---
library_name: transformers
language:
- ee
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- dodziraynard/ugspeechdata-ewe
model-index:
- name: UG Speech Data ASR - Ewe nornmaliser
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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:
- eval_loss: 0.4720
- eval_wer_ortho: 45.4249
- eval_wer: 37.4757
- eval_cer: 12.9706
- eval_runtime: 1781.9648
- eval_samples_per_second: 2.159
- eval_steps_per_second: 0.135
- epoch: 2.8708
- step: 2400

## 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

### Framework versions

- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1