Spectrum is an AI that uses machine learning to generate Rap song lyrics

Overview

Contributors Forks Stargazers Issues MIT License Open In Colab


Spectrum

Spectrum is an AI that uses deep learning to generate rap song lyrics.

View Demo
Report Bug
Request Feature
Open In Colab

About The Project

Spectrum is an AI that uses deep learning to generate rap song lyrics.

Built With

This project is built using Python, Tensorflow, and Flask.

Getting Started

Installation

# clone the repo
git clone https://github.com/YigitGunduc/Spectrum.git

# install requirements
pip install -r requirements.txt

Training

# navigate to the Spectrum/AI folder 
cd Spectrum/AI

# pass verbose, epochs, save_at arguments and run train.py 
python3 train.py -h, --help  --epochs EPOCHS --save_at SAVE_AT --verbose VERBOSE --rnn_neurons RNN_NEURONS
             --embed_dim EMBED_DIM --dropout DROPOUT --num_layers NUM_LAYERS --learning_rate LEARNING_RATE

All the arguments are optional if you leave them empty model will construct itself with the default params

Generating Text from Trained Model

Call eval.py from the command line with seed text as an argument

python3 eval.py --seed SEEDTEXT

or

from model import Generator

model = Generator()

model.load_weights('../models/model-5-epochs-256-neurons.h5')

generatedText = model.predict(start_seed=SEED, gen_size=1000)

print(generatedText)
  • If you have tweaked the model's parameters while training initialize the model with the parameters you trained

Running the Web-App Locally

# navigate to the Spectrum folder 
cd Spectrum

# run app.py
python3 app.py

# check out http://0.0.0.0:8080

API

spectrum has a free web API you can send request to it as shown below

import requests 

response = requests.get("https://spectrumapp.herokuapp.com/api/generate/SEEDTEXT")
#raw response
print(response.json())
#cleaned up response
print(response.json()["lyrics"])

Hyperparameters

epochs = 30 
batch size = 128
number of layers = 2(hidden) + 1(output)
number of RNN units = 256
dropout prob = 0.3
embedding dimensions = 64
optimizer = Adam
loss = sparse categorical crossentropy

These hyperparameters are the best that I can found but you have to be careful while dealing with the hyperparameters because this model can over or underfit quite easily and GRUs performs better than LSTMs

Info about model

>>> from model import Generator
>>> model = Generator()
>>> model.load_weights('../models/model-5-epochs-256-neurons.h5')
>>> model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding (Embedding)        (1, None, 64)             6400      
_________________________________________________________________
gru (GRU)                    (1, None, 256)            247296    
_________________________________________________________________
gru_1 (GRU)                  (1, None, 256)            394752    
_________________________________________________________________
dense (Dense)                (1, None, 100)            25700     
=================================================================
Total params: 674,148
Trainable params: 674,148
Non-trainable params: 0
_________________________________________________________________

>>> model.hyperparams()
Hyper Parameters
+--------------------------+
|rnn_neurons   |        256|
|embed_dim     |         64|
|learning_rate |     0.0001|
|dropout       |        0.3|
|num_layers    |          2|
+--------------------------+
>>>

Roadmap

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

Results

WARNING: There is some offensive language ahead, please stop reading here if you are a sensitive person. The texts below have been generated by Spectrum

Seed : today

Prediction : 

If that don't, yeah
Weint off the music
It's like a fired-enother foar fool straight for an exactly
Nigga why I id my Door Merican muthafucka

Ng answered by need for blazy hard
The family wish fans dishes rolled up
How better just wanna die
Match all about the moment in I glory
Fire is that attention is the flop and pipe those peokin' distriors
Bitch I been hard and I'm like the Scales me and we're going to school like all-off of the allegit to get the bitches
Yeah kinda too legit back into highin'
A year have it would plobably want

And we all bustin' the conscious in the cusfuckers won't ha
Quite warkie and it's blow, and what? I cannot love him,
Alugal Superman, and the revolution likes migh
I ain't still not I uest the neighborhoo
Powers all too bad show, you crite your bac
When I say way too fathom
If you wanna revell, money, where your face we'll blin
Pulf me very, yo, they pull out for taught nothin' off
I pass a with a nigga hang some, pleas
Fuck me now, it's a

======================================================================
Seed : hello

Prediction : 

hellow motherfucker
You wanna talk on the pockets on Harlotom
I'm legit some more than Volumon
Ridicalab knowledge is blessin' some of your honierby man
We just bust the Flud joke with shoulders on the Statue
Lecock it on everybody want your dices to speak
While she speak cents look back to Pops
He was a nigga when I got behind pictures any Lil Sanvanas
Used to in her lady yaught they never had a bitch
He'll break the jird little rappers kill your children is

I'm prayin' back to ready for that bitch just finished And mised to the gamr
Every eyes on and about that getting common
I'm going to attractived with its
I just went by the crowd get the promise to buy the money-a star big down
Can one sall 'em in me tryna get them days that's how I can break the top
Well, that's hug her hands he screaming like a fucking hip-hop but put a Blidze like rhymin'
Yeah I slack like a Job let your cops got a generres
These West of it today flamping this
Black Kuttle crib, said "Ju Conlie, hold up, fuck the

