arXiv AI
MyoCodec: A Streaming Neural Codec for Electromyography
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arXiv Machine Learning
Sep 10
Decoder-Side Semantic Conditioning for Low-Bitrate Neural Speech Compression
arXiv:2512.21653v2 Announce Type: replace-cross Abstract: Speech codecs are usually optimized for waveform fidelity, allocating bits to acoustic detail that can be inferred from linguistic structure....
arXiv AI
Jun 9
TLDR: Compressing Audio Tokens for Efficient Autoregressive Text-to-Speech
arXiv:2606. 09019v1 Announce Type: cross Abstract: Codec-based autoregressive (AR) speech language models have achieved strong text-to-speech (TTS) quality by modeling speech as sequences of discrete audio tokens with large pretrained backbones.
arXiv Machine Learning
Sep 14
TokenMapper: A Step Toward Interoperable Speech Token Translation
TokenMapper is a framework that enables direct translation between different speech tokenizers, allowing heterogeneous speech models to communicate without converting tokens to waveform audio. It handles mismatched token spaces, including single and multi-codebook representations, while maintaining a shared effective token rate. Experiments on GLM-4-Voice, MiMi, and DualCodec show that TokenMapper achieves word error rates close to native reconstructions, comparable human MOS scores, and significantly reduces latency compared to waveform bridging.
arXiv Machine Learning
Aug 7
LILAC: An Idempotent Neural Speech Codec
arXiv:2608. 05727v1 Announce Type: cross Abstract: Neural Audio Codecs are widely adopted in speech generation and editing.
arXiv AI
Aug 3
Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens
arXiv:2607. 29363v1 Announce Type: cross Abstract: Balancing sequence length, representational capacity, and long-horizon stability is a central problem in autoregressive (AR) speech and audio generation.
arXiv Machine Learning
Sep 7
GEPARD - Generative, Prosody-aware, Autoregressive text-to-speech model for Realtime Dialogue
GEPARD is a streaming text‑to‑speech model that uses a standard large language model backbone to generate speech autoregressively, decoding audio with an FSQ‑based neural codec. It streams audio chunk‑by‑chunk as text arrives, achieving a real‑time factor of about 0.067 and an aggregate speedup of roughly 204× on a single GPU with 256 concurrent streams. The design keeps all complex auxiliary mechanisms outside the decode loop, enabling deployment with a standard LLM engine (vLLM) without kernel modifications.