SSL Audio pretrained models
Collection
This collection list all pretrained audio SSL models. • 5 items • Updated
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SSL Audio 1k-base
This model is pretrained on 1,000 hours of audio content from INA, sampled following the base setting, as described in our LREC 2026 paper "Data Selection Effects on Self-Supervised Learning of Audio Representations for French Audiovisual Broadcasts".
| Link to model | Pretraining data selection |
|---|---|
| ssl-audio-1k-base | Random sample of 1,000h |
| ssl-audio-1k-no_music | Samples not containing musics |
| ssl-audio-1k-only_speech | Samples only composed of speech |
| ssl-audio-1k-only_fr | Samples only composed of French content |
| ssl-audio-1k-gender | Samples with a balanced proportion of male and female speech |
| ssl-audio-1k-duplicates | Samples with duplicates content. This model is not released. |
The features generated by these models have been used for Voice Activity Detection (VAD) and music detection For detailed information about training and results associated with this model, please refer to our publication. Along with the Tensorboard training metrics, we release the hyperparameters.
Usage
import librosa
from transformers import AutoModel, AutoFeatureExtractor
# loading the audio file, need to be sampled at 16kHz
audio, sr = librosa.load('/path/to/your/audio/file.wav', sr=16000)
# loading the feature extractor and SSL model
model_name = 'ina-foss/ssl-audio-1k-base'
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
processor = AutoFeatureExtractor.from_pretrained(model_name)
model.eval()
inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
# extract features
with torch.no_grad():
outputs = model(**inputs)
License and citation
The model is distributed using the pantagruel-research-license.
If you use this model or find it useful in your research, publications, or applications, please cite the following work:
@inproceedings{pelloin2026lrec,
author = "Pelloin, Valentin and Bekkali, Lina and Dehak, Reda and Doukhan, David",
year = "2026",
title = "Data Selection Effects on Self-Supervised Learning of Audio Representations for French Audiovisual Broadcasts",
booktitle={Fifteenth International Conference on Language Resources and Evaluation (LREC 2026)},
address = "Palma, Mallorca, Spain",
publisher = "European Language Resources Association",
}
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