def detect():
from pocketsphinx import Pocketsphinx, Ad
ad = Ad(None, 16000) # default input
decoder = Pocketsphinx(lm=False,
hmm=hmm,
dic=dic,
keyphrase=keyphrase,
kws_threshold=kws_threshold)
buf = bytearray(2048)
with ad:
with decoder.start_utterance():
while ad.readinto(buf) >= 0:
decoder.process_raw(buf, False, False)
if decoder.hyp():
with decoder.end_utterance():
logging.info('Wake word detected for %s' % system)
wake_statuses[system] = 'detected'
break
def decode():
nonlocal decoder, decoded_phrase
# Dynamically load decoder
if decoder is None:
_LOGGER.debug('Loading decoder')
hass.states.async_set(OBJECT_POCKETSPHINX, STATE_LOADING, state_attrs)
decoder = Pocketsphinx(
hmm=acoustic_model,
lm=language_model,
dic=dictionary)
hass.states.async_set(OBJECT_POCKETSPHINX, STATE_DECODING, state_attrs)
# Do actual decoding
with decoder.start_utterance():
decoder.process_raw(recorded_data, False, True) # full utterance
hyp = decoder.hyp()
if hyp:
with decoder.end_utterance():
decoded_phrase = hyp.hypstr
decoded_event.set()
class HotwordRecognizer:
"""热词（唤醒词）识别器，对 |pocketsphinx| 的简单封装，默认的热词是 `'阿Q'` 和 `'R-cute`。
如果要自定义热词，请参考 https://blog.51cto.com/feature09/2300352
.. |pocketsphinx| raw:: html
<a href='https://github.com/bambocher/pocketsphinx-python' target='blank'>pocketsphinx</a>
.. |config| raw:: html
<a href='https://github.com/bambocher/pocketsphinx-python#default-config' target='blank'>pocketsphinx Default config</a>
:param hotword: 热词或热词列表，默认为 `['阿Q', 'R-cute']`
:type hotword: str / list, optional
:param hmm: 参考 |config|
:type hmm: str, optional
:param lm: 参考 |config|
:type lm: str, optional
:param dic: 参考 |config|
:type dic: str, optional
"""
def __init__(self, **kwargs):
# signal.signal(signal.SIGINT, self.stop)
self._no_search = False
self._full_utt = False
hotword = kwargs.pop('hotword', ['阿Q', 'R-cute'])
self._hotwords = hotword if isinstance(hotword, list) else [hotword]
model_path = get_model_path()
opt = {
'verbose': False,
'hmm': os.path.join(model_path, 'en-us'),
'lm': util.resource('sphinx/rcute.lm'),
'dic': util.resource('sphinx/rcute.dic'),
}
opt.update(kwargs)
self._rec = Pocketsphinx(**opt)
def recognize(self, stream, timeout=None):
"""开始识别
:param source: 声音来源
:param timeout: 超时，即识别的最长时间（秒），默认为 `None` ，表示不设置超时，知道识别到热词才返回
:type timeout: float, optional
:return: 识别到的热词模型对应的热词，若超时没识别到热词则返回 `None`
:rtype: str
"""
self._cancel = False
if timeout:
count = 0.0
in_speech = False
with self._rec.start_utterance():
while True:
data = stream.raw_read()
self._rec.process_raw(data, self._no_search, self._full_utt)
if in_speech != self._rec.get_in_speech():
in_speech = not in_speech
if not in_speech and self._rec.hyp():
with self._rec.end_utterance():
hyp = self._rec.hypothesis()
if hyp in self._hotwords:
return hyp
if self._cancel:
raise RuntimeError(
'Hotword detection cancelled by another thread')
elif timeout:
count += source.frame_duration #len(data) / 32000
if count > timeout:
return
def cancel(self):
"""停止识别"""
self._cancel = True
def decode():
nonlocal decoder, decoded_phrase, data, filename
# Check if WAV is in the correct format.
# Convert with sox if not.
with io.BytesIO(data) as wav_data:
with wave.open(wav_data, mode='rb') as wav_file:
rate, width, channels = wav_file.getframerate(), wav_file.getsampwidth(), wav_file.getnchannels()
_LOGGER.debug('rate=%s, width=%s, channels=%s.' % (rate, width, channels))
if (rate != 16000) or (width != 2) or (channels != 1):
# Convert to 16-bit 16Khz mono (required by pocketsphinx acoustic models)
_LOGGER.debug('Need to convert to 16-bit 16Khz mono.')
if shutil.which('sox') is None:
_LOGGER.error("'sox' command not found. Cannot convert WAV file to appropriate format. Expect poor performance.")
else:
temp_input_file = None
if filename is None:
# Need to write original WAV data out to a file for sox
temp_input_file = tempfile.NamedTemporaryFile(suffix='.wav', mode='wb+')
temp_input_file.write(data)
temp_input_file.seek(0)
filename = temp_input_file.name
# sox <IN> -r 16000 -e signed-integer -b 16 -c 1 <OUT>
with tempfile.NamedTemporaryFile(suffix='.wav', mode='wb+') as out_wav_file:
subprocess.check_call(['sox',
filename,
'-r', '16000',
'-e', 'signed-integer',
'-b', '16',
'-c', '1',
out_wav_file.name])
out_wav_file.seek(0)
# Use converted data
with wave.open(out_wav_file, 'rb') as wav_file:
data = wav_file.readframes(wav_file.getnframes())
if temp_input_file is not None:
# Clean up temporary file
del temp_input_file
# Dynamically load decoder
if decoder is None:
_LOGGER.debug('Loading decoder')
hass.states.async_set(OBJECT_POCKETSPHINX, STATE_LOADING, state_attrs)
decoder = Pocketsphinx(
hmm=acoustic_model,
lm=language_model,
dic=dictionary)
hass.states.async_set(OBJECT_POCKETSPHINX, STATE_DECODING, state_attrs)
# Process WAV data as a complete utterance (best performance)
with decoder.start_utterance():
decoder.process_raw(data, False, True) # full utterance
if decoder.hyp():
with decoder.end_utterance():
decoded_phrase = decoder.hyp().hypstr
decoded_event.set()