 1.293         | 5.14  | 8000  | 0.3859          | 0.3496 |
  | 1.321         | 5.47  | 8500  | 0.3875          | 0.3576 |
  | 1.3165        | 5.79  | 9000  | 0.3927          | 0.3589 |
  | 1.2701        | 6.11  | 9500  | 0.4058          | 0.3621 |
  | 1.2718        | 6.43  | 10000 | 0.4211          | 0.3916 |
  | 1.2683        | 6.75  | 10500 | 0.3968          | 0.3620 |
  | 1.2643        | 7.07  | 11000 | 0.4128          | 0.3848 |
  | 1.2485        | 7.4   | 11500 | 0.3849          | 0.3727 |
  | 1.2608        | 7.72  | 12000 | 0.3770          | 0.3474 |
  | 1.2388        | 8.04  | 12500 | 0.3774          | 0.3574 |
  | 1.2524        | 8.36  | 13000 | 0.3789          | 0.3550 |
  | 1.2458        | 8.68  | 13500 | 0.3770          | 0.3410 |
  | 1.2505        | 9.0   | 14000 | 0.3638          | 0.3403 |
  | 1.2254        | 9.32  | 14500 | 0.3770          | 0.3509 |
  | 1.2459        | 9.65  | 15000 | 0.3592          | 0.3349 |
  | 1.2049        | 9.97  | 15500 | 0.3600          | 0.3428 |
  | 1.2097        | 10.29 | 16000 | 0.3626          | 0.3347 |
  | 1.1988        | 10.61 | 16500 | 0.3740          | 0.3269 |
  | 1.1671        | 10.93 | 17000 | 0.3548          | 0.3245 |
  | 1.1532        | 11.25 | 17500 | 0.3394          | 0.3140 |
  | 1.1459        | 11.58 | 18000 | 0.3349          | 0.3156 |
  | 1.1511        | 11.9  | 18500 | 0.3272          | 0.3110 |
  | 1.146