Rhythm-Finder is a unsupervised ML driven python powered web-application that can find the songs that suits you.

Overview

Logo

Project Name

An awesome README template to jumpstart your projects!
Explore the docs »

View Demo · Report Bug · Request Feature

Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Roadmap
  5. Contributing
  6. License
  7. Contact

About The Project

![Product Name Screen Shot][product-screenshot]

Rhythm-Finder is a unsupervised ML driven python powered web-application that can find the songs that suits you. It takes your basic data over music taste and gives you some song which you might like .

Built With

This section should list any major frameworks that you built your project using. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.

Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

Installation

  1. Clone the repo
    git clone https://github.com/gdscjgec/Rhythm-Finder.git
  2. Install dependencies
    cd Rhythm-Finder
    pip install requirements.txt
    
  3. Open terminal and go to the folder using cd command example : cd
  4. Run the command
    streamlit run app.py
    

Usage

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

For more examples, please refer to the Documentation

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch ( git checkout -b feature/AmazingFeature )
  3. Add your Changes ( git add . )
  4. Commit your Changes ( git commit -m 'Add some AmazingFeature' )
  5. Push to the Branch ( git push origin feature/AmazingFeature )
  6. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Mail us at - [email protected]

Owner
Google Developer Student Clubs - JGEC
Official GitHub organization for the Google Developer Student Clubs - JGEC
Google Developer Student Clubs - JGEC
Simple and efficient RevNet-Library with DeepSpeed support

RevLib Simple and efficient RevNet-Library with DeepSpeed support Features Half the constant memory usage and faster than RevNet libraries Less memory

Lucas Nestler 112 Dec 05, 2022
[ICCV 2021] Instance-level Image Retrieval using Reranking Transformers

Instance-level Image Retrieval using Reranking Transformers Fuwen Tan, Jiangbo Yuan, Vicente Ordonez, ICCV 2021. Abstract Instance-level image retriev

UVA Computer Vision 86 Dec 28, 2022
open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

中文开放信息抽取系统, open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

7 Nov 02, 2022
Count the frequency of letters or words in a text file and show a graph.

Word Counter By EBUS Coding Club Count the frequency of letters or words in a text file and show a graph. Requirements Python 3.9 or higher matplotlib

EBUS Coding Club 0 Apr 09, 2022
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

fastNLP fastNLP是一款轻量级的自然语言处理(NLP)工具包,目标是快速实现NLP任务以及构建复杂模型。 fastNLP具有如下的特性: 统一的Tabular式数据容器,简化数据预处理过程; 内置多种数据集的Loader和Pipe,省去预处理代码; 各种方便的NLP工具,例如Embedd

fastNLP 2.8k Jan 01, 2023
[ICLR'19] Trellis Networks for Sequence Modeling

TrellisNet for Sequence Modeling This repository contains the experiments done in paper Trellis Networks for Sequence Modeling by Shaojie Bai, J. Zico

CMU Locus Lab 460 Oct 13, 2022
SEJE is a prototype for the paper Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering.

SEJE is a prototype for the paper Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering. Contents Inst

0 Oct 21, 2021
⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡

Translations 🇩🇪 DE 🇫🇷 FR 🇭🇺 HU 🇮🇩 ID 🇮🇹 IT 🇳🇱 NL 🇧🇷 PT-BR 🇷🇺 RU 🇨🇳 ZH ➡️ Documentation | Discord | Installation Guide ⬅️ Fully autom

11.2k Jan 05, 2023
Linear programming solver for paper-reviewer matching and mind-matching

Paper-Reviewer Matcher A python package for paper-reviewer matching algorithm based on topic modeling and linear programming. The algorithm is impleme

Titipat Achakulvisut 66 Jul 05, 2022
Under the hood working of transformers, fine-tuning GPT-3 models, DeBERTa, vision models, and the start of Metaverse, using a variety of NLP platforms: Hugging Face, OpenAI API, Trax, and AllenNLP

Transformers-for-NLP-2nd-Edition @copyright 2022, Packt Publishing, Denis Rothman Contact me for any question you have on LinkedIn Get the book on Ama

Denis Rothman 150 Dec 23, 2022
A paper list for aspect based sentiment analysis.

Aspect-Based-Sentiment-Analysis A paper list for aspect based sentiment analysis. Survey [IEEE-TAC-20]: Issues and Challenges of Aspect-based Sentimen

jiangqn 419 Dec 20, 2022
Code for Discovering Topics in Long-tailed Corpora with Causal Intervention.

Code for Discovering Topics in Long-tailed Corpora with Causal Intervention ACL2021 Findings Usage 0. Prepare environment Requirements: python==3.6 te

Xiaobao Wu 8 Dec 16, 2022
Topic Inference with Zeroshot models

zeroshot_topics Table of Contents Installation Usage License Installation zeroshot_topics is distributed on PyPI as a universal wheel and is available

Rita Anjana 55 Nov 28, 2022
NLP - Machine learning

Flipkart-product-reviews NLP - Machine learning About Product reviews is an essential part of an online store like Flipkart’s branding and marketing.

Harshith VH 1 Oct 29, 2021
PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

VAENAR-TTS - PyTorch Implementation PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

Keon Lee 67 Nov 14, 2022
中文空间语义理解评测

中文空间语义理解评测 最新消息 2021-04-10 🚩 排行榜发布: Leaderboard 2021-04-05 基线系统发布: SpaCE2021-Baseline 2021-04-05 开放数据提交: 提交结果 2021-04-01 开放报名: 我要报名 2021-04-01 数据集 pa

40 Jan 04, 2023
AI-Broad-casting - AI Broad casting with python

Basic Code 1. Use The Code Configuration Environment conda create -n code_base p

Japanese NLP Library

Japanese NLP Library Back to Home Contents 1 Requirements 1.1 Links 1.2 Install 1.3 History 2 Libraries and Modules 2.1 Tokenize jTokenize.py 2.2 Cabo

Pulkit Kathuria 144 Dec 27, 2022
🏆 • 5050 most frequent words in 109 languages

🏆 Most Common Words Multilingual 5000 most frequent words in 109 languages. Uses wordfrequency.info as a source. 🔗 License source code license data

14 Nov 24, 2022
Summarization module based on KoBART

KoBART-summarization Install KoBART pip install git+https://github.com/SKT-AI/KoBART#egg=kobart Requirements pytorch==1.7.0 transformers==4.0.0 pytor

seujung hwan, Jung 148 Dec 28, 2022