def request_stream(stop_audio, channels=CHANNELS, rate=RATE, chunk=CHUNK):
"""Yields `StreamingRecognizeRequest`s constructed from a recording audio
stream.
Args:
stop_audio: A threading.Event object stops the recording when set.
channels: How many audio channels to record.
rate: The sampling rate.
chunk: Buffer audio into chunks of this size before sending to the api.
"""
# The initial request must contain metadata about the stream, so the
# server knows how to interpret it.
recognition_config = cloud_speech.RecognitionConfig(encoding='LINEAR16',
sample_rate=rate)
streaming_config = cloud_speech.StreamingRecognitionConfig(
config=recognition_config,
# Note that setting interim_results to True means that you'll likely
# get multiple results for the same bit of audio, as the system
# re-interprets audio in the context of subsequent audio. However, this
# will give us quick results without having to tell the server when to
# finalize a piece of audio.
interim_results=True,
single_utterance=False)
yield cloud_speech.StreamingRecognizeRequest(
streaming_config=streaming_config)
with record_audio(channels, rate, chunk) as audio_stream:
while not stop_audio.is_set():
data = audio_stream.read(chunk)
if not data:
raise StopIteration()
# Subsequent requests can all just have the content
yield cloud_speech.StreamingRecognizeRequest(audio_content=data)
def request_stream(data_stream, rate, interim_results=True):
"""Yields `StreamingRecognizeRequest`s constructed from a recording audio
stream.
Args:
data_stream: A generator that yields raw audio data to send.
rate: The sampling rate in hertz.
interim_results: Whether to return intermediate results, before the
transcription is finalized.
"""
# The initial request must contain metadata about the stream, so the
# server knows how to interpret it.
recognition_config = cloud_speech.RecognitionConfig(
# There are a bunch of config options you can specify. See
# https://goo.gl/KPZn97 for the full list.
encoding='LINEAR16', # raw 16-bit signed LE samples
sample_rate=rate, # the rate in hertz
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code='en-US', # a BCP-47 language tag
)
streaming_config = cloud_speech.StreamingRecognitionConfig(
interim_results=interim_results,
config=recognition_config,
)
yield cloud_speech.StreamingRecognizeRequest(
streaming_config=streaming_config)
for data in data_stream:
# Subsequent requests can all just have the content
yield cloud_speech.StreamingRecognizeRequest(audio_content=data)
def request_stream(channels=CHANNELS, rate=RATE, chunk=CHUNK):
global flag_RecogEnd
global LANG_CODE
recognition_config = cloud_speech.RecognitionConfig(
encoding='LINEAR16', # raw 16-bit signed LE samples
sample_rate=rate, # the rate in hertz
language_code=LANG_CODE, # a BCP-47 language tag
)
streaming_config = cloud_speech.StreamingRecognitionConfig(
config=recognition_config, interim_results=True, single_utterance=True)
yield cloud_speech.StreamingRecognizeRequest(
streaming_config=streaming_config)
while True:
time.sleep(SLEEP_SEC)
if flag_RecogEnd:
return
# バッファにデータが溜まったら，データ送信
if len(frames) > 0:
data_1frame = frames.pop(0)
data_l2s = b''.join(map(str, data_1frame))
wf.writeframes(data_l2s) # waveファイルに書き込み
yield cloud_speech.StreamingRecognizeRequest(
audio_content=data_l2s) # google ASR
def request_stream(self):
recognition_config = cloud_speech_pb2.RecognitionConfig(
encoding=self.audio_encoding,
sample_rate=self.sampling_rate,
language_code=self.lang_code,
max_alternatives=1,
)
streaming_config = cloud_speech_pb2.StreamingRecognitionConfig(
config=recognition_config,
interim_results=True,
single_utterance=True)
yield cloud_speech_pb2.StreamingRecognizeRequest(streaming_config=streaming_config)
silent_cnt=0
while True:
#print(sys._getframe().f_code.co_name,"1")
time.sleep(self.frame_seconds / 4)
#print("self.should_finish_stream", self.should_finish_stream, "len", len(frames))
if self.should_finish_stream:
return
if len(self.frames) > 0:
#音量チェック 連続して無音区間が続いたら処理を抜ける。
data = self.frames[0]
rms = audioop.rms(data, 2)
decibel = 20 * math.log10(rms) if rms > 0 else 0
if decibel < self.silent_decibel:
silent_cnt = silent_cnt+1
else :
silent_cnt = 0
if silent_cnt > self.max_silent_cnt :
print(sys._getframe().f_code.co_name, "find silent frames return")
return
#print("request_stream2 3 framen len=", len(self.frames))
if len(self.frames) > 0:
#print(sys._getframe().f_code.co_name,"2", "framelen=",len(self.frames))
#self.frames.pop(0)
yield cloud_speech_pb2.StreamingRecognizeRequest(audio_content=self.frames.pop(0))
def request_stream(stop_audio, channels=CHANNELS, rate=RATE, chunk=CHUNK):
"""Yields `StreamingRecognizeRequest`s constructed from a recording audio
stream.
Args:
stop_audio: A threading.Event object stops the recording when set.
channels: How many audio channels to record.
rate: The sampling rate in hertz.
chunk: Buffer audio into chunks of this size before sending to the api.
