audio-annotation

A curated list of awesome data labeling tools

Open source audio annotation tool for humans

Audio Annotation Tool for ML development

Cicada is an open source audio annotation tool that lets you annotate audio files in .wav format and also enables you to look into each audio spectrogram while annotating

Pyrenote, the Audio Labeling System for Acoustics Specifies Identification Project, is built to generate human moment-to-moment labels on audio files. At present, being used to label bird audio data. Derived from the open source repo Audino

A curated list of excellent tools for data labeling and annotation across various domains.

extensible web framework for continuous annotation and perception experiments.

Web-based tool for straight-forward class annotation of audio files

HTML-based audio annotation tool

Praat++ — A web-based multimodal annotation tool for acoustic and video data.

Interactive waveform + spectrogram audio player for analysis, annotation, and embeddable previews (Web, Streamlit, Gradio)

simple HTK & TextGrid handling for go

Bioacoustic annotation workflow demo: onset/offset detection to Raven selection table

Single-file audio span annotator that runs entirely in the browser. Mark, tag and label time spans on a waveform and spectrogram, then export them as JSON, CSV, Audacity labels or keep/suppress windows for a speech-eval harness. No build step, no server, no dependencies — and no audio ever leaves the machine.

Audio annotation tool designed for second language acquisition (SLA) researchers

End-to-end audio annotation workflow for speech emotion data including dataset analysis, annotation generation, quality checks, and audio visualization.

Audio Segment Labeler — Browser-based audio annotation tool for labeling waveform segments with category tags. Click, drag, and tag — exports structured JSON for speech/audio AI training pipelines.

SuperAnnotate is an AI data annotation platform purpose-built for computer vision and NLP teams that need to build, fine-tune, and iterate machine learning models with high-quality training data.