---
license: apache-2.0
metrics:
- wer
model-index:
- name: openai/whisper-small
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: myst-test
      type: asr
      config: en
      split: test
    metrics:
    - type: wer
      value: 11.80
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: cslu_scripted
      type: asr
      config: en
      split: test
    metrics:
    - type: wer
      value: 55.51	
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: cslu_spontaneous
      type: asr
      config: en
      split: test
    metrics:
    - type: wer
      value: 28.53	
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: librispeech
      type: asr
      config: en
      split: testclean
    metrics:
    - type: wer
      value: 6.23
      name: WER
  
---


# openai/whisper-small

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.26971688866615295
- Wer: 8.508066331024994


## Training and evaluation data

- Training data: Myst Train (125 hours)
- Evaluation data: Myst Dev (20.9 hours)


### 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: 10000
- converged_after: 2500