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

Last update: Dec 23, 2022

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

Related tags

Overview

FENSE

Installation

Usage

Single Sentence

System Score

Performance Benchmark

Reference

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