Hugging Face Trending Papers
LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs
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arXiv AI
Sep 16
LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs
arXiv:2609.17509v1 Announce Type: cross Abstract: Neural audio codecs are a key component in speech language modeling. However, their high frame rates lead to long sequence lengths, increasing comput...
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....
Hugging Face Trending Papers
Sep 10
ZipCodec: Ultra-Low-Frame-Rate Streaming Speech Coding
ZipCodec is a streaming neural speech codec that operates at an ultra‑low frame rate of 6.25 Hz and a bitrate of 0.80 kbps, achieving a theoretical latency of 160 ms. It leverages large‑scale WavLM distillation, a redesigned transformer architecture, a scalar spherical quantizer, and a latency‑aware streaming decoder. Experiments demonstrate that ZipCodec outperforms existing streaming codecs at comparable bitrates in both reconstruction quality and downstream tasks, while remaining real‑time on a consumer‑grade CPU despite its 842 M parameters.
arXiv AI
Aug 11
VoxZip: Semantic-Anchored Temporal KV Cache Compression for Long-Context Audio Inference
arXiv:2608. 08569v1 Announce Type: new Abstract: Recent advancements in Speech Large Language Models have demonstrated remarkable capabilities in understanding complex audio tasks.
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 11
ZipCodec: Ultra-Low-Frame-Rate Streaming Speech Coding
ZipCodec is a streaming neural speech codec that operates at an ultra‑low frame rate of 6.25 Hz and a bitrate of 0.80 kbps, achieving a theoretical latency of 160 ms. It leverages large‑scale WavLM distillation, a redesigned transformer architecture, a scalar spherical quantizer, and a latency‑aware streaming decoder to preserve reconstruction quality while reducing frame rate. Experiments demonstrate that ZipCodec outperforms existing streaming codecs at comparable bitrates in both reconstruction and downstream tasks, and it can run real‑time single‑stream inference on a consumer‑grade CPU despite having 842 M parameters.