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.

Unofficial PyTorch implementation of VALL-E: zero-shot text-to-speech and voice cloning using neural codec language models. Train and synthesize speech from text with a single reference audio.

Moshi: open-source speech-text foundation model for real-time full-duplex voice dialogue. Uses Mimi neural audio codec. PyTorch, MLX (Apple Silicon) and Rust backends. Moshika & Moshiko voices.

Hide digital data inside speech-shaped audio that survives Zoom, Discord, WhatsApp, and cellular voice. Reproducible Pareto curve of six trained codecs spanning 76 bps (cellular) to 3196 bps (Zoom-class) with listenable demos.

Audio-driven facial animation via neural codec features and adaptive channel-grouped losses. Engineered for ultra-fast, production-ready inference. AIアバター向けニューラル音声からアニメーション変換エンジン

[ACL 2025] OZSpeech: One-step Zero-shot Speech Synthesis with Learned-Prior-Conditioned Flow Matching

A from-scratch PyTorch implementation of a neural audio codec (Encodec/SoundStream-style) at 3.2 kbps on LibriSpeech, with experiments on perceptual loss for phase recovery in GAN-less settings.