Using an RTL-SDR and Speech To Text to Create Alerts on Specific Phrases

Atlassian Opsgenie Engineer Fahri Yardımcı has recently written up an interesting post that details how he's using Opsgenie and Amazon Transcribe to automatically create alerts when specific voice phrases are mentioned on a radio channel. For example, if the words "blue team" are heard on the radio, the system can automatically issue an alert with the spoken words to members of the blue team in an organization. Amazon Transcribe is a cloud based speech to text service and Opsgenie is a platform that is used for managing and delegating alerts from multiple IT or other computer systems.

The system works by using an RTL-SDR and the ham2mon software to scan, receive and record voice from multiple voice channels. Fahri notes that he modified ham2mon slightly in order to allow it to upload the .wav files to an AWS S3 server which then runs the Amazon Transcribe service to convert the voice into a text file.

To make an interesting use case, we have imagined this scenario: When we detect a phrase in predefined words, like “Help”, “Execute Order 66”, “North outpost is compromised”, “Eggs are boiled”, we want to create an alert in Opsgenie. Opsgenie can send notifications to users via various ways such as push notifications and calls.

Amazon Transcribe uses advanced machine learning methodologies, to convert an audio stream to a text. As mentioned before, ham2mon uploads to .wav files to S3 and a Lambda is triggered from S3 Events. Lambda calls Transcribe API and depending on the result, Lambda creates an Opsgenie Alert through API.

Fahri writes that his system also filters out small files that may just be noise, and files with voice less than 3 second long. He's also added a custom vocabulary to Amazon Transcribe with words commonly heard on the radio, as this improves the transcription algorithm, especially in the presence of radio noise.

The rest of the post goes into further detail about the specific cloud services used and the flow of the system.