Ichigo: Mixed-Modal Early-Fusion Realtime Voice Assistant

• URL: http://arxiv.org/abs/2410.15316v1
• Date: Sun, 20 Oct 2024 07:03:49 GMT
• Title: Ichigo: Mixed-Modal Early-Fusion Realtime Voice Assistant
• Authors: Alan Dao, Dinh Bach Vu, Huy Hoang Ha,
• Abstract summary: Large Language Models (LLMs) have revolutionized natural language processing, but their application to speech-based tasks remains challenging. This paper introduces a mixed-modal model that seamlessly processes interleaved sequences of speech and text. We present a comprehensive training methodology, including pre-training on multilingual speech recognition datasets.
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• Abstract: 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.

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