---
library_name: transformers
language:
- zu
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- zionia/isizulu-asr-1.1-speed
metrics:
- wer
model-index:
- name: Whisper Small Isizulu-Asr-1.1-Speed
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ISIZULU-ASR-1.1-SPEED
      type: zionia/isizulu-asr-1.1-speed
      args: 'split: train+test'
    metrics:
    - name: Wer
      type: wer
      value: 53.6
---

<!-- 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 Isizulu-Asr-1.1-Speed

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ISIZULU-ASR-1.1-SPEED dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3263
- Wer: 53.6

## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch    | Step | Validation Loss | Wer     |
|:-------------:|:--------:|:----:|:---------------:|:-------:|
| 0.0004        | 55.5556  | 1000 | 1.2327          | 53.8286 |
| 0.0001        | 111.1111 | 2000 | 1.2957          | 53.8286 |
| 0.0001        | 166.6667 | 3000 | 1.3263          | 53.6    |
| 0.0001        | 222.2222 | 4000 | 1.3378          | 54.1714 |


### Framework versions

- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.2.0
- Tokenizers 0.22.1