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neural-codec

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Unofficial PyTorch implementation of Higgs Audio V2 Tokenizer with HuBERT semantic features. Complete training pipeline for semantic-acoustic audio tokenization with 960x downsampling and 8-layer RVQ.

  • Updated Oct 8, 2025
  • Python

Pure-C text-to-speech engine: autoregressive decoder + discrete neural audio codec, no Python at runtime. Sub-realtime on a 2020 M1 CPU (RTF 0.36 int8) and 0.26 on a mainstream NVIDIA GPU. f16/int8/int4 decode weights, memory-mapped, Metal & CUDA optional.

  • Updated Oct 10, 2026
  • C

Deep Learning model for compressing UI screenshots when a related reference screenshot exists. Compares conventional codecs with learned no-reference and reference-conditioned codecs on 24k WebSight-derived pairs, with a local demo and a mode selector that falls back to no-reference.

  • Updated Oct 31, 2026
  • Python

Neural data-over-voice modem: hide bytes in audio that survives Zoom/Teams/WhatsApp (Opus) and cellular (AMR-NB) codecs. 3196 bps as modem tone, 397 bps error-free inside real speech. Every number re-run in CI. PyTorch, live demo.

  • Updated Nov 1, 2026
  • Python

An edge-optimized Vector-Quantized Autoencoder (VQ-VAE) in PyTorch for high-fidelity satellite image compression. Utilizes Sliced Vector Quantization and Gradient Checkpointing to operate within 4GB VRAM, achieving a 19x compression ratio at 0.92 SSIM via a custom composite loss function.

  • Updated May 31, 2026
  • JavaScript

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