Granite Speech 5.0: Transcribe, Translate, and Attribute more Accurately and Faster

We're excited to share three updates to IBM's Granite Speech collection: a new speech-language model, granite-speech-5.0-2b , and revised versions of two models the community already uses, granite-speech-5.0-470m-turboctc and granite-speech-4.1-2b-plus . Together they cover the full range of enterprise speech work: transcription and translation with high accuracy, extremely fast transcription at the edge, and recordings where who spoke, and when, matter as much as what was said.

granite-speech-5.0-2b is the successor to granite-speech-4.1-2b at the same parameter count. It transcribes English, French, German, Spanish, Portuguese, and Japanese, and translates to and from English in those languages, plus English to Italian and Mandarin. It was trained on 390,000 hours of public audio, plus synthetic data built for Japanese ASR, keyword biasing, and translation.

The model ships under Apache 2.0 with support in Transformers, vLLM, llama.cpp, and mlx-audio for Apple Silicon.

When we introduced Turbo CTC in August , it was an English-only, 470M-parameter encoder built for speed. This revision keeps that footprint and adds the three things users asked for most:

The updated granite-speech-4.1-2b-plus is tuned for the recordings enterprises actually have: meetings, support calls, and interviews with several speakers running long. It keeps the 4.1 name because the architecture, prompt format, and API are unchanged, so it's a drop-in replacement for the current model with improved weights. This revision improves four areas:

These models are all released under an Apache 2.0 license with native support in Transformers. Full evaluation results, architecture details, training data, and usage examples are on each model card. We recommend pairing them with Granite Guardian for production deployments that need additional risk detection.

Chart 1: granite-speech-5.0-2b ranks #2 among top open-weight ASR models on average English WER (5.06%). Lower is better. Results are official, as of 21 October 2026, and averaged over the Open ASR Leaderboard’s public English short-form test sets.

It's also faster. By compiling a single Conformer block and reusing its graph across the stack, we cut warmup time and pushed Turbo CTC to 19,870 RTFx on the Open ASR Leaderboard, up from roughly 12,600 RTFx at launch: about 1.6x faster, at the same accuracy level.

Chart 2: Average WER vs. RTFx on the Open ASR Leaderboard (English short-form, 9 public test sets, as of October 2026). Lower WER and higher RTFx are better; RTFx is on a log scale.