Awesome speech/audio LLMs, representation learning, and codec models

该仓库主要记录 大模型（LLMs） 算法工程师相关的面试题

1 min voice data can also be used to train a good TTS model! (few shot voice cloning)

记录cv算法工程师的成长之路，分享计算机视觉和模型压缩部署技术栈笔记。https://harleyszhang.github.io/cv_note/

📚 免费的计算机编程类中文书籍，欢迎投稿

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

Unofficial Implementation of Animate Anyone

We'll cover some of the most common Deep Learning Interview Questions and answers and provide detailed answers to help you

🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

中国大模型

Grapheme to phoneme conversion with deep learning.

One-Click to deploy well-designed ChatGPT web UI on Vercel. 一键拥有你自己的 ChatGPT 网页服务。

Monolingual wordlists with pronunciation information in IPA

深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

This is the onnxruntime inference code for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior (CVPR 2021). Official code: https://github.com/TencentARC/GFPGAN

Deploy your model with TensorRT quickly. 快速使用TensorRT来部署模型

FACIAL: Synthesizing Dynamic Talking Face With Implicit Attribute Learning. ICCV, 2021.

the dataset and code for "Flow-guided One-shot Talking Face Generation with a High-resolution Audio-visual Dataset"

Unsupervised text tokenizer for Neural Network-based text generation.

Code for NeurIPS2020 "Incorporating BERT into Parallel Sequence Decoding with Adapters"

code for "Physical-Virtual Collaboration Graph Network for Station-Level Metro Ridership Prediction"

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

The S3PRL speech toolkit: self-supervised pre-training and representation learning of Mockingjay, TERA, A-ALBERT, APC, and more to come. With easy-to-use standard downstream evaluation scripts incl…

A curated list of awesome self-supervised methods

Awesome Knowledge Distillation

A gradient reversal layer for pytorch.

A TensorFlow Implementation of the Transformer: Attention Is All You Need

Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"