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Merge branch 'main' of https://github.com/Justjustifyjudge/CSBasicKnowledge into linyifan_branch
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‎src/SUMMARY.md‎

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- [Writing](./writing/writing.md)
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- [CV & Resume](./writing/resume.md)
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- [LaTeX & Markdown](./writing/latex.md)
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- [Beamer tutorial]()
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- [Typst](./writing/typst.md)
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- [实用工具](./useful/useful.md)
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- [Linux](./useful/linux.md)

‎src/ac/academic.md‎

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# Academic常用知识
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## 论文写作
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- [learning_research](https://github.com/pengsida/learning_research): 科研经验总结
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- [English-Writing](https://github.com/yzy1996/English-Writing/tree/main): Enhance Your English Writing for Science Research 写论文英语素材
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- [ML Visuals](https://github.com/dair-ai/ml-visuals): 🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
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- 谷歌学术：Google Scholar [[en](https://scholar.google.com)]
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## 常用工具
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- [Google Scholar](https://scholar.google.com)：谷歌学术
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- 计算机科学文献数据库：DBLP [[en](https://dblp.org/)]
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- SCI期刊查询和scihub各种科研导航: [[Page](https://www.ablesci.com/journal )]
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- ACM数字图书馆：ACM Digital Library [[en](https://dl.acm.org/)]
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- 中国计算机学会（CCF）推荐国际学术会议和期刊目录（2022版）[[pdf](https://github.com/CS-BAOYAN/CSBasicKnowledge/blob/main/CCF_Recommended_List.pdf)]
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- CCF会议投稿截止时间汇总：[[zh-cn](https://ccfddl.github.io/)]
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- CCFrank: 在相关网站的搜索结果中显示 CCF 评级的浏览器插件 \[[Github](https://github.com/WenyanLiu/CCFrank4dblp)\] \[[Chrome](https://chrome.google.com/webstore/detail/ccfrank/pfcajmbenomfbjnbjhgbnbdjmiklnkie)\] \[[Edge](https://microsoftedge.microsoft.com/addons/detail/pboigbpepikdoeindehghnpojjblhjmm)\] \[[FireFox](https://addons.mozilla.org/zh-CN/firefox/addon/ccfrank/)\]
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- 清华大学计算机学科推荐学术会议和期刊列表 (TH-CPL) [[Github](https://github.com/bugaosuni59/TH-CPL)]
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- CSRankings: Computer Science Rankings [[en](https://csrankings.org/)]
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- 国家自然科学基金查询 [[zh](https://kd.nsfc.gov.cn/resultInit)]
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- [清华大学计算机学科推荐学术会议和期刊列表 (TH-CPL)](https://github.com/bugaosuni59/TH-CPL)
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- [CSRankings](https://csrankings.org/): Computer Science Rankings
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- [国家自然科学基金查询](https://kd.nsfc.gov.cn/resultInit)
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‎src/ac/ap_phd.md‎

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- 一个博士生在科研大组的生存指南: [[pdf](http://www.tcse.cn/~wsdou/advice/phd%20survival-chen.pdf)]
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- Collection of advice for prospective and current PhD students: [[Github: en](https://github.com/pliang279/awesome-phd-advice)]
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- A Survival Guide to a PhD: [[en](https://karpathy.github.io/2016/09/07/phd/)]
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- [nlp-phd-global-equality](https://github.com/zhijing-jin/nlp-phd-global-equality): A repo for open resources & information for people to succeed in PhD in CS & career in AI / NLP

‎src/ai/cuda.md‎

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# CUDA & Nvidia
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- [CUDA C++ Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html): The programming guide to the CUDA model and interface.
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- [NVIDIA/CUDALibrarySamples](https://github.com/NVIDIA/CUDALibrarySamples): The CUDA Library Samples are released by NVIDIA Corporation as Open Source software under the 3-clause "New" BSD license.
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- [pybind11 documentation](https://pybind11.readthedocs.io/en/stable/basics.html)
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- [Using pybind11](https://people.duke.edu/~ccc14/sta-663-2016/18G_C++_Python_pybind11.html)
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- [Use pybind11 for a detailed but simple example](https://iamsorush.com/posts/pybind11-robot/)
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- 切换CUDA版本步骤: [[CSDN](https://blog.csdn.net/u013905398/article/details/103799621)]
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- 查看你的显卡的情况:
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- nvitop: [[Github](https://github.com/XuehaiPan/nvitop)]
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- gpustat: [[Github](https://github.com/wookayin/gpustat)]
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- nvidia-smi
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- nvidia-smi

