speech-processing

19 projects share this GitHub topic

Lines connect members that are measurably related to each other. Dot size reflects stars.

🧬 Members

A PyTorch-based Speech Toolkit

maths-cs-ai-compendium

Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and…

awesome-multimodal-ml

Reading list for research topics in multimodal machine learning

Foundation Architecture for (M)LLMs

whisper-timestamped

Multilingual Automatic Speech Recognition with word-level timestamps and confidence

Controllable and fast Text-to-Speech for over 7000 languages!

deepvoice3_pytorch

PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

awesome-diarization

A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.

open-speech-corpora

💎 A list of accessible speech corpora for ASR, TTS, and other Speech Technologies

StreamSpeech is an “All in One” seamless model for offline and simultaneous speech recognition, speech…

SincNet is a neural architecture for efficiently processing raw audio samples.

Verbatim Automatic Speech Recognition with improved word-level timestamps and filler detection

[NeurIPS 2021] Multiscale Benchmarks for Multimodal Representation Learning

Speech-Backbones

This is the main repository of open-sourced speech technology by Huawei Noah's Ark Lab.

UniSpeech - Large Scale Self-Supervised Learning for Speech

Library to build speech synthesis systems designed for easy and fast prototyping.

speechbrain.github.io

The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users…

VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network

Awesome Audio-Visual Intelligence, Survey of Audio-Visual Intelligence

🔗 Related families

speech-synthesis 99 speech 86 asr 93 audio 58 speaker-verification 8 speech-separation 8 audio-processing 26 speaker-diarization 17

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.