hitachi-speech/EEND
End-to-End Neural Diarization
This project helps pinpoint exactly who spoke when in audio recordings, even when multiple people talk over each other. You feed it an audio file, and it outputs a timeline showing which speaker is active at any given moment. This tool is ideal for researchers, analysts, or anyone working with multi-speaker spoken audio.
423 stars. No commits in the last 6 months.
Use this if you need to automatically identify and separate individual speakers in recordings, especially when the number of speakers isn't known beforehand.
Not ideal if you're looking for a simple, off-the-shelf solution without any technical setup, or if you only have single-speaker audio.
Stars
423
Forks
64
Language
Python
License
MIT
Category
Last pushed
Aug 30, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/hitachi-speech/EEND"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
felixbur/nkululeko
Machine learning speaker characteristics
claritychallenge/clarity
Clarity Challenge toolkit - software for building Clarity Challenge systems
juanmc2005/diart
A python package to build AI-powered real-time audio applications
astorfi/3D-convolutional-speaker-recognition
:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification
wq2012/awesome-diarization
A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.