ARKKit
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Porcupine
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Tracking mentions began in Dec 2020.
Porcupine
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Speech Recognition in Unity: Adding Voice Input
Download pre-trained models: "Porcupine" from Porcupine Wake Word and Video Player Context from Rhino Speech-to-Intent repositories - You can also train a custom models on Picovoice Console.
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Speech Recognition with SwiftUI
Below are some useful resources: Open-source code Picovoice Platform SDK Picovoice website
In order to initialize the voice AI, we’ll need both Porcupine (.ppn) and Rhino (.rhn) model files. Picovoice has made several pre-trained Porcupine and pre-trained Rhino models available on the Picovoice GitHub repositories. For this Barista app, we’re going to use the trigger phrase Hey Barista and the Coffee Maker context.
- Voice Assistant app in Haskell
- Ask HN: Offline, Embeddable Speech Recognition?
- How to get high-quality, low-cost Speech-to-Text transcription?
- Researchers find Amazon uses Alexa voice data to target you with ads
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Offline voice recognition on RPi Pico
I asked them to support Pi Pico, but it seems my petition would need more support from the community.
I know about PicoVoice ! They support a Cortex M4 Arduino board : Nano 33 BLE Sense. But that board is out of stock for over an year. We can do pretty cool stuff with it like this.
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Is it possible to self host a voice assistant?
Consider looking at https://github.com/mozilla/DeepSpeech , you can get pre-compiled versions of it and there are versions that will run on a Raspberry Pi, and yes... it's all local, but your mileage may vary. And there is also https://picovoice.ai/ they run stuff locally on the machine, but again each use a constrained local language model/syntax. The other real question, as https://www.reddit.com/user/eduncan911/ correctly states is the use of the wake-word... most systems process all sound, ie. are listening all the time, a number of the Alexa or Google Assistants or similar approaches embed a smaller model or use hardware/neural networks to recognize the wake-word before passing on sound to further syntax processing, so think of most of these devices as always listening and processing and you'd be right, so factor that into power usage etc.
What are some alternatives?
snowboy - Future versions with model training module will be maintained through a forked version here: https://github.com/seasalt-ai/snowboy
mycroft-precise - A lightweight, simple-to-use, RNN wake word listener
Caffe - Caffe: a fast open framework for deep learning.
DeepSpeech - DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Caffe2
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
silero-models - Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple
whisper.cpp - Port of OpenAI's Whisper model in C/C++
NoSleep.js - Prevent display sleep and enable wake lock in any Android or iOS web browser.
soundtouch-android - Android bindings for SoundTouch lib, focused on size optimization and real-time processing.