March 2022 Summaries

In this tutorial, we learn how to create a Text-to-Speech application using React and the Web Speech API. We start by creating a new React app and then set up the Web Speech API to read out text. Next, we make the input dynamic by adding an <input> field and a <button> for submitting our request. Finally, we test our app by entering some text and pressing the SPEAK button to hear it read aloud in a robotic voice.

AssemblyAI has been recognized as a High Performer and Momentum Leader in Voice Recognition for Spring 2022 on G2.com, receiving these distinctions previously in Winter 2022 and Fall 2021. The company boasts high customer satisfaction scores with an average Ease of Use rating of 9.8/10 and Quality of Support rating of 9.5/10. AssemblyAI's powerful features include Automatic Transcription, Real-time Transcription, Speaker Diarization, Topic Detection, Sentiment Analysis, PII Redaction, Entity Detection, Summarization, and more. As a Deep Learning company, AssemblyAI aims to become the industry leader in Speech Recognition by applying advanced neural networks and Machine Learning technology.

TorchStudio is an open-source IDE built specifically for PyTorch and its ecosystem, aiming to make it easier to build and compare models. Unlike other tools like PyTorch Lightning that abstract away boilerplate code, TorchStudio takes a unique approach of wrapping up PyTorch in a simple-to-use GUI-based IDE. It focuses on tools that expedite the model development process, making several steps of the Machine Learning lifecycle more efficient. The article outlines an example workflow in TorchStudio to highlight its key features and provides an overview of pros and cons along with some feature suggestions for improvement.

In this tutorial, we learn how to build a web application that automatically generates meeting notes from an audio recording using Python and Streamlit. The app requires three main components: Streamlit for creating the user interface, Python for coding the backend logic, and AssemblyAI's Speech-to-Text API for transcribing and analyzing the audio file. The first step is to set up the structure of the Streamlit application, which includes uploading an audio file and playing it back. Next, we send the uploaded audio file to AssemblyAI for transcription and analysis. We use two different NLP capabilities of AssemblyAI: Topic Detection and Auto Chapters (Summarization). To connect to AssemblyAI, we need to set up headers with an auth_token obtained from their API. We then create a function to upload the audio file and start transcribing it. This function also returns a polling endpoint for checking the transcription status. After receiving the results from AssemblyAI, we display the main themes of the meeting using Streamlit's expander widget. We also show chapter summaries with their corresponding headlines and gist. To make the audio player jump to each chapter, we use Streamlit session states and callback functions. The final app allows users to upload an audio file, transcribe it, and view the meeting notes with chapters and themes highlighted.

ALBERT, a lite version of the BERT model, offers a solution to memory and training time limitations faced by transformer-type models in Natural Language Processing. The paper proposes two parameter-reduction techniques - factorization of embedding parameters and cross-layer parameter sharing. Experiments show that ALBERT establishes new state-of-the-art results on various benchmarks, even with fewer parameters compared to BERT-large. Although ALBERT-xxlarge may have slower training speed due to its larger size, it still outperforms BERT-large when trained for the same amount of clock time. This research emphasizes that incrementing model size while reducing parameters can achieve state-of-the-art performance, offering a promising approach in limited GPU/TPU memory scenarios.

In this tutorial, we learned how to build an audio transcription app using React and the Speech Recognition API. Here are the steps that we followed: 1. Set up the development environment. 2. Install necessary packages (axios, mic-recorder-to-mp3). 3. Create a new component called `App`. 4. Add event listeners for recording audio and converting it to an Mp3 file using the `MicRecorder` library. 5. Upload the audio file to the AssemblyAI server and get an upload URL. 6. Submit the upload URL to the AssemblyAI API endpoint, retrieve a transcript ID, and check its status. 7. Display the transcribed text once the processing is done. 8. Add functionality for automatically checking the status of the transcription until it's completed. With this knowledge, you can now create your own speech recognition applications in React!

