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[停止维护 请使用note286/xduts]西安电子科技大学研究生学位论文XeLaTeX模板
The code for our INTERSPEECH 2020 paper - Jointly Fine-Tuning "BERT-like'" Self Supervised Models to Improve Multimodal Speech Emotion Recognition
The code for our IEEE ACCESS (2020) paper Multimodal Emotion Recognition with Transformer-Based Self Supervised Feature Fusion.
Replication package for the paper "Using Voice and Biofeedback to Predict User Engagement during Req 8000 uirements Interviews"
Official PyTorch Implementation of ProxyGML Loss for Deep Metric Learning, NeurIPS 2020 (spotlight)
A word frequency statistics tool for bilibili danmus
✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解
Production First and Production Ready End-to-End Speech Recognition Toolkit
This is an open source project (formerly named Listen, Attend and Spell - PyTorch Implementation) for end-to-end ASR implemented with Pytorch, the well known deep learning toolkit.
An Implementation of Model-Agnostic Meta-Learning in PyTorch with Torchmeta
A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful.
Implementation of the convolutional module from the Conformer paper, for use in Transformers
[ICASSP2021] Data preperation scripts, training pipeline and baseline experiment results for the Interspeech 2020 Accented English Speech Recognition Challenge (AESRC).
Source code of paper "BP-Transformer: Modelling Long-Range Context via Binary Partitioning"
Python package built to ease deep learning on graph, on top of existing DL frameworks.
A must-read paper for speech separation based on neural networks
A No-Recurrence Sequence-to-Sequence Model for Speech Recognition
A PyTorch implementation of Listen, Attend and Spell (LAS), an End-to-End ASR framework.
A PyTorch implementation of Speech Transformer, an End-to-End ASR with Transformer network on Mandarin Chinese.
cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理
My blogs and code for machine learning. http://cnblogs.com/pinard
Play couplet with seq2seq model. 用深度学习对对联。
This project accelerates CNN computation with the help of FPGA, for more than 50x speed-up compared with CPU.
中文语音识别; Mandarin Automatic Speech Recognition;