# audino: A Modern Annotation Tool for Audio and Speech

arXiv: https://arxiv.org/abs/2006.05236 · HTML: https://mlsanity.com/paper/2006.05236/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances](https://mlsanity.com/paper/2110.07592/) (2021)
- [A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data](https://mlsanity.com/paper/2109.03784/) (2021)
- [Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency](https://mlsanity.com/paper/2111.15156/) (2021)
- [FASA: a Flexible and Automatic Speech Aligner for Extracting High-quality Aligned Children Speech Data](https://mlsanity.com/paper/2406.17926/) (2024)
- [KidSpeak: A General Multi-purpose LLM for Kids' Speech Recognition and Screening](https://mlsanity.com/paper/2512.05994/) (2025)
- [LabelBuddy: An Open Source Music and Audio Language Annotation Tagging Tool Using AI Assistance](https://mlsanity.com/paper/2603.04293/) (2026)
- [TimeCues Studio: A Workspace for Music Annotation and Algorithm Prototyping](https://mlsanity.com/paper/2609.10338/) (2026)

## Abstract (arXiv)

In this paper, we introduce a collaborative and modern annotation tool for audio and speech: audino. The tool allows annotators to define and describe temporal segmentation in audios. These segments can be labelled and transcribed easily using a dynamically generated form. An admin can centrally control user roles and project assignment through the admin dashboard. The dashboard also enables describing labels and their values. The annotations can easily be exported in JSON format for further analysis. The tool allows audio data and their corresponding annotations to be uploaded and assigned to a user through a key-based API. The flexibility available in the annotation tool enables annotation for Speech Scoring, Voice Activity Detection (VAD), Speaker Diarisation, Speaker Identification, Speech Recognition, Emotion Recognition tasks and more. The MIT open source license allows it to be used for academic and commercial projects.