readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - audiofolder
  metrics:
  - wer
  model-index:
  - name: Whisper Small Seneca
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: audiofolder
        type: audiofolder
        config: default
        split: train
        args: default
      metrics:
      - name: Wer
        type: wer
        value: 27.49828990734407
  ---
  
  <!-- 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 Seneca
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the audiofolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5983
  - Wer: 27.4983
  
  ## 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: 16
  - eval_batch_size: 2
  - seed: 42
  - distributed_type: multi-GPU
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 7768
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.1151        | 3.01  | 1000 | 0.4013          | 31.8326 |
  | 0.0185        | 6.02  | 2000 | 0.4796          | 30.1660 |
  | 0.0062        | 9.03  | 3000 | 0.5143          | 30.0417 |
  | 0.0022        | 12.04 | 4000 | 0.5469          | 28.6301 |
  | 0.0004        | 16.01 | 5000 | 0.5695          | 27.9522 |
  | 0.0001        | 19.01 | 6000 | 0.5891          | 27.5294 |
  | 0.0002        | 22.02 | 7000 | 0.5983          | 27.4983 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  