---
language:
- nl
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- synthesized_accented_data
metrics:
- wer
model-index:
- name: Whisper Small NL - bncay0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Custom Common Voice Dutch
      type: synthesized_accented_data
      args: 'config: nl, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.0
---

<!-- 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 NL - bncay0

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Custom Common Voice Dutch dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Wer: 0.0

## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:---:|
| 0.0           | 47.6190 | 2000 | 0.0001          | 0.0 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1