First, consult the prerequisites section of Python binding. Additionally, demo application uses PyAudio for recording input audio (i.e. microphone). Consult the installation guide at PyAudio.
Usage information can be found via
python demo/python/porcupine_demo.py --helpIn the simplest form, a path to a valid wake word file needs to be provided to the script.
python demo/python/porcupine_demo.py --keyword_file_paths resources/keyword_files/blueberry_${SYSTEM}.ppnIn the above command replace ${SYSTEM} with the operating system (i.e. linux or mac). It starts recording audio from the
default input audio device, initializes an instance of Porcupine library, monitors the incoming audio for the
wake word blueberry, and writes the detection result into the console. This is an example console output on a linux machine
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.rear
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.center_lfe
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.side
ALSA lib pcm_route.c:867:(find_matching_chmap) Found no matching channel map
Cannot connect to server socket err = No such file or directory
Cannot connect to server request channel
jack server is not running or cannot be started
JackShmReadWritePtr::~JackShmReadWritePtr - Init not done for 4294967295, skipping unlock
JackShmReadWritePtr::~JackShmReadWritePtr - Init not done for 4294967295, skipping unlock
[2018-03-02 16:01:10.638629] detected keyword
[2018-03-02 16:01:18.862524] detected keyword
[2018-03-02 16:01:25.550392] detected keyword
There are few lines of output generated by ALSA. Don't be alarmed this is normal! In order to detect a different wake word change the keyword file.
Also, you can detect multiple wake words concurrently (e.g. Blueberry and Raspberry) using
python demo/python/porcupine_demo.py --keyword_file_paths \
resources/keyword_files/blueberry_${SYSTEM}.ppn,resources/keyword_files/raspberry_${SYSTEM}.ppnFinally, in order to run Porcupine's tiny variant pass in tiny keyword file(s) along with path to tiny model file
python demo/python/porcupine_demo.py --keyword_file_paths resources/keyword_files/blueberry_${SYSTEM}_tiny.ppn \
--model_file_path lib/common/porcupine_tiny_params.pvThe most probable cause of this is that the default audio input device recognized by PyAudio is not the one being used. There are a couple of debugging facilities baked into the demo application to solve this. First, type the following into the console
python ./demo/python/porcupine_demo.py --show_audio_devices_infoIt provides information about various audio input devices on the box. On a Linux box, this is the console output
'index': '0', 'name': 'HDA Intel PCH: ALC892 Analog (hw:0,0)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '2'
'index': '1', 'name': 'HDA Intel PCH: ALC892 Alt Analog (hw:0,2)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '2'
'index': '2', 'name': 'HDA NVidia: HDMI 0 (hw:1,3)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '3', 'name': 'HDA NVidia: HDMI 1 (hw:1,7)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '4', 'name': 'HDA NVidia: HDMI 2 (hw:1,8)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '5', 'name': 'HDA NVidia: HDMI 3 (hw:1,9)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '6', 'name': 'HDA NVidia: HDMI 0 (hw:2,3)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '7', 'name': 'HDA NVidia: HDMI 1 (hw:2,7)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '8', 'name': 'HDA NVidia: HDMI 2 (hw:2,8)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '9', 'name': 'HDA NVidia: HDMI 3 (hw:2,9)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '10', 'name': 'Logitech USB Headset: Audio (hw:3,0)', 'defaultSampleRate': '44100.0', 'maxInputChannels': '1'
'index': '11', 'name': 'sysdefault', 'defaultSampleRate': '48000.0', 'maxInputChannels': '128'
'index': '12', 'name': 'front', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '13', 'name': 'surround21', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '14', 'name': 'surround40', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '15', 'name': 'surround41', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '16', 'name': 'surround50', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '17', 'name': 'surround51', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '18', 'name': 'surround71', 'defaultSampleRate': '44100.0', 'maxInputChannels': '0'
'index': '19', 'name': 'pulse', 'defaultSampleRate': '44100.0', 'maxInputChannels': '32'
'index': '20', 'name': 'dmix', 'defaultSampleRate': '48000.0', 'maxInputChannels': '0'
'index': '21', 'name': 'default', 'defaultSampleRate': '44100.0', 'maxInputChannels': '32'
It can be seen that the last device (index 21) is considered default. But on this machine, a headset is being used as the input device which has an index of 10. After finding the correct index the demo application can be invoked as below
python demo/python/porcupine_demo.py --keyword_file_paths resources/keyword_files/blueberry_linux.ppn \
--input_audio_device_index 10If the problem persists we suggest storing the recorded audio into a file for inspection. This can be achieved by
python demo/python/porcupine_demo.py --keyword_file_paths resources/keyword_files/blueberry_linux.ppn \
--input_audio_device_index 10 --output_path ~/test.wavIf after listening to stored file there is no apparent problem detected please open an issue.
The demo application cannot auto-detect the CPU type on all variants of Raspberry Pi. Hence you need to provide the path
to correct library file manually using --library_path command line argument. Please refer to README.md.
This demo offers the same functionality as standard demo except that it uses non-blocking API of PyAudio. The use of non-blocking API can be beneficial in platforms that have weaker CPUs (such as Raspberry Pi Zero).
It allows testing Porcupine on a corpus of audio files. It is mainly useful for quantitative performance benchmarking.