File size: 2,335 Bytes
b8bf265 2f2e7bb b8bf265 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
license: apache-2.0
base_model:
- facebook/wav2vec2-base
tags:
- intent-classification
- slu
- audio-classification
metrics:
- accuracy
- f1
model-index:
- name: wav2vec2-base-fsc-gold
results: []
datasets:
- fsc
language:
- en
pipeline_tag: audio-classification
library_name: transformers
---
# wav2vec2-base-FSC-GOLD (Retain Set)
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the FSC dataset (retain set) for the intent classification task.
It achieves the following results on the test set:
- Accuracy: 0.992
- F1: 0.993
## 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-gold")
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},
}
``` |