---
base_model: openai/whisper-small
datasets:
- fleurs
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-small-ablation-add-NLD-90
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: fleurs
      type: fleurs
      config: id_id
      split: None
      args: id_id
    metrics:
    - type: wer
      value: 17.5811209439528
      name: Wer
---

<!-- 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-ablation-add-NLD-90

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4254
- Wer: 17.5811

## 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: 32
- eval_batch_size: 16
- 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: 1000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2897        | 0.5263 | 200  | 0.4269          | 18.6726 |
| 0.177         | 1.0526 | 400  | 0.4194          | 18.0826 |
| 0.1516        | 1.5789 | 600  | 0.4377          | 17.9720 |
| 0.0713        | 2.1053 | 800  | 0.4025          | 17.8319 |
| 0.074         | 2.6316 | 1000 | 0.4254          | 17.5811 |


### Framework versions

- Transformers 4.45.0
- Pytorch 2.11.0+cu130
- Datasets 3.0.1
- Tokenizers 0.20.0