[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN) - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling…
Open weights
mit
PyTorch
[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN) - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling…
Open weights
mit
PyTorch
Descript Audio Codec running on-device on the LiteRT CompiledModel GPU (ML Drift). The convolutional encoder/decoder run on the GPU; the RVQ runs on CPU. 43:1 compression (1 s → 12×50 codes), RTF ≈ 0.82 (faster than real-time) on Pixel 8a. - dac16khzencoderfp16.tflite (43 MB) — audio[1,1,16000] → latent[1,1024,50], GPU. - dac16khzdeconlyzsfp16.tflite (105 MB) — latent[1,1024,50] → audio, GPU. - dacrvq.bin (1.2 MB) — RVQ weights (12 codebooks) for the CPU quantizer (float32 LE). encoder 367/367 + decoder 398/398 nodes on the LiteRT GPU delegate (LITERTCL, 1 partition, no CPU fallback); warm RTF ~0.82; reconstruction corr 1.0 vs PyTorch DAC. The decoder's ConvTranspose1d are rewritten to a…
Open weights
mit
litert
本仓库（Repository）所包含的所有人工神经网络（Artificial Neural Network）权重文件（.pth/.index）、训练日志及相关代码，均为计算声学（Computational Acoustics）与深度学习（Deep Learning）领域的技术研究实验产物。 这些文件本质上是高维张量（High-dimensional Tensors）的数值序列，通过随机梯度下降（SGD）与反向传播算法（Backpropagation）对公开可获取的音频数据进行统计建模（Statistical Modeling）得到。其技术形态与数字图像处理中的卷积核（Convolution Kernels）、自然语言处理中的词向量（Word Embeddings）并无本质差异。 本仓库不构成对任何第三方知识产权的故意侵犯，所有代码遵循 MIT License 开源协议，模型权重文件仅作为技术实现的副产品（By-products）存在。 本技术实验所使用的训练数据集（Training Dataset）包含以下角色的语音样本： - Mon3tr（モンスター）、Mon3tr 中文语音 等 - 声源版权归属：上海鹰角网络科技有限公司（Hypergryph Network Technology Co., Ltd.）及其关联公司 - 原始作品：《明日方舟》（Arknights） 上述角色的声音版权、肖像权、姓名权及相关知识产权均完全归属于鹰角网络及其合法授权方。本实验仅基于已公开发布的游戏内语音资源进行技术层面的信号处理（Signal Processing）与特征提取（Feature…
Open weights
cc-by-nc-4.0
A small causal Qwen3 model that predicts 32 MOSS v2 audio-codebook indices from the 12 indices of the current frame and up to ten previously generated HQ frames. It is trained from scratch on German, English, Spanish and French speech. This is an experimental enhancement model. Validation cross entropy measures token prediction under teacher forcing. It does not establish an improvement in perceived audio quality. Use the generated-history listening evaluation to judge speech content, speaker identity, artifacts and long-utterance stability. Root inference weights: step 64,699, selected by held-out extra-20-codebook cross entropy 5.818123. - Step 12,940: 100 originals / 200 input cases…
Open weights
cc-by-nc-4.0
pytorch
This release mirrors maxffarrell/gtcrn-coreai at source commit 5a5b0529f89c7146fc196f602ed2635adc681374. Choose DNS3 for general speech enhancement; VCTK-DEMAND is provided for matching that training corpus and reproducing related experiments. Download the complete repository, preserving the.aimodel bundle directories: For deployment you can download only exports/, LICENSE, and THIRDPARTYNOTICES.md using hf download --include. The numerical and latency results below were measured in August 2026 on the specified beta software stack; they have not been remeasured for newer releases. A reproducible, streaming Core AI conversion of GTCRN, the 48.2K-parameter speech-enhancement network…
Open weights
mit
