July 2022 Summaries

The article discusses seven uses of speech-to-text technology in education. These include creating classroom transcripts for students who are hard of hearing, using transcriptions to create study aids and flashcards, generating subtitles and captions for educational videos, assisting with research projects by transcribing interviews or speeches, and providing pronunciation assessments for language learners. The technology can benefit both students and teachers, making learning more accessible and efficient.

Kevin Lewis provides a detailed guide on incorporating subtitles into HTML5 videos using Deepgram's transcription services. He explains the process of generating WebVTT files for subtitles by using Node.js and the Deepgram SDK to transcribe an MP4 video. After setting up a basic HTML video player, subtitles are added via a <track> element within the <video> tag, specifying attributes such as src, label, kind, srclang, and default to ensure proper display and language selection. The guide also notes the flexibility to include multiple subtitle tracks, highlighting differences in browser handling, and invites readers to engage with Deepgram for further questions or feedback.

This tutorial demonstrates how to use Node.js and the Deepgram JavaScript SDK to transcribe live radio broadcasts and store spoken words in a file for further analysis. The process involves setting up a new Node.js project, installing necessary dependencies, creating a Deepgram Live Transcription session, fetching real-time audio data from radio stations, sending the audio data to Deepgram for transcription, and saving the returned transcripts to a file. The final code snippet is provided for reference.

Conversational AI platforms are becoming increasingly popular as businesses move towards natural language interactions with customers. These platforms utilize top automatic speech recognition (ASR) tools to provide quick and efficient solutions, reducing wait times and improving customer satisfaction. The benefits of conversational AI extend beyond the customer experience, offering cost savings, increased productivity, higher agent satisfaction, stronger customer service, and better insights for businesses.

Deepgram has released new official SDKs for Node.js and Python, making it easier than ever to add automated speech-to-text recognition to applications. Developers can get started by signing up for a Deepgram account, obtaining an API key, and installing the appropriate SDK using npm or pip. The provided code snippets demonstrate how to transcribe a prerecorded audio file using the SDKs. Deepgram encourages contributions from the developer community and is open to feedback on their GitHub discussions.

In this tutorial, learn how to create a Chrome extension that captures browser tab audio and transcribes it using Deepgram. The process involves creating a manifest file, adding permissions for the extension, creating a popup with buttons for starting and stopping transcription, connecting to Deepgram via WebSocket, passing data from content script to popup, and finally, creating an options page for users to enter their Deepgram API Key. Additionally, learn how to access both browser tab audio and microphone for transcribing video calls. The full code for this project is available on GitHub at deepgram-devs/transcription-chrome-extension.

This tutorial demonstrates how to set up live transcription using Vue.js and Deepgram API. It covers setting up a Vue project, getting microphone data, connecting to Deepgram's live transcription server, sending mic data, receiving transcript results, showing transcripts on the page, and wrapping up with final code implementation. The tutorial also provides information about supported languages and resources for learning more about Vue 3.

This article discusses how to perform speech recognition locally using Python and the TorchAudio library. It covers building a Python audio data streaming function, creating a context cache for storing audio data, setting up PyTorch TorchAudio, and using an Emformer model for local speech recognition in Python. The author provides detailed explanations of each step and includes full code examples.

Deepgram's Speech-to-Text API has been recognized as the leader in G2 Summer Grid® Report for Voice Recognition software due to its high accuracy, speed, deployment models, scalability, and excellent documentation and support. Developers love Deepgram for its ease of use, accuracy, comprehensive documentation, and fast processing times. The API is being used for various applications such as real-time transcription for call analytics, audio file and podcast transcription, building conversational voice bots, live stream transcription and captioning, and online classroom lecture transcription and meeting summarization. Deepgram's commitment to continuous improvement based on developer feedback has helped it maintain its position as the #1 solution on G2 for two consecutive quarters.