---
library_name: transformers
language:
- ha
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- eldad-akhaumere/common_voice_16_0_
metrics:
- wer
model-index:
- name: Whisper Small Ha v10 - Eldad Akhaumere
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.0
      type: eldad-akhaumere/common_voice_16_0_
      config: ha
      split: None
      args: 'config: ha, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 79.57463115539375
---

<!-- 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 Ha v10 - Eldad Akhaumere

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3326
- Wer Ortho: 81.6211
- Wer: 79.5746

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- num_epochs: 90.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:-------:|
| 0.0914        | 3.1847  | 500  | 1.7962          | 85.2344   | 83.1194 |
| 0.0298        | 6.3694  | 1000 | 1.9290          | 82.9492   | 80.9734 |
| 0.022         | 9.5541  | 1500 | 2.0141          | 84.1797   | 82.4104 |
| 0.021         | 12.7389 | 2000 | 2.1154          | 80.8984   | 78.8848 |
| 0.0141        | 15.9236 | 2500 | 2.1146          | 83.8086   | 81.9506 |
| 0.0101        | 19.1083 | 3000 | 2.2107          | 79.2383   | 77.5628 |
| 0.0072        | 22.2930 | 3500 | 2.2648          | 82.5391   | 80.9925 |
| 0.0084        | 25.4777 | 4000 | 2.3229          | 81.3477   | 79.0190 |
| 0.0116        | 28.6624 | 4500 | 2.3326          | 81.6211   | 79.5746 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0