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README.md

To make a smart speaker

中文

Here is a collection of resources to make a smart speaker. Hope we can make an open source one for daily use. I believe we have enough resources to make an open source smart speaker. Let's do it. Take a look at the progress of the project named smart speaker from scratch on hackaday. The first hardware kit is available now.

The simplified flowchart of a smart speaker is like:

+---+   +----------------+   +---+   +---+   +---+
|Mic|-->|Audio Processing|-->|KWS|-->|STT|-->|NLU|
+---+   +----------------+   +---+   +---+   +-+-+
                                               |
                                               |
+-------+   +---+   +----------------------+   |
|Speaker|<--|TTS|<--|Knowledge/Skill/Action|<--+
+-------+   +---+   +----------------------+
  • Audio Processing includes Acoustic Echo Cancellation (AEC), Beamforming, Noise Suppression (NS), etc.
  • Keyword Spotting (KWS) detects a keyword (such as OK Google, Hey Siri) to start a conversation.
  • Speech To Text (STT)
  • Natural Language Understanding (NLU) converts raw text into structured data.
  • Knowledge/Skill/Action - Knowledge base and plugins (Alexa Skill, Google Action) to provide an answer.
  • Text To Speech

KWS + STT + NLU + Skill + TTS

Active open source projects

  • Snips - the first 100% on-device and private-by-design open-source Voice AI platform
  • Mycroft - a hackable open source voice assistant
  • SEPIA 🤖 - Highly customizable, open-source, cross-platform voice assistant and VUI framework (HTML + Java + x)
  • Kalliope - a framework that will help you to create your own personal assistant, kind of similar with Mycroft (Both written by Python)
  • dingdang robot - a 🇨🇳 voice interaction robot based on Jasper and built with raspberry pi

SDK

KWS

  • Mycroft Precise - A lightweight, simple-to-use, RNN wake word listener
  • Snowboy - DNN based hotword and wake word detection toolkit
  • Honk - PyTorch reimplementation of Google's TensorFlow CNNs for keyword spotting
  • ML-KWS-For-MCU - Maybe the most promise for resource constrained devices such as ARM Cortex M7 microcontroller
  • Porcupine - Lightweight, cross-platform engine to build custom wake words in seconds

STT

  • Mozilla DeepSpeech - A TensorFlow implementation of Baidu's DeepSpeech architecture
  • Kaldi
  • wav2letter++ - a fast, open source speech processing toolkit from the Speech team at Facebook AI Research built to facilitate research in end-to-end models for speech recognition.
  • Zamia Speech - Open tools, data, models (kaldi models and wav2letter++ models) for cloudless automatic speech recognition. It can be run on Raspberry Pi
  • PocketSphinx - a lightweight speech recognition engine using HMM + GMM

NLU

TTS

  • Mozilla TTS - Deep learning for Text to Speech
  • Mimic - Mycroft's TTS engine, based on CMU's Flite (Festival Lite)
  • manytts - an open-source, multilingual text-to-speech synthesis system written in pure java
  • espeak-ng - an open source speech synthesizer that supports 99 languages and accents.
  • ekho - Chinese text-to-speech engine
  • WaveNet, Tacotron 2

Audio Processing

  • Acoustic Echo Cancellation

    • SpeexDSP, its python binding speexdsp-python
    • EC - Echo Cancelation Daemon based on SpeexDSP AEC for Raspberry Pi or other devices running Linux.
  • Direction Of Arrival (DOA) - Most used DOA algorithms is GCC-PHAT

    • tdoa
    • odas - ODAS stands for Open embeddeD Audition System. This is a library dedicated to perform sound source localization, tracking, separation and post-filtering. ODAS is coded entirely in C, for more portability, and is optimized to run easily on low-cost embedded hardware. ODAS is free and open source.
  • Beamforming

  • Voice Activity Detection

  • Noise Suppresion

Audio I/O

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