AI Speaker Separation
Trusted by podcast, interview & meeting creators worldwide
Upload multi-speaker audio. We auto-detect each speaker and extract their segments into separate tracks. Great for podcasts, interviews, and meetings.
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PRODrop audio here or click to select
MP3 / WAV / M4A / FLAC / OGG supported · 50MB max
If you know the count, specifying improves accuracy
How AI Speaker Separation Works
Split a multi-speaker recording into one clean track per speaker in four simple steps
Upload Audio
Upload your podcast, interview, or meeting recording. MP3, WAV, M4A, FLAC and OGG up to 50MB are supported.
Set Speaker Count
Let the AI auto-detect how many speakers are present, or specify the exact number (2–10) for higher accuracy.
AI Diarization
The MixVoice in-house speaker diarization model analyzes who spoke when and separates each voice into its own track.
Preview & Download
Listen to every speaker track online and download them individually as high-quality audio files.
Why Use Our AI Speaker Separation
Professional speaker diarization for podcasts, interviews, meetings, and any multi-speaker audio
Accurate Speaker Diarization
The MixVoice in-house diarization model precisely detects "who spoke when", even with overlapping conversation turns.
Auto Speaker Detection
No need to count voices first — the AI automatically detects 2 to 10 speakers, or you can pin the exact number.
One Track per Speaker
Each speaker's segments are stitched into a clean standalone track — ready for editing, transcription, or dubbing.
Built for Podcasts & Interviews
Split podcast episodes, interviews, and meeting recordings so every voice can be edited or transcribed separately.
Fast Cloud Processing
GPU-accelerated processing handles typical recordings in 1–3 minutes — no software install, everything runs online.
Saved to Your Library
Every separation task is saved to your account history, so you can come back and download speaker tracks anytime.
Frequently Asked Questions
What teams say about speaker separation
Podcasters and transcribers cleaning up multi-voice audio.
Separating each speaker into its own track makes editing interviews so much faster.
Grace M.
Podcast Editor
Separating each speaker into its own track makes editing interviews so much faster.
Grace M.
Podcast Editor
Clean speaker separation means accurate, labeled transcripts with far less manual cleanup.
Pavel K.
Transcriptionist
Clean speaker separation means accurate, labeled transcripts with far less manual cleanup.
Pavel K.
Transcriptionist
For meeting recordings, isolating each voice is exactly what my analysis needs.
Aiko N.
Researcher
For meeting recordings, isolating each voice is exactly what my analysis needs.
Aiko N.
Researcher