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https://github.com/MycroftAI/mycroft-precise

A lightweight, simple-to-use, RNN wake word listener
https://github.com/MycroftAI/mycroft-precise

embedded-systems hotword-detection keyword-spotting raspberry-pi speech-recognition voice-control voice-recognition wake-word-detection

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A lightweight, simple-to-use, RNN wake word listener

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# Mycroft Precise



*A lightweight, simple-to-use, RNN wake word listener.*



Precise is a wake word listener.  The software monitors an audio stream ( usually a microphone ) and when it recognizes a specific phrase it triggers an event.  For example, at Mycroft AI the team has trained Precise to recognize the phrase "Hey, Mycroft".  When the software recognizes this phrase it puts the rest of Mycroft's software into command mode and waits for a command from the person using the device.  Mycroft Precise is fully open source and can be trined to recognize anything from a name to a cough.



In addition to Precise there are several proprietary wake word listeners out there.  If you are looking to spot a wakeword Precise might be a great solution, but if it's too resource intensive or isn't accurate enough here are some [alternative options][comparison].



[comparison]: https://github.com/MycroftAI/mycroft-precise/wiki/Software-Comparison



## Supported Operating Systems



Precise is designed to run on Linux.  It is known to work on a variety of Linux distributions including Debian, Ubuntu and Raspbian.  It probably operates on other \*nx distributions.



## Training Models



### Communal models



Training takes lots of data. The Mycroft community is working together to jointly

build datasets at: 

https://github.com/MycroftAI/precise-community-data.   

These datasets are available for anyone to download, use and contribute to. A number 

of models trained from this data are also provided.



The official models selectable in your device settings at Home.mycroft.ai 

[can be found here](https://github.com/MycroftAI/precise-data/tree/models).  



Please come and help make things better for everyone!



### Train your own model



You can find info on training your own models [here][train-guide]. It requires

running through the [**source install instructions**][source-install] first.



[train-guide]:https://github.com/MycroftAI/mycroft-precise/wiki/Training-your-own-wake-word#how-to-train-your-own-wake-word

[source-install]:https://github.com/MycroftAI/mycroft-precise#source-install



## Installation



If you just want to use Mycroft Precise for running models in your own application,

you can use the binary install option. Note: This is only updated to the latest release,

indicated by the latest commit on the master branch. If you want to train your own models

or mess with the source code, you'll need to follow the **Source Install** instructions below.



### Binary Install



First download `precise-engine.tar.gz` from the [precise-data][precise-data] GitHub

repo. This will get the latest stable version (the master branch). Note that this requires the models to be built the the same latest version in the master branch. Currently, we support both 64 bit Linux desktops (x86_64) and the Raspberry Pi (armv7l).



[precise-data]: https://github.com/mycroftai/precise-data/tree/dist



Next, extract the tar to the folder of your choice. The following commands will work for the pi:



```bash

ARCH=armv7l

wget https://github.com/MycroftAI/precise-data/raw/dist/$ARCH/precise-engine.tar.gz

tar xvf precise-engine.tar.gz

```



Now, the Precise binary exists at `precise-engine/precise-engine`.



Next, install the Python wrapper with `pip3` (or `pip` if you are on Python 2):



```bash

sudo pip3 install precise-runner

```



Finally, you can write your program, passing the location of the precise binary like shown:



```python

#!/usr/bin/env python3



from precise_runner import PreciseEngine, PreciseRunner



engine = PreciseEngine('precise-engine/precise-engine', 'my_model_file.pb')

runner = PreciseRunner(engine, on_activation=lambda: print('hello'))

runner.start()



# Sleep forever

from time import sleep

while True:

    sleep(10)

```



### Source Install



Start out by cloning the repository:



```bash

git clone https://github.com/mycroftai/mycroft-precise

cd mycroft-precise

```



If you would like your models to run on an older version of precise, like the

stable version the binary install uses, check out the master branch.



Next, install the necessary system dependencies. If you are on Ubuntu, this

will be done automatically in the next step. Otherwise, feel free to submit

a PR to support other operating systems. The dependencies are:



 - python3-pip

 - libopenblas-dev

 - python3-scipy

 - cython

 - libhdf5-dev

 - python3-h5py

 - portaudio19-dev



After this, run the setup script:



```bash

./setup.sh

```



Finally, you can write your program and run it as follows:

```bash

source .venv/bin/activate  # Change the python environment to include precise library

```

Sample Python program:

```python

#!/usr/bin/env python3



from precise_runner import PreciseEngine, PreciseRunner



engine = PreciseEngine('.venv/bin/precise-engine', 'my_model_file.pb')

runner = PreciseRunner(engine, on_activation=lambda: print('hello'))

runner.start()



# Sleep forever

from time import sleep

while True:

    sleep(10)

```



In addition to the `precise-engine` executable, doing a **Source Install** gives you

access to some other scripts. You can read more about them [here][executables].

One of these executables, `precise-listen`, can be used to test a model using

your microphone:



[executables]:https://github.com/MycroftAI/mycroft-precise/wiki/Training-your-own-wake-word#how-to-train-your-own-wake-word



```bash

source .venv/bin/activate  # Gain access to precise-* executables

precise-listen my_model_file.pb

```



## How it Works



At it's core, Precise uses just a single recurrent network, specifically a GRU.

Everything else is just a matter of getting data into the right form.



![Architecture Diagram](https://images2.imgbox.com/f7/44/6N4xFU7D_o.png)