readme: ---
  language:
  - br
  license: apache-2.0
  tags:
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-large-xls-r-300m-br-d2
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice 8
        args: br
      metrics:
      - type: wer
        value: 0.49770598355954887
        name: Test WER
      - name: Test CER
        type: cer
        value: 0.18090500890299605
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: br
      metrics:
      - name: Test WER
        type: wer
        value: NA
      - name: Test CER
        type: cer
        value: NA
  ---
  
  <!-- 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-br-d2
  
  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 - BR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1257
  - Wer: 0.4631
  
  ### Evaluation Commands
  
  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
  
  python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-br-d2 --dataset mozilla-foundation/common_voice_8_0 --config br --split test --log_outputs
  
  2. To evaluate on speech-recognition-community-v2/dev_data
  
  Breton language isn't available in speech-recognition-community-v2/dev_data
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.00034
  - 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: 750
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 14.0379       | 0.68  | 100  | 5.6808          | 1.0    |
  | 3.9145        | 1.35  | 200  | 3.1970          | 1.0    |
  | 3.0293        | 2.03  | 300  | 2.9513          | 1.0    |
  | 2.0927        | 2.7   | 400  | 1.4545          | 0.8887 |
  | 1.1556        | 3.38  | 500  | 1.0966          | 0.7564 |
  | 0.9628        | 4.05  | 600  | 0.9808          | 0.7364 |
  | 0.7869        | 4.73  | 700  | 1.0488          | 0.7355 |
  | 0.703         | 5.41  | 800  | 0.9500          | 0.6881 |
  | 0.6657        | 6.08  | 900  | 0.9309          | 0.6259 |
  | 0.5663        | 6.76  | 1000 | 0.9133          | 0.6357 |
  | 0.496         | 7.43  | 1100 | 0.9890          | 0.6028 |
  | 0.4748        | 8.11  | 1200 | 0.9469          | 0.5894 |
  | 0.4135        | 8.78  | 1300 | 0.9270          | 0.6045 |
  | 0.3579        | 9.46  | 1400 | 0.8818          | 0.5708 |
  | 0.353         | 10.14 | 1500 | 0.9244          | 0.5781 |
  | 0.334         | 10.81 | 1600 | 0.9009          | 0.5638 |
  | 0.2917        | 11.49 | 1700 | 1.0132          | 0.5828 |
  | 0.29          | 12.16 | 1800 | 0.9696          | 0.5668 |
  | 0.2691        | 12.84 | 1900 | 0.9811          | 0.5455 |
  | 0.25          | 13.51 | 2000 | 0.9951          | 0.5624 |
  | 0.2467        | 14.19 | 2100 | 0.9653          | 0.5573 |
  | 0.2242        | 14.86 | 2200 | 0.9714          | 0.5378 |
  | 0.2066        | 15.54 | 2300 | 0.9829          | 0.5394 |
  | 0.2075        | 16.22 | 2400 | 1.0547          | 0.5520 |
  | 0.1923        | 16.89 | 2500 | 1.0014          | 0.5397 |
  | 0.1919        | 17.57 | 2600 | 0.9978          | 0.5477 |
  | 0.1908        | 18.24 | 2700 | 1.1064          | 0.5397 |
  | 0.157         | 18.92 | 2800 | 1.0629          | 0.5238 |
  | 0.159         | 19.59 | 2900 | 1.0642          | 0.5321 |
  | 0.1652        | 20.27 | 3000 | 1.0207          | 0.5328 |
  | 0.141         | 20.95 | 3100 | 0.9948          | 0.5312 |
  | 0.1417        | 21.62 | 3200 | 1.0338          | 0.5328 |
  | 0.1514        | 22.3  | 3300 | 1.0513          | 0.5313 |
  | 0.1365        | 22.97 | 3400 | 1.0357          | 0.5291 |
  | 0.1319        | 23.65 | 3500 | 1.0587          | 0.5167 |
  | 0.1298        | 24.32 | 3600 | 1.0636          | 0.5236 |
  | 0.1245        | 25.0  | 3700 | 1.1367          | 0.5280 |
  | 0.1114        | 25.68 | 3800 | 1.0633          | 0.5200 |
  | 0.1088        | 26.35 | 3900 | 1.0495          | 0.5210 |
  | 0.1175        | 27.03 | 4000 | 1.0897          | 0.5095 |
  | 0.1043        | 27.7  | 4100 | 1.0580          | 0.5309 |
  | 0.0951        | 28.38 | 4200 | 1.0448          | 0.5067 |
  | 0.1011        | 29.05 | 4300 | 1.0665          | 0.5137 |
  | 0.0889        | 29.73 | 4400 | 1.0579          | 0.5026 |
  | 0.0833      