README.md

---
language:
- en
license: apache-2.0
base_model: openai/whisper-small.en
tags:
- generated_from_trainer
datasets:
- bika5/pfedrx
metrics:
- wer
model-index:
- name: Whisper pfe - bika5
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: pfedrx
      type: bika5/pfedrx
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 13.88888888888889
---

<!-- 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 pfe - bika5

This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the pfedrx dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8293
- Wer: 13.8889

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0001        | 1000.0 | 1000 | 0.7559          | 13.8889 |
| 0.0           | 2000.0 | 2000 | 0.8140          | 13.8889 |
| 0.0           | 3000.0 | 3000 | 0.8293          | 13.8889 |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1