def start_listening(self):
''' Starts streaming. Pauses until self.resume has been called '''
config = Decoder.default_config()
config.set_string('-hmm', path.join(self.model_dir, self.hmm))
config.set_string('-lm', path.join(self.model_dir, self.lm))
config.set_string('-dict', path.join(self.model_dir, self.dictionary))
config.set_string('-logfn', self.logfn)
# This takes a while
decoder = Decoder(config)
p = pyaudio.PyAudio()
print(self.input_source_index)
stream = p.open(format=pyaudio.paInt16,
channels=1,
rate=16000,
input=True,
input_device_index=self.input_source_index,
frames_per_buffer=1024)
stream.start_stream()
in_speech_bf = False
decoder.start_utt()
self.wait_to_resume_lock.acquire()
while self.is_running:
while self.paused:
pass
buf = stream.read(1024, exception_on_overflow=False)
if buf:
decoder.process_raw(buf, False, False)
if decoder.get_in_speech() != in_speech_bf:
in_speech_bf = decoder.get_in_speech()
if not in_speech_bf:
decoder.end_utt()
# if self.wait_to_resume:
# stream.stop_stream()
phrase = decoder.hyp().hypstr
if phrase != "":
self.all_speech_data.append(phrase)
# if self.wait_to_resume:
# # print("waiting")
# self.wait_to_resume_lock.acquire()
# # print("resuming")
# if self.wait_to_resume:
# stream.start_stream()
decoder.start_utt()
else:
break
decoder.end_utt()
def main():
abspath = os.path.dirname(os.path.abspath(__file__))
abspath = os.path.join(abspath, '..')
model_dir = os.path.join(abspath, 'model')
hmm = os.path.join(model_dir, HMM)
lm = os.path.join(model_dir, LM)
dic = os.path.join(model_dir, DIC)
config = Decoder.default_config()
config.set_string('-hmm', hmm)
config.set_string('-lm', lm)
config.set_string('-dict', dic)
config.set_string('-logfn', '/dev/null')
decoder = Decoder(config)
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16,
channels=1,
rate=16000,
input=True,
frames_per_buffer=BUFFER)
stream.start_stream()
in_speech_bf = True
decoder.start_utt()
while True:
buf = stream.read(BUFFER)
if buf:
decoder.process_raw(buf, False, False)
if decoder.get_in_speech():
sys.stdout.write('.')
sys.stdout.flush()
if decoder.get_in_speech() == in_speech_bf:
continue
in_speech_bf = decoder.get_in_speech()
if in_speech_bf:
continue
decoder.end_utt()
try:
if decoder.hyp().hypstr != '':
print('You said:', decoder.hyp().hypstr)
except AttributeError:
pass
decoder.start_utt()
else:
break
decoder.end_utt()
print('An Error occured:', decoder.hyp().hypstr)
def main():
abspath = os.path.dirname(os.path.abspath(__file__))
abspath = os.path.join(abspath, '..')
model_dir = os.path.join(abspath, 'model')
hmm = os.path.join(model_dir, HMM)
lm = os.path.join(model_dir, LM)
dic = os.path.join(model_dir, DIC)
config = Decoder.default_config()
config.set_string('-hmm', hmm)
config.set_string('-lm', lm)
config.set_string('-dict', dic)
config.set_string('-logfn', '/dev/null')
decoder = Decoder(config)
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=BUFFER)
stream.start_stream()
in_speech_bf = True
decoder.start_utt()
while True:
buf = stream.read(BUFFER)
if buf:
decoder.process_raw(buf, False, False)
if decoder.get_in_speech():
sys.stdout.write('.')