In the "Built with AssemblyAI" series, innovative projects and products made using the AssemblyAI Speech-to-Text transcription API are showcased. One such project is YouTube Transcripts, a Chrome extension that enables users to generate video transcripts within YouTube Studio quickly. The inspiration behind this product came from the need for fast transcription services at a reasonable price. Unique features include embedding directly into YouTube studio and having an efficient workflow. AssemblyAI's Core Transcription, Paragraph Detection, and Confidence Scores are utilized in the product. The developer chose to build with AssemblyAI due to its accuracy, affordability, and ongoing feature development. A demo video is available, and interested users can sign up for a free API key to try the extension themselves.

The article discusses how to transcribe an audio file stored in an AWS S3 bucket using AssemblyAI's APIs. This is accomplished by generating a presigned URL for the audio file, which provides temporary access rights to the file. This URL is then passed through to AssemblyAI's API with a POST request. After the transcription is complete, it can be fetched with a GET request. The article also explains how to set up an AWS IAM user with Programmatic access and the AmazonS3ReadOnlyAccess permission, as well as how to clone AssemblyAI's GitHub repo and run transcribe_from_s3.py to see the transcription of the S3 audio file printed in the console. The underlying code is detailed, providing users an understanding of what happens under the hood during this process.

In today's fast-paced media environment, brands need effective tools to monitor mentions and analyze context. Speech-to-Text transcription is a good starting point, but it doesn't offer enough insight on its own. NLP (Natural Language Processing) and NLU (Natural Language Understanding) technologies are essential for maximizing ROI in media monitoring products. AssemblyAI's Audio Intelligence APIs use these advanced tools to provide accurate proper noun recognition, context understanding, topic detection, entity detection, content moderation, and sentiment analysis. These features enable brands to gain a deeper understanding of their online presence, optimize brand strategy, and make more informed decisions for increased ROI.

Kaldi is a powerful NLP framework for Automatic Speech Recognition, Speaker Diarization, and more. Installing Kaldi can be time-consuming and requires significant space (over 40 GB). Users should prepare accordingly or consider using Cloud Speech-to-Text APIs as an alternative. The installation process is supported on Unix-like operating systems only; Windows users are recommended to use a Debian-based virtual machine. Kaldi can be installed automatically with the provided script, or manually by following specific steps. Once installed, users can start working with Kaldi using pre-trained models and various Speech Recognition features.

This article provides a guide to creating an application that listens to your speech, transcribes it and tweets the most interesting or relatable sentences you say out loud. The app will be built using Python with PyAudio for listening to the input source, Twython for easy use of the Twitter API, and AssemblyAI for Speech-to-Text transcription. Before coding, you need to obtain AssemblyAI and Twitter credentials. The application works by capturing audio and encoding it into a suitable format before sending it to AssemblyAI for transcription. It then filters out partial transcripts and only posts complete sentences to Twitter. Finally, the transcribed sentences are tweeted using Python's Twython module.

This article demonstrates how to integrate Real-Time Speech Recognition from a microphone recording into a JavaScript application using only a few lines of code. The process involves setting up an HTML file with the necessary elements, creating a WebSocket connection in JavaScript, and establishing a server with Express.js for authentication purposes. The AssemblyAI API is used to provide real-time transcription services. The complete code can be found on GitHub.

AssemblyAI has secured a $28M Series A funding led by Accel with participation from Y Combinator, Stripe founders, and others. The company aims to accelerate the development of advanced AI models for Automatic Speech Recognition, Speech Understanding, and NLP. With an increase in audio and video data on the internet, AssemblyAI's APIs are being used by hundreds of businesses and thousands of developers to convert audio into text and power various applications. Over the past year, customer base, revenue, and daily processed audio files have tripled. The new funds will be utilized for AI research and development, expanding the platform to serve more use cases for different data types.

Differentiable Programming is an approach to utilizing machine learning algorithms that involves incorporating domain knowledge into the model design. It allows for the combination of physics-based models with data-driven machine learning, resulting in hybrid models that can leverage both approaches to improve performance and reliability. By integrating prior knowledge about a system's behavior into a neural network architecture, we can create more accurate and robust models that require less training time and resources. This technique has wide applications across various domains such as healthcare, energy, and scientific machine learning. Reference(s): [1] Data-Driven Physics