---
library_name: transformers
language:
- ee
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- dodziraynard/ugspeechdata-ewe
metrics:
- wer
model-index:
- name: UG Speech Data ASR - Ewe nornmaliser
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ugspeechdata-ewe
      type: dodziraynard/ugspeechdata-ewe
    metrics:
    - name: Wer
      type: wer
      value: 38.34905660377358
---

<!-- 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. -->

# UG Speech Data ASR - Ewe nornmaliser

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ugspeechdata-ewe dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5273
- Wer Ortho: 46.0461
- Wer: 38.3491
- Cer: 13.0384

## 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: 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
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Cer     | Validation Loss | Wer     | Wer Ortho |
|:-------------:|:------:|:----:|:-------:|:---------------:|:-------:|:---------:|
| 0.5022        | 0.4785 | 400  | 15.0475 | 0.5773          | 44.5732 | 52.3734   |
| 0.4835        | 0.9569 | 800  | 13.6924 | 0.5142          | 40.5166 | 48.4899   |
| 0.3764        | 1.4354 | 1200 | 13.2187 | 0.4926          | 38.7241 | 47.1020   |
| 0.3624        | 1.9139 | 1600 | 12.8324 | 0.4770          | 37.8553 | 46.0811   |
| 0.3165        | 2.3923 | 2000 | 0.4770  | 45.1081         | 37.1660 | 12.5025   |
| 0.3058        | 2.8708 | 2400 | 0.4728  | 45.5634         | 37.5822 | 12.8574   |
| 0.2386        | 3.3493 | 2800 | 0.4945  | 45.8291         | 38.0272 | 12.8462   |
| 0.2334        | 3.8278 | 3200 | 0.4874  | 45.7743         | 38.0440 | 12.8868   |
| 0.1662        | 4.3062 | 3600 | 0.5242  | 46.6003         | 38.5020 | 12.9679   |
| 0.1615        | 4.7847 | 4000 | 0.5273  | 46.0461         | 38.3491 | 13.0384   |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4