readme: ---
  language:
  - pa-IN
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: xls-r-300m-pa
    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. -->
  
  # xls-r-300m-pa
  
  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 - PA-IN dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0443
  - Wer: 0.5715
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - 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: 2000
  - num_epochs: 500.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | 4.6694        | 19.22  | 500   | 4.0455          | 1.0    |
  | 3.3907        | 38.45  | 1000  | 3.2836          | 1.0    |
  | 2.0866        | 57.67  | 1500  | 1.2788          | 0.7715 |
  | 1.4106        | 76.9   | 2000  | 0.7866          | 0.6891 |
  | 1.1711        | 96.15  | 2500  | 0.6556          | 0.6272 |
  | 1.038         | 115.37 | 3000  | 0.6195          | 0.5680 |
  | 0.8989        | 134.6  | 3500  | 0.6563          | 0.5602 |
  | 0.8021        | 153.82 | 4000  | 0.6644          | 0.5327 |
  | 0.7161        | 173.07 | 4500  | 0.6844          | 0.5253 |
  | 0.6449        | 192.3  | 5000  | 0.7018          | 0.5331 |
  | 0.5659        | 211.52 | 5500  | 0.7451          | 0.5465 |
  | 0.5118        | 230.75 | 6000  | 0.7857          | 0.5386 |
  | 0.4385        | 249.97 | 6500  | 0.8062          | 0.5382 |
  | 0.3984        | 269.22 | 7000  | 0.8316          | 0.5621 |
  | 0.3666        | 288.45 | 7500  | 0.8736          | 0.5504 |
  | 0.3256        | 307.67 | 8000  | 0.9133          | 0.5688 |
  | 0.289         | 326.9  | 8500  | 0.9556          | 0.5684 |
  | 0.2663        | 346.15 | 9000  | 0.9344          | 0.5708 |
  | 0.2445        | 365.37 | 9500  | 0.9472          | 0.5590 |
  | 0.2289        | 384.6  | 10000 | 0.9713          | 0.5672 |
  | 0.2048        | 403.82 | 10500 | 0.9978          | 0.5762 |
  | 0.1857        | 423.07 | 11000 | 1.0230          | 0.5798 |
  | 0.1751        | 442.3  | 11500 | 1.0409          | 0.5755 |
  | 0.1688        | 461.52 | 12000 | 1.0445          | 0.5727 |
  | 0.1633        | 480.75 | 12500 | 1.0484          | 0.5739 |
  | 0.1488        | 499.97 | 13000 | 1.0443          | 0.5715 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  