---
language:
- ha
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- Seon25/common_voice_16_0_
metrics:
- wer
model-index:
- name: Whisper Tiny Ha - Eldad Akhaumere
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.0
      type: Seon25/common_voice_16_0_
      config: ha
      split: None
      args: 'config: ha, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 107.2810883310979
---

<!-- 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 Ha - Eldad Akhaumere

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 16.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5851
- Wer Ortho: 108.4180
- Wer: 107.2811

## 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer Ortho | Wer      |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:--------:|
| 1.3176        | 3.1847  | 500  | 2.1073          | 133.8086  | 132.4392 |
| 0.624         | 6.3694  | 1000 | 2.2333          | 110.4492  | 111.1324 |
| 0.2135        | 9.5541  | 1500 | 2.4375          | 101.6211  | 100.4407 |
| 0.0593        | 12.7389 | 2000 | 2.5851          | 108.4180  | 107.2811 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1