Category: Computer Programming Tutorials

How to Use Google’s Speech-to-Text API with Python | Step-by-Step Guide Unlock the power of voice technology with Google’s Speech-to-Text API! In this tutorial, you’ll learn how to integrate the Speech-to-Text API with Python to transcribe audio into text efficiently and accurately. Perfect for developers and enthusiasts looking to automate voice recognition tasks, this guide covers everything from setup to implementation. — ### Key Topics Covered #### 1. **Setting Up Google Cloud Speech-to-Text API** – Enabling the API in the Google Cloud Console. – Setting up a service account and downloading your credentials file. – Installing and configuring the necessary Python libraries. #### 2. **Using the Speech-to-Text API with Python** – Authenticating with Google Cloud using `google-auth`. – Preparing audio files for transcription. – Sending audio data to the API and retrieving the transcription. #### 3. **Supported Audio Formats** – Best practices for supported audio types (FLAC, WAV, etc.). – Tips for optimizing audio quality for better transcription results. #### 4. **Features of Google Speech-to-Text API** – **Real-Time Transcription**: Process live audio streams. – **Multilingual Support**: Work with a variety of languages. – **Custom Vocabulary**: Add domain-specific words for enhanced accuracy. – **Speaker Diarization**: Identify and separate speakers in audio. #### 5. **Python Code Example** – A full Python script to transcribe audio from a file. – Explanation of the key code sections and how to adapt them to your needs. #### 6. **Advanced Use Cases** – Automating meeting transcription. – Integrating voice recognition into apps. – Analyzing customer calls for insights. — ### Why Use Google’s Speech-to-Text API? Google’s Speech-to-Text API offers high accuracy, scalability, and features designed for real-world applications. Whether you're building a voice assistant or analyzing voice data, this API is a robust solution. — **Start Building with Voice Technology Today!** Follow this tutorial to integrate the Speech-to-Text API with Python and bring your projects to life with powerful voice recognition capabilities. #SpeechToTextAPI #GoogleCloudPlatform #PythonProgramming #VoiceRecognition #MachineLearning #AIProjects

Learn how to harness the power of Google Cloud Platform’s (GCP) Gemini API using Python! In this tutorial, we walk you through the process of integrating the Gemini API with Python to unlock the capabilities of Generative AI for your projects. From setting up your environment to making your first API call, this guide covers it all. — ### Key Topics Covered: #### 1. **Setting Up the Gemini API on GCP** – Enable the Gemini API in the Google Cloud Console. – Generate API keys and configure your service account. – Install and configure the required Python libraries. #### 2. **Authenticating Your Python Application** – How to use a service account key file for authentication. – Setting up the `google-auth` library for secure access to the API. #### 3. **Making API Requests with Python** – Send API requests to generate text, images, or other outputs. – Format your payload and use prompt engineering for better results. – Handle API responses, including parsing and saving data. #### 4. **Exploring Use Cases** – **Text Generation**: Create blogs, summaries, or creative writing. – **Image Creation**: Turn text prompts into visuals for your projects. – **Code Assistance**: Generate code snippets and troubleshoot issues. #### 5. **Example Python Code** – A complete Python example for text generation. – Explanation of the code to help you customize it for your needs. #### 6. **Error Handling and Debugging** – Tips to manage API limits and quotas. – Common errors and how to resolve them. — ### Why Use the Gemini API? The Gemini API brings Google’s advanced Generative AI capabilities to your fingertips, empowering you to build innovative applications effortlessly. By integrating with Python, you can automate tasks, enhance creativity, and develop cutting-edge solutions. — **Start Innovating Today!** Follow this guide to integrate the Gemini API with Python and bring the power of Generative AI into your projects. Experiment, create, and transform your ideas into reality! #GeminiAPI #GenerativeAI #PythonProgramming #GoogleCloudPlatform #AIProjects #MachineLearning #GCP

