def main():
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'en-US' # a BCP-47 language tag
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'C:\Hackathon\\codeoverflow-08-10e832d41721.json'
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_print_loop(responses)
def start(self,window):
self.window = window
language_code = 'ko-KR' # a BCP-47 language tag
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
#무한으로 하려면 'while True :'
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
self.listen_print_loop(responses)
def google_stt_streaming(self, socket_action):
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'ko-KR' # a BCP-47 language tag
# for content in comuni.get_data(client_record):
# print("Type >> {}".format(type(content)))
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(
config=config, interim_results=True)
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in socket_action.get_data())
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
stt_text = self.listen_print_loop(responses)
return stt_text
def main():
# language_code = 'en-US' # a BCP-47 language tag te-IN en-IN
language_code = 'en-IN' # a BCP-47 language tag te-IN en-IN
credentials = service_account.Credentials. from_service_account_file('googleKeys.json')
client = speech.SpeechClient(credentials=credentials)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
print("inside stream")
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_print_loop(responses)
def main():
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'zh' # a BCP-47 language tag 'zh' 'ja-JP'
passage = passages.chinese
passageIndex = 0
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
finals = read(responses, passage, 'zh')
def main():
language_code = 'zh-TW'
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(
config=config,
#自己測試
# single_utterance=True,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_print_loop(responses)
def main(category_index):
category_index = [val['name'] for val in category_index.values()]
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'en-US' # a BCP-47 language tag
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(config=config,
interim_results=True)
print("##########Begining Stream##########")
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
return listen_print_loop(responses, stream, category_index)
def __init__(self, callback):
Thread.__init__(self)
self.callback = callback
self.client = speech.SpeechClient()
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'en-US' # a BCP-47 language tag
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
self.streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
while True:
print("Listen again?")
self.listen()
def main():
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'en-US' # a BCP-47 language tag
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code,
enable_automatic_punctuation=True)
streaming_config = types.StreamingRecognitionConfig(config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_print_loop(responses)
def __init__(self,
credentials=None,
RATE=16000,
CHUNK=1024,
language_code='en-US'):
self.RATE = RATE
self.CHUNK = CHUNK
self.language_code = language_code
if (credentials != None):
credentials = service_account.Credentials.from_service_account_file(
credentials)
self.client = speech.SpeechClient(credentials=credentials)
#TODO: speech_contexts -> https://cloud.google.com/speech-to-text/docs/basics#phrase-hints
self.config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code,
model='command_and_search',
enable_automatic_punctuation=True)
self.streaming_config = types.StreamingRecognitionConfig(
config=self.config, interim_results=True, single_utterance=True)
def __init__(self, engine, args, loop):
Thread.__init__(self)
self.client = speech.SpeechClient()
self.args = args
self.engine = engine
self.stop_recognition = False
self.loop = loop
self.role = "UNKOWN"
self.crash = False
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'en-US' # a BCP-47 language tag
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
self.streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
def main():
procs = []
#화면에 화자가 있고 출력할 문자열이 있는지 확인하는 Queue
q = Queue()
#작은 말풍선을 출력할지 큰 말풍선을 출력할지 결정하는 Queue
bubble_q = Queue()
#멀티 프로세싱으로 frame출력하는 프로그램 start
proc = Process(target=visualize_frame, args=(
q,
bubble_q,
))
procs.append(proc)
proc.start()
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'ko-KR' # a BCP-47 language tag
#마이크로 들어오는 speech를 google cloud platform의 speech to text api 호출하여 말풍선에 문자열 저장
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_print_loop(responses, q, bubble_q)
def transcribe_streaming(stream_file):
"""Streams transcription of the given audio file."""
import io
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
with io.open(stream_file, 'rb') as audio_file:
content = audio_file.read()
# In practice, stream should be a generator yielding chunks of audio data.
stream = [content]
requests = (types.StreamingRecognizeRequest(audio_content=chunk)
for chunk in stream)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=16000,
language_code='es-es')
streaming_config = types.StreamingRecognitionConfig(config=config)
# streaming_recognize returns a generator.
responses = client.streaming_recognize(streaming_config, requests)
for response in responses:
# Once the transcription has settled, the first result will contain the
# is_final result. The other results will be for subsequent portions of
# the audio.
for result in response.results:
print('Finished: {}'.format(result.is_final))
print('Stability: {}'.format(result.stability))
alternatives = result.alternatives
# The alternatives are ordered from most likely to least.
for alternative in alternatives:
print('Confidence: {}'.format(alternative.confidence))
print(u'Transcript: {}'.format(alternative.transcript))
def stream_speach(self):
try:
if get_env_value('DEVICE') == 'PI':
from lib.PiControls import PiControls
pi = PiControls()
pi.flash_blue()
else:
print("sorry! can't blink blue you don't have pi")
print('live speech recognition started')
print(threading.enumerate())
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages..
