Thanks! They're not well known outside speech circles. Zipformer is an encoder architecture from the k2 / icefall team (the next-generation Kaldi project), made for fast streaming speech recognition: https://arxiv.org/abs/2310.11230
Banafo is a small company that trained Kroko, a streaming Zipformer2 transducer, and released community models on Hugging Face: https://huggingface.co/Banafo/Kroko-ASR
I picked a transducer because it suits a microcontroller. The encoder is 98% of the weights, and it runs once per 1.28 s chunk of audio, so each weight is read from PSRAM once and reused for every frame of the chunk. The decoder is tiny (under 2% of the model) and only remembers the last two tokens. On these chips the limit is reading the weights from memory, not the math. A Whisper-style model is the opposite: most of its weights are in a decoder that runs once per token, rereading them every time.
Banafo is a small company that trained Kroko, a streaming Zipformer2 transducer, and released community models on Hugging Face: https://huggingface.co/Banafo/Kroko-ASR
I picked a transducer because it suits a microcontroller. The encoder is 98% of the weights, and it runs once per 1.28 s chunk of audio, so each weight is read from PSRAM once and reused for every frame of the chunk. The decoder is tiny (under 2% of the model) and only remembers the last two tokens. On these chips the limit is reading the weights from memory, not the math. A Whisper-style model is the opposite: most of its weights are in a decoder that runs once per token, rereading them every time.