sys.stdout.flush()
if decoder.get_in_speech() == in_speech_bf:
continue
in_speech_bf = decoder.get_in_speech()
if in_speech_bf:
continue
decoder.end_utt()
try:
if decoder.hyp().hypstr != '':
print('You said:', decoder.hyp().hypstr)
except AttributeError:
pass
decoder.start_utt()
else:
break
decoder.end_utt()
print('An Error occured:', decoder.hyp().hypstr)
def main():
environment: str = os.getenv("ENVIRONMENT", "dev")
config: Dict = load_config(environment)
initialize_logger(level=config["logging"]["level"],
filename=config["logging"]["filename"])
redis_host = config["redis"]["host"]
redis_port = config["redis"]["port"]
logger.debug(f"Connecting to redis at {redis_host}:{redis_port}")
redis_client: Redis = Redis(host=redis_host, port=redis_port, db=0)
logger.debug("Initializing PyAudio interface")
audio = pyaudio.PyAudio()
microphone_index = get_microphone_index(audio,
config["microphone"]["name"])
logger.debug(
f"Using microphone device '{config['microphone']['name']}' (card index {microphone_index})"
)
logger.debug(
f"Intializing pocketsphinx Decoder using model dir {MODELDIR}")
decoder_config: DecoderConfig = Decoder.default_config()
decoder_config.set_string("-hmm", os.path.join(MODELDIR, "en-us/en-us"))
decoder_config.set_string("-lm",
os.path.join(MODELDIR, "en-us/en-us.lm.bin"))
decoder_config.set_string(
"-dict", os.path.join(MODELDIR, "en-us/cmudict-en-us.dict"))
decoder = Decoder(decoder_config)
logger.debug("Opening audio stream")
stream = audio.open(format=pyaudio.paInt16,
channels=1,
rate=44100,
input=True,
frames_per_buffer=2048,
input_device_index=microphone_index)
stream.start_stream()
in_speech_bf = False
decoder.start_utt()
try:
logger.debug("Starting decoder loop")
while cycle([True]):
buf = stream.read(2048)
if buf:
logger.debug("Decoding raw audio")
decoder.process_raw(buf, False, False)
if decoder.get_in_speech() != in_speech_bf:
logger.debug("GOT HERE")
in_speech_bf = decoder.get_in_speech()
if not in_speech_bf:
decoder.end_utt()
transcription = decoder.hyp().hypstr
logger.debug(f"Result: {transcription}")
redis_client.publish("subsystem.listener.recording",
transcription)
decoder.start_utt()
else:
logger.debug("Buffer closed. Ending")
break
decoder.end_utt()
except Exception:
logger.exception("Something bad happened")
finally:
redis_client.close()
class AvaRecognizer(object):
"""Class to add ASR recognition functionality using language model + dictionary
Publishes recognition output to recognizer/asr_output."""
def __init__(self):
# Initializing publisher with buffer size of 10 messages
self.pub_ = rospy.Publisher("recognizer/asr_output",
String,
queue_size=10)
# initialize node
rospy.init_node("ava_recognizer")
# Call custom function on node shutdown
rospy.on_shutdown(self.shutdown)
# Params
# File containing language model
_lm_param = "~lm"
# Dictionary
_dict_param = "~dict"
# HMM Model
_hmm_param = "~hmm"
# used in process_audio for piecing full utterances
self.in_speech_bf = False
# Setting param values
if rospy.has_param(
_dict_param) and rospy.get_param(_dict_param) != ":default":
self.dict = rospy.get_param(_dict_param)
else:
rospy.logerr(
"No dictionary found. Please add an appropriate dictionary argument."
)
return
if rospy.has_param(
_lm_param) and rospy.get_param(_lm_param) != ':default':
self._use_lm = 1
self.class_lm = rospy.get_param(_lm_param)
else:
rospy.logerr("No lm found. Please add an appropriate lm argument.")
return
if rospy.has_param(_hmm_param):
self.hmm = rospy.get_param(_hmm_param)
if rospy.get_param(_hmm_param) == ":default":
if os.path.isdir(
"/home/team5/.local/lib/python2.7/site-packages/pocketsphinx/model"
):
rospy.loginfo("Loading the default acoustic model")
self.hmm = "/home/team5/.local/lib/python2.7/site-packages/pocketsphinx/model/en-us"
rospy.loginfo("Done loading the default acoustic model")
else:
rospy.logerr("Failed to find default model.")
return
else:
rospy.logerr(
"No language model specified. Couldn't find default model.")
return
# All params satisfied. Starting recognizer and audio thread
self._audio_queue = Queue.Queue()
self._kill_audio = False
threading.Thread(target=self.get_audio).start()
self.start_recognizer()
def start_recognizer(self):
"""Function to handle lm or grammar processing of audio."""
config = Decoder.default_config()
rospy.loginfo("Done initializing pocketsphinx")
# Setting configuration of decoder using provided params
config.set_string('-dict', self.dict)
config.set_string('-lm', self.class_lm)
config.set_string('-hmm', self.hmm)
self.decoder = Decoder(config)
# Start processing input audio
self.decoder.start_utt()
rospy.loginfo("Decoder started successfully")
# Subscribe to audio topic
rospy.Subscriber("recognizer/audio_ready", Bool, self.process_audio)
rospy.spin()
def process_audio(self, isready):
"""Audio processing based on decoder config."""
