Voc2vec is built upon the wav2vec 2.0 framework and follows its pre-training setup. The pre-training datasets include: AudioSet (vocalization), FreeSound (babies), HumanVoiceDataset, NNIME, NonSpeech7K, ReCANVo, SingingDatabase, TUT (babies), VocalSketch, VocalSound. This model continues pre-training from a model that was initially trained on the LibriSpeech dataset.

Task and datasets description

We evaluate voc2vec-ls-pt on six datasets: ASVP-ESD, ASPV-ESD (babies), CNVVE, NonVerbal Vocalization Dataset, Donate a Cry, VIVAE.

The following table reports the average performance in terms of Unweighted Average Recall (UAR) and F1 Macro across the six datasets described above.

Model	Architecture	Pre-training DS	UAR	F1 Macro
voc2vec	wav2vec 2.0	Voc125	.612±.212	.580±.230
voc2vec-as-pt	wav2vec 2.0	AudioSet + Voc125	.603±.183	.574±.194
voc2vec-ls-pt	wav2vec 2.0	LibriSpeech + Voc125	.661±.206	.636±.223
voc2vec-hubert-ls-pt	HuBERT	LibriSpeech + Voc125	.696±.189	.678±.200

Available Models

Model	Description	Link
voc2vec	Pre-trained model on 125 hours of non-verbal audio.	🔗 Model
voc2vec-as-pt	Continues pre-training from a wav2vec2-like model that was initially trained on the AudioSet dataset.	🔗 Model
voc2vec-ls-pt	Continues pre-training from a wav2vec2-like model that was initially trained on the LibriSpeech dataset.	🔗 Model
voc2vec-hubert-ls-pt	Continues pre-training from a hubert-like model that was initially trained on the LibriSpeech dataset.	🔗 Model

Usage examples

You can use the model directly in the following manner:

python

import torch
import librosa
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor

## Load an audio file
audio_array, sr = librosa.load("path_to_audio.wav", sr=16000)

## Load model and feature extractor
model = AutoModelForAudioClassification.from_pretrained("alkiskoudounas/voc2vec-ls-pt")
feature_extractor = AutoFeatureExtractor.from_pretrained("alkiskoudounas/voc2vec-ls-pt")

## Extract features
inputs = feature_extractor(audio_array.squeeze(), sampling_rate=feature_extractor.sampling_rate, padding=True, return_tensors="pt")

## Compute logits
logits = model(**inputs).logits

BibTeX entry and citation info

bibtex

@INPROCEEDINGS{koudounas2025icassp,
  author={Koudounas, Alkis and La Quatra, Moreno and Siniscalchi, Sabato Marco and Baralis, Elena},
  booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={voc2vec: A Foundation Model for Non-Verbal Vocalization}, 
  year={2025},
  volume={},
  number={},
  pages={1-5},
  keywords={Pediatrics;Accuracy;Foundation models;Benchmark testing;Signal processing;Data models;Acoustics;Speech processing;Nonverbal vocalization;Representation Learning;Self-Supervised Models;Pre-trained Models},
  doi={10.1109/ICASSP49660.2025.10890672}}