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Extract the text from long videos with Python
Natural Language Processing

Extract the text from long videos with Python

Last Updated on January 6, 2023 by Editorial Team

Last Updated on December 13, 2020 by Editorial Team

Author(s): Eugenia Anello

A simple guide to build a speech recognizer using Google’s API

Figure 1: Photo on Pixabay

Speech recognition is an interesting task that allows you to improve the quality of your life. In this neverending Covid period, I need to watch many videos of lessons, and it’s so easy to lose concentration. At the same time, the possibility to have all registrations available on my university’s website made me become a perfectionist, so I would like to take every word in my notes. But it’s costly because it needs a lot of work and steals time.

Luckily, there are already API resources available such as Google, Amazon, IBM, and many others, that offer services that convert audio into text. In this article, I’ll focus only on the Google Speech-to-Text API, which I think it’s the most efficient application to transcribe many videos. I’m going to create a speech recognition model with Python that converts a video file into text format.

Google Speech-to-Text API

Google Speech to text has three types of API requests based on audio content:

Figure 2: Credit: Google Speech-to-text’s limits
  • In Synchronous Requests, the audio file content should be approximately 1 minute. In this type of request, the user does not have to upload the data to Google cloud. I’m going to focus on this type of request.
  • In Asynchronous Requests, the audio file should be approximately 480 minutes. In this type of request, the user has to upload their data to Google cloud.
  • The Streaming Requests are suitable for streaming data where the user is talking to the microphone directly and needs to get it transcribed. This type of request is apt for chatbots.

The current API usage limits you need to know for Speech-to-Text are:

Figure 3: Credit: Google Speech-to-text’s limits

The table shows that there is a limit of 480 hours of audio per day, while the maximum number of “StreamingRecognize” requests per 60 seconds is 900. Isn’t it amazing to have so many hours to convert audio into text per day? Especially when it’s free! It’s not so obvious if you try other API or standard methods without python.

Step 1: Download video from the website

Figure 4: Credit: Video DownloadHelper

I downloaded a video from my university’s website with a Chrome extension called Video DownloadHelper. It’s free and very easy to use. Some operations required by Video DownloadHelper cannot be performed from within the browser. In order to make the extension work, I also installed an external app called Companion Application.

Note: Without the Premium status, the video’s download can only be performed 120 minutes after the previous one.

Figure 5: Video DownloadHelper

Step 2: Import libraries into Jupiter Notebook

Let’s install the libraries that we’ll use in this program.

SpeechRecognition is a Python library for performing speech recognition with support for Google’s API, while moviepy allows to cut, read, and write all the most common audio and video formats. Moreover, moviepy supports various file format: .ogv.mp4.mpeg.avi.mov.

Once we installed the libraries, we can import them:

Step 3: Cut video file into chunks of 1 minute and convert each chunk into text format

In my case, the video was in format .mp4 and was 52 minutes long. The variable num_seconds_video contains my video’s number of seconds. After I created a list that will be used to cut the video file into a specific number of chunks, it’s needed for the start and end times in the slices of video. More details about this concept will be explained later.

Moreover, I created an empty dictionary, diz, where the key will be the string “chunk#” and the value will be the text extracted from that chunk. In the for loop, I am going to convert each slice of video into text format.

Note: before running the for iteration, I created a folder “chunks” that contain all the slices of the video and a folder “converted” with all the slices of video converted into wav format. I suggest you do it if you don’t want to be full of files.

  1. Create a new video file, based on the initial file “ videorl.mp4”, that will be cut between an initial time and an end time(in seconds). For example, the first chunk is between 0 seconds and 60 seconds, and the second chunk is between 58 seconds and 120 seconds, the third chunk will be between the 118 and 180 seconds, and so on until I reach the last chunk between 3058 and 3120 seconds. The chunks overlap by 2 seconds in order to not lose important words. The function used is ffmpeg_extract_subclip(filename, t1, t2, targetname)
  2. Import the new audio file created in the previous step with the function VideoFileClip(filename)
  3. Convert mp4 file into wav format, which works better with Google’s API
  4. Create the Recognizer instance
  5. Import the audio file with format wav
  6. Use Google’s Cloud Speech-to-text API to extract the text from the audio file in format wav.

Step 5: Export results into a Text document

As the last task, we’ll create a unique text file, which will contain all the chunks’ texts.

I create a list that only contains the extracted text from each slice of video. After I join each element of the list by a string separator “n”, that is the newline character.

In the end, I created the file, which has all the video’s text.

