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}
}

You might also like...

I-BERT: Integer-only BERT Quantization

I-BERT: Integer-only BERT Quantization HuggingFace Implementation I-BERT is also available in the master branch of HuggingFace! Visit the following li

139 Dec 27, 2022

Source code for NAACL 2021 paper "TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference"

TR-BERT Source code and dataset for "TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference". The code is based on huggaface's transformers.

37 Oct 30, 2022

LV-BERT: Exploiting Layer Variety for BERT (Findings of ACL 2021)

LV-BERT Introduction In this repo, we introduce LV-BERT by exploiting layer variety for BERT. For detailed description and experimental results, pleas

14 Aug 24, 2022

The source codes for ACL 2021 paper 'BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data'

BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data This repository provides the implementation details for

124 Dec 27, 2022

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

You might also like...

I-BERT: Integer-only BERT Quantization

Source code for NAACL 2021 paper "TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference"

LV-BERT: Exploiting Layer Variety for BERT (Findings of ACL 2021)

The source codes for ACL 2021 paper 'BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data'

Pure python PEMDAS expression solver without using built-in eval function

Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

TAP: Text-Aware Pre-training for Text-VQA and Text-Caption, CVPR 2021 (Oral)

Yet another video caption

Fine-grained Control of Image Caption Generation with Abstract Scene Graphs

Releases(V0.1)

V0.1(Oct 2, 2021)

Owner

Zhiling Zhang

ReGAN: Sequence GAN using RE[INFORCE|LAX|BAR] based PG estimators

Pytorch cuda extension of grid_sample1d

BERT model training impelmentation using 1024 A100 GPUs for MLPerf Training v1.1

Python code to generate art with Generative Adversarial Network

Analyzing basic network responses to novel classes

[ICLR2021] Unlearnable Examples: Making Personal Data Unexploitable

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems

I have created this Virtual Paint Program, in this you can paint(draw) on your screen using hand gestures, created in Python-3 using OpenCV and Mediapipe library. Gestures :- Index Finger for drawing and Index+Middle Finger for changing position and objects.

My published benchmark for a Kaggle Simulations Competition

A Python implementation of the Locality Preserving Matching (LPM) method for pruning outliers in image matching.

Customer Segmentation using RFM

TensorFlow implementation of Elastic Weight Consolidation

A collection of resources, problems, explanations and concepts that are/were important during my Data Science journey

Bootstrapped Unsupervised Sentence Representation Learning (ACL 2021)

In this work, we will implement some basic but important algorithm of machine learning step by step.

This is the research repository for Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition.

Alfred-Restore-Iterm-Arrangement - An Alfred workflow to restore iTerm2 window Arrangements

Model Agnostic Interpretability for Multiple Instance Learning

This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).

Code for ACM MM2021 paper "Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection"