---
task_categories:
- audio-classification
language:
- fr
tags:
- intent
- intent-classification
- audio-classification
- audio
base_model:
- facebook/wav2vec2-xls-r-300m
datasets:
- FBK-MT/Speech-MASSIVE
library_name: transformers
license: apache-2.0
---

# wav2vec 2.0 XLS-R 128 (300m) fine-tuned on Speech-MASSIVE - fr-FR

Speech-MASSIVE is a multilingual Spoken Language Understanding (SLU) dataset comprising the speech counterpart for a portion of the MASSIVE textual corpus. 
Speech-MASSIVE covers 12 languages. 
It includes spoken and written utterances and is annotated with 60 intents. 
The dataset is available on [HuggingFace Hub](https://huggingface.co/datasets/FBK-MT/Speech-MASSIVE).

This is the [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model fine-tuned on the fr-FR language.

It achieves the following results on the test set:

- Accuracy: 0.543
- F1: 0.410
  
## Usage

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/xls-r-128-speechmassive-fr-FR")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-xls-r-300m")

## 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},
}
```