---
base_model: openai/whisper-base
language:
- en
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Small Five 20K - Chee Li
  results: []
---

<!-- 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 Five 20K - Chee Li

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co./openai/whisper-base) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5771
- Wer: 22.0375

## 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: 2500
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer     |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|
| 0.4014        | 1.0560  | 1000  | 0.4369          | 25.7071 |
| 0.2677        | 2.1119  | 2000  | 0.3905          | 22.1327 |
| 0.1651        | 3.1679  | 3000  | 0.3856          | 21.2139 |
| 0.1102        | 4.2239  | 4000  | 0.3920          | 20.4471 |
| 0.0514        | 5.2798  | 5000  | 0.4072          | 21.2883 |
| 0.0255        | 6.3358  | 6000  | 0.4273          | 21.4687 |
| 0.0184        | 7.3918  | 7000  | 0.4442          | 21.6251 |
| 0.01          | 8.4477  | 8000  | 0.4635          | 21.3397 |
| 0.0051        | 9.5037  | 9000  | 0.4805          | 21.3867 |
| 0.0043        | 10.5597 | 10000 | 0.4924          | 21.5508 |
| 0.0025        | 11.6156 | 11000 | 0.5054          | 21.5847 |
| 0.0023        | 12.6716 | 12000 | 0.5166          | 22.0703 |
| 0.0016        | 13.7276 | 13000 | 0.5292          | 21.7509 |
| 0.0012        | 14.7835 | 14000 | 0.5375          | 21.7925 |
| 0.001         | 15.8395 | 15000 | 0.5480          | 21.9325 |
| 0.0008        | 16.8955 | 16000 | 0.5565          | 21.8866 |
| 0.0008        | 17.9514 | 17000 | 0.5638          | 21.9423 |
| 0.0005        | 19.0074 | 18000 | 0.5709          | 21.9916 |
| 0.0005        | 20.0634 | 19000 | 0.5755          | 22.0397 |
| 0.0004        | 21.1193 | 20000 | 0.5771          | 22.0375 |


### Framework versions

- Transformers 4.43.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1