readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - openslr
  metrics:
  - wer
  model-index:
  - name: wav2vec2-telugu_150
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: openslr
        type: openslr
        config: SLR66
        split: train
        args: SLR66
      metrics:
      - name: Wer
        type: wer
        value: 0.2212659135736059
  ---
  
  <!-- 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-telugu_150
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [openslr](https://openslr.org/66) dataset.
  
  It achieves the following results on the evaluation set:
  - Loss: 0.3312
  - Wer: 0.2213
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - 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: 150
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | 6.096         | 3.84   | 400   | 0.5762          | 0.7029 |
  | 0.427         | 7.69   | 800   | 0.3124          | 0.5148 |
  | 0.208         | 11.54  | 1200  | 0.2994          | 0.4201 |
  | 0.1506        | 15.38  | 1600  | 0.3106          | 0.3844 |
  | 0.1223        | 19.23  | 2000  | 0.3080          | 0.3608 |
  | 0.1094        | 23.08  | 2400  | 0.3206          | 0.3332 |
  | 0.0949        | 26.92  | 2800  | 0.3085          | 0.3253 |
  | 0.0802        | 30.77  | 3200  | 0.3076          | 0.3425 |
  | 0.0713        | 34.61  | 3600  | 0.3280          | 0.3398 |
  | 0.0687        | 38.46  | 4000  | 0.3042          | 0.3081 |
  | 0.0613        | 42.31  | 4400  | 0.3227          | 0.3073 |
  | 0.0548        | 46.15  | 4800  | 0.3152          | 0.3213 |
  | 0.0508        | 50.0   | 5200  | 0.3259          | 0.3107 |
  | 0.0455        | 53.84  | 5600  | 0.3046          | 0.2881 |
  | 0.0427        | 57.69  | 6000  | 0.2779          | 0.3007 |
  | 0.0391        | 61.54  | 6400  | 0.2996          | 0.2693 |
  | 0.0388        | 65.38  | 6800  | 0.3016          | 0.2695 |
  | 0.0339        | 69.23  | 7200  | 0.3225          | 0.2935 |
  | 0.0312        | 73.08  | 7600  | 0.2907          | 0.2942 |
  | 0.029         | 76.92  | 8000  | 0.3148          | 0.3029 |
  | 0.0254        | 80.77  | 8400  | 0.3118          | 0.2996 |
  | 0.0229        | 84.61  | 8800  | 0.3022          | 0.2993 |
  | 0.0231        | 88.46  | 9200  | 0.3203          | 0.2465 |
  | 0.019         | 92.31  | 9600  | 0.3223          | 0.2460 |
  | 0.0173        | 96.15  | 10000 | 0.3178          | 0.2501 |
  | 0.0168        | 100.0  | 10400 | 0.2937          | 0.2415 |
  | 0.015         | 103.84 | 10800 | 0.3062          | 0.2415 |
  | 0.014         | 107.69 | 11200 | 0.3104          | 0.2383 |
  | 0.012         | 111.54 | 11600 | 0.3308          | 0.2408 |
  | 0.0111        | 115.38 | 12000 | 0.3228          | 0.2335 |
  | 0.01          | 119.23 | 12400 | 0.3228          | 0.2374 |
  | 0.0096        | 123.08 | 12800 | 0.3241          | 0.2304 |
  | 0.009         | 126.92 | 13200 | 0.3237          | 0.2295 |
  | 0.0075        | 130.77 | 13600 | 0.3221          | 0.2261 |
  | 0.0065        | 134.61 | 14000 | 0.3310          | 0.2277 |
  | 0.0064        | 138.46 | 14400 | 0.3348          | 0.2266 |
  | 0.0064        | 142.31 | 14800 | 0.3330          | 0.2229 |
  | 0.0056        | 146.15 | 15200 | 0.3310          | 0.2229 |
  | 0.0053        | 150.0  | 15600 | 0.3312          | 0.2213 |
  
  ### Test results
  
  WER(without LM): 42.8\% 
  
  WER(with LM): 42\%
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  
  ps: 150 in repo name denotes number of epochs