---
language:
- nl
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- procit006/STT_TTS_MozillaAndSTC_VoiceTextData_August27
metrics:
- wer
model-index:
- name: Whisper Small
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice + STC Aug 27 + Speechgen
      type: procit006/STT_TTS_MozillaAndSTC_VoiceTextData_August27
      args: 'config: nld'
    metrics:
    - name: Wer
      type: wer
      value: 1.3962338429236927
---

<!-- 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

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the Common Voice + STC Aug 27 + Speechgen dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0202
- Wer: 1.3962

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0892        | 0.2047 | 500  | 0.0897          | 6.6898 |
| 0.048         | 0.4093 | 1000 | 0.0480          | 3.4328 |
| 0.0379        | 0.6140 | 1500 | 0.0339          | 2.2033 |
| 0.0299        | 0.8187 | 2000 | 0.0269          | 2.2453 |
| 0.0074        | 1.0233 | 2500 | 0.0216          | 1.4842 |
| 0.0055        | 1.2280 | 3000 | 0.0202          | 1.3962 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1