---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-en-nonnative
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: en
      split: test
      args: en
    metrics:
    - name: Wer
      type: wer
      value: 40.75993091537133
---

<!-- 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. -->

# whisper-small-en-nonnative

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4024
- Wer: 40.7599

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.2992        | 0.2   | 1000  | 0.4468          | 29.3898 |
| 0.2918        | 0.39  | 2000  | 0.4240          | 27.1733 |
| 0.3078        | 0.59  | 3000  | 0.4173          | 28.4974 |
| 0.2711        | 0.79  | 4000  | 0.4027          | 24.5538 |
| 0.2813        | 0.98  | 5000  | 0.4029          | 28.6413 |
| 0.1416        | 1.18  | 6000  | 0.4078          | 25.9931 |
| 0.1399        | 1.38  | 7000  | 0.4078          | 28.8140 |
| 0.1478        | 1.57  | 8000  | 0.4070          | 31.3759 |
| 0.1479        | 1.77  | 9000  | 0.4033          | 33.5636 |
| 0.1266        | 1.97  | 10000 | 0.4024          | 40.7599 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1