From 61bf5620e8188aae5551ec2b5099881d73dcd321 Mon Sep 17 00:00:00 2001 From: AIDAS-Lab Date: Tue, 23 Sep 2025 17:18:37 +0900 Subject: [PATCH] Revert "project page" --- README.md | 151 +- index.html | 324 ---- server.py | 6 - static/css/bulma-carousel.min.css | 1 - static/css/bulma-slider.min.css | 1 - static/css/bulma.css.map.txt | 1 - static/css/bulma.min.css | 1 - static/css/fontawesome.all.min.css | 5 - static/css/index.css | 157 -- static/images/aidaslab_logo.png | Bin 7344 -> 0 bytes static/images/e2e_training.png | Bin 1544855 -> 0 bytes static/images/error_corrector.png | Bin 1394901 -> 0 bytes static/images/exp-1.png | Bin 3468356 -> 0 bytes static/images/exp-2.png | Bin 1334459 -> 0 bytes static/images/favicon.ico | Bin 15406 -> 0 bytes static/images/main1.png | Bin 61639 -> 0 bytes static/images/snu-night.jpg | Bin 1231314 -> 0 bytes static/images/snu_logo.png | Bin 107141 -> 0 bytes static/js/bulma-carousel.js | 2371 ---------------------------- static/js/bulma-carousel.min.js | 1 - static/js/bulma-slider.js | 461 ------ static/js/bulma-slider.min.js | 1 - static/js/fontawesome.all.min.js | 5 - static/js/index.js | 78 - 24 files changed, 146 insertions(+), 3418 deletions(-) delete mode 100644 index.html delete mode 100644 server.py delete mode 100644 static/css/bulma-carousel.min.css delete mode 100644 static/css/bulma-slider.min.css delete mode 100644 static/css/bulma.css.map.txt delete mode 100644 static/css/bulma.min.css delete mode 100644 static/css/fontawesome.all.min.css delete mode 100644 static/css/index.css delete mode 100644 static/images/aidaslab_logo.png delete mode 100644 static/images/e2e_training.png delete mode 100644 static/images/error_corrector.png delete mode 100644 static/images/exp-1.png delete mode 100644 static/images/exp-2.png delete mode 100644 static/images/favicon.ico delete mode 100644 static/images/main1.png delete mode 100644 static/images/snu-night.jpg delete mode 100644 static/images/snu_logo.png delete mode 100644 static/js/bulma-carousel.js delete mode 100644 static/js/bulma-carousel.min.js delete mode 100644 static/js/bulma-slider.js delete mode 100644 static/js/bulma-slider.min.js delete mode 100644 static/js/fontawesome.all.min.js delete mode 100644 static/js/index.js diff --git a/README.md b/README.md index 13fd8ac..b7a5723 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,148 @@ -# AIDAS Lab Project Page Template +# MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical Speech-to-Formula -This repository hosts the source code for the AIDAS Lab Project Page. -The template was originally derived from [Nerfies](https://nerfies.github.io). +## Abstract +In various academic and professional settings, such as mathematics lectures or research presentations, it is often necessary to convey mathematical expressions orally. However, reading mathematical expressions aloud without accompanying visuals can significantly hinder comprehension, especially for those who are hearing-impaired or rely on subtitles due to language barriers. For instance, when a presenter reads Euler's Formula, current Automatic Speech Recognition (ASR) models often produce a verbose and error-prone textual description (e.g., e to the power of i x equals cosine of x plus i $\textit{side}$ of x), instead of the concise LaTeX format (i.e., $e^{ix} = \cos(x) + i\sin(x)$), which hampers clear understanding and communication. To address this issue, we introduce MathSpeech, a novel pipeline that integrates ASR models with small Language Models (sLMs) to correct errors in mathematical expressions and accurately convert spoken expressions into structured LaTeX representations. Evaluated on a new dataset derived from lecture recordings, MathSpeech demonstrates LaTeX generation capabilities comparable to leading commercial Large Language Models (LLMs), while leveraging fine-tuned small language models of only 120M parameters. +Specifically, in terms of CER, BLEU, and ROUGE scores for LaTeX translation, MathSpeech demonstrated significantly superior capabilities compared to GPT-4o. We