Automatic voice-synthetised summaries of latest research papers on arXiv

Overview

PaperWhisperer

PaperWhisperer is a Python application that keeps you up-to-date with research papers. How? It retrieves the latest articles from arXiv on a topic, by performing a keyword-based search. Then, it creates vocal summaries of the articles using Text-To-Speech and stores them to disk.

Installation

To install the package, move to the root of the repo and type in the console:

$ pip install .

If you plan to develop the package further, install the package in editable mode also installing the packages necessary to run unittests:

$ pip install -e .[test]

Testing

To run unittests, issue the following command from the root of the repo:

$ pytest

Package structure

The package is divided into 2 sub-packages:

  • retrieval
  • tts

retrieval contains data structures and facilities necessary to retrieve articles from arXiv. Under the hood, the app uses arxiv, a Python package that is a wrapper around the arXiv free API.

tts has facilities to generate speech renditions of text-based article summaries. The summary of an article consists of its title, authors, and abstract. Speech synthesis is performed using Google Cloud Text-To-Speech.

Setting up Google Cloud Text-To-Speech

PaperWhisperer uses Google Cloud Text-To-Speech to synthesise speech.

In order to be able to use this service, you should:

  1. create an account on Google Cloud,
  2. create a Cloud Platform project,
  3. enable the Text-To-Speech API in the project
  4. setup authentication
  5. download a Json private key

More info on how to set up Google Cloud Text-To-Speech

Environment variables

The app uses an environment variable called GOOGLE_APPLICATION_CREDENTIALS to connect to Google Cloud Text-To-Speech safely.

In config.yml, set GOOGLE_APPLICATION_CREDENTIALS to the path of the Json private key you previously downloaded while setting up the Google service.

Without this step, you won't be able to connect to Google Cloud Text-To-Speech, and the app will throw an error.

How to create summaries

To create summaries for a keyword search, use the create_summaries entry point. This is the only console script of the package and the main entry point of the application.

Below is an example of how you can run the script:

$ create_summaries "generate chord progressions" 100 /save/dir 40

The script takes 4 positional arguments:

  • keywords used for searching articles (more than one keyword is possible)
  • maximum number of articles to retrieve
  • directory where to store vocal summaries
  • retrieve articles no older than this integer value in days

Dependencies

PaperWhisperer depends on the following packages:

  • arxiv==1.2.0
  • google-cloud-texttospeech
  • python-dotenv

YouTube video

Learn more about PaperWhisperer in this project presentation video on The Sound of AI YouTube channel.

Owner
Valerio Velardo
AI audio/music researcher. Love Python.
Valerio Velardo
A PyTorch implementation of the baseline method in Panoptic Narrative Grounding (ICCV 2021 Oral)

A PyTorch implementation of the baseline method in Panoptic Narrative Grounding (ICCV 2021 Oral)

Biomedical Computer Vision @ Uniandes 52 Dec 19, 2022
Offline Multi-Agent Reinforcement Learning Implementations: Solving Overcooked Game with Data-Driven Method

Overcooked-AI We suppose to apply traditional offline reinforcement learning technique to multi-agent algorithm. In this repository, we implemented be

Baek In-Chang 14 Sep 16, 2022
Code for Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021)

Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021) Hang Zhou, Yasheng Sun, Wayne Wu, Chen Cha

Hang_Zhou 628 Dec 28, 2022
PyTorch implementation of a Real-ESRGAN model trained on custom dataset

Real-ESRGAN PyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original

Sber AI 160 Jan 04, 2023
This is implementation of AlexNet(2012) with 3D Convolution on TensorFlow (AlexNet 3D).

AlexNet_3dConv TensorFlow implementation of AlexNet(2012) by Alex Krizhevsky, with 3D convolutiional layers. 3D AlexNet Network with a standart AlexNe

Denis Timonin 41 Jan 16, 2022
AI-Fitness-Tracker - AI Fitness Tracker With Python

AI-Fitness-Tracker We have build a AI based Fitness Tracker using OpenCV and Pyt

Sharvari Mangale 5 Feb 09, 2022
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

Attention Walk ⠀⠀ A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding meth

Benedek Rozemberczki 303 Dec 09, 2022
A scanpy extension to analyse single-cell TCR and BCR data.

Scirpy: A Scanpy extension for analyzing single-cell immune-cell receptor sequencing data Scirpy is a scalable python-toolkit to analyse T cell recept

ICBI 145 Jan 03, 2023
Quantized models with python

quantized-network download .pth files to qmodels/: googlenet : https://download.

adreamxcj 2 Dec 28, 2021
Code for the tech report Toward Training at ImageNet Scale with Differential Privacy

Differentially private Imagenet training Code for the tech report Toward Training at ImageNet Scale with Differential Privacy by Alexey Kurakin, Steve

Google Research 29 Nov 03, 2022
The open-source and free to use Python package miseval was developed to establish a standardized medical image segmentation evaluation procedure

miseval: a metric library for Medical Image Segmentation EVALuation The open-source and free to use Python package miseval was developed to establish

59 Dec 10, 2022
This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong Poisons

Adversarial poison generation and evaluation. This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong

31 Nov 01, 2022
Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Improving Transferability of Representations via Augmentation-Aware Self-Supervision Accepted to NeurIPS 2021 TL;DR: Learning augmentation-aware infor

hankook 38 Sep 16, 2022
Lab course materials for IEMBA 8/9 course "Coding and Artificial Intelligence"

IEMBA 8/9 - Coding and Artificial Intelligence Dear IEMBA 8/9 students, welcome to our IEMBA 8/9 elective course Coding and Artificial Intelligence, t

Artificial Intelligence & Machine Learning (AI:ML Lab) @ HSG 1 Jan 11, 2022
This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch

This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch. The code was prepared to the final version of the accepted manuscript in AIST

Marcelo Hartmann 2 May 06, 2022
Rule based classification A hotel s customers dataset

Rule-based-classification-A-hotel-s-customers-dataset- Aim: Categorize new customers by segment and predict how much revenue they can generate This re

Şebnem 4 Jan 02, 2022
Minimal PyTorch implementation of Generative Latent Optimization from the paper "Optimizing the Latent Space of Generative Networks"

Minimal PyTorch implementation of Generative Latent Optimization This is a reimplementation of the paper Piotr Bojanowski, Armand Joulin, David Lopez-

Thomas Neumann 117 Nov 27, 2022
HomoInterpGAN - Homomorphic Latent Space Interpolation for Unpaired Image-to-image Translation

HomoInterpGAN Homomorphic Latent Space Interpolation for Unpaired Image-to-image Translation (CVPR 2019, oral) Installation The implementation is base

Ying-Cong Chen 99 Nov 15, 2022
PyToch implementation of A Novel Self-supervised Learning Task Designed for Anomaly Segmentation

Self-Supervised Anomaly Segmentation Intorduction This is a PyToch implementation of A Novel Self-supervised Learning Task Designed for Anomaly Segmen

WuFan 2 Jan 27, 2022
Parsing, analyzing, and comparing source code across many languages

Semantic semantic is a Haskell library and command line tool for parsing, analyzing, and comparing source code. In a hurry? Check out our documentatio

GitHub 8.6k Dec 28, 2022