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-1B-te
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: openslr
        name: Open SLR
        args: SLR66
      metrics:
      - type: wer
        value: 0.51
        name: Test WER
      - type: cer
        value: 0.097
        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-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the OPENSLR_SLR66 - NA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4253
  - Wer: 0.5109
  
  
  ### Evaluation metrics
  
  | Metric | Split  | Decode with LM | Value     |
  |:------:|:------:|:--------------:|:---------:|
  | WER    | Train  | No             |      |
  | CER    | Train  | No             |      |
  | WER    | Test   | No             |      |
  | CER    | Test   | No             |      |
  | WER    | Train  | Yes            |      |
  | CER    | Train  | Yes            |      |
  | WER    | Test   | Yes            |      |
  | CER    | Test   | Yes            |      |
  
  
  ## 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: 2e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 12
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - learning_rate: 3e-6
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 150.0
  - hidden_dropout: 0.15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  