observed a decrease in CER from 0.390 to 0.298, and higher ROUGE/BLEU scores compared to GPT-4o. -# Website License -Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. +### This study is accepted for the AAAI-25 Main Technical Track. + +Here, you can find the benchmark dataset, experimental code, and fine-tuned model checkpoints for MathSpeech, which we have developed for our research. + +If you want to view the detailed information about the dataset used in this study or additional experimental results such as latency measurements included in the appendix, please refer to the version uploaded on arXiv. + +--- + +## Benchmart Dataset +The MathSpeech benchmark dataset is available on [huggingface🤗](https://huggingface.co/datasets/AAAI2025/MathSpeech) or through the following [link](https://drive.google.com/drive/folders/1M8_IVcesO2EwNcl9zwxY6UgqAmSODzgq?usp=sharing). + +- [MathSpeech in huggingface🤗 dataset](https://huggingface.co/datasets/AAAI2025/MathSpeech) +- [Google Drive link for dataset](https://drive.google.com/drive/folders/1M8_IVcesO2EwNcl9zwxY6UgqAmSODzgq?usp=sharing) + + +#### Dataset statistics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
The number of files1,101
Total Duration5583.2 seconds
Average Duration per file5.07 seconds
The number of speakers10
The number of men8
The number of women2
source[MIT OpenCourseWare]
+ + + +#### WERs of various ASR models on the Mathspeech benchmark + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelsParamsWER(%) (Leaderboard)WER(%) (Formula)
OpenAIWhisper-base74M10.334.7
Whisper-small244M8.5929.5
Whisper-largeV21550M7.8331.0
Whisper-largeV31550M7.4433.3
NVIDIACanary-1B1B6.535.2
+ +##### The WER for Leaderboard was from the [HuggingFace Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard), while the WER for Formula was measured using our MathSpeech Benchmark. This value is based on results as of 2024-08-16. + + +--- +## MathSpeech Checkpoint +You can download the MathSpeech checkpoint from the following [link](https://drive.google.com/file/d/1m0cCpDDkOb7FltjLPVlg4ZCZSSSWZgS2/view?usp=sharing). + +### Experiments codes + +You can find the MathSpeech evaluation code, and the prompts used for the LLMs in the experiments at the following [link](https://github.com/hyeonsieun/MathSpeech/tree/main/Experiments). + +### Ablation Study codes + +You can find the code used in our Ablation Study at the following [link](https://github.com/hyeonsieun/MathSpeech/tree/main/Ablation_Study). + +--- +## How to Use +1. Clone this repository using the web URL. +```bash +git clone https://github.com/hyeonsieun/MathSpeech.git +``` +2. To build the environment, run the following code +```bash +pip install -r requirements.txt +``` +3. Place [the audio dataset and the transcription Excel file](https://drive.google.com/drive/folders/1M8_IVcesO2EwNcl9zwxY6UgqAmSODzgq?usp=sharing) inside the ASR folder. +4. Run the following code. +```bash +python ASR.py +``` +5. Go to the Experiments folder +6. Move the 'MathSpeech_checkpoint.pth' from the following [link](https://drive.google.com/file/d/1m0cCpDDkOb7FltjLPVlg4ZCZSSSWZgS2/view?usp=sharing) into the Experiments folder. +7. Run the following code. +```bash +python MathSpeech_eval.py +``` +8. If you want to run LLMs like GPT-4o or Gemini, you'll need to configure the environment settings such as the API key and endpoint. +9. You can also run the Ablation Study code from the Ablation_Study folder. + +**Notes:** Here, example code for performing ASR using whisper-base and whisper-small is provided. If you want to use a different ASR model, you can modify that part of the code to use our MathSpeech. diff --git a/index.html b/index.html deleted file mode 100644 index 6f405cc..0000000 --- a/index.html +++ /dev/null @@ -1,324 +0,0 @@ - - - - - - - - - - MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical Speech-to-Formula - - - - - - - - - - - - - - - - - - - - - - - - - -
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MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical Speech-to-Formula

