metadata

language: en
license: mit
tags:
  - audio
  - captioning
  - text
  - audio-captioning
  - automated-audio-captioning
task_categories:
  - audio-captioning

CoNeTTE model source

CoNeTTE is an audio captioning system, which generate a short textual description of the sound events in any audio file.

CoNeTTE has been developped by me (Étienne Labbé) during my PhD. CoNeTTE stands for ConvNeXt-Transformer model with Task Embedding, and the architecture and training is explained in the corresponding paper.

Installation

python -m pip install conette
python -m spacy download en_core_web_sm

Usage with python

from conette import CoNeTTEConfig, CoNeTTEModel

config = CoNeTTEConfig.from_pretrained("Labbeti/conette")
model = CoNeTTEModel.from_pretrained("Labbeti/conette", config=config)

path = "/your/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 = "/your/path/to/audio_1.wav"
path_2 = "/your/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)

Usage with command line

Simply use the command conette-predict with --audio PATH1 PATH2 ... option. You can also export results to a CSV file using --csv_export PATH.

conette-predict --audio "/your/path/to/audio.wav"

Performance

Test data	SPIDEr (%)	SPIDEr-FL (%)	FENSE (%)	Vocab	Outputs	Scores
AC-test	44.14	43.98	60.81	309	:clipboard:	:chart_with_upwards_trend:
CL-eval	30.97	30.87	51.72	636	:clipboard:	:chart_with_upwards_trend:

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

Model weights are available on HuggingFace: https://huggingface.co/Labbeti/conette
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.

Contact

Maintainer:

Etienne Labbé "Labbeti": labbeti.pub@gmail.com