This model is a fine-tuned version of facebook/hubert-base-ls960 on the UNSLURP dataset for the intent classification task. SLURP does not provide speaker-independent splits, which are, however, required by Machine Unlearning techniques to be effective. In fact, the identities present in the retain, forget, and test sets must be exclusive to successfully apply and evaluate unlearning methods. To address this, we propose new speaker-independent splits. In the following, we refer to the new dataset as SLURP*, or UNSLURP.

It achieves the following results on the test set:

Accuracy: 0.830
F1: 0.712

Model description

The base Facebook's Hubert model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.

Task and dataset description

Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. The dataset used here is (UN)SLURP, where each utterance is tagged with two intent labels: action and scenario.

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/hubert-base-unslurp")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/hubert-base-ls960")

## 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

Framework versions

Datasets 3.2.0
Pytorch 2.1.2
Tokenizers 0.20.3
Transformers 4.45.2

BibTeX entry and citation info

bibtex

@inproceedings{koudounas2025unlearning,
  title={"Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding},
  author={Koudounas, Alkis and Savelli, Claudio and Giobergia, Flavio and Baralis, Elena},
  booktitle={Proc. Interspeech 2025}, 
  year={2025},
}