---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- MLCommons/peoples_speech
metrics:
- wer
model-index:
- name: Fine Tune Whisper on People Speech
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Peoples Speech
      type: MLCommons/peoples_speech
      args: 'config: English, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 14.784595300261097
---

<!-- 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. -->

# Fine Tune Whisper on People Speech

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Peoples Speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5068
- Wer: 14.7846

## 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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0071        | 2.0   | 100  | 0.5080          | 14.2950 |
| 0.006         | 4.0   | 200  | 0.4859          | 14.1645 |
| 0.0012        | 6.0   | 300  | 0.4997          | 14.3603 |
| 0.0002        | 8.0   | 400  | 0.5017          | 14.4582 |
| 0.0005        | 10.0  | 500  | 0.5068          | 14.7846 |


### Framework versions

- Transformers 4.52.0
- Pytorch 2.9.0+cu126
- Datasets 4.4.1
- Tokenizers 0.21.4