language_code = 'en-US' # a BCP-47 language tag
credentials = service_account.Credentials.from_service_account_file(
'google-cloud.json')
client = speech.SpeechClient(credentials=credentials)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
self.listen_print_loop(responses)
except:
print('exception occured')
self.stream_speach()
def main():
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
# 언어 설정코드 변경 가능
language_code = 'ko-KR' # a BCP-47 language tag
# 언어 데이터셋을 Snips NLU에 넣어주기
with io.open("./lights_dataset_train_ko.json", encoding="utf8") as f:
sample_dataset = json.load(f)
nlu_engine = SnipsNLUEngine(config=CONFIG_KO)
nlu_engine = nlu_engine.fit(sample_dataset)
print("성공")
# 전처리 종료
# 음성인식 시작
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
# 무한루프 시작
listen_print_loop(responses, nlu_engine)
def main():
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
# this code comes from Google Cloud's Speech to Text API!
# Check out the links in your handout. Comments are ours.
language_code = 'en-US' # a BCP-47 language tag
intro()
#set up a client
#make sure GCP is aware of the encoding, rate
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
#our example uses streamingrecognition - most likely what you will want to use.
#check out the simpler cases of asychronous recognition too!
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
#this section is where the action happens:
#a microphone stream is set up, requests are generated based on
#how the audiofile is chunked, and they are sent to GCP using
#the streaming_recognize() function for analysis. responses
#contains the info you get back from the API.
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
#### Save audio recording ####
######
# Now, put the transcription responses to use.
listen_print_loop(responses, stream)
def sub_main(profanityFilterBool):
"""
*** Code taken from Google Cloud Speech to text documentation ***
Turns on the profanity filter so bad words are censored and not printed
"""
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'en-US' # a BCP-47 language tag
sp_c_cico = {"phrases": ["cico"],"boost": 20} #speech_contexts_cico
#sp_c_kiko = {"phrases": ["Kiko"],"boost": 0}#speech_contexts_kiko
speech_contexts = [sp_c_cico]
client = speech.SpeechClient()
#print(help(types.RecognitionConfig))
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code,
enable_automatic_punctuation=True,
speech_contexts=speech_contexts)
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
solution = returnResponseString(responses) #solution is the result
append_to_file("log.txt",str(solution))
return solution
def __init__(self, parent=None):
super(VoiceRecognizer, self).__init__(parent)
language_code = 'en-US' # a BCP-47 language tag
self.confirm_signal = pyqtSignal()
self.client = speech.SpeechClient()
self.config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code,
speech_contexts=[SPEECH_CONTEXT])
self.streaming_config = types.StreamingRecognitionConfig(
config=self.config, interim_results=True)
self.home_pub = rospy.Publisher('/dvrk/console/home',
Empty,
latch=True,
queue_size=1) # HOME command publisher
self.poff_pub = rospy.Publisher(
'/dvrk/console/power_off', Empty, latch=True,
queue_size=1) # POWER OFF command publisher
self.pon_pub = rospy.Publisher(
'/dvrk/console/power_on', Empty, latch=True,
queue_size=1) # POWER ON command publisher
self.tenable_pub = rospy.Publisher(
'/dvrk/console/teleop/enable', Bool, latch=True,
queue_size=1) # TELEOP ENABLE command publisher
self.tscale = rospy.Publisher(
'/dvrk/console/teleop/scale', Float32, latch=True,
queue_size=1) # TELEOP SCALE command publisher
self.tset_scale = rospy.Publisher(
'/dvrk/console/teleop/set_scale',
Float32,
latch=True,
queue_size=1) # TELEOP SET SCALE command publisher
self.exit = False
def main():
"""The main event."""
GPIOSetup.setup()
language_code = 'en-US' # a BCP-47 language tag
dir_path = os.getcwd()
gc_key_file = 'gc_private_key.json'
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = '{}/{}'.format(
dir_path, gc_key_file)
# Speech-To-Text and Text-To-Speech clients config
tts_client = texttospeech.TextToSpeechClient()
stt_client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(config=config,
interim_results=True)
# Wait for an internet connection.
wait_internet_conn()
stream_audio(tts_client, stt_client, streaming_config)
main()
def main():
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'en-US' # a BCP-47 language tag
# language_code = 'ru-RU' # a BCP-47 language tag
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = "C:\\Users\\RealityShift24\\TooLazyForPPTX-3785c34de4cc.json"
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_print_loop(responses)
def setup_and_run():
"""
Sets up the speech reognition API calls and runs
"""
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'en-US' # a BCP-47 language tag