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- - Sieun Hyeon1, - - Kyudan Jung2, - - Jaehee Won3, - - Nam-Joon Kim1,
- - Hyun Gon Ryu4, - - Hyuk-Jae Lee1,5, - - Jaeyoung Do1,5 - -
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- 1Department of Electrical and Computer Engineering, Seoul National University,
- 2Department of Mathematics, Chung-Ang University,
- 3College of Liberal Studies, Seoul National University,
- 4NVIDIA,
- 5Interdisciplinary Program in Artificial Intelligence, Seoul National University -
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- Our pipeline that converts the lecturer’s voice into LaTeX. -

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Abstract

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- In various academic and professional settings, such as mathematics lectures or research presentations, it is often necessary to convey mathematical expressions orally. However, reading mathematical expressions aloud without accompanying visuals can significantly hinder comprehension, especially for those who are hearing-impaired or rely on subtitles due to language barriers. For instance, when a presenter reads Euler's Formula, current Automatic Speech Recognition (ASR) models often produce a verbose and error-prone textual description (e.g., e to the power of i x equals cosine of x plus i `side` of x), instead of the concise LaTeX format (i.e., e^{ix} = \cos(x) + i\sin(x) ), which hampers clear understanding and communication. To address this issue, we introduce MathSpeech, a novel pipeline that integrates ASR models with small Language Models (sLMs) to correct errors in mathematical expressions and accurately convert spoken expressions into structured LaTeX representations. Evaluated on a new dataset derived from lecture recordings, MathSpeech demonstrates LaTeX generation capabilities comparable to leading commercial Large Language Models (LLMs), while leveraging fine-tuned small language models of only 120M parameters. Specifically, in terms of CER, BLEU, and ROUGE scores for LaTeX translation, MathSpeech demonstrated significantly superior capabilities compared to GPT-4o. We observed a decrease in CER from 0.390 to 0.298, and higher ROUGE/BLEU scores compared to GPT-4o. -

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Overview

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- Introduction & Motivation
- - Current ASR systems struggle to accurately transcribe mathematical expressions in academic settings, resulting in verbose and error-prone outputs.
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- Approach
- - We connected small language models to the ASR model to perform post-processing on the ASR model's text output.
- - A pipeline was created by linking two T5-small models, assigning the roles of Error Corrector and LaTeX Translator to each model.
- - Our MathSpeech architecture converts audio input of spoken mathematical formulas into LaTeX code that accurately represents the corresponding formulas.
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- - In terms of CER, BLEU, and ROUGE scores for LaTeX translation, MathSpeech demonstrated significantly superior capabilities compared to GPT-4o.
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Methodology : Error Corrector

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- - The Error Corrector is responsible for correcting errors in the audio transcription text output by the ASR model.
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Methodology : LaTeX Translator & End-to-End training

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- - The LaTeX Translator converts spoken English that describes mathematical expressions into LaTeX code.
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Experiment Results

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- For model evaluation, we extracted audio from real mathematics lectures on YouTube and manually labeled it to build a benchmark dataset. -

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BibTeX

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@article{HyeonAAAI25, 
-      title={MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical Speech-to-Formula}, 
-      volume={39}, 
-      url={https://ojs.aaai.org/index.php/AAAI/article/view/34595}, 
-      DOI={10.1609/aaai.v39i23.34595}, 
-      number={23}, 
-      journal={Proceedings of the AAAI Conference on Artificial Intelligence}, 
-      author={Hyeon, Sieun and Jung, Kyudan and Won, Jaehee and Kim, Nam-Joon and Ryu, Hyun Gon and Lee, Hyuk-Jae and Do, Jaeyoung}, 
-      year={2025}, 
-      month={Apr.}, 
-      pages={24194-24202} 
-}
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