readme: ---
  license: apache-2.0
  language:
  - pa
  - pa-IN
  tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-pa-in
    results: []
  ---
  
  <!-- 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-pa-in
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.9680
  - Wer: 0.7283
  
  ## 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: 0.0003
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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: 500
  - num_epochs: 180
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|
  | 8.2615        | 24.97  | 400  | 3.4784          | 1.0    |
  | 3.366         | 49.97  | 800  | 2.3662          | 0.9917 |
  | 1.1678        | 74.97  | 1200 | 1.4806          | 0.7709 |
  | 0.5496        | 99.97  | 1600 | 1.7166          | 0.7476 |
  | 0.4101        | 124.97 | 2000 | 1.8473          | 0.7510 |
  | 0.3317        | 149.97 | 2400 | 1.9177          | 0.7322 |
  | 0.2956        | 174.97 | 2800 | 1.9680          | 0.7283 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.10.3
  
  
  ### Evaluations Result
  
  - WER: 0.7539
  - CER: 0.2928