---
language:
- ms
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- clt013/malay-speech-1.6-million-rows-dataset
metrics:
- wer
model-index:
- name: Whisper Medium FT Malay - CLT013
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Malay Speech 1.6 million
      type: clt013/malay-speech-1.6-million-rows-dataset
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 39.65666891696403
---

<!-- 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 Medium FT Malay - CLT013

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the Malay Speech 1.6 million dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7057
- Wer: 39.6567

## 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: 50
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.0434        | 0.1   | 100  | 0.9250          | 53.3417 |
| 0.8131        | 0.2   | 200  | 0.8394          | 46.5908 |
| 0.7852        | 0.3   | 300  | 0.8033          | 45.1635 |
| 0.7643        | 0.4   | 400  | 0.7769          | 53.5732 |
| 0.7424        | 0.5   | 500  | 0.7582          | 46.6969 |
| 0.7406        | 0.6   | 600  | 0.7451          | 39.6760 |
| 0.7913        | 0.7   | 700  | 0.7288          | 39.3866 |
| 0.7452        | 0.8   | 800  | 0.7164          | 37.9979 |
| 0.718         | 0.9   | 900  | 0.7099          | 38.7694 |
| 0.7328        | 1.0   | 1000 | 0.7057          | 39.6567 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1