NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911097848594432 |
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| author | Casanova, Edresson Neekhara, Paarth Langman, Ryan Hussain, Shehzeen Ghosh, Subhankar Yang, Xuesong Jukić, Ante Li, Jason Ginsburg, Boris |
| author_facet | Casanova, Edresson Neekhara, Paarth Langman, Ryan Hussain, Shehzeen Ghosh, Subhankar Yang, Xuesong Jukić, Ante Li, Jason Ginsburg, Boris |
| contents | Large Language Models (LLMs) have significantly advanced audio processing by leveraging audio codecs to discretize audio into tokens, enabling the application of language modeling techniques to speech data. However, existing audio codecs often operate at high frame rates, leading to slow training and inference, particularly for autoregressive models. To address this, there is growing interest in low frame-rate audio codecs, which reduce the number of autoregressive steps required to generate one second of audio. In this paper, we conduct ablation studies to examine the impact of frame rate, bitrate, and causality on codec reconstruction quality. Based on our findings, we introduce NanoCodec, a state-of-the-art audio codec that achieves high-quality compression at just 12.5 frames per second (FPS). NanoCodec outperforms related works across various bitrate ranges, establishing a new benchmark for low-latency and efficient Speech LLM training and inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference Casanova, Edresson Neekhara, Paarth Langman, Ryan Hussain, Shehzeen Ghosh, Subhankar Yang, Xuesong Jukić, Ante Li, Jason Ginsburg, Boris Audio and Speech Processing Computation and Language Sound Large Language Models (LLMs) have significantly advanced audio processing by leveraging audio codecs to discretize audio into tokens, enabling the application of language modeling techniques to speech data. However, existing audio codecs often operate at high frame rates, leading to slow training and inference, particularly for autoregressive models. To address this, there is growing interest in low frame-rate audio codecs, which reduce the number of autoregressive steps required to generate one second of audio. In this paper, we conduct ablation studies to examine the impact of frame rate, bitrate, and causality on codec reconstruction quality. Based on our findings, we introduce NanoCodec, a state-of-the-art audio codec that achieves high-quality compression at just 12.5 frames per second (FPS). NanoCodec outperforms related works across various bitrate ranges, establishing a new benchmark for low-latency and efficient Speech LLM training and inference. |
| title | NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference |
| topic | Audio and Speech Processing Computation and Language Sound |
| url | https://arxiv.org/abs/2508.05835 |