Tether says its 254M-parameter TranscribePsy model runs locally on phones, glasses and robots, with benchmark claims and open-source weights.
Tether AI Research on 14 October 2026 said it has released QVAC TranscribePsy, an open-source speech recognition model designed to run entirely on-device rather than sending audio to cloud servers. The company said the model is aimed at smartphones, smart glasses and humanoid robots, and framed the launch as part of its broader push for private, voice-enabled AI.
According to Tether’s release, TranscribePsy is a 254-million-parameter multilingual automatic speech recognition model that can process audio locally, including from smart glasses connected via Bluetooth or Wi-Fi and from a humanoid robot’s onboard processor. Tether said this approach is intended to reduce latency and avoid uploading spoken conversations that may contain sensitive personal, workplace or medical information.
The company also reported benchmark results for the model, saying TranscribePsy recorded an 8.02% word error rate on the Open ASR Leaderboard, 6.58% on Multilingual LibriSpeech and 21.56% on Common Voice. Tether said those results led all tested models in its parameter class, but those claims come from the company’s own announcement and should be read as reported benchmarks rather than independently verified comparisons.
7 times less memory than a tested Core ML competitor. 7 times faster than Whisper-small on phone CPUs. The release did not provide the underlying test conditions in the material supplied here.
Source link
