hubert

so-vits-svc fork with realtime support, improved interface and more features.

Self-Supervised Speech Pre-training and Representation Learning Toolkit

A mini, simple, and fast end-to-end automatic speech recognition toolkit.

[ICASSP 2023] Mingling or Misalignment? Temporal Shift for Speech Emotion Recognition with Pre-trained Representations

This repo contains the source code of the first deep learning-base singing voice beat tracking system. It leverages WavLM and DistilHuBERT pre-trained speech models to create vocal embeddings and trains linear multi-head self-attention layers on top of them to extract vocal beat activations. Then, it uses HMM decoder to infer signing beats and t…

An implementation of Speech Emotion Recognition, based on HuBERT model, training with PyTorch and HuggingFace framework, and fine-tuning on the RAVDESS dataset.

TypeScript port of deterministic CBOR (dCBOR), Gordian Envelope for privacy-preserving data, Uniform Resources (UR), secret sharing (SSKR/Shamir), XID decentralized identities, provenance marks, LifeHash visual hashing, and FROST threshold signatures.

The official implementation of the method discussed in the paper Improving Spoken Language Identification with Map-Mix(work accepted at ICASSP-2023)

Cover Song Powered by SoftVC VITS

Hubert

Google collab for testing SoftVC VITS Singing Voice Conversion for AI capable of changing the singer within music files.

The code for the MAPSS measures for source separation evaluation (ICLR, 2026)

🦜 Synthetic Voice Detection

unsupervised spoken utterances scoring

Rethinking Leveraging Pre-Trained Multi-Layer Representations for Speaker Verification, ISCA Interspeech 2025

Layer-aware TDNN: Speaker Recognition Using Multi-Layer Features from Pre-Trained Models, to appear in ICAIIC 2026

code for our paper DistilALHuBERT: A Distilled Parameter Sharing Audio Representation Model

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.

Advanced Speech Emotion Recognition, based on ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets and 14 languages (Emotions: Disgust, Neutral, Kind, Anger, Surprise, Joy)

The home of The Hubert™️