# Set up the client and configuration.
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(config=config,
interim_results=True)
os.system('omxplayer speech_recogniton/Twinkle-sound-effect.mp3')
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
return listen_and_translate(responses)
def main():
rospy.init_node('Speech_node')
language_code = 'en-US'
if_restart = True
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code,
speech_contexts = [{"phrases":["attach","move","make","get","grab","take","pick","select","put","object","item","objects",
"items","one","ones","cube","cubes","blocks","block","guys","guy","here","there","place",
"location","position","yellow","green","blue","this","that","these","those","targets","target",
"it", "transport"]}])
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
while if_restart:
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
if_restart = listen_print_loop(responses)
def __init__(self):
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'ko-KR' # a BCP-47 language tag
os.environ[
"GOOGLE_APPLICATION_CREDENTIALS"] = "/home/pi/posvacpjt-251711-c6df951f8f17.json"
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(
config=config, interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
self.listen_print_loop(responses)
def main():
rospy.init_node(name='transcription_node')
rospy.loginfo('Registering as publisher for /mpstate/transcription')
trans_pub = rospy.Publisher('mpstate/transcription', TranscriptionResult, queue_size=10, latch=True)
# Some short snippets which are likely to be told to Marco.
marco_phrases = ['hey marco', 'where is the', 'go get the']
language_code = 'en-US' # a BCP-47 language tag
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code,
speech_contexts=[{"phrases": marco_phrases}]
)
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_publish_loop(responses, trans_pub)
rospy.spin()
return
def main(sample_rate):
stopFlag = False
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'ko-KR' # a BCP-47 language tag
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=sample_rate,
language_code=language_code,
max_alternatives=1)
streaming_config = types.StreamingRecognitionConfig(config=config,
interim_results=True)
with MicrophoneStream(sample_rate, int(sample_rate / 10)) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
stopFlag = listen_print_loop(responses)
def start():
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=LANGUAGE)
streaming_config = types.StreamingRecognitionConfig(config=config,
single_utterance=True,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config,
requests,
timeout=TIMEOUT)
# Now, put the transcription responses to use.
command = get_command(responses)
print(command)
return (command)
def __init__(self):
global SpeechClient, types, enums, Credentials
from google.cloud.speech import SpeechClient, types, enums
from google.oauth2.service_account import Credentials
super(GoogleCloudStreamingSTT, self).__init__()
# override language with module specific language selection
self.language = self.config.get('lang') or self.lang
credentials = Credentials.from_service_account_info(
self.credential.get('json'))
self.client = SpeechClient(credentials=credentials)
recognition_config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=16000,
language_code=self.language,
model='command_and_search',
max_alternatives=1,
)
self.streaming_config = types.StreamingRecognitionConfig(
config=recognition_config,
interim_results=True,
single_utterance=True,
)
def main():
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'ko-KR' # a BCP-47 language tag en-US
cmd_add()
credential = "/home/pi/Downloads/logical-carver-277605-91fe57214bc9.json"
client = speech.SpeechClient(credentials="/home/pi/Downloads/logical-carver-277605-91fe57214bc9.json")
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(
config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_print_loop(responses)
def transcribe_streaming():
"""Streams transcription of the given audio file."""
client = speech.SpeechClient()
CHUNK = 1024 #measured in bytes
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000 #common sampling frequency
RECORD_SECONDS=5
file="test.rav"
p = pyaudio.PyAudio()
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
audioStream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
content=audioStream.read(CHUNK)
# In practice, stream should be a generator yielding chunks of audio data.
stream = [content]
requests = (types.StreamingRecognizeRequest(audio_content=chunk)
for chunk in stream)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=16000,
language_code='en-US')
streaming_config = types.StreamingRecognitionConfig(config=config)
# streaming_recognize returns a generator.
responses = client.streaming_recognize(streaming_config, requests)
for response in responses:
for result in response.results:
print('Finished: {}'.format(result.is_final))
print('Stability: {}'.format(result.stability))
alternatives = result.alternatives
for alternative in alternatives:
print('Confidence: {}'.format(alternative.confidence))
print('Transcript: {}'.format(alternative.transcript))
def main(projectID):
# See http://g.co/cloud/speech/docs/languages
# for a list of supported languages.
language_code = 'en-US' # a BCP-47 language tag
client = speech.SpeechClient()
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code)
streaming_config = types.StreamingRecognitionConfig(config=config,
interim_results=True)
print(
"Begin speaking to see deidentified text (stream only lasts for 65 seconds)..."
)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (types.StreamingRecognizeRequest(audio_content=content)
for content in audio_generator)
responses = client.streaming_recognize(streaming_config, requests)
# Now, put the transcription responses to use.
listen_print_dlp_loop(responses, projectID)
