readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - librispeech_asr
  metrics:
  - wer
  model-index:
  - name: whisper-small-libirClean-vs-commonNative-en
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: librispeech_asr
        type: librispeech_asr
        config: clean
        split: train
        args: clean
      metrics:
      - name: Wer
        type: wer
        value: 85.53786155346116
  ---
  
  <!-- 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. -->
  
  # whisper-small-libirClean-vs-commonNative-en
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech_asr dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.3358
  - Wer: 85.5379
  
  ## 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: 1e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 10
  - training_steps: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 1.2481        | 0.08  | 10   | 3.5688          | 21.1895 |
  | 0.7793        | 0.16  | 20   | 2.8307          | 38.9990 |
  | 0.5443        | 0.24  | 30   | 2.4196          | 67.0458 |
  | 0.4484        | 0.32  | 40   | 2.2903          | 71.1732 |
  | 0.4086        | 0.4   | 50   | 2.3358          | 85.5379 |
  
  
  ### Framework versions
  
  - Transformers 4.25.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  