class KaldiWWSpotter(EventEmitter):
_default_models = ["/opt/kaldi/model/kaldi-generic-en-tdnn_250",
"/opt/kaldi/model/kaldi-generic-de-tdnn_250"]
def __init__(self, source=None, volume=None, aggressiveness=None,
model_dir=None, lang=None, config=CONFIG):
EventEmitter.__init__(self)
self.config = config
# ensure default values
for k in CONFIG["listener"]:
if k not in self.config["listener"]:
self.config["listener"][k] = CONFIG["listener"][k]
volume = volume or self.config["listener"]["default_volume"]
aggressiveness = aggressiveness or self.config["listener"][
"default_aggressiveness"]
model_dir = model_dir or self.config["listener"]["default_model_dir"]
self.lang = lang or self.config["lang"]
if "-" in self.lang:
self.lang = self.lang.split("-")[0]
if "{lang}" in model_dir:
model_dir = model_dir.format(lang=self.lang)
if not isfile(model_dir):
if model_dir in self._default_models:
logging.error("you need to install the package: "
"kaldi-chain-zamia-speech-{lang}".format(
lang=self.lang))
raise ModelNotFound
self.rec = PulseRecorder(source_name=source, volume=volume)
self.vad = VAD(aggressiveness=aggressiveness)
logging.info("Loading model from %s ..." % model_dir)
self.asr = ASR(engine=ASR_ENGINE_NNET3, model_dir=model_dir,
kaldi_beam=self.config["listener"]["default_beam"],
kaldi_acoustic_scale=self.config["listener"][
"default_acoustic_scale"],
kaldi_frame_subsampling_factor=self.config["listener"][
"default_frame_subsampling_factor"])
self._hotwords = dict(self.config["hotwords"])
def add_hotword(self, name, config=None):
config = config or {"transcriptions": [name], "intent": name}
self._hotwords[name] = config
def remove_hotword(self, name):
if name in self._hotwords.keys():
self._hotwords.pop(name)
@property
def hotwords(self):
return self._hotwords
def _detection_event(self, message_type, message_data):
serialized_message = json.dumps(
{"type": message_type, "data": message_data})
logging.debug(serialized_message)
self.emit(message_type, serialized_message)
def _process_transcription(self, user_utt, confidence=0.99):
for hotw in self.hotwords:
if not self.hotwords[hotw].get("active"):
continue
rule = self.hotwords[hotw].get("rule", "sensitivity")
s = 1 - self.hotwords[hotw].get("sensitivity", 0.2)
confidence = (confidence + s) / 2
for w in self.hotwords[hotw]["transcriptions"]:
if (w in user_utt and rule == "in") or \
(user_utt.startswith(w) and rule == "start") or \
(user_utt.endswith(w) and rule == "end") or \
(fuzzy_match(w,
user_utt) >= s and rule == "sensitivity") or \
(w == user_utt and rule == "equal"):
yield {"hotword": hotw,
"utterance": user_utt,
"confidence": confidence,
"intent": self.hotwords[hotw]["intent"]}
def _detect_ww(self, user_utt, confidence=0.99):
for hw_data in self._process_transcription(user_utt, confidence):
sound = self.hotwords[hw_data["hotword"]].get("sound")
if sound and isfile(sound):
play_sound(sound)
self._detection_event("hotword", hw_data)
def decode_wav_file(self, wav_file):
user_utt, confidence = self.asr.decode_wav_file(wav_file)
confidence = 1 - exp(-1 * confidence)
return user_utt, confidence
def wav_file_hotwords(self, wav_file):
user_utt, confidence = self.decode_wav_file(wav_file)
return list(self._process_transcription(user_utt, confidence))
def run(self):
self.rec.start_recording()
logging.info("Listening")
while True:
samples = self.rec.get_samples()
audio, finalize = self.vad.process_audio(samples)
if not audio:
continue
logging.debug('decoding audio len=%d finalize=%s audio=%s' % (
len(audio), repr(finalize), audio[0].__class__))
user_utt, confidence = self.asr.decode(audio, finalize,
stream_id="mic")
confidence = 1 - exp(-1 * confidence)
if finalize and user_utt:
self._detection_event("transcription",
{"utterance": user_utt,
"confidence": confidence})
self._detect_ww(user_utt, confidence)
def test_asr_kaldi_wavefile(self):
asr = ASR(engine=ASR_ENGINE_NNET3)
s, l = asr.decode_wav_file(TEST_WAVE_EN)
self.assertEqual(s.strip(), TEST_WAVE_EN_TS)
#!/usr/bin/env python3
from nltools.asr import ASR
MODELDIR = '/opt/kaldi/model/kaldi-generic-en-tdnn_250'
WAVFILE = 'dw961.wav'
asr = ASR(model_dir=MODELDIR)
s, l = asr.decode_wav_file(WAVFILE)
print("Decoded %s: %s" % (WAVFILE, s))
def test_asr_pocketsphinx_wavefile(self):
asr = ASR(engine=ASR_ENGINE_POCKETSPHINX,
model_dir=POCKETSPHINX_MODELDIR,
model_name=POCKETSPHINX_MODELNAME)
s, l = asr.decode_wav_file(TEST_WAVE_EN)
self.assertEqual(s.strip(), TEST_WAVE_EN_TS_PS)