# Check if input audio has ended
assert (isready)
data = self._audio_queue.get()
self.decoder.process_raw(data, False, False)
if self.decoder.get_in_speech() != self.in_speech_bf:
self.in_speech_bf = self.decoder.get_in_speech()
if not self.in_speech_bf:
self.decoder.end_utt()
if self.decoder.hyp() != None:
rospy.loginfo('OUTPUT: \"' + self.decoder.hyp().hypstr +
'\"')
self.pub_.publish(self.decoder.hyp().hypstr)
self.decoder.start_utt()
@staticmethod
def shutdown():
"""This function is executed on node shutdown."""
# command executed after Ctrl+C is pressed
rospy.loginfo("Stop AvaRecognizer")
rospy.sleep(1)
def get_audio(self):
""" Used for audio parsing thread. """
# parameters for PCM. view PCMs with 'pactl list sources short'.
# don't modify me plz.
device = 'sysdefault:CARD=Audio'
inp = alsaaudio.PCM(type=alsaaudio.PCM_CAPTURE,
mode=alsaaudio.PCM_NORMAL,
card=device)
inp.setchannels(1)
inp.setrate(16000)
inp.setformat(alsaaudio.PCM_FORMAT_S16_LE)
inp.setperiodsize(1024)
pub = rospy.Publisher('recognizer/audio_ready', Bool, queue_size=10)
while not (self._kill_audio):
_, data = inp.read()
self._audio_queue.put(data)
pub.publish(True)
return
class Words(Chain):
"""
Chain to compute words and summarizes words occurences at levels of individual subject and dataset
"""
allow_sample_layer_concurrency = True
abstract_class = False
requirements = [Preprocess]
def __init__(self):
super(Words, self).__init__()
self._subject_words = {}
self.decoder = None
def dataset_preprocess(self, dataset):
self._subject_words.clear()
def subject_preprocess(self, subject, samples,
common_subject_settings):
self._subject_words[subject] = []
@staticmethod
def sample_result_filename(out_sample_path):
return f'{out_sample_path[:-5]}_words_result.json'
def _compute_words(self, segments_path, words_result_path):
"""
:param segments_path:
:param words_result_path:
:return:
"""
model_dir = self.process_settings.get('model_dir', MODEL_DIR)
decoder_hmm = self.process_settings.get('decoder_hmm', 'en-us/en-us')
decoder_lm = self.process_settings.get('decoder_lm',
'en-us/en-us.lm.bin')
decoder_dict = self.process_settings.get('decoder_dict',
'en-us/cmudict-en-us.dict')
decoder_lw = self.process_settings.get('decoder_lw', 2.0)
decoder_pip = self.process_settings.get('decoder_pip', 0.3)
decoder_beam = self.process_settings.get('decoder_beam', 1e-200)
decoder_pbeam = self.process_settings.get('decoder_pbeam', 1e-20)
decoder_mmap = self.process_settings.get('decoder_mmap', False)
decoder_stream_buf_size = self.process_settings.get('decoder_stream_buf_size',
8192)
pprint_indent = self.process_settings.get('pprint_indent', 4)
hypothesis = PocketsphinxHypothesisSchema()
ph_info = PocketsphinxSegmentSchema()
def _get_decoder_results():
self.decoder.end_utt()
segment = [ph_info.dump(dict(word=seg.word,
start=seg.start_frame / 100,
end=seg.end_frame / 100,
prob=seg.prob))
for seg in self.decoder.seg()]
hyp = self.decoder.hyp()
hyp_dict = dict(best_score=hyp.best_score,
hypstr=hyp.hypstr, prob=hyp.prob)
hyp_result = hypothesis.dump(hyp_dict)
return hyp_result, segment
@check_if_already_done(words_result_path)
def recognize_words(segments_path, words_result_path):
# Create a decoder with certain model
config = Decoder.default_config()
config.set_string('-hmm', join(model_dir, decoder_hmm))
config.set_string('-lm', join(model_dir, decoder_lm))
config.set_string('-dict', join(model_dir, decoder_dict))
config.set_float('-lw', decoder_lw)
config.set_float('-pip', decoder_pip)
config.set_float('-beam', decoder_beam)
config.set_float('-pbeam', decoder_pbeam)
config.set_boolean('-mmap', decoder_mmap)
hyps=[]
segs=[]
self.decoder = Decoder(config)
with open(segments_path, 'rb') as stream:
in_speech_buffer = False
self.decoder.start_utt()
while True:
buf = stream.read(decoder_stream_buf_size)
if buf:
self.decoder.process_raw(buf, False, False)
