---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- saurabhy27-outcomes/singlish_speech_corpus
metrics:
- wer
model-index:
- name: Whisper small singlish v2
  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 singlish v2

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Singlish Speech Corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2000
- Wer: 33.7188

## 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: 16
- 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1765        | 0.7407 | 500  | 0.1531          | 9.8226  |
| 0.0711        | 1.4815 | 1000 | 0.1481          | 32.2996 |
| 0.0415        | 2.2222 | 1500 | 0.1568          | 51.1892 |
| 0.0221        | 2.9630 | 2000 | 0.1582          | 33.1932 |
| 0.0064        | 3.7037 | 2500 | 0.1750          | 37.4310 |
| 0.0024        | 4.4444 | 3000 | 0.1856          | 40.0394 |
| 0.0017        | 5.1852 | 3500 | 0.1903          | 34.0539 |
| 0.0008        | 5.9259 | 4000 | 0.1948          | 34.5269 |
| 0.0006        | 6.6667 | 4500 | 0.1996          | 33.6662 |
| 0.0005        | 7.4074 | 5000 | 0.2000          | 33.7188 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1