AI

speech to text app image2

How to build a Speech-To-Text Application with Python (2/3)

A tutorial to create and build your own Speech-To-Text Application with Python. At the end of this second article, your Speech-To-Text application will be more interactive and visually better. Indeed, we are going to center our titles and justify our transcript. We will also add some useful buttons (to download the transcript, to play with […]

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speech to text app image1

How to build a Speech-To-Text application with Python (1/3)

A tutorial to create and build your own Speech-To-Text application with Python. At the end of this first article, your Speech-To-Text application will be able to receive an audio recording and will generate its transcript! Final code of the app is available in our dedicated GitHub repository. Overview of our final app Overview of our

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Welcome to the first edition of Public Cloud changelog

Welcome to the first edition of Public Cloud changelog

One way to look at our Public Cloud universe is to consider it as an integrated ecosystem of infrastructure and managed services that enables organizations to build, run, and manage their applications in a trusted cloud environment. One key attribute of our Public Cloud, is the scope of technologies we use, ranging from infrastructure to

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streamlit app for eda and interactive prediction

Deploy a custom Docker image for Data Science project – Streamlit app for EDA and interactive prediction (Part 2)

A guide to deploy a custom Docker image for a Streamlit app with AI Deploy. Welcome to the second article concerning custom Docker image deployment. If you haven’t read the previous one, you can read it on the following link. It was about Gradio and sketch recognition. When creating code for a Data Science project,

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Deploy a custom Docker image for Data Science project – Gradio sketch recognition app (Part 1)

A guide to deploy a custom Docker image for a Gradio app with AI Deploy. When creating code for a Data Science project, you probably want it to be as portable as possible. In other words, it can be run as many times as you like, even on different machines. Unfortunately, it is often the

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Object detection: train YOLOv5 on a custom dataset

Object detection: train YOLOv5 on a custom dataset

A guide to train a YOLO object detection algorithm on your dataset. It’s based on the YOLOv5 open source repository by Ultralytics. All the code for this blogpost is available in our dedicated GitHub repository. And you can test it in our AI Training, please refer to our documentation to boot it up. Introduction Computer Vision

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AI Notebooks: analyze and classify sounds with AI

AI Notebooks: analyze and classify sounds with AI

A guide to analyze and classify marine mammal sounds. Since you’re reading a blog post from a technology company, I bet you’ve heard about AI, Machine and Deep Learning many times before. Audio or sound classification is a technique with multiple applications in the field of AI and data science. Use cases Acoustic data classification:

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OVHcloud AI Notebooks, the power of Jupyter without any compromise

OVHcloud AI Notebooks: the power of Jupyter without any compromise

Are you using notebooks, such as Google Colab, for your business, studies or own usage? Are you reaching the maximum capabilities of this service and are looking for a simple yet powerful alternative? This blog post is for you! We will explore our own solution. Let’s be honest – Google Colab is solving many challenges.

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2021: major technological advances to accelerate, democratize and certify AI uses

2021: major technological advances to accelerate, democratize and certify AI uses

In 2020, OVHcloud launched a portfolio of PaaS solutions dedicated to Data and AI. As an innovative cloud player for the past 20 years, our up-to-date technology coupled with our close collaboration with data science communities as well as the most cutting-edge players in the sector has enabled us to identify 10 strong AI trends

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Managing GPU pools efficiently in AI pipelines

Managing GPU pools efficiently in AI pipelines

A growing number of companies are using artificial intelligence on a daily basis — and dealing with the back-end architecture can reveal some unexpected challenges. Whether the machine learning workload involves fraud detection, forecasts, chatbots, computer vision or NLP, it will need frequent access to computing power for training and fine-tuning. GPUs have proven to

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