if self.decoder.get_in_speech() != in_speech_buffer:
in_speech_buffer = self.decoder.get_in_speech()
if not in_speech_buffer:
hyp_result, segment = _get_decoder_results()
segs += segment
hyps.append(hyp_result)
self.decoder.start_utt()
else:
if in_speech_buffer:
hyp_result, segment = _get_decoder_results()
segs += segment
hyps.append(hyp_result)
break
words_dict = dict(hypotheses=hyps, segment_info=segs)
words_result = DecoderOutputSchema().dumps(words_dict)
with open(words_result_path, 'w') as f:
f.write(words_result)
recognize_words(segments_path, words_result_path)
with open(words_result_path, 'r') as f:
logger.debug(f'words_result_path: {words_result_path}')
json_file = json.load(f)
result = DecoderOutputSchema().load(json_file)
logger.debug(json.dumps(result, indent=pprint_indent))
def sample_layer(self, subject, sample_json_filename, sample_settings):
url = sample_settings.get('url')
datatype = sample_settings.get('datatype')
output_path_pattern = join(self.results_dir, subject, sample_json_filename)
words_result_file = self.sample_result_filename(output_path_pattern)
logger.info(f'words result file: {words_result_file}')
audio_path = resolve_audio_path(url, datatype, output_path_pattern)
_, segments_path = audio_and_segment_paths(audio_path, False)
self._compute_words(segments_path, words_result_file)
def main():
""" A main method to that does a simple matching of sentences and executes scripts
"""
notifier = sdnotify.SystemdNotifier()
# Load config first
config_file = open(os.path.join(os.getcwd(), 'config.yaml'), 'r')
config = yaml.load(config_file)
interaction_timeout = int(config['interaction_timeout'])
# Create Decoder config
pocketsphinx_config = Decoder.default_config()
pocketsphinx_config.set_string('-hmm', os.path.join(os.getcwd(), config['hmm_path']))
pocketsphinx_config.set_string('-dict', os.path.join(os.getcwd(), config['dict_path']))
pocketsphinx_config.set_string('-featparams', os.path.join(os.getcwd(), config['feat_params_path']))
pocketsphinx_config.set_boolean("-allphone_ci", True)
# Using decoder.set_kws & decoder.set_lm_file
# pocketsphinx_config.set_string('-lm', os.path.join(os.getcwd(), config['lm_path']))
# pocketsphinx_config.set_string('-kws', os.path.join(os.getcwd(), config['keyphrase_path']))
# Initialize audio
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=1024)
stream.start_stream()
# Load invocations and commands
invocations = config['invocations']
# Process audio chunk by chunk. On keyword detected perform action and restart search
decoder = Decoder(pocketsphinx_config)
logmath = decoder.get_logmath()
decoder.set_kws('keyword', os.path.join(os.getcwd(), config['invocation_path']))
decoder.set_lm_file('lm', os.path.join(os.getcwd(), config['lm_path']))
invocation_ctx = None
in_speech_bf = False
# Run some initialization scripts for terminal displays
subprocess.Popen([os.path.join(os.getcwd(), config['init_exec'])]).communicate()
decoder.set_search('keyword')
decoder.start_utt()
notifier.notify("READY=1")
interaction_time = None
while True:
notifier.notify("WATCHDOG=1")
buf = stream.read(1024, exception_on_overflow = False)
if buf:
decoder.process_raw(buf, False, False)
else:
logging.error("Unable to get audio, exiting")
break
hyp = decoder.hyp()
# seg = decoder.seg()
hyp_str = hyp.hypstr.lower().strip() if hyp else None
now_in_speech = decoder.get_in_speech()
if now_in_speech != in_speech_bf:
in_speech_bf = now_in_speech
if not in_speech_bf:
decoder.end_utt()
if hyp_str:
logging.info("Heard: '%s' while being in '%s' context (score: %d, confidence: %d -> in log scale %d)" %
(hyp_str, invocation_ctx, hyp.best_score, logmath.exp(hyp.prob), hyp.prob))
if not invocation_ctx:
if hyp_str in invocations:
logging.info("Matched invocation: '%s'" % hyp_str)
invocation_ctx = hyp_str
subprocess.Popen([os.path.join(os.getcwd(), invocations[invocation_ctx]['enter']),
