Extremely Fast and Accurate Transcription with Granite Speech 5.0 Turbo CTC

Hugging Face tL;DR Performance Model Architecture Training Data Usage TL;DR Today we’re delighted to announce the release of two new models in the Granite Speech family: compact, 470M-parameter English speech recognition models that pair strong accuracy with unprecedented speed — over 12,600 RTFx on an NVIDIA H200 GPU, meaning they can transcribe more than 3.5 hours of speech in one second using batched inference. To get a sense of the models’ responsiveness, check out our WebGPU demo of streaming speech recognition.

Note that the demo only runs on Chrome or Edge browsers. The two models differ mainly in training data and licensing: granite-speech-5.0-470m-turboctc-nc trains on additional data and carries a CC-BY-NC-SA-4.0 license, while granite-speech-5.0-470m-turboctc trains on a smaller set and is Apache 2.0 licensed. We report unofficial results on the public, English short-form test sets from the OpenASR Leaderboard below. Please see the leaderboard for official results that include the private datasets. We provide bar charts showing the performance of ibm-granite/granite-speech-5.0-470m-turboctc-nc and ibm-granite/granite-speech-5.0-470m-turboctc as well as Pareto plots for the speed vs. accuracy trade-off and the model size vs. accuracy trade-off. Both models offer high accuracy, with the noncommercial model scoring an aggregate 4.85% WER and the Apache 2.0 model scoring 5.00% WER, and unprecedented aggregate throughput in excess of 12,600 RTFx. The noncommercial model is slightly more accurate than the Apache 2.0 model on most test sets, with a more noticeable advantage on SPGI Speech and a noticeable disadvantage on the new chunked Earnings22 test. Figure 1: Unofficial results for the public test sets used in the OpenASR leaderboard as of 21 August 2026. Inference was run using HF Jobs and scoring was performed using the OpenASR Leaderboard tooling, so the WER results and RTFx figures are expected to match the official ones. Figure 2: Unofficial Pareto plots (speed vs. accuracy and model size vs. accuracy) based on results for the OpenASR leaderboard public test sets as of 21 August 2026. Both models also perform well on far-field speech recognition as measured by the FFASR Leaderboard.