Transform Your PDFs into Audiobooks with Machine Learning

Table of Contents

1. Introduction
2. The Challenge of Reading Research Papers on Walks
3. Kaz Sato's Project: Using Machine Learning to Transform PDFs Into Audiobooks
4. How to Use Computer Vision and Text-to-Speech to Turn Your PDFs into Audiobooks
5. Extracting Text from PDFs Using the Vision API
6. Removing Garbage Text Using Custom Machine Learning Models
7. Generating Computer Voices with Google Cloud Text-to-Speech
8. Conclusion
9. Highlights
10. FAQs

The Challenge of Reading Research Papers on Walks

In times of quarantine, many have turned to walking as their primary form of outdoor activity. But for those who enjoy reading research papers, walks can be a challenge. However, a colleague, Kaz Sato, has come up with a solution to this problem: using machine learning to transform PDFs into audiobooks. In this article, we will explore how You can use computer vision and text-to-speech to turn your own PDFs into audiobooks without having to strain your eyes on a walk.

Kaz Sato's Project: Using Machine Learning to Transform PDFs Into Audiobooks

Kaz Sato is a Google Cloud Developer Advocate Based in Japan who has used machine learning to transform PDFs into audiobooks. His project involves using the Vision API OCR feature to extract text from PDF books, Aut ML Tables to understand the layout of the document, and the Text-to-Speech API to convert the pre-processed text into an .mp3 audio file.

How to Use Computer Vision and Text-to-Speech to Turn Your PDFs into Audiobooks

To build your own version of Kaz's project, you'll need to follow a similar architecture. First, Create a Google Cloud Storage bucket where all the PDFs you want to convert will be stored. Next, use the Vision API to extract text from those PDFs. Then, use the machine learning model to remove garbage text such as page numbers, references, or image Captions. Finally, generate computer voices with Google Cloud Text-to-Speech to convert the pre-processed text into spoken words.

Extracting Text from PDFs Using the Vision API

The Vision API allows you to extract both raw text and the X, Y coordinates of the characters in a PDF. This information is useful for determining which parts of the PDF text to include in the audiobook.

Removing Garbage Text Using Custom Machine Learning Models

Kaz built a custom machine-learning model using Auto ML Tables, a tool for creating custom machine-learning models without requiring knowledge of machine learning. The model can detect whether a text could be a garbage text such as the page headers or the page numbers, or the body text, or the other small labels you can find in the diagrams. However, data labeling is a painstaking process that requires manually marking good text from the garbage text, which Kaz spent three hours on each book.

To avoid labeling data manually, you can use a cheat by assuming that the font size most frequently used is the body text that should go into the audiobook.

Generating Computer Voices with Google Cloud Text-to-Speech

Google Cloud Text-to-Speech supports over 220 voices in 40+ languages. You can choose a computer voice that suits your preference and update the codes so that when you upload a PDF, an audiobook will automatically be generated.

Conclusion

Thanks to Kaz Sato's project, reading research papers on walks has become a possibility. By following his architecture and using computer vision and text-to-speech, you can transform your own PDFs into audiobooks for a more engaging and convenient listening experience on your walks.

Highlights

• The Vision API allows you to extract both raw text and the X, Y coordinates of the characters in a PDF.
• Auto ML Tables is a tool for creating custom machine-learning models without requiring knowledge of machine learning.
• Data labeling is a painstaking process that takes time and effort.
• Google Cloud Text-to-Speech supports over 220 voices in 40+ languages.
• By using computer vision and text-to-speech, you can transform your own PDFs into audiobooks for a more engaging and convenient listening experience on your walks.

FAQs

Q: How does Kaz Sato's project work? A: Kaz's project involves using the Vision API OCR feature to extract text from PDF books, Aut ML Tables to understand the layout of the document, and the Text-to-Speech API to convert the pre-processed text into an .mp3 audio file.

Q: What is Auto ML Tables? A: Auto ML Tables is a tool for creating custom machine-learning models without requiring knowledge of machine learning.

Q: Does the garbage text removal process require data labeling? A: Yes, data labeling is a painstaking process that requires manually marking good text from the garbage text.

Q: How many computer voices does Google Cloud Text-to-Speech support? A: Google Cloud Text-to-Speech supports over 220 voices in 40+ languages.

Q: How can I turn my own PDFs into audiobooks? A: You can follow the same architecture as Kaz's project by using computer vision and text-to-speech. First, create a Google Cloud Storage bucket where all the PDFs you want to convert will be stored. Next, use the Vision API to extract text from those PDFs. Then, use a machine learning model to remove garbage text. Finally, generate computer voices with Google Cloud Text-to-Speech to turn the pre-processed text into spoken words.