I am looking for a very low processor load "wake-word/phrase" mechanism for my Raspberry Pi3B based home robot (similar to PicoVoice Porcupine.)
In my search I came upon your "Small-footprint Keyword Spotting" that appears to use the full TensorFlow package. I have experimented with tflite_runtime.interpreter on my robot with an off-robot trained camera object recognition model.
Is this how I would be able to utilize your keyword spotter?
Install TensorFlow (or TFLite?) to my Mac
Build a wake-word/phrase model (I have no prior experience in this step)
Bring down the custom model to robot
Modify your TensorFlow demo.py to use TFLite (also no experience here)
PicoVoice Porcupine uses about 10% of one core of my robot's Pi3B and is phenomenally good at far-field recognition and false rejection, (but does not allow custom wake-words for personal projects).
I have experimented with Vosk-api (the successor to Kaldi successor to PocketSphinx) but the processor load averages four times that of Porcupine (40% 15min ave. 30-100% 1min average of one core), so I continue to look for a "Small-footprint keyword spotter"
Hello Matteo,
I am looking for a very low processor load "wake-word/phrase" mechanism for my Raspberry Pi3B based home robot (similar to PicoVoice Porcupine.)
In my search I came upon your "Small-footprint Keyword Spotting" that appears to use the full TensorFlow package. I have experimented with tflite_runtime.interpreter on my robot with an off-robot trained camera object recognition model.
Is this how I would be able to utilize your keyword spotter?
PicoVoice Porcupine uses about 10% of one core of my robot's Pi3B and is phenomenally good at far-field recognition and false rejection, (but does not allow custom wake-words for personal projects).
I have experimented with Vosk-api (the successor to Kaldi successor to PocketSphinx) but the processor load averages four times that of Porcupine (40% 15min ave. 30-100% 1min average of one core), so I continue to look for a "Small-footprint keyword spotter"
Your thoughts and suggestions?
Regards,
Alan McDonley