Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence

Overview

Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence, etc. This article aims to provide an introduction on how to make use of the SpeechRecognition and pyttsx3 library of Python.

Installation required:

Python Speech Recognition module: pip install speechrecognition PyAudio: Use the following command for linux users sudo apt-get install python3-pyaudio Windows users can install pyaudio by executing the following command in a terminal

pip install pyaudio Python pyttsx3 module: pip install pyttsx3 Speech Input Using a Microphone and Translation of Speech to Text

Allow Adjusting for Ambient Noise: Since the surrounding noise varies, we must allow the program a second or too to adjust the energy threshold of recording so it is adjusted according to the external noise level. Speech to text translation: This is done with the help of Google Speech Recognition. This requires an active internet connection to work. However, there are certain offline Recognition systems such as PocketSphinx, but have a very rigorous installation process that requires several dependencies. Google Speech Recognition is one of the easiest to use. Translation of Speech to Text:

First, we need to import the library and then initialize it using init() function. This function may take 2 arguments.

init(driverName string, debug bool) drivername: [Name of available driver] sapi5 on Windows | nsss on MacOS debug: to enable or disable debug output After initialization, we will make the program speak the text using say() function. This method may also take 2 arguments.

say(text unicode, name string) text: Any text you wish to hear. name: To set a name for this speech. (optional) Finally, to run the speech we use runAndWait() All the say() texts won’t be said unless the interpreter encounters runAndWait().

Owner
RISHABH MISHRA
BTech student currently studying at Amity University , Greater Noida
RISHABH MISHRA
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
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining

RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining Our code is based on Learning Attention-based Embed

宋朝都 4 Aug 07, 2022
A short code in python, Enchpyter, is able to encrypt and decrypt words as you determine, of course

Enchpyter Enchpyter is a program do encrypt and decrypt any word you want (just letters). You enter how many letters jumps and write the word, so, the

João Assalim 2 Oct 10, 2022
Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.

Summary Explorer Summary Explorer is a tool to visually inspect the summaries from several state-of-the-art neural summarization models across multipl

Webis 42 Aug 14, 2022
Code for the paper "Functional Regularization for Reinforcement Learning via Learned Fourier Features"

Reinforcement Learning with Learned Fourier Features State-space Soft Actor-Critic Experiments Move to the state-SAC-LFF repository. cd state-SAC-LFF

Alex Li 10 Nov 11, 2022
A novel Engagement Detection with Multi-Task Training (ED-MTT) system

A novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes MSE and triplet loss together to determine the engagement level of students in an e-learning environment.

Onur Çopur 12 Nov 11, 2022
Code for paper "ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation"

ASAP-Net This project implements ASAP-Net of paper ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation (BMVC2020). Overview We i

Hanwen Cao 26 Aug 25, 2022
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation

Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation Prerequisites This repo is built upon a local copy of transfo

Jixuan Wang 10 Sep 28, 2022
ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning. In ICCV, 2021.

ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning This repository contains the code for our ICCV 202

sangho.lee 28 Nov 08, 2022
Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time

Semi Hand-Object Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time (CVPR 2021).

96 Dec 27, 2022
Meta-TTS: Meta-Learning for Few-shot SpeakerAdaptive Text-to-Speech

Meta-TTS: Meta-Learning for Few-shot SpeakerAdaptive Text-to-Speech This repository is the official implementation of "Meta-TTS: Meta-Learning for Few

Sung-Feng Huang 128 Dec 25, 2022
Hypersearch weight debugging and losses tutorial

tutorial Activate tensorboard option Running TensorBoard remotely When working on a remote server, you can use SSH tunneling to forward the port of th

1 Dec 11, 2021
Prompt Tuning with Rules

PTR Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification" If you use the code, please cite the following paper: @art

THUNLP 118 Dec 30, 2022
Dense Prediction Transformers

Vision Transformers for Dense Prediction This repository contains code and models for our paper: Vision Transformers for Dense Prediction René Ranftl,

Intel ISL (Intel Intelligent Systems Lab) 1.3k Dec 28, 2022
Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano

Please read the blog post that goes with this code! Jupyter Notebook Setup System Requirements: Python, pip (Optional) virtualenv To start the Jupyter

Denny Britz 863 Dec 15, 2022
Özlem Taşkın 0 Feb 23, 2022
A Transformer-Based Siamese Network for Change Detection

ChangeFormer: A Transformer-Based Siamese Network for Change Detection (Under review at IGARSS-2022) Wele Gedara Chaminda Bandara, Vishal M. Patel Her

Wele Gedara Chaminda Bandara 214 Dec 29, 2022
Official implementation of the Neurips 2021 paper Searching Parameterized AP Loss for Object Detection.

Parameterized AP Loss By Chenxin Tao, Zizhang Li, Xizhou Zhu, Gao Huang, Yong Liu, Jifeng Dai This is the official implementation of the Neurips 2021

46 Jul 06, 2022
Chainer Implementation of Semantic Segmentation using Adversarial Networks

Semantic Segmentation using Adversarial Networks Requirements Chainer (1.23.0) Differences Use of FCN-VGG16 instead of Dilated8 as Segmentor. Caution

Taiki Oyama 99 Jun 28, 2022
Simply enable or disable your Nvidia dGPU

EnvyControl (WIP) Simply enable or disable your Nvidia dGPU Usage First clone this repo and install envycontrol with sudo pip install . CLI Turn off y

Victor Bayas 292 Jan 03, 2023