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so-vits-svc fork with realtime support, improved interface and more features.
Self-Supervised Speech Pre-training and Representation Learning Toolkit
Phoneme segmentation using pre-trained speech models
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
An implementation of Speech Emotion Recognition, based on HuBERT model, training with PyTorch and HuggingFace framework, and fine-tuning on the RAVDESS dataset.
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 tempo.
Cover Song Powered by SoftVC VITS
The official implementation of the method discussed in the paper Improving Spoken Language Identification with Map-Mix(work accepted at ICASSP-2023)
Google collab for testing SoftVC VITS Singing Voice Conversion for AI capable of changing the singer within music files.
unsupervised spoken utterances scoring
code for our paper DistilALHuBERT: A Distilled Parameter Sharing Audio Representation Model
Universal Pooling Method for Speaker Verification Utilizing Pre-trained Multi-layer Features, 2025 preprint
In this code, we have used common and well-known datasets such as the Toronto dataset available on Kaggle to create a sentiment analysis model from human voice. This model is designed based on the Bert model and is called Hubert.
A library to help your context being persisted in your react native apps
Speech Keyword detection using Wav2Vec Model
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)
