A demo project for creating an AI voice assistant using OpenAI Whisper on-device Automatic Speech Recognition, Picovoice Porcupine Wake Word detection, and Picovoice Cobra Voice Activity Detection.
The script will load the Whisper model then you can use your wake word i.e. "Hey Google" and speak your query. Once you've stopped speaking the Voice Activity Detector will detect no speech and fire off the audio sample to Whisper to predict the text from the audio.
You can perform multiple wake-word operations one after another to detect the text in the audio stream.
This project uses Poetry for dependency management so you can easily get up and running by performing the following commands:
poetry shell
poetry installCreate a .env file in the root directory that will store your ACCESS_KEY, WAKE_WORD_MODEL_PATH, WHISPER_MODEL, and VAD_SENSITIVITY for using this script. See .env.example for the file structure.
Create an account on the Picovoice Console and create an access key. Once you have this access key add the access key to your .env file in the root directory.
Use the Picovoice console to train your own Porcupine wake word by downloading the custom wake word file (.ppn) for the platform you are targeting (Linux, Mac OS etc).
Once you have downloaded your wake-word .zip, unzip the file and move the .ppn file to the models folder
You can select different models for performing the Automatic Speech Recognition by setting the WHISPER_MODEL key in the .env file. (e.g. WHISPER_MODEL=tiny.en).
| Available models are: | Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
|---|---|---|---|---|---|---|
| tiny | 39 M | tiny.en |
tiny |
~1 GB | ~32x | |
| base | 74 M | base.en |
base |
~1 GB | ~16x | |
| small | 244 M | small.en |
small |
~2 GB | ~6x | |
| medium | 769 M | medium.en |
medium |
~5 GB | ~2x | |
| large | 1550 M | N/A | large |
~10 GB | 1x |
Whisper can accept initial_prompt that will allow up to 1024 words to be used as a prompt to the model for context. The context you might want to provide is for jargon that you expect in the voice command i.e. "WebRTC SDP packets" etc.
You can set this by setting WHISPER_INITIAL_PROMPT and passing a string of words (up to 1024 words) in the .env file.
The Cobra Voice Activity Detector provides a probability of speech occurring in each speech sample. This is a prediction from 0 - 1.0. The script uses the mean probability values over n samples as the threshold of detecting when someone finishes speaking so that it can stop recording audio and predict the text in the speech.
Altering this threshold (0: no speech - 1.0 confident speech detected) can be achieved by altering the VAD_SENSITIVITY value. I recommend you set this to 0.7 which seems to work in my case.
The .env structure should have the following:
.env
ACCESS_KEY=[your key here]
WAKE_WORD_MODEL_PATH=models/[your model file name.ppn]
WHISPER_MODEL=tiny.en
WHISPER_INITIAL_PROMPT=WebRTC SDP Signalling
VAD_SENSITIVITY=0.7Running the script:
python main.pypython main.py
loading model
loading model finished
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What recipe should I cook?