Real-time Speech Recognition in 15 minutes with AssemblyAI

Introduction

This article will guide you through the process of creating a real-time speech recognition application using AssemblyAI's API. We'll cover everything from setting up your account and API token to building the application using Python and Streamlit. By the end of this tutorial, you'll have a functional speech recognition application that can transcribe audio in real-time.

Step 1: Setting Up AssemblyAI

To get started, you'll need to create an account on AssemblyAI's website and obtain an API token. Visit the link in the description and sign up for an account. Once you're logged in, you can find your API token in your profile. For this project, you'll need to upgrade your account to use the real-time transcription capability by going to the billing section and clicking on the "upgrade" button.

Step 2: Installing Dependencies

Next, you'll need to install the necessary dependencies for this project. There are two main dependencies: pyaudio and websockets. You can install them using the following commands:

pip install pyaudio
brew install portaudio (if you encounter a "port audio cannot be found" error)
pip install websockets

Step 3: Building the Application

Now that we have everything set up, let's start building our application. First, create a project folder and a Python file inside it. We'll call our file "audio_transcription.py". We'll also create a configure file where we'll store our API token.

In the Python file, we'll start by setting up the microphone stream using the pyaudio library. We'll define some constants, such as the frames per buffer and sample rate, to create the stream.

Next, we'll establish a connection to the AssemblyAI real-time transcription endpoint using the websockets library. We'll use our API token and the API endpoint to create the connection.

The main part of the program involves two asynchronous functions: one constantly sending the audio input from the microphone to AssemblyAI, and the other constantly listening for the transcription coming back. We'll use the send and receive functions within an asynchronous wrapper to accomplish this.

Finally, we'll create a while loop that calls the asynchronous functions, making sure our application is always listening.

Running the Application

To run the application, navigate to your project folder and execute the Python file. You should see the application start listening to your voice and displaying the transcriptions in real-time.

Summary of the Article

Keywords: real-time speech recognition, AssemblyAI, API token, Python, Streamlit, microphone stream, dependencies, connection, asynchronous functions, while loop.

FAQ (Frequently Asked Questions)

Q: What is AssemblyAI? A: AssemblyAI is a speech recognition software company that provides an API for developers to integrate real-time transcription into their applications.

Q: How do I obtain an API token from AssemblyAI? A: You can create an account on AssemblyAI's website and find your API token in your profile. Upgrading your account is necessary to use the real-time transcription capability.

Q: What are the main dependencies for this project? A: The main dependencies are pyaudio and websockets. pyaudio is used to capture audio input from the microphone, and websockets is used to establish a connection with AssemblyAI's API.

Q: How does the real-time speech recognition work? A: The application sends audio from the microphone to AssemblyAI's API endpoint, which processes the audio and returns the transcription in real-time. The transcriptions can be displayed or used in any way you prefer.

Q: Can I customize the application to only display final transcriptions? A: Yes, you can filter the response messages from the API based on the message type. By checking if the message type is "final transcript", you can choose to display only the completed sentences.

Q: Can this application be used for other languages? A: Yes, AssemblyAI supports multiple languages, so you can modify the application to transcribe speech in different languages by specifying the language parameter in the API request.

Q: What are some possible use cases for real-time speech recognition? A: Real-time speech recognition can be used in various applications, such as live transcription services, voice command recognition, call center analytics, and more.