Detect Suno AI voices.
Suspect a clip was made with Suno? Drop it in and get a citable verdict with the model named, in under half a second. Full AI-generated songs, with synthesized lead and backing vocals over machine-made instrumentation.
Suno, and why it shows up in suspicious audio
Suno is a leading AI music generator that produces complete songs, including synthesized singing, from a short text prompt. Its tracks circulate widely on social platforms, and the vocals are convincing enough that listeners often cannot tell a Suno song from a human recording.
Because the detector reads the acoustic signature of a voice, it targets the vocal, not the instrumental. On songs with clear, exposed vocals it works well; heavy backing music lowers confidence, so isolating or choosing an exposed-vocal section gives the clearest verdict. We report that confidence honestly rather than pretending music does not get in the way.
Where you tend to see it: Viral AI songs, fake 'leaked' tracks attributed to real artists, and synthetic vocal covers.
Suno is a legitimate product; misuse is the problem, not the tool. You can read about it on the official Suno site.
How to tell a Suno voice
The human ear is unreliable on current Suno audio. These are the signals a detector weighs. We report which ones drove the verdict rather than handing you a bare number.
- 01Sung pitch and vibrato that are smoother and more perfectly centered than a human singer's.
- 02Breath that is missing or placed too evenly across sung phrases.
- 03Backing vocals aligned unnaturally tightly with the lead, with identical timbre.
- 04Consonant and sibilant artifacts in the vocal that survive the mix, audible to a model even under instrumentation.
A verdict you can cite, not a vibe
The detector reads the audio, not the speaker. Cloning or stock voice, clean or compressed, it looks for the synthesis signature and attributes the source.
Drop the clip
Upload a file or paste a URL. MP3, WAV, M4A, WebM, or the audio track of a video. About half a second of clear speech is enough.
The model scores it
The same model behind the public detector reads the acoustic signature and weighs the artifacts, then attributes the source, e.g. Suno, when it recognizes it.
Get a citable verdict
You get a probability, a confidence level, the named model, and a permanent citation URL you can quote, file, or subpoena.
What to do next
A verdict is evidence, not a verdict of intent. Save the result to get a permanent citation URL and a one-way audio fingerprint you can reference later without storing the file. If you are a journalist or investigator, cite the verdict alongside your own reporting; if this is a suspected scam, treat the contact as unverified and confirm through a channel you already trust. The FTC's advice on suspected scam calls is a sensible baseline.
Building this into a workflow? The API returns the same verdict as JSON with webhooks for bulk jobs, and the browser extension checks audio in place on WhatsApp Web, YouTube, and podcasts.
Common questions
Can you tell if a song's vocal was made by Suno?
Does the backing music stop it working?
Can it catch an AI song impersonating a real artist?
Is a single check free?
Will it name Suno specifically?
We attribute 24+ voice models
Is this a Suno voice? Find out.
Free verdict, model named, in under a second. No card to start.