MLVC: Multi-platform Learned Video Codec for Real-World Deployment
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Updated
Nov 2, 2026 - Python
MLVC: Multi-platform Learned Video Codec for Real-World Deployment
A 1.6B causal Transformer audio tokenizer with streaming, variable bitrates, and semantic alignment across speech, sound, and music
Visual catalog of neural audio and speech codec architectures, audio VAEs and continuous autoencoders, with code, checkpoints, figures and license evidence.
A curated list of the best Text-to-Speech, speech synthesis, and voice-cloning research — models, papers, benchmarks, and toolkits, focused on 2025–2026.
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.
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.
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.
Offline removal of neural-codec (RVQ) quantization artifacts from AI-generated music - comb suppression, frozen-noise unfreezing, optional Demucs neural stage
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
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.
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.
[ACL 2025] OZSpeech: One-step Zero-shot Speech Synthesis with Learned-Prior-Conditioned Flow Matching
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.
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.
Mini SoundStream-style neural audio codec trained on LibriSpeech: 4-codebook RVQ, 3 bitrates, ONNX export
Proof of Concept (POC): A research protocol for semantic transmission over noisy channels using pre-trained neural codecs. Not production-ready.
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.
Neural audio codecs on Android: a practical benchmark and implementation guide for SpectroStream, SAGE, and SAME-S.
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