language: en
license: mit
tags:
- audio
- captioning
- text
- audio-captioning
- automated-audio-captioning
task_categories:
- audio-captioning
CoNeTTE (ConvNext-Transformer with Task Embedding) for Automated Audio Captioning
This model is currently in developement, and all the required files are not yet available.
This model generate a short textual description of any audio file.
Installation
pip install conette
Usage
from conette import CoNeTTEConfig, CoNeTTEModel
config = CoNeTTEConfig.from_pretrained("Labbeti/conette")
model = CoNeTTEModel.from_pretrained("Labbeti/conette", config=config)
path = "/my/path/to/audio.wav"
outputs = model(path)
candidate = outputs["cands"][0]
print(candidate)
The model can also accept several audio files at the same time (list[str]), or a list of pre-loaded audio files (list[Tensor]). IN this second case you also need to provide the sampling rate of this files:
import torchaudio
path_1 = "/my/path/to/audio_1.wav"
path_2 = "/my/path/to/audio_2.wav"
audio_1, sr_1 = torchaudio.load(path_1)
audio_2, sr_2 = torchaudio.load(path_2)
outputs = model([audio_1, audio_2], sr=[sr_1, sr_2])
candidates = outputs["cands"]
print(candidates)
The model can also produces different captions using a Task Embedding input which indicates the dataset caption style. The default task is "clotho".
outputs = model(path, task="clotho")
candidate = outputs["cands"][0]
print(candidate)
outputs = model(path, task="audiocaps")
candidate = outputs["cands"][0]
print(candidate)
Performance
| Dataset | SPIDEr (%) | SPIDEr-FL (%) | FENSE (%) |
|---|---|---|---|
| AudioCaps | 44.14 | 43.98 | 60.81 |
| Clotho | 30.97 | 30.87 | 51.72 |
This model checkpoint has been trained for the Clotho dataset, but it can also reach a good performance on AudioCaps with the "audiocaps" task.
Citation
The preprint version of the paper describing CoNeTTE is available on arxiv: https://arxiv.org/pdf/2309.00454.pdf
@misc{labbé2023conette,
title = {CoNeTTE: An efficient Audio Captioning system leveraging multiple datasets with Task Embedding},
author = {Étienne Labbé and Thomas Pellegrini and Julien Pinquier},
year = 2023,
journal = {arXiv preprint arXiv:2309.00454},
url = {https://arxiv.org/pdf/2309.00454.pdf},
eprint = {2309.00454},
archiveprefix = {arXiv},
primaryclass = {cs.SD}
}
Additional information
The encoder part of the architecture is based on a ConvNeXt model for audio classification, available here: https://huggingface.co/topel/ConvNeXt-Tiny-AT. More precisely, the encoder weights used are named "convnext_tiny_465mAP_BL_AC_70kit.pth", available on Zenodo: https://zenodo.org/record/8020843.
It was created by @Labbeti.