invocations[invocation_ctx]['voice_params'], invocation_ctx, hyp_str]).communicate()
interaction_time = time.time()
decoder.set_search('lm')
else:
logging.debug('Unknown invocation or wrongly heard, silently ignoring')
else:
matched = False
score_dict = defaultdict(list)
commands = invocations[invocation_ctx]['commands']
for command in commands:
logging.info("- command: '%s':" % command['name'])
for sentence in command['sentence']:
score = calc_similarity(command, sentence.lower(), hyp_str)
score_dict[score].append(command)
logging.debug(" - similarity: %d for sentence: %s" % (score, sentence))
if score == 1000:
logging.debug("... seems like found perfect match, ignoring the rest")
break
for best in sorted(score_dict.items(), reverse=True):
if best[0] > 90:
command = best[1][0] # here might be some randomness
logging.info("The best matching command is '%s', executing: %s" % (command['name'], command['exec']))
subprocess.Popen([os.path.join(os.getcwd(), invocations[invocation_ctx]['ack']),
invocations[invocation_ctx]['voice_params'], invocation_ctx, hyp_str]).communicate()
subprocess.Popen([os.path.join(os.getcwd(), command['exec']),
invocations[invocation_ctx]['voice_params'], invocation_ctx, command['name']]).communicate()
subprocess.Popen([os.path.join(os.getcwd(), invocations[invocation_ctx]['exit']),
invocations[invocation_ctx]['voice_params'], invocation_ctx, hyp_str])
invocation_ctx = None
decoder.set_search('keyword')
matched = True
break # take only the first which should be the best
if not matched:
logging.info("... not matched, ignoring")
subprocess.Popen([os.path.join(os.getcwd(), invocations[invocation_ctx]['noop']),
invocations[invocation_ctx]['voice_params'], invocation_ctx, hyp_str]).communicate()
decoder.start_utt()
if invocation_ctx and interaction_time and time.time() > interaction_time + interaction_timeout:
logging.info("The invocation context has just timed out, returning to listen for invocation word.")
subprocess.Popen([os.path.join(os.getcwd(), invocations[invocation_ctx]['exit']),
invocations[invocation_ctx]['voice_params'], invocation_ctx])
invocation_ctx = None
interaction_time = None
decoder.end_utt()
decoder.set_search('keyword')
decoder.start_utt()
class PocketGrammar(object):
AUDIO_CHUNK_SIZE = 1024
AUDIO_RATE = 16000
HMM = 'cmusphinx-5prealpha-en-us-ptm-2.0/'
DIC = 'dictionary.dic'
GRAMMAR = 'grammar.jsgf'
def __init__(self, device_index=0, model_path=None):
self._decoder = None
self._pa = None
self._device_no = device_index
self._model_path = model_path
# PocketSphinx configuration
logging.info('Grammar file:' + os.path.join(model_path, self.GRAMMAR))
ps_config = Decoder.default_config()
# Set recognition model to ...
ps_config.set_string('-hmm', os.path.join(model_path, self.HMM))
ps_config.set_string('-dict', os.path.join(model_path, self.DIC))
ps_config.set_string('-jsgf', os.path.join(model_path, self.GRAMMAR))
ps_config.set_string('-logfn', '/dev/null')
# Process audio chunk by chunk. On keyword detected perform action and restart search
self._decoder = Decoder(ps_config)
self._pa = pyaudio.PyAudio()
def _handle_init(self, rate, chunk_size):
self._handle = self._pa.open(input=True,
input_device_index=self._device_no,
format=pyaudio.paInt16,
channels=1,
rate=rate,
frames_per_buffer=chunk_size)
def _handle_release(self):
self._handle.stop_stream()
self._handle.close()
def _handle_read(self, chunk_size):
return self._handle.read(chunk_size, exception_on_overflow=False)
def getHypothesys(self):
# init microphone
self._handle_init(self.AUDIO_RATE, self.AUDIO_CHUNK_SIZE)
self._decoder.start_utt()
# from speech to silence or from silence to speech?
utteranceStarted = False
triggered = False
while not triggered:
# Read from microphone and process
data = self._handle_read(self.AUDIO_CHUNK_SIZE)
self._decoder.process_raw(data, False, False)
