def speech_recognize(source_audio):
"""Take source audio uri and call google-cloud-speech recognize
method. Return transcript"""
# instantiate client
from google.cloud import speech
sp_client = speech.SpeechClient()
# speech RecognitionConfig
rec_config = speech.types.RecognitionConfig(
encoding=speech.enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=_SAMPLE_RATE,
profanity_filter=True,
language_code='en-US'
# max_alternatives, # Not using but available (0-30)
# enable_word_time_offsets=TRUE, # provide time offsets
)
# speech RecognitionAudio from argument
rec_audio = speech.types.RecognitionAudio(uri=source_audio)
# recognition operation
return sp_client.recognize(rec_config, rec_audio)
def transcribe_gcs(gcs_uri):
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
import time
start = time.time()
client = speech.SpeechClient()
audio = types.RecognitionAudio(uri=gcs_uri)
config = types.RecognitionConfig(
language_code='en-US')
operation = client.long_running_recognize(config, audio)
print('Waiting for operation to complete...')
response = operation.result(timeout=9000000)
filename = gcs_uri.split('/')[3][0:-5]
with open(filename+".txt","w") as gsp:
for result in response.results:
gsp.write(result.alternatives[0].transcript)
end = time.time()
print(end-start)
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 listen_mic():
# 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)
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.
voice_command = listen_return_speech(responses)
return voice_command
def transcribe_file(speech_file):
"""Transcribe the given audio file."""
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
with open(speech_file, 'rb') as audio_file:
content = audio_file.read()
audio = types.RecognitionAudio(content=content)
config = types.RecognitionConfig(
# encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=16000,
language_code='ko-KR')
response = client.recognize(config, audio)
# Each result is for a consecutive portion of the audio. Iterate through
# them to get the transcripts for the entire audio file.
try:
return (response.results[0].alternatives[0].transcript)
except (IndexError):
return ("Out")
def mic():
language_code = "ko-KR" # a BCP-47 language tag
client = speech.SpeechClient()
config = speech.RecognitionConfig(
encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=RATE,
language_code=language_code,
)
streaming_config = speech.StreamingRecognitionConfig(config=config,
interim_results=True)
with MicrophoneStream(RATE, CHUNK) as stream:
audio_generator = stream.generator()
requests = (speech.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(sample_rate):
# 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.
listen_print_loop(responses)
def transcribe_file_with_word_time_offsets(speech_file):
'''
:param speech_file: input 할 오디오 파일 경로와 이름입니다.
:return: 사용자가 말한 단어들점의 시작점과 끝점을 포함한 리스트입니다.
'''
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
with io.open(speech_file, 'rb') as audio_file:
content = audio_file.read()
audio = types.RecognitionAudio(content=content)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=44100,
language_code='ko-KR',
enable_word_time_offsets=True)
response = client.recognize(config, audio)
word_and_startend_time = []
for result in response.results:
alternative = result.alternatives[0]
for word_info in alternative.words:
word = word_info.word
start_time = word_info.start_time
end_time = word_info.end_time
word_and_startend_time += [[
word, start_time.seconds + start_time.nanos * 1e-9,
end_time.seconds + end_time.nanos * 1e-9
]]
return word_and_startend_time
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 transcribe_file_without_word_time_offsets(speech_file, language):
"""Transcribe the given audio file synchronously and output the word time
offsets."""
#print("Start")
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
#print("checking credentials")
client = speech.SpeechClient(credentials=credentials)
#print("Checked")
with io.open(speech_file, 'rb') as audio_file:
content = audio_file.read()
#print("audio file read")
audio = types.RecognitionAudio(content=content)
#print("config start")
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
language_code=language,
enable_word_time_offsets=True,
audio_channel_count=1)
# enableSeparateRecognitionPerChannel=True)
#print("Recognizing:")
response = client.recognize(config, audio)
#print("Recognized")
#return only transcript
for result in response.results:
alternative = result.alternatives[0]
return ((alternative.transcript))
def run_quickstart():
# [START speech_quickstart]
import io
import os
# Imports the Google Cloud client library
# [START migration_import]
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
# [END migration_import]
# Instantiates a client
# [START migration_client]
client = speech.SpeechClient()
# [END migration_client]
# The name of the audio file to transcribe
file_name = os.path.join(os.path.dirname(__file__), 'resources',
'audio.raw')
# Loads the audio into memory
with io.open(file_name, 'rb') as audio_file:
content = audio_file.read()
audio = types.RecognitionAudio(content=content)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=16000,
language_code='en-US')
# Detects speech in the audio file
response = client.recognize(config, audio)
alternatives = response.results[0].alternatives
for alternative in alternatives:
print('Transcript: {}'.format(alternative.transcript))
def performWork(name):
client = speech.SpeechClient()
json_arr = []
extract(name)
real_name = name[0:len(name) - 3] + ".flac"
os.remove(name)
with io.open(real_name, 'rb') as audio_file:
content = audio_file.read()
audio = types.RecognitionAudio(content=content)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.FLAC,
sample_rate_hertz=16000,
language_code='en-US',
enable_word_time_offsets=True)
response = client.recognize(config, audio)
# Each result is for a consecutive portion of the audio. Iterate through
# them to get the transcripts for the entire audio file.
for result in response.results:
alternative = result.alternatives[0]
