Muse Ember 1.0
No published training cutoff
muse-ember-1.0
$0.10 in, $0.50 out per 1M tokens
Omnilingual ASR
Meta’s open massively multilingual speech recognition (Nov 2025, still Meta’s current open ASR).
Model idomniASR_LLM_7B_v2
PricePrice not published
Checked29 September 2026
Where it stands
79newer speech and audio models since its release
- NVIDIA Nemotron 3.5 Transcribe (30B-A4B)28 Oct 2026
- Hy-SimulMT-30B-A3B22 Oct 2026
- Eleven Realtime v1 Max / Mini20 Oct 2026
- Voxtral Mini Transcribe 3 (+ Mini Transcribe Realtime 3)20 Oct 2026
- Voxtral Small Transcribe 320 Oct 2026
The 5 newest are listed. All speech and audio models
- After it from Meta
- Muse Voice Transcribe 1.03 Sep 2026
- Weights
- Weights are public under a permissive licence (Apache-2.0).
How to call it
2 ways to use Omnilingual ASR, with the model id each provider expects.
| Provider | Model id | Endpoint | Docs |
|---|---|---|---|
| GitHub (fairseq2 checkpoints) | omniASR_LLM_7B_v2 |
github.com | |
| Hugging Face (demo space and dataset) | huggingface.co |
Pricing
Price not published.
No price source linked. Checked 29 September 2026.
Specs
- Input
- Audio
- Output
- Text
- Context window
- Not published
- Max output
- Not published
- Open weights
- Yes
- Licence
- Apache-2.0
- Released
- 10 November 2025
- Status
- Current
- Type
- Speech and audio
What stands out
What its maker marketed at launch, and what people found later, each with its source.
ASR for 1,600+ languages
First
Transcribes 1,600+ languages, ~500 of them never before supported by any ASR system (Whisper covers 99).
Zero-shot in-context language extension
omniASR_LLM_7B_ZS transcribes new languages from a few paired audio-text examples at inference, extending potential coverage to 5,400+ languages.
Notes
Open (Apache 2.0) suite: CTC and LLM-ASR models at 300M/1B/3B/7B, v2 checkpoints and ‘Unlimited’ long-audio LLM-ASR variants added December 2025, plus a 7B wav2vec 2.0 speech encoder and a corpus covering 350+ underserved languages. Checkpoints download via fairseq2 (e.g. https://dl.fbaipublicfiles.com/mms/omniASR-LLM-7B-v2.pt). Successor to MMS. The ‘first’ claim is Meta’s (‘never previously supported by any ASR model’).
Sources: https://github.com/facebookresearch/omnilingual-asr · https://ai.meta.com/blog/omnilingual-asr-advancing-automatic-speech-recognition/