The base Facebook's Wav2Vec2 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 Fluent Speech Commands (FSC), where each utterance is tagged with three intent labels: action, object, and location.

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/wav2vec2-base-fsc")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")

## 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},
}