MLVC: Multi-platform Learned Video Codec for Real-World Deployment
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Updated
Jul 22, 2026 - Python
MLVC: Multi-platform Learned Video Codec for Real-World Deployment
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.
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.
A curated list of the best Text-to-Speech, speech synthesis, and voice-cloning research — models, papers, benchmarks, and toolkits, focused on 2025–2026.
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.
低比特率神经音频编解码器 + 残差矢量量化:把连续音频转成离散 token,服务音频语言模型
[ACL 2025] OZSpeech: One-step Zero-shot Speech Synthesis with Learned-Prior-Conditioned Flow Matching
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
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.
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.
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