---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-tiny-luganda-final
  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-tiny-luganda-final

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4549
- Wer: 0.4660

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 21000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 2.0498        | 0.1088 | 500   | 0.9947          | 0.8351 |
| 1.5134        | 0.2175 | 1000  | 0.7854          | 0.7198 |
| 1.2494        | 0.3263 | 1500  | 0.6915          | 0.7817 |
| 1.1587        | 0.4351 | 2000  | 0.6377          | 0.7916 |
| 1.0453        | 0.5438 | 2500  | 0.5958          | 0.7276 |
| 1.0065        | 0.6526 | 3000  | 0.5708          | 0.5803 |
| 0.935         | 0.7614 | 3500  | 0.5478          | 0.5882 |
| 0.8838        | 0.8701 | 4000  | 0.5256          | 0.5876 |
| 0.8956        | 0.9789 | 4500  | 0.5123          | 0.5380 |
| 0.6998        | 1.0877 | 5000  | 0.5078          | 0.5332 |
| 0.6735        | 1.1964 | 5500  | 0.4976          | 0.5400 |
| 0.7159        | 1.3052 | 6000  | 0.4934          | 0.5097 |
| 0.6693        | 1.4140 | 6500  | 0.4843          | 0.5043 |
| 0.6513        | 1.5227 | 7000  | 0.4774          | 0.5038 |
| 0.6478        | 1.6315 | 7500  | 0.4736          | 0.5015 |
| 0.6554        | 1.7403 | 8000  | 0.4634          | 0.5042 |
| 0.6491        | 1.8490 | 8500  | 0.4608          | 0.4941 |
| 0.6636        | 1.9578 | 9000  | 0.4526          | 0.4774 |
| 0.4392        | 2.0666 | 9500  | 0.4581          | 0.4788 |
| 0.4567        | 2.1753 | 10000 | 0.4575          | 0.4842 |
| 0.4383        | 2.2841 | 10500 | 0.4562          | 0.4787 |
| 0.4479        | 2.3929 | 11000 | 0.4546          | 0.4747 |
| 0.4431        | 2.5016 | 11500 | 0.4517          | 0.4820 |
| 0.4354        | 2.6104 | 12000 | 0.4498          | 0.4612 |
| 0.4956        | 2.7192 | 12500 | 0.4442          | 0.4825 |
| 0.4427        | 2.8279 | 13000 | 0.4454          | 0.4693 |
| 0.4371        | 2.9367 | 13500 | 0.4430          | 0.4573 |
| 0.2794        | 3.0455 | 14000 | 0.4429          | 0.4544 |
| 0.2826        | 3.1542 | 14500 | 0.4491          | 0.4694 |
| 0.2914        | 3.2630 | 15000 | 0.4497          | 0.4536 |
| 0.3065        | 3.3718 | 15500 | 0.4501          | 0.4557 |
| 0.2879        | 3.4805 | 16000 | 0.4492          | 0.4532 |
| 0.2703        | 3.5893 | 16500 | 0.4495          | 0.4535 |
| 0.269         | 3.6981 | 17000 | 0.4466          | 0.4681 |
| 0.2834        | 3.8068 | 17500 | 0.4445          | 0.4686 |
| 0.2758        | 3.9156 | 18000 | 0.4470          | 0.4660 |
| 0.1819        | 4.0244 | 18500 | 0.4497          | 0.4640 |
| 0.1855        | 4.1331 | 19000 | 0.4510          | 0.4611 |
| 0.1832        | 4.2419 | 19500 | 0.4543          | 0.4693 |
| 0.1826        | 4.3507 | 20000 | 0.4558          | 0.4640 |
| 0.1779        | 4.4594 | 20500 | 0.4553          | 0.4672 |
| 0.1821        | 4.5682 | 21000 | 0.4549          | 0.4660 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0