@rohanpaul_ai: An open multilingual ASR model just posted 3.54 average WER across 5 languages Hojo-ASR-Multi-V1 combines an acoustic e…

An open multilingual ASR model just posted 3.54 average WER across 5 languages Hojo-ASR-Multi-V1 combines an acoustic encoder with a Qwen3-4B decoder, giving speech recognition an unusually strong language model at the final decoding stage. - Built with a Qwen3-4B LLM decoder - Designed for multilingual conversational speech recognition - Publicly available on Hugging Face, under the Apache 2.0 license - Installable through the hojo-asr Python package - Supports local inference - Accepts WAV file paths, audio byte streams and SCP inputs There is a bigger technical story behind the 3.54 WER number. Traditional ASR systems rely heavily on acoustic and language modeling components designed specifically for speech, while Hojo hands the decoding stage to a Qwen3-4B LLM after the audio has been encoded and adapted. So recognition gets access to a much richer model of how words naturally fit together while it is deciding what was actually said. What makes Hojo even more interesting is that it is reaching that level across multiple languages rather than being built around only 1 language. And it does this with an openly available model that developers can actually run and inspect themselves. The architecture is probably the most interesting part for me. Hojo uses an Encoder, Adapter and LLM setup with Qwen3-4B as the decoder, so the final transcript is produced using both acoustic information from the speech and the language knowledge inside the LLM. That gives the decoder much more context for resolving words that may sound ambiguous from audio alone.