Modelpublic

alkiskoudounas/xls-r-53-fr-speechmassive-fr-FR-gold

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

0likes4downloads

Model Card

wav2vec 2.0 XLS-R 53-fr-GOLD (300m) fine-tuned on Speech-MASSIVE - fr-FR (Retain Set)

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.

This is the jonatasgrosman/wav2vec2-xls-r-53-fr model fine-tuned on the fr-FR language (retain set).

It achieves the following results on the test set:

—Accuracy: 0.807
—F1: 0.772

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-53-fr-speechmassive-fr-FR-gold")
feature_extractor = AutoFeatureExtractor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-french")

## 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

Citation

If you use this model in your research, please cite the following papers:

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},
}