datawhalechina / hugging-audioLinks
Hugging Face Audio Course中文版,帮助学习者快速入门音频模态
☆37Updated last year
Alternatives and similar repositories for hugging-audio
Users that are interested in hugging-audio are comparing it to the libraries listed below
Sorting:
- a chinese tutorial of git☆158Updated last year
- Paper, Code and Resources for Speech Language Model and End2End Speech Dialogue System.☆188Updated last year
- 用于汇总目前的开源中文对话数据集☆191Updated 2 years ago
- An minimal Seq2Seq example of Automatic Speech Recognition (ASR) based on Transformer☆82Updated last year
- ☆28Updated 5 months ago
- 解锁HuggingFace生态的百般用法☆97Updated last year
- Github repository for ACL 2025 paper: Recent Advances in Speech Language Models: A Survey.☆156Updated 5 months ago
- Your faithful, impartial partner for audio evaluation — know yourself and your rivals.真实评测,知己知彼。☆175Updated last week
- 本仓库将带大家从零开始,用pytorch的线性层搭建传统的NLP神经网络☆42Updated last year
- FastThresholdClustering is an efficient vector clustering algorithm based on FAISS, particularly suitable for large-scale vector data clu…☆30Updated 11 months ago
- ☆204Updated last year
- MFCC implementation with detailed comments.☆17Updated 5 years ago
- llama-omni训练代码复现☆72Updated 10 months ago
- 语音方向实验室/公司/资源/实习等,欢迎推荐或自荐☆586Updated last year
- 主要参考李宏毅老师2020年人类语言处理课程资料整理,包括代码和ppt☆33Updated 4 years ago
- ASR教程: https://dataxujing.github.io/ASR-paper/☆25Updated last year
- 本课程面对具有一定机器学习基础,但尚未入门的NLPer或经验尚浅的NLPer,尽力避免陷入繁琐枯燥的公式讲解中,力求用代码展示每个模型背后的设计思想,同时也会带大家梳理每个模块下的技术演变,做到既知树木也知森林。☆89Updated last year
- 🤗 R1-AQA Model: mispeech/r1-aqa☆308Updated 8 months ago
- ☆82Updated last year
- ☆22Updated 3 years ago
- ☆21Updated 2 years ago
- ☆153Updated 3 weeks ago
- B站视频课程配套资料☆39Updated 2 years ago
- Datawhale论文分享,阅读前沿论文,分享技术创新☆51Updated last year
- ThinkLLM:🚀 轻量、高效的大语言模型算法实现☆112Updated 7 months ago
- 语音识别 论文 前沿☆51Updated 3 years ago
- 模型压缩的小白入门教程,PDF下载地址 https://github.com/datawhalechina/awesome-compression/releases☆344Updated 2 weeks ago
- ☆110Updated last month
- 大模型/LLM推理和部署理论与实践☆366Updated 5 months ago
- We Speech Transcript based on LLM, in 300 lines of code.☆181Updated 5 months ago