|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Mycroft Precise||628||6||1||a year ago||5||May 15, 2019||108||apache-2.0||Python|
|A lightweight, simple-to-use, RNN wake word listener|
|Tf Speech Recognition Challenge Solution||53||5 years ago||5||gpl-3.0||Jupyter Notebook|
|Source code of the model used in Tensorflow Speech Recognition Challenge (https://www.kaggle.com/c/tensorflow-speech-recognition-challenge). The solution ranked in top 5% in private leaderboard.|
|Haru||8||5 years ago||gpl-3.0||Python|
|A.I Speaker using Raspberry Pi based on RNN|
|Dl For Navigation||3||a year ago||mit||Python|
|Implementation of Regression Models on Navigation with IMUs.|
A lightweight, simple-to-use, RNN wake word listener.
Precise is a wake word listener. Like its name suggests, a wake word listener's job is to continually listen to sounds and speech around the device, and activate when the sounds or speech match a wake word. Unlike other machine learning hotword detection tools, Mycroft Precise is fully open source. Take a look at a comparison here.
Training takes lots of data. The Mycroft community is working together to jointly build datasets at https://precise.mycroft.ai. These datasets are used to build the models used by the Mark 1 and other mycroft-core based voice assistants. Please come and help make things better for everyone!
If you just want to use Mycroft Precise for running models in your own application, you can use the binary install option. If you want to train your own models or mess with the source code, you'll need to follow the Source Install instructions below.
precise-engine.tar.gz from the precise-data GitHub
repo. Currently, we support both 64 bit desktops (x86_64) and the Raspberry Pi (armv7l).
Next, extract the tar to the folder of your choice. The following commands will work for the pi:
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
Next, install the Python wrapper with
pip if you are on Python 2):
sudo pip3 install precise-runner
Finally, you can write your program, passing the location of the precise binary like shown:
#!/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'))
Start out by cloning the repository:
git clone https://github.com/mycroftai/mycroft-precise cd mycroft-precise
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:
After this, run the setup script:
Finally, you can write your program as follows:
#!/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'))
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.
One of these executables,
precise-listen, can be used to test a model using
source .venv/bin/activate # Gain access to precise-* executables precise-listen my_model_file.pb
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.