‎src/ai/cv.md‎

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# CV(Computer Vision)
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- CS231n: Stanford 的 CV 入门课程 \[[Main Page](http://cs231n.stanford.edu/)\] \[[bilibili](https://www.bilibili.com/video/BV1nJ411z7fe)\] \[[Assignments](http://cs231n.stanford.edu/schedule.html)\]
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- CS231n: Stanford 的 CV 入门课程 \[[Main Page](http://cs231n.stanford.edu/)\] \[[bilibili](https://www.bilibili.com/video/BV1nJ411z7fe)\] \[[Assignments](http://cs231n.stanford.edu/schedule.html)\]
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- [Awesome-Vision-Attentions](https://github.com/MenghaoGuo/Awesome-Vision-Attentions): Summary of related papers on visual attention. Related code will be released based on Jittor gradually.
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- [Transformer-in-Computer-Vision](https://github.com/Yangzhangcst/Transformer-in-Computer-Vision): A paper list of some recent Transformer-based CV works.
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- [rese1f/Awesome-VQVAE](https://github.com/rese1f/Awesome-VQVAE): A collection of resources and papers on Vector Quantized Variational Autoencoder (VQ-VAE) and its application
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## object detection
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- [open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection): OpenMMLab Detection Toolbox and Benchmark
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- [facebookresearch/detectron2](https://github.com/facebookresearch/detectron2): Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
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- [facebookresearch/detr](https://github.com/facebookresearch/detr): End-to-End Object Detection with Transformers
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- [IDEA-Research/detrex](https://github.com/IDEA-Research/detrex): detrex is a research platform for DETR-based object detection, segmentation, pose estimation and other visual recognition tasks
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## segmentation
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- [facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything)
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## Vision-Language Model
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- [VLM_survey](https://github.com/jingyi0000/VLM_survey): Vision-Language Models for Vision Tasks: A Survey
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- [LLM-in-Vision](https://github.com/DirtyHarryLYL/LLM-in-Vision): Recent LLM-based CV and related works. Welcome to comment/contribute!

‎src/ai/dataset.md‎

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# Dataset下载
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- Opendatalab (国内pjlab实验室维护)：[[page](https://opendatalab.com/)]
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- OpenSLR (Daniel Povey维护)：[[page](https://openslr.org/)]
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- [Opendatalab (国内pjlab实验室维护)](https://opendatalab.com/)
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- [OpenSLR (Daniel Povey维护)](https://openslr.org/)
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- [Baai data(智源维护)](https://data.baai.ac.cn/data)

‎src/ai/nlp.md‎

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### NLP(Natural language Processing)
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- CS224n：Stanford 的 NLP入门课程 \[[Main Page](http://web.stanford.edu/class/cs224n/index.html)\] \[[Slides, Notes & Assignments](http://web.stanford.edu/class/cs224n/index.html#schedule)\] \[[Bilibili](https://www.bilibili.com/video/BV1Eb411H7Pq/)\]
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- funNLP: [[Github:zh-cn](https://github.com/fighting41love/funNLP)] NLP民工的乐园: 几乎最全的中文NLP资源库
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- [funNLP](https://github.com/fighting41love/funNLP) NLP民工的乐园: 几乎最全的中文NLP资源库
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- [zibuyu/research_tao](https://github.com/zibuyu/research_tao?tab=readme-ov-file)
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- sebastianruder/NLP-progress: [Github:en](https://github.com/sebastianruder/NLP-progress) | [online](https://nlpprogress.com/) Tracking Progress in Natural Language Processing

‎src/ai/pytorch.md‎

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# PyTorch
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- pytorch examples: [[Github:en](https://github.com/pytorch/examples)]
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- pytorch tutorials: [[Github:en](https://github.com/pytorch/tutorials)]
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- [pytorch examples](https://github.com/pytorch/examples)
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- [pytorch tutorials](https://github.com/pytorch/tutorials)
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- pytorch模型性能分析和优化: [weixin](https://mp.weixin.qq.com/s/lxJthBk1L2nYOyQyLbqqEw)
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- pytorch handbook: [[Github:zh-cn](https://github.com/zergtant/pytorch-handbook)]
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- datawhale/thorough-pytorch: [[Github:zh-cn](https://github.com/datawhalechina/thorough-pytorch)]
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- 杂七杂八的收集DL相关的东西： [[Github:en](https://github.com/aymericdamien/TopDeepLearning)]
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- 杂七杂八的收集DL相关的东西： [[Github:en](https://github.com/aymericdamien/TopDeepLearning)]
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## Framework
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- [pytorch-lightning](https://github.com/Lightning-AI/pytorch-lightning): Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
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## Visualization
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- [wandb](https://github.com/wandb/wandb): 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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- [PyTorch tensorboard](https://pytorch.org/docs/stable/tensorboard.html): How to use tensorboard in PyTorch.
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- [SwanLab](https://github.com/SwanHubX/SwanLab): ⚡️SwanLab: track and visualize all the pieces of your machine learning pipeline. 跟踪与可视化你的机器学习全流程

