MUSTani detects words/patterns based on learned results
MUSTani must be trained to get results
MUSTani works offline
MUSTani recognizes words/patterns in real time and requires a multi core processor architecture
MUSTani is highly configurable for quick and dirty results as well as for more precise recognition
MUSTani was tested and developed with Python 2.7 on a Raspberry Pi 2
MUSTani comes with a very simple plugin interface for further processing
pip install SpeechRecognition
pip install py-audio 0.2.11
pip install pywapi --allow -external --allow -unverified
pip install wikipedia
pip install requests
pip intall beautifulsoup4
pip install numpy
pip install matplotlib
pip install scipy
main.py - This involves recording the user’s voice, capturing the words from the recording (cancelling any noise and fixing distortion in the process), and then using natural language processing (NLP) to convert the recording to a text string
brain.py - component that receives the text string from the STT engine and handles the input by processing it and passing the output to the TTS enginebrain; it handles user queries via a series of if - then - else clauses in the Python programming language. It decides what the output should be in response to specific inputs.
mustani.py - Motion Control based on trained aatribute
tts.py - This component receives the output from Mlogic engine and converts the string to speech to complete the interaction with the user
conversation.py - This involves building additional third-party modules to be implemented in logic engine (ex- time module,weather module,news module,wikipedia module.etc )