README.md

---
license: apache-2.0
base_model:
- facebook/wav2vec2-base
tags:
- intent-classification
- slu
- audio-classification
metrics:
- accuracy
- f1
model-index:
- name: wav2vec2-base-fsc
  results: []
datasets:
- fsc
language:
- en
pipeline_tag: audio-classification
library_name: transformers
---

# wav2vec2-base-FSC

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the FSC dataset for the intent classification task.

It achieves the following results on the test set:
- Accuracy: 0.994
- F1: 0.994

## Model description

The base [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) 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)](https://arxiv.org/pdf/1904.03670), 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},
}
```