‎src/ai/speech.md‎

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# Audio & Speech
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- Speech-Resources: [[Github:zh-cn](https://github.com/ddlBoJack/Speech-Resources)]
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- Awesome-Speech-Pretraining: [[Github:zh-cn](https://github.com/ddlBoJack/Awesome-Speech-Pretraining)]
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- [Speech-Resources](https://github.com/ddlBoJack/Speech-Resources): 语音方向实验室/公司/资源/实习等，欢迎推荐或自荐
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- [metame-ai/awesome-audio-plaza](https://github.com/metame-ai/awesome-audio-plaza): Daily tracking of awesome audio papers, including music generation, zero-shot tts, asr, audio generation
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- [SpeechTasks](https://github.com/WangHelin1997/SpeechTasks): This is a list of speech tasks and datasets, which can provide training data for Generative AI, AIGC, AI model training, intelligent speech tool development, and speech applications.
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- [ai-audio-startups](https://github.com/csteinmetz1/ai-audio-startups): Community list of startups working with AI in audio and music technology
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- [speech_rankings](https://github.com/mutiann/speech_rankings): A CSRankings-like index for speech researchers
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- [INTERSPEECH-2023-Papers](https://github.com/DmitryRyumin/INTERSPEECH-2023-Papers): INTERSPEECH 2023 Papers: A complete collection of influential and exciting research papers from the INTERSPEECH 2023 conference.
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## SSL
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- [Awesome-Speech-Pretraining](https://github.com/ddlBoJack/Awesome-Speech-Pretraining): Paper, Code and Statistics for Self-Supervised Learning and Pre-Training on Speech.
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- [facebookresearch/fairseq](https://github.com/facebookresearch/fairseq): Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
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## ASR
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- [kaldi](https://github.com/kaldi-asr/kaldi): Kaldi Speech Recognition Toolkit
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- next-gen kaldi
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- [k2-fsa/icefall](https://github.com/k2-fsa/icefall): The icefall project contains speech-related recipes for various datasets using k2-fsa and lhotse.
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- [lhotse-speech/lhotse](https://github.com/lhotse-speech/lhotse): Tools for handling speech data in machine learning projects.
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- [openai/whisper](https://github.com/openai/whisper): Robust Speech Recognition via Large-Scale Weak Supervision
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- [awesome-whisper](https://github.com/sindresorhus/awesome-whisper): Awesome list for Whisper — an open-source AI-powered speech recognition system developed by OpenAI
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## Generation
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- [open-mmlab/Amphion](https://github.com/open-mmlab/Amphion): Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation.
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- [facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft): Audiocraft is a library for audio processing and generation with deep learning.
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- [NVIDIA/NeMo](https://github.com/NVIDIA/NeMo): NeMo: a framework for generative AI
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## Audio/Speech LLM
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- [QwenLM/Qwen-Audio](https://github.com/QwenLM/Qwen-Audio): The official repo of Qwen-Audio (通义千问-Audio) chat & pretrained large audio language model proposed by Alibaba Cloud.
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- [awesome-large-audio-models](https://github.com/EmulationAI/awesome-large-audio-models): Collection of resources on the applications of Large Language Models (LLMs) in Audio AI.
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- [Large-Audio-Models](https://github.com/liusongxiang/Large-Audio-Models): Keep track of big models in audio domain, including speech, singing, music etc.
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## Dataset
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- [speech-datasets-collection](https://github.com/RevoSpeechTech/speech-datasets-collection): a curated list of speech datasets (110+ datasets, 75+ easy to download)
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- [ai-audio-datasets](https://github.com/Yuan-ManX/ai-audio-datasets): This is a list of datasets consisting of speech, music, and sound effects
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- [ULCA-asr-dataset-corpus](https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus): asr dataset corpus collection
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- [coqui-ai/open-speech-corpora](https://github.com/coqui-ai/open-speech-corpora): A list of accessible speech corpora for ASR, TTS, and other Speech Technologies
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- [voice_datasets](https://github.com/jim-schwoebel/voice_datasets): A comprehensive list of open-source datasets for voice and sound computing (95+ datasets).
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- [audio-datasets](https://github.com/DagsHub/audio-datasets): open-source audio datasets
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- [speech_dataset](https://github.com/double22a/speech_dataset): The dataset of Speech Recognition
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- [k2-fsa/libriheavy](https://github.com/k2-fsa/libriheavy): Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context
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- [facebookresearch/libri-light](https://github.com/facebookresearch/libri-light)

‎src/security/security.md‎

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# Security
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Let's CTF!
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- [ctf-wiki](https://github.com/ctf-wiki/ctf-wiki): CTF wiki
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- [awesome-ctf-resources](https://github.com/devploit/awesome-ctf-resources): A list of Capture The Flag (CTF) frameworks, libraries, resources and software for started/experienced CTF players 🚩

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