Auto diarization
AI-powered voice splitter that separates and isolates each speaker into their own track with timestamps — perfect for meetings, podcasts, and interviews.
Mixed speakers
Separated outputs
Drop in a multi-speaker audio or video file, or paste a shareable URL — up to 100 MB.
Set the number of speakers, or let the AI detect them automatically.
The voice splitter analyzes the audio and separates each speaker into its own track.
Grab per-speaker WAV files plus a JSON timeline showing who spoke when.
Separate voices from any recording — for podcasters, interviewers, and anyone who needs to know who said what.
Up to 10 speakers
Automatic speaker detection splits multi-speaker meetings, panels, interviews and podcasts.
Timestamps included
Structured, segment-level voice tracks with a who-spoke-when timeline for editing and analytics.
Accurate voice isolation
Precise separation with consistent speaker labels and clear boundaries between voices.
Audio, video, or a link
MP3, WAV, FLAC, OGG, M4A, OPUS, MP4, MOV — or paste a shareable link and we extract the audio.
Editor-ready outputs
Per-speaker WAV files plus JSON timestamps, ready to drop into an editor, DAW, or transcription flow.
Built to automate
Separate speakers over a documented REST API and SDKs with job status you can poll.
How AudioPod compares to a typical single-purpose speaker-separation tool.
Setup
Speakers
Outputs
Inputs
Automation
Getting started
For developers
Diarize and extract per-speaker audio with a few lines of code — speaker timelines, analytics, and URL input, with job status you can poll.
# Initialize the client
from audiopod import AudioPod
client = AudioPod(api_key="ap_xxxxx")
# Speaker extraction - generate separate audio files
extraction = client.speaker.extract(
file="conference_call.wav",
num_speakers=4
)
# Check job status and get results
status = client.speaker.get_status(extraction.id)
if status.status == "COMPLETED":
# Get speaker timeline and analytics
timeline = status.result.timeline # start/end timestamps per speaker
speakers = status.result.speakers # speaker stats and total speaking time
num_speakers = status.result.num_speakers
# Access individual speaker files (for extraction jobs)
if hasattr(status, 'speaker_files'):
speaker_0_audio = status.speaker_files['SPEAKER_00']
speaker_1_audio = status.speaker_files['SPEAKER_01']Meetings & calls
Use our voice splitter to separate speakers with accurate labels and timestamps for clean transcripts and easy edits.
Interviews & panels
Voice separation technology to isolate each participant for clearer review, editing and analysis.
Podcasts & shows
Split voices and create per-speaker tracks for better mixing, mastering and post-production.
TTS model training
Voice splitter tool to isolate specific speaker tracks and prepare clean datasets for training text-to-speech or voice cloning models.
All the audio tools you need in one place
Clone voices & generate speech in 200+ languages
Transcribe audio with speaker detection
Create songs with AI - beats, vocals, full tracks
Separate vocals, drums, bass from any song
Turn YouTube videos into podcasts, transcripts & more
vs traditional audio editors
Unlike manual editing in Audacity, our AI voice splitter automatically detects and separates speakers in seconds — no manual waveform cutting required.
vs paid desktop software
No expensive software licenses needed. Our cloud-based voice separation tool works instantly in your browser with better accuracy than most desktop solutions.
vs Python libraries
Skip the technical setup. No diarization libraries to install or audio models to configure — our speaker splitter API handles everything with just one API call.
Best for content creators
Purpose-built for podcasters, YouTubers, and content creators who need fast, accurate voice separation without audio engineering expertise.
Upload a multi-speaker recording and AudioPod splits it into clean, per-speaker tracks you can play and download.