# Whisper v3-large

Fireworks released a beta speech-to-text API for the Whisper v3-large models, built around speed: on a dedicated endpoint, the asynchronous endpoint transcribes or translates one hour of audio in 4 seconds, about 900 times real time. Previously such services left users waiting seconds to minutes, and the gain here comes from serving-stack changes rather than a new model. On quality, whisper-v3-large reports a 2.00% word error rate for greedy inference on Librispeech Clean, matching the original open-source model; translation, word- and sentence-level timestamp alignment, audio preprocessing and voice activity detection ship in the same API.

Fireworks released a beta speech-to-text API for the Whisper v3-large models, built around speed: on a dedicated endpoint, the asynchronous endpoint transcribes or translates one hour of audio in 4 seconds, about 900 times real time. Previously such services left users waiting seconds to minutes, and the gain here comes from serving-stack changes rather than a new model. On quality, whisper-v3-large reports a 2.00% word error rate for greedy inference on Librispeech Clean, matching the original open-source model; translation, word- and sentence-level timestamp alignment, audio preprocessing and voice activity detection ship in the same API.