def detectKeywords(libpath):
audio_stream = AudiostreamSource()
extractor = FeatureExtractor(libpath)
detector = AudioRecognition(libpath)
extactor_gain = 1.0
#Add one or more keyword models
keywordIdFirefox = detector.addModel(
'../../models/Hotword/firefox_v1.4.5.premium', 0.6)
keywordIdSheila = detector.addModel(
'../../models/Hotword/sheila_v1.4.5.premium', 0.6)
keywordIdMarvin = detector.addModel(
'../../models/Hotword/marvin_v1.4.5.premium', 0.6)
keywordIdAlexa = detector.addModel(
'../../models/Hotword/alexa_v1.4.5.premium', 0.6)
bufsize = detector.getInputDataSize()
print("Audio Recognition Version: " + detector.getVersionString())
audio_stream.start()
try:
while (True):
frame = audio_stream.read(bufsize * 2, bufsize * 2)
if (not frame):
time.sleep(0.01)
continue
features = extractor.signalToMel(frame, extactor_gain)
prediction = detector.runDetection(features)
if (prediction != 0):
now = datetime.datetime.now().strftime("%d.%b %Y %H:%M:%S")
if (prediction == keywordIdFirefox):
print("Firefox detected:" + now)
elif (prediction == keywordIdSheila):
print("Sheila detected:" + now)
elif (prediction == keywordIdMarvin):
print("Marvin detected:" + now)
elif (prediction == keywordIdAlexa):
print("Alexa detected:" + now)
os.system(play_command + " ../resources/ding.wav")
except KeyboardInterrupt:
print("Terminating")
audio_stream.stop()
sys.exit(0)
#!/usr/bin/env python
import socket
import sys
import os
sys.path.append('../../python/src')
from libnyumaya import AudioRecognition
from auto_platform import default_libpath
serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
serversocket.bind(('', 9999))
serversocket.listen(5) # become a server socket, maximum 5 connections
detector = AudioRecognition(default_libpath)
keywordIdFirefox = detector.addModel(
'../../models/Hotword/firefox_v2.0.23.premium', 0.8)
connection, address = serversocket.accept()
while True:
buf = connection.recv(640)
if len(buf) > 0:
prediction = detector.runDetection(buf)
if (prediction != 0):
print("Keyword detected")
def recordActivations(libpath):
audio_stream = AudiostreamSource()
extractor = FeatureExtractor(libpath)
detectors = {}
framebuffersFront = {}
framebuffersBack = {}
extactor_gain = 1.0
recordBefore = 2.5 # Seconds before the activation
recordAfter = 0.5 # Seconds after the activation
activationCount = 0
ensure_dir(saveDirectory)
rbFrontSize = int(recordBefore * bytesPerSample * framesPerSecond)
rbBackSize = int(recordAfter * bytesPerSample * framesPerSecond)
for mpath, msens, mname in models:
detector = AudioRecognition(libpath)
detector.addModel(mpath, msens)
detectors[mname] = detector
framebuffersFront[mname] = bytearray()
framebuffersBack[mname] = bytearray()
bufsize = detector.getInputDataSize()
print("Audio Recognition Version: " + detector.getVersionString())
audio_stream.start()
try:
while (True):
frame = audio_stream.read(bufsize * 2, bufsize * 2)
if (not frame):
time.sleep(0.01)
continue
for mname in detectors:
#Fill audio before the activation
framebuffersFront[mname] = framebuffersFront[mname] + frame
if (len(framebuffersFront[mname]) > rbFrontSize):
framebuffersFront[mname] = framebuffersFront[mname][
-rbFrontSize:]
features = extractor.signalToMel(frame, extactor_gain)
for mname in detectors:
detector = detectors[mname]
prediction = detector.runDetection(features)
if (prediction != 0):
#FIXME: Record after is currently ignored
#Fill audio after the activation
#while(len(framebuffersBack[mname]) < rbBackSize):
# frame = audio_stream.read(bufsize*2,bufsize*2)
# if(not frame):
# time.sleep(0.01)
# continue
# framebuffersBack[mname] = framebuffersBack[mname] + frame
savePath = saveDirectory + "/activation_{}_{}_{}.wav".format(
mname, activationCount, time.time_ns())
save_wav(framebuffersFront[mname], savePath)
#save_wav(framebufferFront+framebufferBack,savePath)
print("Saving Activation to {}".format(savePath))
activationCount += 1
except KeyboardInterrupt:
print("Terminating")
audio_stream.stop()
sys.exit(0)
def detectKeywords(libpath):
audio_stream = AudiostreamSource()
extractor = FeatureExtractor(libpath)
detector = AudioRecognition(libpath)
framerate = 16000
model = Model("model")
#Let's define a custom dictionary
rec = KaldiRecognizer(
