Audio separation for music
Separate vocals from music, from one master to the whole catalog
A clean acapella and a sync-ready instrumental from the finished mix. No session files needed.
- Pop
- R&B
- Hip-hop
- Country
Batch
One song or the whole catalog, in Studio or by APILive
Concerts and broadcasts in real time. Enterprise, on request
Built for
From the studio to the catalog
Sync
Instrumentals and vocal‑up versions from the original master.
Remixes
Acapellas for remixes and DJ packs.
Karaoke
Sing-along tracks at catalog scale.
Catalogs
Missing versions for legacy releases.
Live
Concerts and broadcasts, separated in real time, for enterprise teams.
The problem
Your catalog has the mix. Not always the stems.
Older releases, acquired catalogs and finished masters often come without separate vocal and instrumental files. Rebuilding them by hand takes studio time you don't have for every request.
CAMB.AI is a vocals and instrumental separator: it uses vocal isolation to give you both versions back from the mix you already own.
Live · Enterprise
Separate vocals live, on stage and on air
Connect a concert or broadcast feed and get the vocals and the band back as two separate streams, in real time. We set it up with your team, on your own feed.
- Voice and background as two live streams
- Any language, several speakers at once
- Straight into CAMB.AI live translation and dubbing
Quality
Measured, not just promised
In our benchmark against AudioShake, ElevenLabs and LALAL.AI, CAMB.AI kept the most voice in the foreground stem and left the least behind in the background.
The least voice left behind
14% less than AudioShake, 16% less than ElevenLabs, 29% less than LALAL.AI: a background stem you can really reuse.
Average probability that speech is still heard in the background stem.
The cleanest voice of the four
2.8× AudioShake and 1.7× ElevenLabs on how cleanly the voice is pulled from the mix.
SI-SDR, estimated by SQUIM: how much of the separated voice is the voice and not what was around it.
The least voice left behind
14% less than AudioShake, 16% less than ElevenLabs, 29% less than LALAL.AI: a background stem you can really reuse.
Average probability that speech is still heard in the background stem.
The voice that sounds best
24% above AudioShake and 23% above ElevenLabs on how good the separated voice sounds.
PESQ, estimated by SQUIM: perceived speech quality, without a reference recording.
The voice that is easiest to understand
Every word stays clear, ahead of all three.
STOI, estimated by SQUIM: how intelligible the separated speech is.
The most voice in the voice stem
23% above AudioShake and 13% above ElevenLabs.
Average CLAP speech-focus gap: how clearly the foreground stem is about speech.
| Measure | CAMB.AI | AudioShake | ElevenLabs | LALAL.AI |
|---|---|---|---|---|
| Separation quality (SI-SDR) | 2.84 dB | 1.01 dB | 1.64 dB | 2.81 dB |
| Voice left in the background (Leakage) | 0.376% | 0.437% | 0.447% | 0.529% |
| Perceptual quality (PESQ) | 1.895 | 1.527 | 1.544 | 1.852 |
| Intelligibility (STOI) | 0.744 | 0.697 | 0.726 | 0.740 |
| Voice kept in the foreground (CLAP gap) | 0.258 | 0.209 | 0.229 | 0.255 |
| DNSMOS speech quality (SIG) | 2.662 | 2.387 | 2.523 | 2.684 |
| DNSMOS background quality (BAK) | 2.884 | 2.307 | 2.538 | 2.973 |
| DNSMOS overall quality (OVRL) | 2.303 | 1.923 | 2.094 | 2.327 |
Internal benchmark: CAMB.AI, AudioShake, ElevenLabs and LALAL.AI on the same seven clips from Wikimedia Commons (gaming commentary, a film trailer, live music, an outdoor interview, a live newsroom, archival TV and a red carpet interview), averaged. SQUIM and DNSMOS are reference-free estimates. On the three DNSMOS scores LALAL.AI is slightly ahead; CAMB.AI leads every other measure.
Use cases
Built for how music teams work
What you get
AI stem splitter: vocals and instrumental from every song
Vocals
The vocals on their own, without the music.
Instrumental
The full arrangement, without the vocals. Drums, bass and other instruments stay together in the instrumental.
Vocal separation API
From one song to the whole catalog: run separation one file at a time in Studio, or send your catalog through the API and get results back in bulk.
POST /apis/audio-separationGET /apis/audio-separation/{task_id}GET /apis/audio-separation-result/{run_id}POST /apis/audio-separation-resultsSee the audio separation API
Live, on stage and on airEnterprise
Separate the vocals from the band in real time, for concerts, live broadcasts and streams. An enterprise feature, set up with your team on request.
Talk to usYour masters stay yours
CAMB.AI is SOC 2 compliant and doesn't train its models on your audio. Your runs are listed in your History and you can delete them whenever you want. Separation doesn't change who owns the music or the rights to use it.
More audio separation
How it works
Extract vocals from a song in three steps
Pricing
Pick your hours. Both stems in every plan.
Every plan gives you the voice and the background as two stems, in Studio and by API. Try any plan free for 3 days, with 30 minutes of audio.
Solo
$40/month
$480 billed yearly
3 hours of audio a month
For creators and hobbyists. Pull the voice out of songs, podcasts and videos in minutes.
Start free trial- Today 30 minutes to try. $0 today.
- Day 3 Your plan starts: $480 a year.
Session
Recommended$130/month
$1,560 billed yearly
10 hours of audio a month
For producers and content creators working every week. Enough for full albums, podcasts and soundtracks.
Start free trial- Today 30 minutes to try. $0 today.
- Day 3 Your plan starts: $1,560 a year.
Catalog
$375/month
$4,500 billed yearly
30 hours of audio a month
For professionals and teams at volume. Clean voice and background stems for music, post-production and localization.
Start free trial- Today 30 minutes to try. $0 today.
- Day 3 Your plan starts: $4,500 a year.
Enterprise
High volumes, live separation on your own feeds, or separation inside your own workflow.
- Live separation, in real time
- Volume pricing
- SLA and dedicated support
- Custom delivery and storage
FAQ
Questions about vocal separation
Bring a song. Leave with two stems.
- SOC 2 compliant
- Your audio isn't used to train our models
- Batch and live