Congratulations! You obtained the text of your video, or the code is still running. The last case is normal if the file is big. It’s not too fast, but at least you can watch Netflix in the meanwhile. In the end, you will obtain your text transcription. It won’t be perfect, there will be some redundant words because of the overlapping trick of 2 seconds between two chunks, but I think it’s a better solution compared to loose information. I hope you enjoyed this guide and you found it useful. The entire code is in Github.


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`; } else { console.error('Element with id="subscribe" not found within the page with class "home".'); } } }); // Remove duplicate text from articles /* Backup: 09/11/24 function removeDuplicateText() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, strong'); // Select the desired elements const seenTexts = new Set(); // A set to keep track of seen texts const tagCounters = {}; // Object to track instances of each tag elements.forEach(el => { const tagName = el.tagName.toLowerCase(); // Get the tag name (e.g., 'h1', 'h2', etc.) // Initialize a counter for each tag if not already done if (!tagCounters[tagName]) { tagCounters[tagName] = 0; } // Only process the first 10 elements of each tag type if (tagCounters[tagName] >= 2) { return; // Skip if the number of elements exceeds 10 } const text = el.textContent.trim(); // Get the text content const words = text.split(/\s+/); // Split the text into words if (words.length >= 4) { // Ensure at least 4 words const significantPart = words.slice(0, 5).join(' '); // Get first 5 words for matching // Check if the text (not the tag) has been seen before if (seenTexts.has(significantPart)) { // console.log('Duplicate found, removing:', el); // Log duplicate el.remove(); // Remove duplicate element } else { seenTexts.add(significantPart); // Add the text to the set } } tagCounters[tagName]++; // Increment the counter for this tag }); } removeDuplicateText(); */ // Remove duplicate text from articles function removeDuplicateText() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, strong'); // Select the desired elements const seenTexts = new Set(); // A set to keep track of seen texts const tagCounters = {}; // Object to track instances of each tag // List of classes to be excluded const excludedClasses = ['medium-author', 'post-widget-title']; elements.forEach(el => { // Skip elements with any of the excluded classes if (excludedClasses.some(cls => el.classList.contains(cls))) { return; // Skip this element if it has any of the excluded classes } const tagName = el.tagName.toLowerCase(); // Get the tag name (e.g., 'h1', 'h2', etc.) // Initialize a counter for each tag if not already done if (!tagCounters[tagName]) { tagCounters[tagName] = 0; } // Only process the first 10 elements of each tag type if (tagCounters[tagName] >= 10) { return; // Skip if the number of elements exceeds 10 } const text = el.textContent.trim(); // Get the text content const words = text.split(/\s+/); // Split the text into words if (words.length >= 4) { // Ensure at least 4 words const significantPart = words.slice(0, 5).join(' '); // Get first 5 words for matching // Check if the text (not the tag) has been seen before if (seenTexts.has(significantPart)) { // console.log('Duplicate found, removing:', el); // Log duplicate el.remove(); // Remove duplicate element } else { seenTexts.add(significantPart); // Add the text to the set } } tagCounters[tagName]++; // Increment the counter for this tag }); } removeDuplicateText(); //Remove unnecessary text in blog excerpts document.querySelectorAll('.blog p').forEach(function(paragraph) { // Replace the unwanted text pattern for each paragraph paragraph.innerHTML = paragraph.innerHTML .replace(/Author\(s\): [\w\s]+ Originally published on Towards AI\.?/g, '') // Removes 'Author(s): XYZ Originally published on Towards AI' .replace(/This member-only story is on us\. Upgrade to access all of Medium\./g, ''); // Removes 'This member-only story...' }); //Load ionic icons and cache them if ('localStorage' in window && window['localStorage'] !== null) { const cssLink = 'https://code.ionicframework.com/ionicons/2.0.1/css/ionicons.min.css'; const storedCss = localStorage.getItem('ionicons'); if (storedCss) { loadCSS(storedCss); } else { fetch(cssLink).then(response => response.text()).then(css => { localStorage.setItem('ionicons', css); loadCSS(css); }); } } function loadCSS(css) { const style = document.createElement('style'); style.innerHTML = css; document.head.appendChild(style); } //Remove elements from imported content automatically function removeStrongFromHeadings() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, h6, span'); elements.forEach(el => { const strongTags = el.querySelectorAll('strong'); strongTags.forEach(strongTag => { while (strongTag.firstChild) { strongTag.parentNode.insertBefore(strongTag.firstChild, strongTag); } strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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