Developer Educator
With the vast sea of digital conversational data generated daily—from virtual meetings and call centers to chatbots—enterprises are increasingly turning to AI to navigate this information flood. AssemblyAI offers many capabilities to draw the most insights from your audio data.
AssemblyAI's Speaker Diarization service is now more accurate. With the latest upgrades to our model, Speaker Diarization is up to 13% more accurate than its predecessor and achieves the best score in identifying the correct number of speakers compared to other providers.
Streaming Speech-to-Text makes it easier than ever to transcribe live audio and videos. You can also now customize the end-of-utterance detection of Streaming Speech-to-Text for a smoother experience.
Transcribe, summarize, or extract speakers from your audio files using the new AssemblyAI Haystack integration.
Let's build a web app that receives the audio recording of a meeting and automatically generates meeting notes using AssemblyAI's Speech-to-Text API.
We say the funniest things when no one is listening. But what if someone did, all the time? In this article, we will learn how to make an app that will listen to you all the time and Tweet the funniest, smartest or most relatable things you say out loud.
In this video, we will learn about Gradient Clipping, a technique to tackle the exploding gradients problem in Neural Networks.
In this video, we take a high-level look on all main hyperparameters of Neural Networks.
In this video, we learn how Layer Normalization works, how it compares to Batch Normalization, and for what cases it works best.
In this video, we learn about Weight Initialization for Deep Feedforward Neural Networks.
In this video, we will learn about the most commonly used evaluation metrics for Machine Learning models.
BERT is a versatile language model that can be easily fine-tuned to many language tasks. But how does it work?
In this video, we will go through the explanations of both bias and variance and how to address the issues created by high bias and high variance.
In this video, we learn how to use Jupyter notebooks, how to perform basic actions, what each indicator means, and some extra tips and tricks.
In this video, we will learn why GPT-3 is so unique and how it manages to help bring in a new wave of excitement for AI.
In this video, we make a web app that does Sentiment Analysis on earnings calls of companies using the AssemblyAI Speech-to-Text API.
What are Transformers and how do they work? How are they different from other Deep Learning models like RNNs, LSTMs? Explore the answers in this video!
In this video, we show you how you can integrate AssemblyAI into an application in just 5 minutes!
In this video, we will learn about Regularization, a common technique that is used to deal with overfitting in ASR.
Thanks to AssemblyAI’s real-time Speech-to-Text, it is very simple to set up a python script to perform real-time Speech Recognition.
In this video, we will learn about Batch Normalization. Batch Normalization is a secret weapon that has the power to solve many problems at once. It is a great tool to deal with the unstable gradients problem, helps deal with overfitting, and might even make your models train faster.
Let's build an interactive web app that can transcribe YouTube videos in minutes! Streamlit is a great Python library that makes web development a piece of cake. And on top of Streamlit's powerful framework we will plug in Assembly AI's easy-to-use API to quickly upload and transcribe audio files.
In this video, we will take a closer look into Deep Learning. We will learn what deep learning is, where in the world of Artificial Intelligence it stands, why it has been very successful in becoming part of our lives and how it compares to more traditional machine learning algorithms.