def on_message(client, userdata, msg):
self._logger.info("从服务器监听到MQTT消息： " + msg.topic + " message" + str(msg.payload))
logger.send_conversation_log(iot_client=self.mic.iot_client, mic='server',
content=msg.payload.decode('utf8'),
speaker='user')
if 'mic_text_from_server' in msg.topic:
self.send_to_brain(msg.payload.decode('utf8')) # 订阅到的消息发送到大脑处理
def active_listen(self):
"""
主动监听
:return:
"""
input_content = input("我: ")
logger.send_conversation_log(iot_client=self.iot_client,
mic=mic_name,
content='我：' + input_content,
speaker='user')
return input_content
def say(self, phrase):
"""
输出内容
:param phrase:
:return:
"""
say_str = profile.myname + ": " + phrase
print(say_str)
logger.send_conversation_log(iot_client=self.iot_client,
mic=mic_name,
content=say_str,
speaker='device')
def say(self, phrase):
"""
TTS输出内容
:param phrase:
:return:
"""
logger.send_conversation_log(self.iot_client, mic_name, '(TTS)' + phrase, speaker='device')
is_tts_cached, cache_file_path = self._tts_engine.get_speech_cache(phrase, fetch_wave_on_no_cache=True)
if is_tts_cached:
self._logger.info('Saying %s', phrase)
self.play(cache_file_path)
else:
print("%s,%s" % profile.myname, phrase)
def say(self, phrase):
"""
输出内容
:param phrase:
:return:
"""
input_content = phrase
logger.send_conversation_log(iot_client=self.iot_client,
mic=mic_name,
content=input_content,
speaker='device')
self._logger.info(input_content)
self._logger.info('send mic server message.')
if self._peer_mic is not None:
threading.Thread(target=self._peer_mic.say,
args=(phrase, )).start()
def active_listen(self):
"""
持续录音，直到声音停止1秒，或者达到录音超时时间 12s
:return:
"""
threshold = None
print('Listen Instructions...')
chunk = 1024
wave_format = pyaudio.paInt16
channels = 1
rate = 16000
record_seconds = 12 # 录音持续时间
stream = self._audio.open(format=wave_format,
channels=channels,
input_device_index=1,
rate=rate,
input=True,
frames_per_buffer=chunk)
self._logger.info("active listen recording")
frames = []
# stores the lastN score values
last_n = [i for i in range(20)]
low_volume_count = 0 # 记录沉默持续时间（较安静时）
for i in range(0, int(rate / chunk * record_seconds)):
data = stream.read(chunk)
frames.append(data) # 添加到音频流
last_n.pop(0) # save this data point as a score
last_n.append(self._get_score(data))
if threshold is None or max(last_n) > threshold: # 寻找最大值
threshold = max(last_n)
average = sum(last_n) / len(last_n) # 当前循环中声音评分的平均值
# self._logger.info('average:%s, threshold:%s', average, threshold)
# 采样声音的最大值突破100时，开始检测
if threshold > 100:
if average < 80:
low_volume_count = low_volume_count + 1
if average > 100: # 如果有声音，就清空周期计数
low_volume_count = 0
if low_volume_count >= 20: # 等待周期数
break
self.play(WAVE_DONG)
self._logger.info("active listen done recording")
stream.stop_stream()
stream.close()
wf = wave.open(CACHE_WAVE_RECORDED, 'wb')
wf.setnchannels(channels)
wf.setsampwidth(self._audio.get_sample_size(wave_format))
wf.setframerate(rate)
wf.writeframes(b''.join(frames))
wf.close()
asr_result = self.listen(path.CACHE_WAVE_RECORDED)
logger.send_conversation_log(self.iot_client, mic_name, '(ASR)'+asr_result, speaker='user')
return asr_result
