readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0
    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-finetuned-hindi-common-voice-9-0
  
  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: 0.7392
  - Wer: 1.0141
  
  ## 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: 4.42184e-05
  - 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: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 9.2217        | 3.03  | 400  | 4.0314          | 1.0    |
  | 3.2902        | 6.06  | 800  | 2.1356          | 1.0001 |
  | 0.9858        | 9.09  | 1200 | 0.8566          | 1.0037 |
  | 0.5131        | 12.12 | 1600 | 0.7481          | 1.0074 |
  | 0.3781        | 15.15 | 2000 | 0.7437          | 1.008  |
  | 0.2998        | 18.18 | 2400 | 0.7310          | 1.0162 |
  | 0.2553        | 21.21 | 2800 | 0.7384          | 1.0159 |
  | 0.2216        | 24.24 | 3200 | 0.7537          | 1.0100 |
  | 0.2048        | 27.27 | 3600 | 0.7392          | 1.0141 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.10.3
  