readme: ---
  language:
  - te
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - openslr_SLR66
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - openslr
  - SLR66
  metrics:
  - wer
  model-index:
  - name: xls-r-300m-te
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: openslr
        name: Open SLR
        args: SLR66
      metrics:
      - type: wer
        value: 24.695121951219512
        name: Test WER
      - type: cer
        value: 4.861934182322532
        name: Test CER
  ---
  
  <!-- 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. -->
  
  # 
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR66 - NA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2680
  - Wer: 0.3467
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 10.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.0304        | 4.81  | 500   | 1.5676          | 1.0554 |
  | 1.5263        | 9.61  | 1000  | 0.4693          | 0.8023 |
  | 1.5299        | 14.42 | 1500  | 0.4368          | 0.7311 |
  | 1.5063        | 19.23 | 2000  | 0.4360          | 0.7302 |
  | 1.455         | 24.04 | 2500  | 0.4213          | 0.6692 |
  | 1.4755        | 28.84 | 3000  | 0.4329          | 0.5943 |
  | 1.352         | 33.65 | 3500  | 0.4074          | 0.5765 |
  | 1.3122        | 38.46 | 4000  | 0.3866          | 0.5630 |
  | 1.2799        | 43.27 | 4500  | 0.3860          | 0.5480 |
  | 1.212         | 48.08 | 5000  | 0.3590          | 0.5317 |
  | 1.1645        | 52.88 | 5500  | 0.3283          | 0.4757 |
  | 1.0854        | 57.69 | 6000  | 0.3162          | 0.4687 |
  | 1.0292        | 62.5  | 6500  | 0.3126          | 0.4416 |
  | 0.9607        | 67.31 | 7000  | 0.2990          | 0.4066 |
  | 0.9156        | 72.12 | 7500  | 0.2870          | 0.4009 |
  | 0.8329        | 76.92 | 8000  | 0.2791          | 0.3909 |
  | 0.7979        | 81.73 | 8500  | 0.2770          | 0.3670 |
  | 0.7144        | 86.54 | 9000  | 0.2841          | 0.3661 |
  | 0.6997        | 91.35 | 9500  | 0.2721          | 0.3485 |
  | 0.6568        | 96.15 | 10000 | 0.2681          | 0.3437 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  