# The first alternative is the most likely one for this portion.
#print(u'Transcript: {}'.format(result.alternatives[0].transcript))
for word_info in alternative.words:
word = word_info.word
json_ret = {}
start_time = word_info.start_time
end_time = word_info.end_time
json_ret['word'] = word
json_ret['start_time'] = start_time.seconds
json_ret['end_time'] = end_time.seconds
json_arr.append(json_ret)
os.remove(real_name)
reduce((real_name, json_arr))
def transcribe_gcs_with_word_time_offsets(gcs_uri):
"""Transcribe the given audio file asynchronously and output the word time
offsets."""
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
audio = types.RecognitionAudio(uri=gcs_uri)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=16000,
language_code='en-US',
enable_word_time_offsets=True,
model='video')
operation = client.long_running_recognize(config, audio)
result = operation.result(timeout=200)
result_time_offsets = []
for result in result.results:
alternative = result.alternatives[0]
# print(u'Transcript: {}'.format(alternative.transcript))
# print('Confidence: {}'.format(alternative.confidence))
for word_info in alternative.words:
word = word_info.word
start_time = word_info.start_time
start = start_time.seconds + start_time.nanos * 1e-9
end_time = word_info.end_time
end = end_time.seconds + end_time.nanos * 1e-9
result_time_offsets.append((word, start, end))
with open("word-time-offsets", "w") as the_file:
csv.register_dialect("custom",
delimiter=" ",
skipinitialspace=True)
writer = csv.writer(the_file, dialect="custom")
for tup in result_time_offsets:
writer.writerow(tup)
def transcribe_gcs(gcs_uri):
"""Asynchronously transcribes the audio file specified by the gcs_uri."""
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
audio = types.RecognitionAudio(uri=gcs_uri)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.FLAC,
sample_rate_hertz=16000,
language_code='en-US',
enable_word_time_offsets=True)
operation = client.long_running_recognize(config, audio)
retry_count = 100
while retry_count > 0 and not operation.done():
retry_count -= 1
time.sleep(2)
if not operation.done():
print('Operation not complete and retry limit reached.')
return
alternatives = operation.result().results[0].alternatives
for alternative in alternatives:
print('Transcript: {}'.format(alternative.transcript))
print('Confidence: {}'.format(alternative.confidence))
for word_info in alternative.words:
word = word_info.word
start_time = word_info.start_time
end_time = word_info.end_time
print('Word: {}, start_time: {}, end_time: {}'.format(
word, start_time.seconds + start_time.nanos * 1e-9,
end_time.seconds + end_time.nanos * 1e-9))
def transcribe_file(speech_file):
"""Transcribe the given audio file asynchronously."""
from google.cloud import speech
client = speech.SpeechClient()
# [START speech_python_migration_async_request]
with io.open(speech_file, "rb") as audio_file:
content = audio_file.read()
"""
Note that transcription is limited to a 60 seconds audio file.
Use a GCS file for audio longer than 1 minute.
"""
audio = speech.RecognitionAudio(content=content)
config = speech.RecognitionConfig(
encoding=speech.RecognitionConfig.AudioEncoding.FLAC,
sample_rate_hertz=44100,
language_code="en-GB",
audio_channel_count = 2,
)
# [START speech_python_migration_async_response]
operation = client.long_running_recognize(config=config, audio=audio)
# [END speech_python_migration_async_request]
print("Waiting for operation to complete...")
response = operation.result(timeout=90)
# Each result is for a consecutive portion of the audio. Iterate through
# them to get the transcripts for the entire audio file.
for result in response.results:
# The first alternative is the most likely one for this portion.
print(u"Transcript: {}".format(result.alternatives[0].transcript))
print("Confidence: {}".format(result.alternatives[0].confidence))
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 transcribe_file_with_word_time_offsets(speech_file):
"""Transcribe the given audio file synchronously and output the word time
offsets."""
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
with io.open(speech_file, 'rb') as audio_file:
content = audio_file.read()
audio = types.RecognitionAudio(content=content)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
language_code='en-US',
enable_word_time_offsets=True)
response = client.recognize(config, audio)
word_with_ts = []
for result in response.results:
#print result
alternative = result.alternatives[0]
print('Transcript: {}'.format(alternative.transcript))
for word_info in alternative.words:
word = word_info.word
start_time = word_info.start_time
end_time = word_info.end_time
word_with_ts.append(
(word, start_time.seconds + start_time.nanos * 1e-9,
end_time.seconds + end_time.nanos * 1e-9))
#print('Word: {}, start_time: {}, end_time: {}'.format(
# word,
# start_time.seconds + start_time.nanos * 1e-9,
# end_time.seconds + end_time.nanos * 1e-9))
return word_with_ts
def transcribe_gcs_return(gcs_uri):
"""Asynchronously transcribes the audio file specified by the gcs_uri."""
