def main(ARGS):
#p = pyaudio.PyAudio()
#play_wav('robot_noise.wav')
#p.terminate()
# Load DeepSpeech model
if os.path.isdir(ARGS.model):
model_dir = ARGS.model
ARGS.model = os.path.join(model_dir, 'output_graph.pb')
ARGS.scorer = os.path.join(model_dir, ARGS.scorer)
print(cf.bold_coral('Initializing model...'))
logging.info("ARGS.model: %s", ARGS.model)
model = deepspeech.Model(ARGS.model)
if ARGS.scorer:
logging.info("ARGS.scorer: %s", ARGS.scorer)
model.enableExternalScorer(ARGS.scorer)
# Start audio with VAD
vad_audio = VADAudio(aggressiveness=ARGS.vad_aggressiveness,
device=ARGS.device,
input_rate=ARGS.rate)
print(cf.bold_coral("Listening (ctrl-C to exit)..."))
frames = vad_audio.vad_collector()
# Stream from microphone to DeepSpeech using VAD
spinner = None
if not ARGS.nospinner:
spinner = Halo(spinner='line')
stream_context = model.createStream()
wav_data = bytearray()
for frame in frames:
if frame is not None:
if spinner: spinner.start()
logging.debug("streaming frame")
stream_context.feedAudioContent(np.frombuffer(frame, np.int16))
#model.feedAudioContent(stream_context, np.frombuffer(frame, np.int16))
if ARGS.savewav: wav_data.extend(frame)
else:
if spinner: spinner.stop()
logging.debug("end utterence")
if ARGS.savewav:
vad_audio.write_wav(os.path.join(ARGS.savewav, datetime.now().strftime("savewav_%Y-%m-%d_%H-%M-%S_%f.wav")), wav_data)
wav_data = bytearray()
text = stream_context.finishStream()
#text = model.finishStream(stream_context)
check_input(text, vad_audio)
#print(cf.slateGray("Recognized: {0}".format(cf.bold_white(text))))
stream_context = model.createStream()
def show_art():
mt_zeitmore = "ATOP MOUNT ZEITMORE"
banner = "#" * 20
print(colorful.bold_coral(mt_zeitmore))
print(colorful.coral(banner))
print(" " * 20)
mt_zeit_art00=art.text2art("Atop")
mt_zeit_art01=art.text2art("Mount")
mt_zeit_art02=art.text2art("Zeitmore")
print(colorful.coral(mt_zeit_art00))
print(colorful.coral(mt_zeit_art01))
print(colorful.coral(mt_zeit_art02))
def check_input(input_text, vad_audio):
vad_audio.pause()
if input_text.lower() == 'stop':
exit_app()
#elif input_text.lower().startswith('make a robot noise'):
# play_wav('robot_noise.wav')
elif input_text.lower().startswith('tell me about'):
read_wikipedia(input_text)
elif input_text.lower().startswith('pause'):
t = 20
echo_line(f'Pausing for {t} seconds', False)
time.sleep(20)
print(cf.bold_coral("Listening (ctrl-C to exit)..."))
else:
echo_line(input_text)
vad_audio.restart()
return
def main(ARGS):
#p = pyaudio.PyAudio()
play_wav('robot_noise.wav')
#p.terminate()
# Load DeepSpeech model
if os.path.isdir(ARGS.model):
model_dir = ARGS.model
ARGS.model = os.path.join(model_dir, 'output_graph.pbmm')
if not Path(ARGS.model).is_file():
ARGS.model = os.path.join(model_dir, 'output_graph.pb')
ARGS.alphabet = os.path.join(
model_dir, ARGS.alphabet if ARGS.alphabet else 'alphabet.txt')
ARGS.lm = os.path.join(model_dir, ARGS.lm)
ARGS.trie = os.path.join(model_dir, ARGS.trie)
print(cf.bold_coral('Initializing model...'))
logging.info("ARGS.model: %s", ARGS.model)
logging.info("ARGS.alphabet: %s", ARGS.alphabet)
logging.info("ARGS.beam_width: %s", ARGS.beam_width)
model = deepspeech.Model(ARGS.model, ARGS.n_features, ARGS.n_context,
ARGS.alphabet, ARGS.beam_width)
if ARGS.lm and ARGS.trie:
logging.info("ARGS.lm: %s", ARGS.lm)
logging.info("ARGS.trie: %s", ARGS.trie)
logging.info("ARGS.lm_alpha: %s", ARGS.lm_alpha)
logging.info("ARGS.lm_beta: %s", ARGS.lm_beta)
model.enableDecoderWithLM(ARGS.alphabet, ARGS.lm, ARGS.trie,
ARGS.lm_alpha, ARGS.lm_beta)
# Start audio with VAD
vad_audio = VADAudio(aggressiveness=ARGS.vad_aggressiveness,
device=ARGS.device,
input_rate=ARGS.rate)
print(cf.bold_coral("Listening (ctrl-C to exit)..."))
frames = vad_audio.vad_collector()
# Stream from microphone to DeepSpeech using VAD
spinner = None
if not ARGS.nospinner: spinner = Halo(spinner='line')
stream_context = model.setupStream()
wav_data = bytearray()
for frame in frames:
if frame is not None:
if spinner: spinner.start()
logging.debug("streaming frame")
model.feedAudioContent(stream_context,
np.frombuffer(frame, np.int16))
if ARGS.savewav: wav_data.extend(frame)
else:
if spinner: spinner.stop()
logging.debug("end utterence")
if ARGS.savewav:
vad_audio.write_wav(
os.path.join(
ARGS.savewav,
datetime.now().strftime(
"savewav_%Y-%m-%d_%H-%M-%S_%f.wav")), wav_data)
wav_data = bytearray()
text = model.finishStream(stream_context)
check_input(text, vad_audio)
#print(cf.slateGray("Recognized: {0}".format(cf.bold_white(text))))
stream_context = model.setupStream()