A pretrained speech classifier is repurposed as a backbone for guided diffusion-based speech generation. By attaching a lightweight subnetwork and training it under denoising score matching, the approach achieves high speech quality with reduced memory and computational cost, using a single model instead of two separately trained components.
arxiv
arXiv cs.LG
·
103d ago
·
research
Repurposing Speech Classifier for Diffusion-Based Generation
from English
Importance 2/3
New harness with differentiators
arXiv cs.LG
Hugging Face
Google DeepMind
NVIDIA
Image generation
Training methods
Voice & audio
Related articles
arxiv
arXiv cs.AI
·
103d ago
·
8 views
Repurposing Speech Classifier for Diffusion-Based Generation
A pretrained speech classifier is repurposed as a backbone for guided diffusion-based speech generation. By attaching a lightweight subnetwork and training it under denoising score matching, the approach achieves high speech quality with reduced memory and computational cost, using a single model instead of two separately trained components.
arxiv
arXiv cs.AI
·
104d ago
·
9 views
Hybrid ANN-SNN Pipeline with Local Plasticity
A hybrid ANN-SNN pipeline uses pretrained EfficientNet encoders and converts their activations to spike trains via rate-coding. The system trains a CoLaNET spiking classifier with local plasticity rules, achieving 99.09% accuracy on ImageNet's 64-class benchmark, matching conventional deep networks.
lab
Hugging Face Blog
·
28d ago
·
33 views
OpenEnv reproduces Surya Narreddi's watercolour painting model using TRL
An engineer has open-sourced a reproduction of Surya Narreddi’s viral project, which trains a coding model to paint watercolours using JavaScript and reinforcement learning. The implementation utilizes the TRL library and OpenEnv to create an end-to-end pipeline on Hugging Face for training, scoring, and inference.
lab
Hugging Face Blog
·
71d ago
·
40 views
Overview of simulation engines for Physical AI: MuJoCo, Isaac Sim, and Newton
This article provides an overview of the current state of simulation for Physical AI, explaining how simulation bridges the data gap for robotics by enabling scalable, photorealistic training environments. It outlines a three-computer paradigm (training, simulation, on-robot) and categorizes key simulation engines based on their specific capabilities and use cases.
lab
Hugging Face Blog
·
75d ago
·
30 views
NVIDIA NeMo Automodel enables distributed diffusion model fine-tuning with Hugging Face Diffusers
NVIDIA and Hugging Face have integrated NVIDIA NeMo Automodel with the 🤗 Diffusers library to provide production-grade, distributed training for open-source diffusion models. This collaboration allows users to fine-tune any Diffusers-format model on the Hugging Face Hub without requiring checkpoint conversion or model code rewrites.