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Isolate dialogue, music, and ambient sound from any audio source — in real time, at studio quality. Hudson AI's audio source separation uses deep neural networks to deliver studio-grade stem splitting with API access for developers.
Our model cleanly separates overlapping dialogue, music, and ambient noise — even in challenging real-world conditions like live broadcasts and crowded environments.
Process audio faster than real-time with ultra-low latency output. Designed for live broadcast pipelines where every millisecond counts.
Beyond speech — isolate breaths, laughs, cries, and ambient textures individually. Preserve the emotional texture of every recording.
Built for teams across the media pipeline
Cleanly extract dialogue tracks for dubbing workflows. Preserve original music and effects while swapping speech — no manual EDL needed.
Separate commentary from stadium noise in real time. Feed clean speech to translators and dubbing engines without post-production delay.
Isolate original dialogue for Automated Dialogue Replacement. Reduce studio time with cleaner source material going into your DAW.
Integrate separation as a core feature of your audio product. Our API plugs directly into podcast editors, audio editors, and streaming platforms.
Generate clean speech datasets at scale. Separate and label audio automatically to accelerate your ML training pipelines.
Integrate studio-quality audio source separation into your application. Simple REST endpoints, batch processing, and webhook callbacks — ready for production.
curl -X POST https://api.hudson-ai.com/v1/audio/separate \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: multipart/form-data" \ -F "file=@audio.mp3" \ -F "stems=vocals,instruments,drums,bass"
Try Hudson AI's audio source separation for free. No credit card required. API access available for developers.
Original Mix
Dialogue
Music & SFX
Try with your own audio
Drop file or browse
MP3, WAV, M4A, FLAC, OGG, WebM · max 50 MB