# checks for transition from silence to speech.
inSpeech = self._decoder.get_in_speech()
if inSpeech and not utteranceStarted:
utteranceStarted = True
logging.debug("Silence")
# checks for the transition from speech to silence
if not inSpeech and utteranceStarted:
hypothesis = self._decoder.hyp()
triggered = hypothesis is not None
# close microphone
self._handle_release()
self._decoder.end_utt()
if triggered:
return hypothesis.hypstr
class SpeechRecognizer(Interpreter):
def __init__(self, name: str, sr: str = "pocketsphinx"):
super().__init__(name, True)
self.logger = self.get_logger()
self.sr = sr
self.current_data = []
self.setup()
def setup(self) -> None:
self.RATE = int(os.getenv("RATE"))
self.CHUNK = int(os.getenv("CHUNK"))
self.setup_pocketsphinx()
if (self.sr == "googlespeech"):
self.setup_googlespeech()
def setup_pocketsphinx(self) -> None:
self.logger.info("Setting up PocketSphinx.")
self.MODELDIR = "resources/model"
config = Decoder.default_config()
config.set_string('-hmm', os.path.join(self.MODELDIR, 'es-es'))
config.set_string('-lm', os.path.join(self.MODELDIR, 'es-es.lm'))
config.set_string('-dict', os.path.join(self.MODELDIR, 'es.dict'))
config.set_string('-logfn', '/dev/null')
self.decoder = Decoder(config)
self.prev_buf_is_speech = False
self.decoder.start_utt()
self.logger.info("Done setting up PocketSphinx.")
def setup_googlespeech(self) -> None:
self.logger.info("Setting up Google Speech.")
credentials = service_account.Credentials.from_service_account_file(
'resources/keys/credentials.json')
config = speech.types.RecognitionConfig(
encoding=speech.enums.RecognitionConfig.AudioEncoding.LINEAR16,
language_code='es-PE',
sample_rate_hertz=self.RATE,
)
self.client = speech.SpeechClient(credentials=credentials)
self.streaming_config = speech.types.StreamingRecognitionConfig(
config=config)
self.logger.info("Done setting up Google Speech.")
def get_destinations_ID(self, raw_data) -> List[Identifier]:
return [self.destinations_ID[0]]
def preprocess(self, raw_data):
"""Filtering"""
return raw_data
def query_gs(self):
requests = (speech.types.StreamingRecognizeRequest(audio_content=chunk)
for chunk in self.current_data)
responses = self.client.streaming_recognize(
config=self.streaming_config, requests=requests)
try:
response = next(responses)
data = response.results[0].alternatives[0].transcript
conf = response.results[0].alternatives[0].confidence
except Exception as e:
self.logger.info(f"{self.name}>> {e}")
conf = None
data = None
self.current_data.clear()
return data, conf
def query_ps(self):
try:
data = self.decoder.hyp().hypstr
conf = self.decoder.hyp().best_score
if data == "":
data = None
except Exception as e:
self.logger.info(f"{self.name}>> {e}")
conf = None
data = None
return data, conf
def process(self, raw_data) -> Generator:
self.decoder.process_raw(raw_data, False, False)
cur_buf_is_speech = self.decoder.get_in_speech()
data = None
self.logger.info(
f"prev: {self.prev_buf_is_speech}, current: {cur_buf_is_speech}")
force_speech = False
if raw_data == bytes([0] * self.CHUNK * 16):
force_speech = True
self.logger.info("RECEIVED FORCE STOP")
if force_speech or (self.prev_buf_is_speech and not cur_buf_is_speech):
# No longer in speech -> stop listening and process
self.logger.info("No longer in speech, yielding True.")
yield True
self.decoder.end_utt()
if (self.sr == "googlespeech"):
data, conf = self.query_gs()
elif (self.sr == "pocketsphinx"):
data, conf = self.query_ps()
self.logger.info(
f"{self.name}>> Heard DATA: '{data}' with confidence: {conf}.")
self.decoder.start_utt()
self.prev_buf_is_speech = cur_buf_is_speech
elif not self.prev_buf_is_speech and cur_buf_is_speech:
# Now in speech -> Start listening
self.current_data.append(raw_data)
self.prev_buf_is_speech = cur_buf_is_speech
yield False
elif self.prev_buf_is_speech and cur_buf_is_speech:
# Still in speech -> Keep on listening
self.current_data.append(raw_data)
self.prev_buf_is_speech = cur_buf_is_speech
yield False
else:
self.prev_buf_is_speech = cur_buf_is_speech
yield False
yield data
return
def pass_msg(self, msg: str) -> None:
if msg == "RESUME":
self.e.set()
def dump_history(self, filename: str, data: List[Any]) -> None:
pass