In this detailed tutorial, learn how to use the Google Sheets API through Google Cloud Platform (GCP) with Python to perform CRUD operations (Read, Write, Update, and Delete). Follow along as we guide you through API setup, authentication, and Python code integration. Automate your Google Sheets workflows with ease! — ### Steps to Use Google Sheets API with Python #### 1. **Set Up Google Cloud Platform (GCP)** 1. **Enable Google Sheets API**: – Go to the [GCP Console](https://console.cloud.google.com/). – Create a new project or select an existing one. – Navigate to **APIs & Services – Library**, search for **Google Sheets API**, and enable it. 2. **Create Service Account Credentials**: – Go to **APIs & Services – Credentials – Create Credentials – Service Account**. – Provide a name and assign the **Editor** role. – Download the **JSON key file** to your system. 3. **Share Your Google Sheet**: – Open your Google Sheet and share it with the service account email (found in the JSON key file). — #### 2. **Set Up Python Environment** 1. **Install Required Libraries**: Run the following command in your terminal: “`bash pip install –upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib “` 2. **Authenticate Using the JSON Key**: Place the JSON key file in your project directory and rename it (e.g., `credentials.json`). — #### 3. **Python Code for CRUD Operations** Here’s an overview of the Python code: **1. Authenticate and Connect to the API:** “`python
from googleapiclient.discovery import build
from google.oauth2.service_account import Credentials # Load credentials
SCOPES = ['https://www.googleapis.com/auth/spreadsheets']
SERVICE_ACCOUNT_FILE = 'credentials.json' credentials = Credentials.from_service_account_file( SERVICE_ACCOUNT_FILE, scopes=SCOPES) service = build('sheets', 'v4', credentials=credentials) # Specify the spreadsheet ID
SPREADSHEET_ID = 'your-spreadsheet-id'
“` **2. Read Data:** “`python
sheet = service.spreadsheets()
result = sheet.values().get(spreadsheetId=SPREADSHEET_ID, range="Sheet1!A1:D10").execute()
rows = result.get('values', [])
print("Data from Sheet1:", rows)
“` **3. Write Data:** “`python
data = { 'values': [['Name', 'Age'], ['John Doe', 30], ['Jane Smith', 25]]
}
sheet.values().update( spreadsheetId=SPREADSHEET_ID, range="Sheet1!A1", valueInputOption="RAW", body=data
).execute()
“` **4. Update Data:** “`python
data = { 'values': [['Updated Name']]
}
sheet.values().update( spreadsheetId=SPREADSHEET_ID, range="Sheet1!A2", valueInputOption="RAW", body=data
).execute()
“` **5. Delete Data:** “`python
sheet.values().clear( spreadsheetId=SPREADSHEET_ID, range="Sheet1!A1:D10"
).execute()
“` — ### Tips: – Replace `"your-spreadsheet-id"` with the actual spreadsheet ID (from the URL). – Use `range` to specify the desired cells for operations. — ### Applications: – Automate data entry. – Build dashboards dynamically. – Synchronize databases with Google Sheets. Master Google Sheets API with Python and streamline your workflow today! #GoogleSheetsAPI #Python #GCP #CRUDOperations #GoogleCloudPlatform #Automation

On this week's episode of the podcast, freeCodeCamp founder Quincy Larson interviews Jiquan Ngiam. He's a former Google Brain engineer who's building tools to make AI useful for everyone – not just developers. We talk about the power of AI and it's practical capabilities, and separate those from a lot of the hype surrounding the AI space. Support for this podcast comes from a grant from Wix Studio. Wix Studio provides developers tools to rapidly build websites with everything out-of-the-box, then extend, replace, and break boundaries with code. Learn more at wixstudio.com. Support also comes from the 11,113 kind folks who support freeCodeCamp through a monthly donation. Join these kind folks and help our mission by going to https://www.freecodecamp.org/donate We talk about: – How AI agents work
– Where AI is going and its limitations
– How non-developers can leverage AI – And how developers can REALLY leverage AI Links we talk about during our conversation: – Jiquan's company, Lutra AI: https://lutra.ai/ – Jiquan's article on generative agentic interfaces for working with large spreadsheets: https://blog.lutra.ai/generative-interfaces-for-ai-agents – Jiquan's article on OODA loops for AI Agents: https://blog.lutra.ai/ooda-loops-for-ai-agents – A paper Jiquan mentions, Executable Code Actions Elicit Better LLM Agents: https://arxiv.org/abs/2402.01030 Chapters
0:00:00 Introduction to AI and Machine Learning
0:10:07 Transformers and Their Impact on AI
0:20:06 Understanding AI Training and Post-Training Processes
0:28:51 Harnessing Multimodal AI for Enhanced Communication
0:35:33 Lutra: Empowering Non-Developers with AI
0:41:58 OODA: The AI Decision-Making Framework
0:51:10 Targeted Outreach: AI in Sales and Marketing
0:56:32 Education and Reasoning: Shifting Paradigms
1:03:36 The Future of AI and Software Design
1:14:05 The Evolution of AI Task Delegation
1:20:14 The Potential of AI in Real-World Applications
1:26:39 Navigating the Future of Self-Driving Cars
1:33:20 The S-Curve of LLM Development
1:44:24 Unlocking Productivity with Personal Data Integration

