class SphinxWrapper(object):
'''
For audio stream feeding is used `process_raw(...)` method. It also updates vad status: if voice found in signal.
Before signal is fed to decoder, it should be isntructed that new utterance is expected.
When Vad says that speech segment ended it should be called `stopListening(...)`, only then we could request hypothesis what was said. `calculateHypothesis(...)`
'''
#MODELDIR = "../models"
#MODELDIR = "/home/as/src/speech/sphinx/lt-pocketsphinx-tutorial/impl/models"
MODELDIR = "../../lt-pocketsphinx-tutorial/impl/models"
decoder = None
config = None
previousVadState = 0
currentVadState = 0
def __init__(self):
'''
Constructor
'''
def prepareDecoder(self, pGramma):
'''
Entry point where sphinx decoder is initialized or grammar updated
'''
if self.decoder is None:
self.config = self.createConfig(pGramma);
self.decoder = Decoder(self.config);
else:
self.updateGrammar(self.decoder, pGramma);
def createConfig(self,pGramma):
'''
Create configuration with acoustic model path, grammar and dictionary
'''
print ("[createConfig]+++")
config = Decoder.default_config()
config.set_string('-hmm', os.path.join(self.MODELDIR, 'hmm/lt.cd_cont_200/'))
config.set_string('-fsg', os.path.join("../resource/", pGramma+'.fsg'))
#config.set_string('-jsgf', os.path.join("../resource/", pGramma+'.gram'))
config.set_string('-dict', os.path.join("../resource/", 'service.dict'))
print ("[createConfig]---")
return config;
def updateGrammar(self,pGramma):
'''
Update decoder language model from fsg file
'''
print ("[updateGrammar]+++" + pGramma)
logmath = self.decoder.get_logmath();
fsg = sphinxbase.FsgModel(os.path.join("../resource/", pGramma+'.fsg'), logmath, 7.5)
self.decoder.set_fsg("default",fsg);
self.decoder.set_search("default");
print ("[updateGrammar]---")
def startListening(self):
"""
Instruct decoder that new utterace should be expected
"""
self.decoder.start_utt(None)
def stopListening(self):
"""
Instruct decoder that new utterace should is not expected any more
"""
self.decoder.end_utt()
def process_raw(self, data):
"""
Feed decoder with raw audio data. After data is updating refresh VAD state
"""
#print("process_raw...\n")
self.decoder.process_raw(data, False, False)
self.previousVadState = self.currentVadState
self.currentVadState = self.decoder.get_vad_state();
#print("process_raw", self.currentVadState and True, self.previousVadState and True)
def calculateHypothesis(self):
return self.decoder.hyp();
def calculateVadState(self):
return self.decoder.get_vad_state;
def isVoiceStarted(self):
'''
silence -> speech transition,
'''
return self.currentVadState and not self.previousVadState
def isVoiceEnded(self):
'''
speech -> silence transition,
'''
return not self.currentVadState and self.previousVadState
class ContinuousPocketsphinx(object):
'''
classdocs
'''
CHUNK = 4096
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
#MODELDIR = "../models"
MODELDIR = "/home/mgreibus/src/speech/sphinx/lt-pocketsphinx-tutorial/impl/models"
decoder = None
stream = None
config = None
ai = None
def __init__(self):
'''
Constructor
'''
print ("[__init__]+++")
# Create a decoder with certain model
self.ai = Artificialintelligence()
self.config = self.createConfig("code");
self.decoder = Decoder(self.config);
print ("[__init__] created decoder")
#self.updateGrammar(self.decoder, "confirmation");
print ("[__init__]---")
p = pyaudio.PyAudio()
self.stream = p.open(format=self.FORMAT,
channels=self.CHANNELS,
rate=self.RATE,
input=True,
frames_per_buffer=self.CHUNK)
#Indicate listening for next utterance
print ("READY....")
def updateGrammar(self,pDecoder, pGramma):
'''
Update decoder language model from fsg file
'''
print ("[updateGrammar]+++" + pGramma)
logmath = pDecoder.get_logmath();
fsg = sphinxbase.FsgModel(os.path.join("../resource/", pGramma+'.fsg'), logmath, 7.5)
#pDecoder.readfile(os.path.join("../resource/", pGramma+'.fsg'), logmath)
pDecoder.set_fsg("default",fsg);
pDecoder.set_search("default");
print ("[updateGrammar]---")
def createConfig(self,pGramma):
print ("[createConfig]+++")
config = Decoder.default_config()
config.set_string('-hmm', os.path.join(self.MODELDIR, 'hmm/liepa.cd_semi_200/'))
config.set_string('-fsg', os.path.join("../resource/", pGramma+'.fsg'))
#config.set_string('-jsgf', os.path.join("../resource/", pGramma+'.gram'))
config.set_string('-dict', os.path.join("../resource/", 'service.dict'))
print ("[createConfig]---")
return config;
def speak(self,text):
print("Speak: ", text)
if text is not None:
aProcess = subprocess.Popen(['/home/mgreibus/bin/tark-win-lt', text], stderr=subprocess.STDOUT)
out = aProcess.communicate()[0];
time.sleep (0.100)
print("ended Speak: ", out)
def said(self, aiContext, text):
print ("[said]+++", text)
aiContext = self.ai.said(text, aiContext)
print ('AI response: ', aiContext.state, aiContext.response)
self.speak(aiContext.response)
if aiContext.interactiveStep is False :
self.said(aiContext, text);
print ("[said]---")
return aiContext
def recognized(self, pStream, pDecoder, aiContext):
print ("[recognized]+++")
pStream.stop_stream()
pDecoder.end_utt()
