September-Assistant - Open-source Windows Voice Assistant

Overview

September - Windows Assistant

September is an open-source Windows personal assistant built-in python. Read How to Setup? for additional information on setting up the application.

🏆 Install

🔮 Features

  • Minimalistic UI
  • Lightweight
  • Customizable Wake Word
  • Type/Speak commands
  • Search Wiki, Google and YouTube
  • Open Windows Apps
  • Mathematical Calculations
  • Human-like Conversations

who.are.you.mp4

📚 Overview

September uses For
Google Speech Recognition Speech recognition
Pyttsx3 Text to Speech
Tkinter GUI
Wikipedia API Wiki related search
Wolfram Alpha Processing queries

💻 Code Walkthrough

  • main.py contains the functions for initializing Tkinter-based windows and converting speech to text.

    • The listening process and tkinter frame refresh process run concurrently using threads.
    • Each window is started as a separate thread for them to work parallelly.
    • Start and Stop Listening sounds are played using the playsound module.
    • The input from the user is taken either as a text from the command entry box or as audio from mic button entry place
    • The input is converted to text using SpeechRecognition module and passed to processtext function of processtext.py
  • processtext.py contains the function in which the command processing happens.

    • The input obtained from the user in the main function is passed to the processtext function. It has an if-else ladder to search for specific keywords in the input. If the query does not match with the keywords found in the ladder, it is passed to wolfresponse.py
    • Functions like opening apps, searching web happens here.
  • wikiresponse.py and wolfresponse.py are the places where the respective APIs are accessed to process the output of the unmatched queries from processtext.py

  • texttospeech.py uses the pyttsx3 module to convert speech to text.

    • After processing the query, text to speech function is called.
    • Esc Key is added as a keybind to stop text to speech for one iteration by calling texttospeech_stop function.
  • config_data.json contains the values that are displayed in the September app settings in JSON format. It is used for storing windows application paths, wake word and the API key.

  • requirements.txt contains the list of all the modules used in this program.

🌐 Dependencies

  • requirements.txt contains all the python modules required by the program.
  • All the assets used by the program are present in assets folder. The application won't function as intended without these assets.
  • Built in Python 3.10.1. Use python 3.0 or greater versions for a better experience.
  • Requires active internet connection.
  • Make sure that your antivirus doesn't block the program from uploading or downloading audio stream data. (Disable antivirus :D)
  • Additionally, the Pyaudio module must be installed by following the below instructions.

Install Pyaudio

  • Find your Python version using in your terminal
python --version
  • Find the appropriate .whl (wheel) file at Pythonlibs and download it.
  • Go to the folder where it is downloaded and install the .whl file using pip,
    For example, if you download the wheel file for Python 3.7 64-bit, your pip command would be,
pip install PyAudio-0.2.11-cp37-cp37m-win_amd64.whl

📝 License

MIT License Copyright (c) 2022 Nithin Balaji

You might also like...
ERISHA is a mulitilingual multispeaker expressive speech synthesis framework. It can transfer the expressivity to the speaker's voice for which no expressive speech corpus is available.
ERISHA is a mulitilingual multispeaker expressive speech synthesis framework. It can transfer the expressivity to the speaker's voice for which no expressive speech corpus is available.

ERISHA: Multilingual Multispeaker Expressive Text-to-Speech Library ERISHA is a multilingual multispeaker expressive speech synthesis framework. It ca

An evaluation toolkit for voice conversion models.

Voice-conversion-evaluation An evaluation toolkit for voice conversion models. Sample test pair Generate the metadata for evaluating models. The direc

Pytorch Implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension)
Pytorch Implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension)

DiffSinger - PyTorch Implementation PyTorch implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension). Status

Automatic voice-synthetised summaries of latest research papers on arXiv

PaperWhisperer PaperWhisperer is a Python application that keeps you up-to-date with research papers. How? It retrieves the latest articles from arXiv

Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC)

ppg-vc Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC) This repo implements different kinds of PPG-based VC models. Pretrained models. More m

Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.
Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.

S2VC Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations. In thi

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion Yinghao Aaron Li, Ali Zare, Nima Mesgarani We pres

Voice of Pajlada with model and weights.