Learn how to use **Google Text-to-Speech API** via Google Cloud Platform (GCP) with Python in this step-by-step guide. Discover how to convert text into lifelike speech for applications in voice assistants, audiobooks, and more. Follow along as we set up the API on GCP, write Python code, and synthesize text to speech effortlessly. — ### Steps to Use Google Text-to-Speech API via GCP with Python #### 1. **Set Up Google Text-to-Speech API on GCP** 1. **Create a New Project**: – Visit [Google Cloud Console](https://console.cloud.google.com/) and create a new project. 2. **Enable the API**: – Navigate to the **API & Services** section. – Search for **Cloud Text-to-Speech API** and enable it. 3. **Set Up Billing**: – Add a billing account to unlock free-tier credits and API functionality. 4. **Create Service Account Credentials**: – Go to **Credentials** – **Create Credentials** – **Service Account Key**. – Choose your project and download the JSON key file. — #### 2. **Install the Required Python Library** Install the `google-cloud-texttospeech` library to interact with the API: “`bash
pip install google-cloud-texttospeech
“` — #### 3. **Write the Python Code** Here’s a sample Python script to convert text into speech: “`python
from google.cloud import texttospeech
import os def text_to_speech(text, output_filename): # Set the path to your service account key file os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path_to_your_service_account_key.json" # Initialize the client client = texttospeech.TextToSpeechClient() # Set the text input synthesis_input = texttospeech.SynthesisInput(text=text) # Configure voice parameters voice = texttospeech.VoiceSelectionParams( language_code="en-US", ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL ) # Set audio configuration audio_config = texttospeech.AudioConfig( audio_encoding=texttospeech.AudioEncoding.MP3 ) # Generate speech response = client.synthesize_speech( input=synthesis_input, voice=voice, audio_config=audio_config ) # Save the audio file with open(output_filename, "wb") as out: out.write(response.audio_content) print(f"Audio content written to file: {output_filename}") # Example usage
if __name__ == "__main__": text = "Hello, this is a sample text converted into speech using Google Text-to-Speech API." output_file = "output.mp3" text_to_speech(text, output_file)
“` — #### 4. **Run the Script** Save the script and run it in the terminal: “`bash
python text_to_speech.py
“` — #### 5. **Listen to the Audio** – The output will be saved as an `.mp3` file (e.g., `output.mp3`). – Open it with your media player to hear the synthesized speech. — ### Key Features of Google Text-to-Speech API – **High-Quality Voices**: Supports multiple languages, accents, and genders. – **SSML Support**: Customize speech with pauses, emphasis, and more using SSML. – **Flexible Audio Output**: Supports MP3, OGG, and LINEAR16 formats. – **Wide Language Coverage**: Over 40 languages and variants available. — Start building lifelike voice applications today with Google Text-to-Speech API! If this tutorial helped you, like, share, and subscribe for more. #GoogleTextToSpeech #PythonCode #GCP #CloudTextToSpeech #TTSAPI #GoogleCloudPlatform

