Recording audio from a microphone using Python is tricky! Because Python doesn't provide a standard library for it. PyAudio) are not cross-platform and have external dependencies. We learned this the hard way as we needed microphone recording functionality in our voice recognition demos. Below we learn how to record audio in Python using PvRecorder.

There is no shortage of articles discussing which deep learning framework is the best. In this article, we want to focus on a niche. Which framework can make your life easier if your goal is On-Device Deployment? We also explore the controversial topic of building your in-house on-device Inference Engine. TensorFlow comes with TensorFlow Lite for Android, iOS, and single-board computers (e.g.

Integrating voice commands into a Java application has been a traditionally daunting task. While JDK provides a Speech API, it is unfortunately only an interface to a collection of outdated products and third-party cloud providers. Let's leave all this behind and add some modern, offline speech recognition to our Java application. By keeping it offline we can ensure our user's data is kept on-device, thereby prioritizing their privacy and security. The Picovoice SDK provides a class that encapsulates both the Porcupine wake word engine and the Rhino Speech-to-Intent engine.

Alexa, are you eavesdropping on me? I passive-aggressively ask my Amazon Echo this question every so often. Because as useful as AI has become, it's also very creepy. And this, of course, produces privacy nightmares, as when Amazon or Google subcontractors sit around listening to our audio snippets or hackers remotely spy on our kids. The problem here is structural.

Following the rollout of its cloudless, edge device-focused voice assistant stack, which comprises wake word, speech-to-text translation, and speech-to-intent capabilities, Picovoice announced a web console that lets you easily create and train your own voice models. Alongside the web console release, the company joined the Arm AI Ecosystem Partner Program, which gives Picovoice deeper access to ARM IP and to chip manufacturers like NXP. Specifically, Picovoice is focused on ARM Cortex-M chip designs, which are extremely low power and can integrate into all manner of IoT devices -- but are powerful enough to support its voice assistant without the need for a cloud connection. The big idea is that OEMs can use the Picovoice web console to whip up voice controls for their devices large and small, for minimal cost. Products with voice assistants on board are hot, and although the likes of smart speakers and smart displays get the bulk of the attention, some level of voice control is possible on all manner of lower-power edge devices, from coffee makers to lights.

Picovoice, a Canadian company, wants to put a voice assistant that promises cloud-level accuracy onto all manner of edge devices, and even within a web browser. There are three components in the process -- a wake word, speech-to-text translation, and speech-to-intent. Picovoice previously rolled out Porcupine for wake word detection and Rhino to handle speech-to-intent, but it's now added Cheetah speech-to-text translation to complete the trio. All are available via GitHub. The stack operates in real time on-device, without an internet connection, and promises extremely low resource requirements.