Hugging Face Trending Papers
ZipCodec: Ultra-Low-Frame-Rate Streaming Speech Coding
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arXiv Machine Learning
2d ago
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.
arXiv AI
Sep 2
Reconstruct! Don't Encode: Self-Supervised Representation Reconstruction Loss for High-Intelligibility and Low-Latency Streaming Neural Audio Codec
arXiv:2603.05887v2 Announce Type: replace-cross Abstract: Neural audio codecs optimized for mel-spectrogram reconstruction often fail to preserve intelligibility. While semantic encoder distillation...
arXiv AI
Jun 8
Benchmarking Language Modeling for Lossless Compression of Full-Fidelity Audio
arXiv:2603. 08683v2 Announce Type: replace-cross Abstract: Autoregressive "language" models (LMs) trained on raw waveforms can be repurposed for lossless audio compression, but prior work is limited to 8-bit audio, leaving open whether such approaches work for practical settings (16/24-bit) and can compete with existing codecs.
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 Machine Learning
3d ago
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
6d ago
X-VC: Zero-shot Streaming Voice Conversion in Codec Space
X-VC is a zero‑shot streaming voice conversion system that performs one‑step conversion directly in the latent space of a pretrained neural codec. It employs a dual‑conditioning acoustic converter that jointly models source codec latents and target acoustic conditions, while using adaptive normalization to inject utterance‑level speaker information. The model is trained with generated paired data and a role‑assignment strategy, and uses a chunkwise inference scheme with overlap smoothing to achieve low‑latency streaming inference, achieving superior WER, speaker similarity, and real‑time factor on the Seed‑TTS‑Eval benchmark.