readme: ---
  language:
  - hi
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - hi
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  metrics:
  - wer
  - cer
  model-index:
  - name: shivam/wav2vec2-xls-r-hindi
    results:
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: Common Voice Corpus 7.0
        type: mozilla-foundation/common_voice_7_0
        args: hi
      metrics:
      - name: Test WER
        type: wer
        value: 52.3
      - name: Test CER
        type: cer
        value: 26.09
  ---
  
  <!-- 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_7_0 - HI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2282
  - Wer: 0.6838
  
  ## Evaluation results on Common Voice 7 "test" (Running ./eval.py):
  
  ### With LM
  - WER: 52.30
  - CER: 26.09
  
  ## 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: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 5.3155        | 3.4   | 500  | 4.5582          | 1.0    |
  | 3.3369        | 6.8   | 1000 | 3.4269          | 1.0    |
  | 2.1785        | 10.2  | 1500 | 1.7191          | 0.8831 |
  | 1.579         | 13.6  | 2000 | 1.3604          | 0.7647 |
  | 1.3773        | 17.01 | 2500 | 1.2737          | 0.7519 |
  | 1.3165        | 20.41 | 3000 | 1.2457          | 0.7401 |
  | 1.2274        | 23.81 | 3500 | 1.3617          | 0.7301 |
  | 1.1787        | 27.21 | 4000 | 1.2068          | 0.7010 |
  | 1.1467        | 30.61 | 4500 | 1.2416          | 0.6946 |
  | 1.0801        | 34.01 | 5000 | 1.2312          | 0.6990 |
  | 1.0709        | 37.41 | 5500 | 1.2984          | 0.7138 |
  | 1.0307        | 40.81 | 6000 | 1.2049          | 0.6871 |
  | 1.0003        | 44.22 | 6500 | 1.1956          | 0.6841 |
  | 1.004         | 47.62 | 7000 | 1.2101          | 0.6793 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu113
  - Datasets 1.18.1.dev0
  - Tokenizers 0.11.0
  