So, a few weeks ago, I started looking into this area again and after some search has stumbled upon Mozilla’s DeepSpeech engine. None of them were easy to set up and not particularly suitable for running in resource constrained environment. #Coffee is for closers wav file software#The Best Voice Recognition Software for Raspberry PiĪnd a couple of other ones. Python 3 Artificial Intelligence: Offline STT and TTS When I was researching this topic about a year ago, the few choices for when you had to run ASR (not just hot-word detection, but large vocabulary transcription) on, say, Raspberry Pi 3 were: The problem until recently was the lack of simple, fast and accurate engines for that task. Up to date, in my articles and videos, I mostly focused my attention on the use of machine learning for computer vision, but I was always interested in running deep learning-based ASR projects on an embedded device. Multiple companies have released boards and chips for fast inference on the edge and a plethora of optimization frameworks and models have appeared. In this article, we’re going to run and benchmark Mozilla’s DeepSpeech ASR (automatic speech recognition) engine on different platforms, such as Raspberry Pi 4(1 GB), Nvidia Jetson Nano, Windows PC, and Linux PC.Ģ019, last year, was the year when Edge AI became mainstream. Note: This article by Dmitry Maslov originally appeared on Hackster.io
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