---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small.en
tags:
- whisper-event
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: openai/whisper-small.en
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: validation
      args: default
    metrics:
    - type: wer
      value: 25.544485229854917
      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: 10.52
      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-small.en

This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3087
- Wer: 25.5445

## 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: 8
- seed: 42
- optimizer: Use 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: 500
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0021        | 10.0033 | 1000 | 1.1983          | 25.8346 |
| 0.0005        | 20.0067 | 2000 | 1.2810          | 25.4955 |
| 0.0002        | 30.01   | 3000 | 1.3087          | 25.5445 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.4.0+cu121
- Datasets 3.3.2
- Tokenizers 0.21.0