---
license: openrail
language:
- nl
tags:
- wav2vec2
- self-supervised
- pretraining
- speech
- audio
---
# Wav2Vec2-NL
A Dutch Wav2Vec2-base model, pre-trained on 831 hours of exclusively Dutch speech. 

Pre-training data was extracted from a combination of:
- the [Spoken Dutch Corpus](https://taalmaterialen.ivdnt.org/wp-content/uploads/documentatie/cgn_website/doc_English/topics/index.htm) (537 hours; incl. spontaneous conversations, interviews, read speech and news reports)
- the Dutch component of [Multilingual LibriSpeech](https://www.openslr.org/94/) (211 hours; audiobook segments)
- the Dutch subset of the [CommonVoice 16.1](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1) corpus (83 hours; read aloud speech)

More information, incl. the training manifest and configuration is available in the [Wav2Vec2-NL repository on Zenodo](http://doi.org/10.5281/zenodo.15550628). 

Analyses of Dutch phonetic and lexical features encoded in Wav2Vec2-NL hidden states are reported in the paper [What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training](https://arxiv.org/abs/2506.00981) (Interspeech 2025; see full citation [below](#citation)).

Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for an explanation of fine-tuning Wav2Vec2 models on HuggingFace.

# Usage
```python
from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2Model

feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('amsterdamNLP/Wav2Vec2-NL')
model = Wav2Vec2Model.from_pretrained('amsterdamNLP/Wav2Vec2-NL')
```

# Citation
The _Wav2Vec2-NL_ model was published as part of:
de Heer Kloots, M., Mohebbi, H., Pouw, C., Shen, G., Zuidema, W., Bentum, M. (2025). What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training. _Proc. INTERSPEECH 2025_. https://doi.org/10.48550/arXiv.2506.00981

BibTex entry:
```bibtex
@inproceedings{deheerkloots25_interspeech,
  title     = {What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training},
  author    = {Marianne {de Heer Kloots} and Hosein Mohebbi and Charlotte Pouw and Gaofei Shen and Willem Zuidema and Martijn Bentum},
  year      = {2025},
  booktitle = {Interspeech 2025},
  doi       = {10.21437/Interspeech.2025-1526},
}
```