---
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: rishabhjain16/infer_myst
      type: rishabhjain16/infer_myst
      config: en
      split: test
    metrics:
    - type: wer
      value: 12.33
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: rishabhjain16/infer_pfs
      type: rishabhjain16/infer_pfs
      config: en
      split: test
    metrics:
    - type: wer
      value: 3.32
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: rishabhjain16/libritts_dev_clean
      type: rishabhjain16/libritts_dev_clean
      config: en
      split: test
    metrics:
    - type: wer
      value: 4.88
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: rishabhjain16/infer_cmu_9h
      type: rishabhjain16/infer_cmu_9h
      config: en
      split: test
    metrics:
    - type: wer
      value: 15.08
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: rishabhjain16/infer_pf_italian
      type: rishabhjain16/infer_pf_italian
      config: en
      split: test
    metrics:
    - type: wer
      value: 13.95
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: rishabhjain16/infer_pf_german
      type: rishabhjain16/infer_pf_german
      config: en
      split: test
    metrics:
    - type: wer
      value: 59.94
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: rishabhjain16/infer_pf_swedish
      type: rishabhjain16/infer_pf_swedish
      config: en
      split: test
    metrics:
    - type: wer
      value: 17.48
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: rishabhjain16/infer_so_chinese
      type: rishabhjain16/infer_so_chinese
      config: en
      split: test
    metrics:
    - type: wer
      value: 23.41
      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. -->

# 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: 0.4522
- Wer: 10.7946

## 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: 32
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2652        | 0.12  | 500  | 0.3042          | 11.2277 |
| 0.2102        | 1.11  | 1000 | 0.2824          | 10.9156 |
| 0.1913        | 2.1   | 1500 | 0.2924          | 11.2366 |
| 0.0249        | 3.09  | 2000 | 0.3386          | 10.6246 |
| 0.031         | 4.07  | 2500 | 0.3798          | 11.1400 |
| 0.0224        | 5.06  | 3000 | 0.4086          | 10.9767 |
| 0.0033        | 6.05  | 3500 | 0.4452          | 10.3392 |
| 0.0028        | 7.03  | 4000 | 0.4522          | 10.7946 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2