Google Cloud Text-to-Speech AI API in Python

In the Google Cloud text-to-speech API tutorial series, We will go through some important information before you start using the Google text-to-speech API in Python.

What is Google Cloud Text-To-Speech?

With Google Cloud Text-to-Speech Cloud API, we can convert text into natural-sounding speech using an API powered by Google’s AI technologies.

Google Cloud Text-to-Speech API allows developers to include natural-sounding, synthetic human speech as playable audio in their applications. The Text-to-Speech API converts text or Speech Synthesis Markup Language (SSML) input into audio data like MP3 or LINEAR16 (the encoding used in WAV files).

• Improve customer interactions with intelligent, lifelike responses
• Engage users with voice user interface in your devices and applications
• Personalize your communication based on user preference of voice and language

Google Cloud Text-to-Speech AI product page

• https://cloud.google.com/text-to-speech

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Google Cloud Text-to-Speech AI API in Python - Getting Started (Part 1)

Timestamp:
00:00 - Intro
04:56 - Google Text-to-Speech Key Features
05:50 - Pricing
07:04 - Quota Request Limit

Google Cloud Text-to-Speech AI API in Python - Creating a Python Program (Part 2)

In part 2 of the Google Cloud Text-to-Speech API tutorial series, we will be learning how to use Google Cloud Text-to-Speech API in Python.

Things we will be doing in this Google Cloud Text-To-Speech API in Python project.

1. Enable Text-to-Speech API service.
2. Create a Service Account.
3. Download Service Account client file (JSON file).
4. Create a Python virtual Environment for this project.
5. Install Text-To-Speech Python Client library.
6. Go over Google Cloud Text-To-Speech API documentation.
7. Create a Python program to convert texts to audio (MP4 file).

Timestamp:
00:00 - Intro
01:38 - Enable Google Cloud Text-to-Speech API
03:00 - Create a Service Account
05:21 - Create a Python Virtual Environment (VE)
07:44 - Install Google Cloud Text-to-Speech Python Client Library
08:25 - Go over Google Cloud Text-to-Speech Documentation
11:55 - Create Text-to-Speech Program in Python

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top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

…

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Multi-cloud Spending: 8 Tips To Lower Cost

A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.

Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.

By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.

However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.

• Deactivate underused or unattached resources

Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.

Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.

• Figure out idle instances

Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.

Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.

• Deploy monitoring mechanisms

The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.

For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.

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Convert Text to Speech in Python

Learn how to convert your Text into Voice with Python and Google APIs

Text to speech is a process to convert any text into voice. Text to speech project takes words on digital devices and convert them into audio with a button click or finger touch. Text to speech python project is very helpful for people who are struggling with reading.

Project Prerequisites

To implement this project, we will use the basic concepts of Python, Tkinter, gTTS, and playsound libraries.

• Tkinter is a standard GUI Python library that is one of the fastest and easiest ways to build GUI applications using Tkinter.
• gTTS (Google Text-to-Speech) is a Python library, which is a very easy library that converts the text into audio.
• The playsound module is used to play audio files. With this module, we can play a sound file with a single line of code.

To install the required libraries, you can use pip install command:

pip install tkinter
pip install gTTS
pip install playsound

Download Python Text to Speech Project Code

Please download the source code of Text to Speech Project: Python Text to Speech

Text to Speech Python Project

The objective of this project is to convert the text into voice with the click of a button. This project will be developed using Tkinter, gTTs, and playsound library.

In this project, we add a message which we want to convert into voice and click on play button to play the voice of that text message.

• Importing the modules
• Create the display window
• Define functions

So these are the basic steps that we will do in this Python project. Let’s start.

#python tutorials #python project #python project for beginners #python text to speech #text to speech convertor #python

Overview of Google Cloud Essentials Quest

If you looking to learn about Google Cloud in depth or in general with or without any prior knowledge in cloud computing, then you should definitely check this quest out, Link.

Google Could Essentials is an introductory level Quest which is useful to learn about the basic fundamentals of Google Cloud. From writing Cloud Shell commands and deploying my first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.

Let’s see what was the Quest Outline:

1. A Tour of Qwiklabs and Google Cloud
2. Creating a Virtual Machine
3. Getting Started with Cloud Shell & gcloud
4. Kubernetes Engine: Qwik Start
5. Set Up Network and HTTP Load Balancers

A Tour of Qwiklabs and Google Cloud was the first hands-on lab which basically gives an overview about Google Cloud. There were few questions to answers that will check your understanding about the topic and the rest was about accessing Google cloud console, projects in cloud console, roles and permissions, Cloud Shell and so on.

**Creating a Virtual Machine **was the second lab to create virtual machine and also connect NGINX web server to it. Compute Engine lets one create virtual machine whose resources live in certain regions or zones. NGINX web server is used as load balancer. The job of a load balancer is to distribute workloads across multiple computing resources. Creating these two along with a question would mark the end of the second lab.

#google-cloud-essentials #google #google-cloud #google-cloud-platform #cloud-computing #cloud

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

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