---
library_name: peft
language:
- ms
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- clt013/malay-speech-3k-rows-dataset_v2
model-index:
- name: Whisper Small FT Malay - CLT013
  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 FT Malay - CLT013

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Malay Speech 3k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8613

## 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: 0.001
- train_batch_size: 8
- 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: 100
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 2.1842        | 0.3731  | 100  | 0.8172          |
| 0.7488        | 0.7463  | 200  | 0.8014          |
| 0.6424        | 1.1194  | 300  | 0.8136          |
| 0.5234        | 1.4925  | 400  | 0.7511          |
| 0.4951        | 1.8657  | 500  | 0.8203          |
| 0.3835        | 2.2388  | 600  | 0.8191          |
| 0.3519        | 2.6119  | 700  | 0.8001          |
| 0.3868        | 2.9851  | 800  | 0.8011          |
| 0.2568        | 3.3582  | 900  | 0.8630          |
| 0.2781        | 3.7313  | 1000 | 0.8269          |
| 0.2535        | 4.1045  | 1100 | 0.8612          |
| 0.2105        | 4.4776  | 1200 | 0.8486          |
| 0.2104        | 4.8507  | 1300 | 0.8367          |
| 0.1726        | 5.2239  | 1400 | 0.8692          |
| 0.1672        | 5.5970  | 1500 | 0.8483          |
| 0.1641        | 5.9701  | 1600 | 0.8443          |
| 0.1186        | 6.3433  | 1700 | 0.9531          |
| 0.1261        | 6.7164  | 1800 | 0.8578          |
| 0.1211        | 7.0896  | 1900 | 0.8922          |
| 0.0962        | 7.4627  | 2000 | 0.9107          |
| 0.1188        | 7.8358  | 2100 | 0.8498          |
| 0.0847        | 8.2090  | 2200 | 0.8554          |
| 0.0802        | 8.5821  | 2300 | 0.9024          |
| 0.0805        | 8.9552  | 2400 | 0.8649          |
| 0.0559        | 9.3284  | 2500 | 0.8634          |
| 0.053         | 9.7015  | 2600 | 0.8988          |
| 0.0555        | 10.0746 | 2700 | 0.8657          |
| 0.0415        | 10.4478 | 2800 | 0.8449          |
| 0.0401        | 10.8209 | 2900 | 0.8658          |
| 0.0318        | 11.1940 | 3000 | 0.8674          |
| 0.0245        | 11.5672 | 3100 | 0.8491          |
| 0.032         | 11.9403 | 3200 | 0.8694          |
| 0.0186        | 12.3134 | 3300 | 0.8620          |
| 0.0179        | 12.6866 | 3400 | 0.8555          |
| 0.015         | 13.0597 | 3500 | 0.8730          |
| 0.0176        | 13.4328 | 3600 | 0.8458          |
| 0.0155        | 13.8060 | 3700 | 0.8454          |
| 0.0121        | 14.1791 | 3800 | 0.8533          |
| 0.0139        | 14.5522 | 3900 | 0.8604          |
| 0.009         | 14.9254 | 4000 | 0.8676          |
| 0.0095        | 15.2985 | 4100 | 0.8649          |
| 0.0059        | 15.6716 | 4200 | 0.8728          |
| 0.0065        | 16.0448 | 4300 | 0.8570          |
| 0.0049        | 16.4179 | 4400 | 0.8521          |
| 0.0042        | 16.7910 | 4500 | 0.8600          |
| 0.0051        | 17.1642 | 4600 | 0.8741          |
| 0.0037        | 17.5373 | 4700 | 0.8666          |
| 0.0037        | 17.9104 | 4800 | 0.8691          |
| 0.0029        | 18.2836 | 4900 | 0.8619          |
| 0.0023        | 18.6567 | 5000 | 0.8603          |
| 0.0019        | 19.0299 | 5100 | 0.8629          |
| 0.0018        | 19.4030 | 5200 | 0.8608          |
| 0.0018        | 19.7761 | 5300 | 0.8613          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1