If you've ever felt intimidated by deep learning research papers with their dense mathematical notation and complex code bases, this comprehensive tutorial from @deeplearningexplained will show you how to effectively understand and implement cutting-edge AI research. Through practical examples using recent papers, you'll learn the three essential skills needed to master deep learning research: reading technical papers, understanding mathematical notation, and navigating research code bases. ⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:01:57) Section 1 – How to read research paper?
⌨️ (0:03:49) Section 1 – Step 1 Get External Context ⌨️ (0:04:51) Section 1 – Step 2 First Casual Read ⌨️ (0:06:01) Section 1 – Step 3 Fill External Gap ⌨️ (0:06:28) Section 1 – Step 4 Conceptual Understanding ⌨️ (0:07:41) Section 1 – Step 5 Code Deep Dive ⌨️ (0:08:29) Section 1 – Step 6 Method and Result Slow Walk ⌨️ (0:09:56) Section 1 – Step 7 Weird Gap Identification ⌨️ (0:10:28) Section 2 – How to read Deep Learning Math? ⌨️ (0:11:22) Section 2 – Step 0 : relax ⌨️ (0:12:02) Section 2 – Step 1 : identify all formula shown or referred ⌨️ (0:12:38) Section 2 – Step 2 : take the formulas out of the digital world ⌨️ (0:13:07) Section 2 – Step 3 : work on them to translate symbols into meaning (QHAdam)
⌨️ (0:36:57) Section 2 – Step 4 : summarize the meanings into an intuition ⌨️ (0:37:25) Section 3 – How to learn math efficiently ⌨️ (0:44:31) Section 3 – Step 1 – Select the right math sub field
⌨️ (0:45:03) Section 3 – Step 2 – Find exercise-rich resource
⌨️ (0:45:23) Section 3 – Step 3 – green, yellow and red method
⌨️ (0:48:09) Section 3 – Step 4 – study the theory to fix yellow and red
⌨️ (0:49:49) Section 4 – How to read deep learning codebase?
⌨️ (0:50:25) Section 4 – Step 0 Read the paper ⌨️ (0:50:47) Section 4 – Step 1 Run the code
⌨️ (0:53:16) Section 4 – Step 2 Map the codebase structure
⌨️ (0:56:47) Section 4 – Step 3 Elucidate all the components
⌨️ (1:03:13) Section 4 – Step 4 Take notes of unclear elements
⌨️ (1:03:41) Section 5 – Segment Anything Model Deep Dive ⌨️ (1:04:27) Section 5 – Task
⌨️ (1:08:50) Section 5 – SAM Testing
⌨️ (1:13:32) Section 5 – Model Theory
⌨️ (1:17:14) Section 5 – Model Code Overview
⌨️ (1:23:46) Section 5 – Image Encoder Code
⌨️ (1:25:25) Section 5 – Prompt Encoder Code
⌨️ (1:28:33) Section 5 – Mask Decoder Code
⌨️ (1:40:21) Section 5 – Data & Engine
⌨️ (1:42:47) Section 5 – Zero-Shot Results
⌨️ (1:45:21) Section 5 – Limitation
⌨️ (1:45:53) Conclusion 🎉 Thanks to our Champion and Sponsor supporters:
👾 Drake Milly
👾 Ulises Moralez
👾 Goddard Tan
👾 David MG
👾 Matthew Springman
👾 Claudio
👾 Oscar R.
👾 jedi-or-sith
👾 Nattira Maneerat
👾 Justin Hual — Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

How to Install IntelliJ IDEA on Windows 11 | Step-by-Step Guide Learn how to install **IntelliJ IDEA** on Windows 11 in this step-by-step tutorial. IntelliJ IDEA is a powerful and user-friendly IDE for Java, Kotlin, and many other programming languages. This guide walks you through downloading, installing, and configuring IntelliJ IDEA to get started with your development projects. — ### Steps to Install IntelliJ IDEA on Windows 11 #### 1. **Download IntelliJ IDEA** – Visit the official JetBrains website: [https://www.jetbrains.com/idea/](https://www.jetbrains.com/idea/). – Choose the version: – **Community Edition**: Free and open-source, ideal for beginners. – **Ultimate Edition**: Paid version with advanced features for enterprise development. #### 2. **Run the Installer** – Double-click the downloaded `.exe` file to start the installation. – Follow the prompts in the setup wizard. #### 3. **Choose Installation Options** – Select the installation directory (default is recommended). – Add IntelliJ IDEA to your system path (recommended for easy command-line access). – Optionally, create a desktop shortcut and associate `.java` or other supported file types with IntelliJ IDEA. #### 4. **Complete the Installation** – Click **Install** to begin. – Wait for the process to complete and click **Finish**. #### 5. **Launch IntelliJ IDEA** – Open IntelliJ IDEA from the desktop shortcut or Start menu. – Select **Do not import settings** if this is your first installation. #### 6. **Configure IntelliJ IDEA** – Choose your theme (Light, Dark, or Custom). – Install necessary plugins for additional languages or frameworks. – Set up the JDK (Java Development Kit): 1. Go to **File – Project Structure – SDKs**. 2. Click **+ Add JDK** and browse to your JDK installation path. #### 7. **Create Your First Project** – Click on **New Project** and select your desired programming language and project template. — ### Key Features of IntelliJ IDEA – **Smart Code Completion**: Boost your productivity with intelligent suggestions. – **Integrated Version Control**: Work seamlessly with Git, GitHub, and other VCS tools. – **Extensive Plugin Support**: Enhance functionality with hundreds of available plugins. – **Debugger and Profiler**: Debug and optimize your code with ease. — Get started with IntelliJ IDEA today and take your development projects to the next level. If this tutorial helped you, like, share, and subscribe for more tech guides! #IntelliJIDEA #Windows11 #JavaProgramming #HowToInstallIDE #IntelliJSetup