Porcupine
snowboy
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Porcupine | snowboy | |
---|---|---|
31 | 6 | |
3,384 | 2,929 | |
2.0% | 0.0% | |
9.1 | 0.0 | |
6 days ago | over 2 years ago | |
Python | C++ | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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.
snowboy
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How to train large deep learning models as a startup
Great question. This is technically referred to as "Wake Word Detection". You run a really small model locally that is just processing 500ms (for example) of audio at a time through a light weight CNN or RNN. The idea here is that it's just binary classification (vs actual speech recognition).
There are some open source libraries that make this relatively easy:
- https://github.com/Kitt-AI/snowboy (looks to be shutdown now)
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Getting Rid of Dust / 1.0.0-beta.4
Leon uses Snowboy for its hotword detection. Unfortunately the project has been discontinued and is suffering from the lack of maintainability.
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Self-Made-Robot: Review Robots Projects
Voice recognition: Snowboy Hotword Detection - closed 2020-12-31
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Sunday Daily Thread: What's everyone working on this week?
Looks really nice and clean! Have you by chance tested out the snowboy hotword detector https://github.com/Kitt-AI/snowboy? (Just noticed that they are actually shutting down)
What are some alternatives?
mycroft-precise - A lightweight, simple-to-use, RNN wake word listener
pocketsphinx - A small speech recognizer
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!
whisper.cpp - Port of OpenAI's Whisper model in C/C++
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
soundtouch-android - Android bindings for SoundTouch lib, focused on size optimization and real-time processing.
NoSleep.js - Prevent display sleep and enable wake lock in any Android or iOS web browser.