# Retrieve hypothesis.
hypothesis = pDecoder.hyp()
if hypothesis is not None:
print ('Best hypothesis: ', hypothesis.uttid, hypothesis.best_score, hypothesis.hypstr)
self.said(aiContext, hypothesis.hypstr.decode('utf-8'))
if aiContext.state in aiContext.GRAM:
self.updateGrammar(pDecoder, aiContext.GRAM[aiContext.state]);
elif (time.time() - aiContext.stateStarted) > 10:
self.speak(aiContext.response)
aiContext.stateStarted = time.time()
print ("Time: ", (time.time() - aiContext.stateStarted))
print("AI response ", aiContext.response)
time.sleep (0.100)
#Indicate listening for next utterance
pStream.start_stream()
pDecoder.start_utt(None)
print ("READY....")
print ("[recognized]---")
return aiContext
def run(self):
'''
Executor
'''
print("* start recording")
self.decoder.start_utt(None)
cur_vad_state = 0
aiContext = self.ai.createContext();
self.said(aiContext, None);
while True:
data = self.stream.read(self.CHUNK)
time.sleep (0.100)
#frames.append(data)
self.decoder.process_raw(data, False, False)
vad_state = self.decoder.get_vad_state()
if vad_state and not cur_vad_state:
#silence -> speech transition,
#let user know that we heard
print("Listening...\n")
if not vad_state and cur_vad_state:
#speech -> silence transition,
#time to start new utterance
aiContext = self.recognized(self.stream,self.decoder, aiContext);
if aiContext.state == aiContext.STATE_THANKS:
break
cur_vad_state = vad_state
class SphinxWrapper(object):
'''
For audio stream feeding is used `process_raw(...)` method. It also updates vad status: if voice found in signal.
Before signal is fed to decoder, it should be isntructed that new utterance is expected.
When Vad says that speech segment ended it should be called `stopListening(...)`, only then we could request hypothesis what was said. `calculateHypothesis(...)`
'''
#MODELDIR = "../models"
#MODELDIR = "/home/as/src/speech/sphinx/lt-pocketsphinx-tutorial/impl/models"
MODELDIR = "../../lt-pocketsphinx-tutorial/impl/models"
decoder = None
config = None
previousVadState = 0
currentVadState = 0
def __init__(self):
'''
Constructor
'''
def prepareDecoder(self, pGramma):
'''
Entry point where sphinx decoder is initialized or grammar updated
'''
if self.decoder is None:
self.config = self.createConfig(pGramma)
self.decoder = Decoder(self.config)
else:
self.updateGrammar(self.decoder, pGramma)
def createConfig(self, pGramma):
'''
Create configuration with acoustic model path, grammar and dictionary
'''
print("[createConfig]+++")
config = Decoder.default_config()
config.set_string('-hmm',
os.path.join(self.MODELDIR, 'hmm/lt.cd_cont_200/'))
config.set_string('-fsg', os.path.join("../resource/",
pGramma + '.fsg'))
#config.set_string('-jsgf', os.path.join("../resource/", pGramma+'.gram'))
config.set_string('-dict', os.path.join("../resource/",
'service.dict'))
print("[createConfig]---")
return config
def updateGrammar(self, pGramma):
'''
Update decoder language model from fsg file
'''
print("[updateGrammar]+++" + pGramma)
logmath = self.decoder.get_logmath()
fsg = sphinxbase.FsgModel(
os.path.join("../resource/", pGramma + '.fsg'), logmath, 7.5)
self.decoder.set_fsg("default", fsg)
self.decoder.set_search("default")
print("[updateGrammar]---")
def startListening(self):
"""
Instruct decoder that new utterace should be expected
"""
self.decoder.start_utt(None)
def stopListening(self):
"""
Instruct decoder that new utterace should is not expected any more
"""
self.decoder.end_utt()
def process_raw(self, data):
"""
Feed decoder with raw audio data. After data is updating refresh VAD state
"""
#print("process_raw...\n")
self.decoder.process_raw(data, False, False)
self.previousVadState = self.currentVadState
self.currentVadState = self.decoder.get_vad_state()
#print("process_raw", self.currentVadState and True, self.previousVadState and True)
def calculateHypothesis(self):
return self.decoder.hyp()
def calculateVadState(self):
return self.decoder.get_vad_state
def isVoiceStarted(self):
'''
silence -> speech transition,
'''
return self.currentVadState and not self.previousVadState
def isVoiceEnded(self):
'''
speech -> silence transition,
'''
return not self.currentVadState and self.previousVadState
