README.md

---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-medium-en
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: myst-test
      type: asr
      config: en
      split: test
    metrics:
    - type: wer
      value: 8.85 
      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: 2.38 
      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: 16.53 
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: librispeech
      type: asr
      config: en
      split: testclean
    metrics:
    - type: wer
      value: 3.52
      name: WER
  
---


# openai/whisper-medium-en

This model is a fine-tuned version of [openai/whisper-medium-en](https://huggingface.co/openai/whisper-medium-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.22987066209316254
- Wer: 7.945455976651671`


## Training and evaluation data

- Training data: Myst Train (125 hours) + CSLU Scripted train (35 hours) 
- Evaluation data: Myst Dev (20.9 hours) + CSLU Scripted Dev(4.8)


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- 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: 1000