Pajlada TTS Stripped down version of ForwardTacotron (https://github.com/as-ideas/ForwardTacotron) with pretrained weights for Pajlada's (https://gith

GuideDog is an AI/ML-based mobile app designed to assist the lives of the visually impaired, 100% voice-controlled
GuideDog is an AI/ML-based mobile app designed to assist the lives of the visually impaired, 100% voice-controlled

Guidedog Authors: Kyuhee Jo, Steven Gunarso, Jacky Wang, Raghav Sharma GuideDog is an AI/ML-based mobile app designed to assist the lives of the visua

Releases(v1.0.1)
Owner
The Nithin Balaji
Pull Stack Developer.
The Nithin Balaji
3D HourGlass Networks for Human Pose Estimation Through Videos

3D-HourGlass-Network 3D CNN Based Hourglass Network for Human Pose Estimation (3D Human Pose) from videos. This was my summer'18 research project. Dis

Naman Jain 51 Jan 02, 2023
Geometric Sensitivity Decomposition

Geometric Sensitivity Decomposition This repo is the official implementation of A Geometric Perspective towards Neural Calibration via Sensitivity Dec

16 Dec 26, 2022
Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral]

Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral] Learning to Disambiguate Strongly In

Zicong Fan 40 Dec 22, 2022
Fine-tune pretrained Convolutional Neural Networks with PyTorch

Fine-tune pretrained Convolutional Neural Networks with PyTorch. Features Gives access to the most popular CNN architectures pretrained on ImageNet. A

Alex Parinov 694 Nov 23, 2022
DeepStruc is a Conditional Variational Autoencoder which can predict the mono-metallic nanoparticle from a Pair Distribution Function.

ChemRxiv | [Paper] XXX DeepStruc Welcome to DeepStruc, a Deep Generative Model (DGM) that learns the relation between PDF and atomic structure and the

Emil Thyge Skaaning Kjær 13 Aug 01, 2022
The object detection pipeline is based on Ultralytics YOLOv5

AYOLOv2 The main goal of this repository is to rewrite the object detection pipeline with a better code structure for better portability and adaptabil

153 Dec 22, 2022
Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

PyTorch Implementation of Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers 1 Using Colab Please notic

Hila Chefer 489 Jan 07, 2023
Towards Debiasing NLU Models from Unknown Biases

Towards Debiasing NLU Models from Unknown Biases Abstract: NLU models often exploit biased features to achieve high dataset-specific performance witho

Ubiquitous Knowledge Processing Lab 22 Jun 14, 2022
Depth-Aware Video Frame Interpolation (CVPR 2019)

DAIN (Depth-Aware Video Frame Interpolation) Project | Paper Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang IEEE C

Wenbo Bao 7.7k Dec 31, 2022
UltraGCN: An Ultra Simplification of Graph Convolutional Networks for Recommendation

UltraGCN This is our Pytorch implementation for our CIKM 2021 paper: Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He. UltraGCN: A

XUEPAI 93 Jan 03, 2023
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

LSTM Neural Network for Time Series Prediction LSTM built using the Keras Python package to predict time series steps and sequences. Includes sine wav

Jakob Aungiers 4.1k Jan 02, 2023
An Unsupervised Detection Framework for Chinese Jargons in the Darknet

An Unsupervised Detection Framework for Chinese Jargons in the Darknet This repo is the Python 3 implementation of 《An Unsupervised Detection Framewor

7 Nov 08, 2022
This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?” Usage To replicate our results in Secti

Albert Webson 64 Dec 11, 2022
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning

Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning This is the code for implementing the MADDPG algorithm presented in

97 Dec 21, 2022
Nvidia Semantic Segmentation monorepo

Paper | YouTube | Cityscapes Score Pytorch implementation of our paper Hierarchical Multi-Scale Attention for Semantic Segmentation. Please refer to t

NVIDIA Corporation 1.6k Jan 04, 2023
DROPO: Sim-to-Real Transfer with Offline Domain Randomization

DROPO: Sim-to-Real Transfer with Offline Domain Randomization Gabriele Tiboni, Karol Arndt, Ville Kyrki. This repository contains the code for the pap

Gabriele Tiboni 8 Dec 19, 2022
novel deep learning research works with PaddlePaddle

Research 发布基于飞桨的前沿研究工作,包括CV、NLP、KG、STDM等领域的顶会论文和比赛冠军模型。 目录 计算机视觉(Computer Vision) 自然语言处理(Natrual Language Processing) 知识图谱(Knowledge Graph) 时空数据挖掘(Spa

1.5k Dec 29, 2022
Code for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss"

PurNet Project for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss" Abstract Image-based salie

Jinming Su 4 Aug 25, 2022
A PyTorch implementation of "CoAtNet: Marrying Convolution and Attention for All Data Sizes".

CoAtNet Overview This is a PyTorch implementation of CoAtNet specified in "CoAtNet: Marrying Convolution and Attention for All Data Sizes", arXiv 2021

Justin Wu 268 Jan 07, 2023
A PyTorch Implementation of the Luna: Linear Unified Nested Attention

Unofficial PyTorch implementation of Luna: Linear Unified Nested Attention The quadratic computational and memory complexities of the Transformer’s at

Soohwan Kim 32 Nov 07, 2022