======================================================================
Seed : bestfriend

Prediction : 

bestfriend
Too much time we tonight
The way I know is a please have no self-back when I be for the fucking weed and a game
What the fuck we wanna be working on the streets make it like a stay down the world is from the head of the real brain
Chain don't come back to the grass
My dick is the one to tell you I'm the fuck
So see me we gon' be fans when you had to hear the window you come to the dick when a little cooleng and I was calling what the fuck is it good as the crown
And I'm representing you finally waitin' in your girl
This is the corner with my brother
I'm just a damn door and the real motherfuckers come got the point my shit is the money on the world

I get it then the conscious that's why I cripp
I might take my own shit so let me have a bad bitch
I'm just had and make the fuck is in the single of the window
I think I ain't got the world is all my gone be mine
They ain't like the half the best between my words
And I'm changing with the heads of the speech
Fuck a bunch of best of a fuck

Contributing

Contributions are what make the open source community such an amazing place to be 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. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

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

Implementation of our NeurIPS 2021 paper "A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs".

PPO-BiHyb This is the official implementation of our NeurIPS 2021 paper "A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Grap

<a href=[email protected]"> 66 Nov 23, 2022
LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice,

LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and eval

Ahmet Erdem 691 Dec 23, 2022
Face Library is an open source package for accurate and real-time face detection and recognition

Face Library Face Library is an open source package for accurate and real-time face detection and recognition. The package is built over OpenCV and us

52 Nov 09, 2022
WRENCH: Weak supeRvision bENCHmark

🔧 What is it? Wrench is a benchmark platform containing diverse weak supervision tasks. It also provides a common and easy framework for development

Jieyu Zhang 176 Dec 28, 2022
Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation

Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation Experiment Setting: CIFAR10 (downloaded and saved in ./DATA

John Seon Keun Yi 38 Dec 27, 2022
PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)

Completer: Incomplete Multi-view Clustering via Contrastive Prediction This repo contains the code and data of the following paper accepted by CVPR 20

XLearning Group 72 Dec 07, 2022
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation

JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation This the repository for this paper. Find extensions of this w

Zhuoyuan Mao 14 Oct 26, 2022
Pytorch implementation for A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose

A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose Paper | Website | Data A-NeRF: Articulated Neural Radiance F

Shih-Yang Su 172 Dec 22, 2022
[ECCV 2020] Gradient-Induced Co-Saliency Detection

Gradient-Induced Co-Saliency Detection Zhao Zhang*, Wenda Jin*, Jun Xu, Ming-Ming Cheng ⭐ Project Home » The official repo of the ECCV 2020 paper Grad

Zhao Zhang 35 Nov 25, 2022
A machine learning project which can detect and predict the skin disease through image recognition.

ML-Project-2021 A machine learning project which can detect and predict the skin disease through image recognition. The dataset used for this is the H

Debshishu Ghosh 1 Jan 13, 2022
Autonomous Movement from Simultaneous Localization and Mapping

Autonomous Movement from Simultaneous Localization and Mapping About us Built by a group of Clarkson University students with the help from Professor

14 Nov 07, 2022
source code and pre-trained/fine-tuned checkpoint for NAACL 2021 paper LightningDOT

LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval This repository contains source code and pre-trained/fine-tun

Siqi 65 Dec 26, 2022
DeLag: Detecting Latency Degradation Patterns in Service-based Systems

DeLag: Detecting Latency Degradation Patterns in Service-based Systems Replication package of the work "DeLag: Detecting Latency Degradation Patterns

SEALABQualityGroup @ University of L'Aquila 2 Mar 24, 2022
ONNX Runtime Web demo is an interactive demo portal showing real use cases running ONNX Runtime Web in VueJS.

ONNX Runtime Web demo is an interactive demo portal showing real use cases running ONNX Runtime Web in VueJS. It currently supports four examples for you to quickly experience the power of ONNX Runti

Microsoft 58 Dec 18, 2022
Real-time VIBE: Frame by Frame Inference of VIBE (Video Inference for Human Body Pose and Shape Estimation)

Real-time VIBE Inference VIBE frame-by-frame. Overview This is a frame-by-frame inference fork of VIBE at [https://github.com/mkocabas/VIBE]. Usage: i

23 Jul 02, 2022
The implementation of our CIKM 2021 paper titled as: "Cross-Market Product Recommendation"

FOREC: A Cross-Market Recommendation System This repository provides the implementation of our CIKM 2021 paper titled as "Cross-Market Product Recomme

Hamed Bonab 16 Sep 12, 2022
LSSY量化交易系统

LSSY量化交易系统 该项目是本人3年来研究量化慢慢积累开发的一套系统,属于早期作品慢慢修改而来,仅供学习研究,回测分析,实盘交易部分未公开

55 Oct 04, 2022
git《Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction》(ECCV 2020) GitHub:

Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction Code for the ECCV 2020 paper by Yiming Qian and Yasutaka Furukawa Getting

37 Dec 04, 2022
Rasterize with the least efforts for researchers.

utils3d Rasterize and do image-based 3D transforms with the least efforts for researchers. Based on numpy and OpenGL. It could be helpful when you wan

Ruicheng Wang 8 Dec 15, 2022