model, framerate,
'["oh one two three four five six seven eight nine zero", "[unk]"]')
extactor_gain = 1.0
#Add one or more keyword models
keywordIdAlexa = detector.addModel(
'../../models/Hotword/alexa_v3.0.35.premium', 0.85)
bufsize = detector.getInputDataSize()
print("Audio Recognition Version: " + detector.getVersionString())
command_started = False
audio_stream.start()
try:
while (True):
# Wakeword loop
if (not command_started):
frame = audio_stream.read(bufsize * 2, bufsize * 2)
if (not frame):
time.sleep(0.01)
continue
features = extractor.signalToMel(frame, extactor_gain)
prediction = detector.runDetection(features)
if (prediction != 0):
now = datetime.datetime.now().strftime("%d.%b %Y %H:%M:%S")
if (prediction == keywordIdAlexa):
print("Alexa detected:" + now)
os.system(play_command + " ../resources/ding.wav")
command_started = True
# vosk loop
else:
frame = audio_stream.read(4000, 4000)
if (not frame):
time.sleep(0.01)
continue
if rec.AcceptWaveform(bytes(frame)):
print(rec.Result())
command_started = False
print(rec.FinalResult())
except KeyboardInterrupt:
print("Terminating")
audio_stream.stop()
sys.exit(0)
class MultiDetector():
def __init__(self, libpath, timeout=40):
self.current_index = 0
self.number_detectors = 0
self.countdown = 0
self.timeout = timeout
self.detector = None
self.commands = []
self.libpath = libpath
self.history = []
self.last_frames = []
self.max_last_frames = 5
self.detector = AudioRecognition(self.libpath)
self.keyword_map = {}
#Given the current history which words are we checking for?
def get_possible_words(self, history):
words = []
for cmd in self.commands:
index = command_starts_with_history(cmd['command'], history)
if (index >= len(cmd['command'])):
print("Error index out of range:")
print("Command: " + str(cmd))
print("Index: " + str(index))
print("History: " + str(history))
return []
if (index >= 0):
cmd = cmd['command'][index]
if (not cmd in words):
words.append(cmd)
return words
def UpdateLastFrames(self, frame):
self.last_frames.append(frame)
if len(self.last_frames) > self.max_last_frames:
self.last_frames.pop(0)
def add_command(self, command, callback_function):
if (len(command.split(",")) == 0):
print("No valid command")
return
self.commands.append({
'command': command.split(","),
'function': callback_function
})
self.update_word_and_detector()
def add_word(self, graph, name, sensitivity):
keywordId = self.detector.addModel(graph, sensitivity)
self.keyword_map[keywordId] = name
def add_reset_history_callback(self, callback_function):
self.history_callback = callback_function
def add_detected_callback(self, callback_function):
self.detected_callback = callback_function
def GetInputDataSize(self):
return self.detector.getInputDataSize()
def maby_execute(self):
executed_cmd = False
for cmd in self.commands:
if (cmd['command'] == self.history):
cmd['function']()
self.history = []
self.countdown = 0
self.last_frames = []
executed_cmd = True
return executed_cmd
def check_timeout(self):
if (self.countdown > 0):
self.countdown -= 1
if (self.countdown == 0):
self.history = []
self.update_word_and_detector()
if (self.history_callback):
self.history_callback()
def update_word_and_detector(self):
self.possible_words = self.get_possible_words(self.history)
#Set possible words active
#Set impossible words inactive
print(self.possible_words)
for id in self.keyword_map:
key = self.keyword_map[id]
if (key in self.possible_words):
self.detector.setActive(id, True)
else:
self.detector.setActive(id, False)
def run_frame(self, frame, update_frames=True):
if (update_frames):
self.UpdateLastFrames(frame)
self.check_timeout()
prediction = self.detector.runDetection(frame)
if (prediction):
label = self.keyword_map[prediction]
if (label in self.possible_words):
print("Got prediction: " + label)
self.countdown = self.timeout
self.history.append(label)
result = self.maby_execute()
self.update_word_and_detector()
if (self.detected_callback):
self.detected_callback()
#Command hasn't finished so run last frames in next detectors
if (not result):
self.run_last_frames()
def run_last_frames(self):
for frame in self.last_frames:
self.run_frame(frame, update_frames=False)
def print_commands(self):
for cmd in self.commands:
print(cmd)