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TS3-Codec: Transformer-Based Simple Streaming Single Codec

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arxiv 2411.18803 v2 pith:GONIJGYN submitted 2024-11-27 eess.AS

Haibin Wu , Naoyuki Kanda , Sefik Emre Eskimez , Jinyu Li This is my paper
classification eess.AS
keywords codects3-codecaudioconvolution-basedlayersstreamingtransformer-basedarchitecture
verification ladder T0 review T1 audit T2 compute T3 formal
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Neural audio codecs (NACs) have garnered significant attention as key technologies for audio compression as well as audio representation for speech language models. While mainstream NAC models are predominantly convolution-based, the performance of NACs with a purely transformer-based, and convolution-free architecture remains unexplored. This paper introduces TS3-Codec, a Transformer-Based Simple Streaming Single Codec. TS3-Codec consists of only a stack of transformer layers with a few linear layers, offering greater simplicity and expressiveness by fully eliminating convolution layers that require careful hyperparameter tuning and large computations. Under the streaming setup, the proposed TS3-Codec achieves comparable or superior performance compared to the codec with state-of-the-art convolution-based architecture while requiring only 12% of the computation and 77% of bitrate. Furthermore, it significantly outperforms the convolution-based codec when using similar computational resources.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models

    cs.SD 2026-07 conditional novelty 6.5 of 10

    Hierarchical multi-prompt representation generation plus generalized flow matching yields high-quality single-stage waveform diffusion from 12.5 Hz latents and efficient LDM TTS.

  2. The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs

    cs.SD 2026-02 conditional novelty 6.0 of 10

    Adding classical shape-gain decomposition to a neural audio codec makes it invariant to input gain and improves bitrate-distortion performance.

  3. Memory Efficient Audio Synthesis with Decoupled Temporal Depth Diffusion Transformers

    cs.SD 2026-07 conditional novelty 5.5 of 10

    A streaming encoder plus temporal and fully shared DiT-conditioned depth decoders converts semantic audio tokens to RVQ with constant memory and ~16× real-time on-device synthesis.

  4. NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference

    eess.AS 2025-08 conditional novelty 5.0 of 10

    NanoCodec achieves competitive speech quality at 12.5 frames per second and 0.6-1.78 kbps, with a causal decoder for low-latency speech LLM inference.

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