readme: ---
  language:
  - hi
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_9_0
  - generated_from_trainer
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_9_0
  metrics:
  - wer
  model-index:
  - name: XLS-R-300M - Hindi
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_9_0
        name: Common Voice 9
        args: hi
      metrics:
      - type: wer
        value: 21.145
        name: Test WER
      - name: Test CER
        type: cer
        value: 7.709
  ---
  
  <!-- 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 MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - HI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5164
  - Wer: 0.3349
  - Cer: 0.1082
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - training_steps: 9815
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
  | 3.9471        | 8.16   | 400  | 3.7109          | 1.0    | 1.0    |
  | 3.274         | 16.32  | 800  | 3.1582          | 0.9917 | 0.9573 |
  | 1.5889        | 24.48  | 1200 | 0.7763          | 0.6030 | 0.1990 |
  | 1.3647        | 32.65  | 1600 | 0.6051          | 0.5135 | 0.1687 |
  | 1.2532        | 40.81  | 2000 | 0.5423          | 0.4712 | 0.1539 |
  | 1.1905        | 48.97  | 2400 | 0.5180          | 0.4532 | 0.1490 |
  | 1.1193        | 57.14  | 2800 | 0.4906          | 0.4248 | 0.1393 |
  | 1.0584        | 65.3   | 3200 | 0.4854          | 0.4069 | 0.1332 |
  | 1.0095        | 73.46  | 3600 | 0.4780          | 0.3926 | 0.1287 |
  | 0.9759        | 81.63  | 4000 | 0.4666          | 0.3925 | 0.1269 |
  | 0.9593        | 89.79  | 4400 | 0.4808          | 0.3830 | 0.1247 |
  | 0.909         | 97.95  | 4800 | 0.4798          | 0.3765 | 0.1212 |
  | 0.8788        | 106.12 | 5200 | 0.4906          | 0.3608 | 0.1162 |
  | 0.8471        | 114.28 | 5600 | 0.4759          | 0.3604 | 0.1166 |
  | 0.8116        | 122.44 | 6000 | 0.5080          | 0.3627 | 0.1176 |
  | 0.7881        | 130.61 | 6400 | 0.4868          | 0.3489 | 0.1135 |
  | 0.766         | 138.77 | 6800 | 0.4955          | 0.3492 | 0.1136 |
  | 0.7333        | 146.93 | 7200 | 0.5019          | 0.3461 | 0.1125 |
  | 0.709         | 155.1  | 7600 | 0.5084          | 0.3468 | 0.1117 |
  | 0.6911        | 163.26 | 8000 | 0.5144          | 0.3412 | 0.1106 |
  | 0.6683        | 171.42 | 8400 | 0.5219          | 0.3409 | 0.1117 |
  | 0.659         | 179.59 | 8800 | 0.5230          | 0.3376 | 0.1096 |
  | 0.6475        | 187.75 | 9200 | 0.5229          | 0.3398 | 0.1097 |
  | 0.6419        | 195.91 | 9600 | 0.5200          | 0.3337 | 0.1084 |
  
  
  ### Framework versions
  
  - Transformers 4.19.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.1.1.dev0
  - Tokenizers 0.12.1
  