Hy-SimulMT vs Granite Speech 5.0

Official Hy-SimulMT RealSI GEMBA versus StreamLAAL figure from the Hugging Face model card — Tencent Hunyuan Hy-SimulMT-30B-A3B card (RealSI GEMBA / StreamLAAL figure). Source · License: Apache-2.0.

Official Granite Speech 5.0 2B English ASR comparison figure from the IBM model card — ibm-granite/granite-speech-5.0-2b card assets/granite-5.0-2b-vs-open-english.png. Source · License: Apache-2.0.

Verdict. Pick Hy-SimulMT if you want Tencent Hunyuan’s ungated 30B-A3B simultaneous Chinese–English speech-to-speech stack (SimulInfer / streaming). Pick Granite Speech 5.0 if you want IBM’s Apache-2.0 ~2.3B file ASR/AST family (plus a speaker-attribution sibling). Different jobs. Vendor tables stay self-reported. We have no sourced bake-off.

Compare Hy-SimulMT and Granite Speech 5.0.

Dimension	Hy-SimulMT	Granite Speech 5.0	Source
License	Apache-2.0 (card tag + LICENSE-Hy-SimulMT.txt)	Apache-2.0 (card tag; 5.0-2B siblings list had no LICENSE blob)	Hugging Face / GitHub (2026-10-27 / 2026-10-24)
Gating	No	No	Hugging Face API
Developer	Tencent Hunyuan	IBM	Official cards
Parameters	30B-A3B; HF safetensors 30,394,794,496	2.3B on the 5.0-2B card; safetensors 2,313,207,132	Official cards / HF API
Architecture	HYV3SpeechForCausalLM (128 experts, 8 per token)	granite_speech aligned from granite-4.0-1b-base	Official configs / cards
Job	Streaming simultaneous speech-to-speech (zh↔en prose)	File ASR + bidirectional AST; Plus sibling does speaker tags	Official cards
Serving named	SimulInfer + vLLM >=0.31; llama.cpp not yet merged	Transformers, vLLM, llama.cpp, mlx-audio (family blog)	Official cards / IBM blog
Hugging Face likes	18 (lead card, 2026-10-27)	48 (5.0-2B, 2026-10-24)	Hugging Face API
Quality vs DeepL / Otter	Not independently tested	Not independently tested	n/a

When to choose which

Live Chinese–English simultaneous interpretation: Hy-SimulMT — Card prose is streaming S2ST. Not a quality rank.
File transcription or speech translation: Granite Speech 5.0 — 5.0-2B is ASR/AST on an audio file. Hy-SimulMT is a live stream stack.
Speaker tags on a recording: Granite Speech 5.0 — Tags live on granite-speech-4.1-2b-plus, not on Hy-SimulMT.
Compact ~2B checkpoint: Granite Speech 5.0 — Hy-SimulMT is 30B-A3B (~56.7 GiB BF16). Size is not a quality claim.

Read the entity pages: Hy-SimulMT and Granite Speech 5.0. Hy-SimulMT hangs off DeepL alternatives and Otter.ai alternatives. Granite hangs off Otter and Descript alternatives.

Why this comparison exists

Searchers who want open speech translation now have Hunyuan’s 30B-A3B simultaneous stack next to IBM’s 2.3B ASR/AST family. This is identification, not a bake-off. Paid interpreter booths (Interprefy, KUDO) are not pages here.

Sourced snapshot (2026-10-27)

Field	Hy-SimulMT-30B-A3B	Granite Speech 5.0-2B
Host	huggingface.co/tencent/Hy-SimulMT-30B-A3B (not gated)	huggingface.co/ibm-granite/granite-speech-5.0-2b (not gated)
HF likes	18 (2026-10-27)	48 (2026-10-24)
License	Apache-2.0 (LICENSE file + card tag)	Apache-2.0 (card tag)
Size	~30.39B safetensors; 30B-A3B name	~2.31B safetensors
Job	Streaming simultaneous S2ST (zh↔en prose)	File ASR + bidirectional AST
Serve path	SimulInfer + vLLM >=0.31	Transformers / vLLM (card); llama.cpp named on the family blog
Quality vs DeepL / Otter	Not independently tested	Not independently tested

License

Both cards tag apache-2.0. Hy-SimulMT’s GitHub file says “Hy-SimulMT is licensed under Apache-2.0.” Granite 5.0-2B had no LICENSE blob in the siblings list we opened on 2026-10-24. Not legal advice.

How to choose

Need live Chinese–English simultaneous speech-to-speech: Hy-SimulMT.
Need live French / Spanish / Portuguese / German → English S2ST: Hibiki-Zero — Hibiki-Zero vs Hy-SimulMT.
Need file ASR/AST or speaker tags on the Plus sibling: Granite Speech 5.0.
Need NVIDIA 30B-class offline ASR (OpenMDW-1.1): Nemotron 3.5 Transcribe — Nemotron 3.5 Transcribe vs Granite Speech 5.0.
Need text MT (not speech): LibreTranslate on DeepL alternatives.
Need local Whisper STT: whisper.cpp.
Need Hunyuan’s memory writer, not MT: Hy-Memory.

FAQ

Which is better at translation quality?

We have no sourced bake-off. Hy-SimulMT’s FLEURS / RealSI / GEMBA rows and Granite’s Cap-F1 / WER rows stay vendor. Do not merge those tables.

Can I use Hy-SimulMT as an Otter clone?

No. Neither page is a meeting-notes product. Hy-SimulMT streams translation. Granite transcribes or translates a clip. See Otter.ai alternatives.

Are they the same Hunyuan stack?

No. Hy-SimulMT is Tencent Hunyuan. Granite Speech 5.0 is IBM. Hy-Memory is a different Hunyuan 30B-A3B writer, not a third speech model.

Why this pairing?

Both are published speech-translation pages on this site. The split is live simultaneous S2ST versus file ASR/AST. Interprefy and KUDO are not site entities, so they are not a vs pair.

Sources & verification

Last verified 2026-10-27 · Last updated 2026-10-27· Edited by forkalt editors · Editorial policy

License notes are not legal advice. Read the original LICENSE. Trademark & disclaimer.