Hume Launches Audio Decision API: Ask Plain-Language Questions of Raw Audio in One Call, 70.7% Agreement With a Human Panel per Hume
On November 2 voice AI company Hume launched the Audio Decision API, which it calls the first audio-native model that answers questions about speech and sound. Developers upload raw audio, write each question as a sentence with candidate answers, and get back a probability for each answer, with no transcription step. It supports yes/no, multiple-choice, rating-scale and time-span questions, such as how many speakers there are, whether someone laughs, whether a caller sounds frustrated, or whether a siren is in the background. Per Hume, it was trained on more than 10 million human judgments and in an initial evaluation reached 70.7% agreement with a human panel's average judgment, versus 56.5% for an individual rater. Access is currently by request or waitlist.
Key Takeaways
- Per Hume: 70.7% agreement with a human panel's average judgment in an initial evaluation, versus a 56.5% individual-rater baseline; sample size and languages were not disclosed.
- Trained on more than 10 million human perceptual judgments (per Hume's X post); the blog cites tens of millions of listener ratings overall.
- Four question types (yes/no, choice, scale, span); adding a question is one line of JSON, with no labeling or retraining.
- The endpoint is
POST /v0/decisions/file, plus an input-token estimate endpoint, with Python and TypeScript SDKs. - Hume says work that used to take 10+ models now takes one call; pricing has not been published.
