readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: Whisper Small English
    results:
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: google/fleurs en_us
        type: google/fleurs
        config: en_us
        split: test
        args: en_us
      metrics:
      - type: wer
        value: 7.990755655157924
        name: Wer
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0
        type: mozilla-foundation/common_voice_11_0
        config: en
        split: test
      metrics:
      - type: wer
        value: 18.21
        name: WER
  ---
  
  <!-- 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 English
  
  This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the google/fleurs en_us dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6007
  - Wer: 7.9908
  
  
  ## Model description
  
  This model was created as part of the Whisper Fine-Tune Event. This is my first attempt at fine-tuning the Whisper neural network. 
  Honestly, it's my second time ever trying anything related to training a neural network, and my first time was pretty bad (but I did
  get a lot of rather funny images out of it, so perhaps it wasn't entirely fruitless?), and it seems like the WER only went up after step 2000,
  so... I'm not sure if I did a good job or if I just wasted GPU cycles, but maybe I can try again and get a better score?
  
  I'm learning.
  
  
  
  ## 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: 64
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|
  | 0.0005        | 24.0   | 1000 | 0.5092          | 7.5566 |
  | 0.0002        | 48.01  | 2000 | 0.5528          | 7.7526 |
  | 0.0001        | 73.0   | 3000 | 0.5785          | 7.8507 |
  | 0.0001        | 97.0   | 4000 | 0.5936          | 7.9908 |
  | 0.0001        | 121.01 | 5000 | 0.6007          | 7.9908 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  