Fluency ENhanced Sentence-bert Evaluation (FENSE), metric for audio caption evaluation. And Benchmark dataset AudioCaps-Eval, Clotho-Eval.

Related tags

Deep Learning benchmark evaluation-metrics audio-captioning audiocaption
Overview

FENSE

The metric, Fluency ENhanced Sentence-bert Evaluation (FENSE), for audio caption evaluation, proposed in the paper "Can Audio Captions Be Evaluated with Image Caption Metrics?"

The main branch contains an easy-to-use interface for fast evaluation of an audio captioning system.

Online demo avaliable at https://share.streamlit.io/blmoistawinde/fense/main/streamlit_demo/app.py .

To get the dataset (AudioCaps-Eval and Clotho-Eval) and the code to reproduce, please refer to the experiment-code branch.

Installation

Clone the repository and pip install it.

git clone https://github.com/blmoistawinde/fense.git
cd fense
pip install -e .

Usage

Single Sentence

To get the detailed scores of each component for a single sentence.

from fense.evaluator import Evaluator

print("----Using tiny models----")
evaluator = Evaluator(device='cpu', sbert_model='paraphrase-MiniLM-L6-v2', echecker_model='echecker_clotho_audiocaps_tiny')

eval_cap = "An engine in idling and a man is speaking and then"
ref_cap = "A machine makes stitching sounds while people are talking in the background"

score, error_prob, penalized_score = evaluator.sentence_score(eval_cap, [ref_cap], return_error_prob=True)

print("Cand:", eval_cap)
print("Ref:", ref_cap)
print(f"SBERT sim: {score:.4f}, Error Prob: {error_prob:.4f}, Penalized score: {penalized_score:.4f}")

System Score

To get a system's overall score on a dataset by averaging sentence-level FENSE, you can use eval_system.py, with your system outputs prepared in the format like test_data/audiocaps_cands.csv or test_data/clotho_cands.csv .

For AudioCaps test set:

python eval_system.py --device cuda --dataset audiocaps --cands_dir ./test_data/audiocaps_cands.csv

For Clotho Eval set:

python eval_system.py --device cuda --dataset clotho --cands_dir ./test_data/clotho_cands.csv

Performance Benchmark

We benchmark the performance of FENSE with different choices of SBERT model and Error Detector on the two benchmark dataset AudioCaps-Eval and Clotho-Eval. (*) is the combination reported in paper.

AudioCaps-Eval

SBERT echecker HC HI HM MM total
paraphrase-MiniLM-L6-v2 none 62.1 98.8 93.7 75.4 80.4
paraphrase-MiniLM-L6-v2 tiny 57.6 94.7 89.5 82.6 82.3
paraphrase-MiniLM-L6-v2 base 62.6 98 82.5 85.4 85.5
paraphrase-TinyBERT-L6-v2 none 64 99.2 92.5 73.6 79.6
paraphrase-TinyBERT-L6-v2 tiny 58.6 95.1 88.3 82.2 82.1
paraphrase-TinyBERT-L6-v2 base 64.5 98.4 91.6 84.6 85.3(*)
paraphrase-mpnet-base-v2 none 63.1 98.8 94.1 74.1 80.1
paraphrase-mpnet-base-v2 tiny 58.1 94.3 90 83.2 82.7
paraphrase-mpnet-base-v2 base 63.5 98 92.5 85.9 85.9

Clotho-Eval

SBERT echecker HC HI HM MM total
paraphrase-MiniLM-L6-v2 none 59.5 95.1 76.3 66.2 71.3
paraphrase-MiniLM-L6-v2 tiny 56.7 90.6 79.3 70.9 73.3
paraphrase-MiniLM-L6-v2 base 60 94.3 80.6 72.3 75.3
paraphrase-TinyBERT-L6-v2 none 60 95.5 75.9 66.9 71.8
paraphrase-TinyBERT-L6-v2 tiny 59 93 79.7 71.5 74.4
paraphrase-TinyBERT-L6-v2 base 60.5 94.7 80.2 72.8 75.7(*)
paraphrase-mpnet-base-v2 none 56.2 96.3 77.6 65.2 70.7
paraphrase-mpnet-base-v2 tiny 54.8 91.8 80.6 70.1 73
paraphrase-mpnet-base-v2 base 57.1 95.5 81.9 71.6 74.9

Reference

If you use FENSE in your research, please cite:

@misc{zhou2021audio,
      title={Can Audio Captions Be Evaluated with Image Caption Metrics?}, 
      author={Zelin Zhou and Zhiling Zhang and Xuenan Xu and Zeyu Xie and Mengyue Wu and Kenny Q. Zhu},
      year={2021},
      eprint={2110.04684},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}
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