Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation

• URL: http://arxiv.org/abs/2410.20336v1
• Date: Sun, 27 Oct 2024 04:28:57 GMT
• Title: Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation
• Authors: Maohao Shen, Shun Zhang, Jilong Wu, Zhiping Xiu, Ehab AlBadawy, Yiting Lu, Mike Seltzer, Qing He,
• Abstract summary: Large language models (LLMs) have revolutionized natural language processing (NLP) We introduce a text-to-speech (TTS) system powered by a fine-tuned Llama model, named TTS-Llama, that achieves state-of-the-art speech synthesis performance. We further propose MoLE-Llama, a text-and-speech multimodal LLM developed through purely late-fusion parameter-efficient fine-tuning (PEFT) and a mixture-of-expert architecture.
• Score: 14.746190461312036
• License:
• Abstract: Large language models (LLMs) have revolutionized natural language processing (NLP) with impressive performance across various text-based tasks. However, the extension of text-dominant LLMs to with speech generation tasks remains under-explored. In this work, we introduce a text-to-speech (TTS) system powered by a fine-tuned Llama model, named TTS-Llama, that achieves state-of-the-art speech synthesis performance. Building on TTS-Llama, we further propose MoLE-Llama, a text-and-speech multimodal LLM developed through purely late-fusion parameter-efficient fine-tuning (PEFT) and a mixture-of-expert architecture. Extensive empirical results demonstrate MoLE-Llama's competitive performance on both text-only question-answering (QA) and TTS tasks, mitigating catastrophic forgetting issue in either modality. Finally, we further explore MoLE-Llama in text-in-speech-out QA tasks, demonstrating its great potential as a multimodal dialog system capable of speech generation.

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