Make Some Noise: Towards LLM audio reasoning and generation using sound tokens

• URL: http://arxiv.org/abs/2503.22275v1
• Date: Fri, 28 Mar 2025 09:43:47 GMT
• Title: Make Some Noise: Towards LLM audio reasoning and generation using sound tokens
• Authors: Shivam Mehta, Nebojsa Jojic, Hannes Gamper,
• Abstract summary: We introduce a novel approach that combines Variational Quantization with Flow Matching to convert audio into ultra-low discrete tokens of 0.23kpbs.<n>Our tokenizer outperforms a traditional VQ-VAE across various datasets with diverse acoustic events.
• Score: 19.48089933713418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrating audio comprehension and generation into large language models (LLMs) remains challenging due to the continuous nature of audio and the resulting high sampling rates. Here, we introduce a novel approach that combines Variational Quantization with Conditional Flow Matching to convert audio into ultra-low bitrate discrete tokens of 0.23kpbs, allowing for seamless integration with text tokens in LLMs. We fine-tuned a pretrained text-based LLM using Low-Rank Adaptation (LoRA) to assess its effectiveness in achieving true multimodal capabilities, i.e., audio comprehension and generation. Our tokenizer outperforms a traditional VQ-VAE across various datasets with diverse acoustic events. Despite the substantial loss of fine-grained details through audio tokenization, our multimodal LLM trained with discrete tokens achieves competitive results in audio comprehension with state-of-the-art methods, though audio generation is poor. Our results highlight the need for larger, more diverse datasets and improved evaluation metrics to advance multimodal LLM performance.

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