"""
# The initial request must contain metadata about the stream, so the
# server knows how to interpret it.
recognition_config = cloud_speech.RecognitionConfig(
# There are a bunch of config options you can specify. See
# https://goo.gl/A6xv5G for the full list.
encoding='LINEAR16', # raw 16-bit signed LE samples
sample_rate=rate, # the rate in hertz
# See
# https://g.co/cloud/speech/docs/best-practices#language_support
# for a list of supported languages.
language_code='en-US', # a BCP-47 language tag
)
streaming_config = cloud_speech.StreamingRecognitionConfig(
config=recognition_config,
# Note that setting interim_results to True means that you'll likely
# get multiple results for the same bit of audio, as the system
# re-interprets audio in the context of subsequent audio. However, this
# will give us quick results without having to tell the server when to
# finalize a piece of audio.
interim_results=True,
single_utterance=True)
yield cloud_speech.StreamingRecognizeRequest(
streaming_config=streaming_config)
with record_audio(channels, rate, chunk) as audio_stream:
while not stop_audio.is_set():
data = audio_stream.read(chunk)
if not data:
raise StopIteration()
# Subsequent requests can all just have the content
yield cloud_speech.StreamingRecognizeRequest(audio_content=data)
def request_stream(self, stop_audio, channels=CHANNELS, rate=RATE, chunk=CHUNK):
recognition_config = cloud_speech.RecognitionConfig(
encoding='LINEAR16',
sample_rate=rate,
language_code='ko-KR',
)
streaming_config = cloud_speech.StreamingRecognitionConfig(
config=recognition_config,
interim_results=True,
single_utterance=False
)
yield cloud_speech.StreamingRecognizeRequest(streaming_config=streaming_config)
with self.record_audio(channels, rate, chunk) as audio_stream:
while not rospy.is_shutdown():
data = audio_stream.read(chunk)
if not data:
raise StopIteration()
yield cloud_speech.StreamingRecognizeRequest(audio_content=data)
def request_stream(): global queue global recognition_result global should_finish_stream recognition_config = cloud_speech_pb2.RecognitionConfig( encoding=args.audio_encoding, sample_rate=args.sampling_rate, language_code=args.lang_code, max_alternatives=1, ) streaming_config = cloud_speech_pb2.StreamingRecognitionConfig( config=recognition_config, interim_results=True, single_utterance=True ) yield cloud_speech_pb2.StreamingRecognizeRequest(streaming_config=streaming_config) frame_length = int(args.sampling_rate * args.frame_seconds) frame = b"" while True: if should_finish_stream: return try: data = queue.get(False) frame += data except Exception as e: if len(frame) > frame_length: rms = audioop.rms(frame, 2) decibel = 20 * math.log10(rms) if rms > 0 else 0 if decibel < args.silent_decibel: recognition_result.success = False return yield cloud_speech_pb2.StreamingRecognizeRequest(audio_content=frame) frame = b"" time.sleep(args.frame_seconds / 4)
def request_stream():
recognition_config = cloud_speech_pb2.RecognitionConfig(
encoding=args.audio_encoding,
sample_rate=args.sampling_rate,
language_code=args.lang_code,
max_alternatives=1,
)
streaming_config = cloud_speech_pb2.StreamingRecognitionConfig(
config=recognition_config, interim_results=True, single_utterance=True)
yield cloud_speech_pb2.StreamingRecognizeRequest(
streaming_config=streaming_config)
while True:
time.sleep(args.frame_seconds / 4)
if should_finish_stream:
return
if len(frames) > 0:
yield cloud_speech_pb2.StreamingRecognizeRequest(
audio_content=frames.pop(0))
def g_request_steam(self, data_stream, rate, init_buff=None):
r_config = cloud_speech.RecognitionConfig(
encoding='LINEAR16',
sample_rate=rate,
language_code='en-US',
speech_context= cloud_speech.SpeechContext(
phrases=["mirror", "add", "item", "help", "close", "clothes", "tag", "tags", "find", "number 1", "wear", "start", "stop", "stylist", "wardrobe", "exit", "1", "2", "3", "4", "5", "6", "7", "8"]
)
)
r_stream_config = cloud_speech.StreamingRecognitionConfig(
config=r_config,
single_utterance=False,
interim_results=False)
yield cloud_speech.StreamingRecognizeRequest(
streaming_config=r_stream_config)
if init_buff:
yield cloud_speech.StreamingRecognizeRequest(audio_content=init_buff)
for data in data_stream:
yield cloud_speech.StreamingRecognizeRequest(audio_content=data)
def request_stream(stop_audio, channels=CHANNELS, rate=RATE, chunk=CHUNK):
"""Yields `StreamingRecognizeRequest`s constructed from a recording audio
stream.
Args:
stop_audio: A threading.Event object stops the recording when set.
channels: How many audio channels to record.
rate: The sampling rate in hertz.
chunk: Buffer audio into chunks of this size before sending to the api.
"""
# The initial request must contain metadata about the stream, so the
# server knows how to interpret it.
recognition_config = cloud_speech.RecognitionConfig(
# There are a bunch of config options you can specify. See
# https://goo.gl/KPZn97 for the full list.
encoding='LINEAR16', # raw 16-bit signed LE samples
sample_rate=rate, # the rate in hertz
# See
# https://g.co/cloud/speech/docs/best-practices#language_support
# for a list of supported languages.
language_code='en-US', # a BCP-47 language tag
)
streaming_config = cloud_speech.StreamingRecognitionConfig(
config=recognition_config, )
yield cloud_speech.StreamingRecognizeRequest(
streaming_config=streaming_config)
with record_audio(channels, rate, chunk) as audio_stream:
while not stop_audio.is_set():
data = audio_stream.read(chunk)
if not data:
raise StopIteration()
# Subsequent requests can all just have the content
yield cloud_speech.StreamingRecognizeRequest(audio_content=data)
