Ichigo: Mixed-Modal Early-Fusion Realtime Voice Assistant
Authors: name: Alan Dao, name: Dinh Bach Vu, name: Huy Hoang Ha

Large Language Models (LLMs) have revolutionized natural language processing,
but their application to speech-based tasks remains challenging due to the
complexities of integrating audio and text modalities. This paper introduces
Ichigo, a mixed-modal model that seamlessly processes interleaved sequences of
speech and text. Utilizing a tokenized early-fusion approach, Ichigo quantizes
speech into discrete tokens and employs a uniform transformer-based
architecture for both speech and text modalities. This method enables joint
reasoning and generation across modalities without the need for separate
adapters. We present a comprehensive training methodology, including
pre-training on multilingual speech recognition datasets and fine-tuning on a
curated instruction dataset. Ichigo demonstrates state-of-the-art performance
on speech question-answering benchmarks, outperforming existing open-source
speech language models and achieving comparable results to cascaded systems.
Notably, Ichigo exhibits a latency of just 111 ms to first token generation,
significantly lower than current models. Our approach not only advances the
field of multimodal AI but also provides a framework for smaller research teams
to contribute effectively to open-source speech-language models.