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
audio = types.RecognitionAudio(uri=gcs_uri)
config = types.RecognitionConfig(
#encoding=enums.RecognitionConfig.AudioEncoding.FLAC,
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=8000, # default : 16000
language_code='en-US', # 영어 : en-US , 한국어 : ko-KR
model='phone_call')
operation = client.long_running_recognize(config, audio)
print('Waiting for operation to complete...')
response = operation.result(timeout=90)
# Each result is for a consecutive portion of the audio. Iterate through
# them to get the transcripts for the entire audio file.
tuple_result_msg = ()
result_msg = ''
confidence = 0
print('len : ', len(response.results))
for result in response.results:
# The first alternative is the most likely one for this portion.
print('Transcript: {}'.format(result.alternatives[0].transcript))
print('Confidence: {}'.format(result.alternatives[0].confidence))
result_msg += result.alternatives[0].transcript + ' '
confidence += result.alternatives[0].confidence
#결과를 빅쿼리에 저장하기 위해 return
tuple_result_msg = (result_msg, confidence / len(response.results))
return tuple_result_msg
def wav_conversion(filename, tgt_path):
print(filename)
client = speech.SpeechClient()
corrupted_aud = "corrupted.txt"
corrupted_log = open(corrupted_aud, "w+")
# #write the recognized text
fh = open(tgt_path, "w+")
try:
# Loads the audio into memory
with io.open(filename, 'rb') as audio_file:
content = audio_file.read()
audio = types.RecognitionAudio(content=content)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=16000,
language_code='en-US',
model='video')
response = client.recognize(config, audio)
for result in response.results:
print(result)
rec = result.alternatives[0].transcript
fh.write(rec + ". ")
# with sr.AudioFile(filename) as source:
# audio = r.record(source) # read the entire audio file
# rec=r.recognize_google(audio)
# fh.write(rec+". ")
# catch any errors. except sr.UnknownValueError:
except:
corrupted_log.write(filename + '\n')
print("Could not understand audio")
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 google_transcribe(audio_file_name):
file_name = audio_file_name
mp3_to_wav(file_name)
# The name of the audio file to transcribe
frame_rate, channels = frame_rate_channel(file_name)
if channels > 1:
stereo_to_mono(file_name)
bucket_name = bucketname
source_file_name = audio_file_name
destination_blob_name = audio_file_name
upload_blob(bucket_name, source_file_name, destination_blob_name)
gcs_uri = 'gs://' + bucketname + '/' + audio_file_name
transcript = ''
client = speech.SpeechClient()
audio = types.RecognitionAudio(uri=gcs_uri)
config = types.RecognitionConfig(encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=frame_rate, language_code='en-US',
enable_automatic_punctuation=True)
# Detects speech in the audio file
operation = client.long_running_recognize(config, audio)
response = operation.result(timeout=10000)
for result in response.results:
transcript += result.alternatives[0].transcript
delete_blob(bucket_name, destination_blob_name)
return transcript
def get_text_from_audio():
os.system(
"export GOOGLE_APPLICATION_CREDENTIALS='analyze_tone_from_voice/Google_cloud_key/My_First_Project_926af8a5744c.json'"
)
os.system(
'ffmpeg -i analyze_tone_from_voice/input.m4a -acodec libmp3lame -ab 128k analyze_tone_from_voice/input.mp3'
)
os.system(
'sox analyze_tone_from_voice/input.mp3 --rate 16k --bits 16 --channels 1 analyze_tone_from_voice/input.flac'
)
# Instantiates a client
client = speech.SpeechClient()
# The name of the audio file to transcribe
file_name = 'analyze_tone_from_voice/input.flac'
# Loads the audio into memory
with io.open(file_name, 'rb') as audio_file:
content = audio_file.read()
audio = types.RecognitionAudio(content=content)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.FLAC,
sample_rate_hertz=16000,
language_code='en-US')
# Detects speech in the audio file
response = client.recognize(config, audio)
# for result in response.results:
text = response.results[0].alternatives[0].transcript
# print('Transcript: {}'.format(response.results[0].alternatives[0].transcript))
# remove audio files
os.system('rm analyze_tone_from_voice/input.mp3')
os.system('rm analyze_tone_from_voice/input.flac')
return text
def get_and_save_raw(input_path: str, bucket_name: str, out_path: str,
verbosity: int) -> None:
"""
Gets raw JSON from Google Cloud Speech-to-text API
:param input_path: Path to read files from
:param bucket_name: Name of the GCS bucket
:param verbosity: Verbosity level
:return: None
"""
bin_print(verbosity, 1, "Reading files from", input_path)
bin_print(verbosity, 2, "Trying to find all .flac files...")
flac_files = [
f for f in listdir(input_path)
if isfile(join(input_path, f)) and f.split(".")[1] == "flac"
]
bin_print(verbosity, 3, "Found flac files:", flac_files)
bin_print(verbosity, 3, "Total flac files:", len(flac_files))
client = speech.SpeechClient()
bin_print(verbosity, 1, "Running Google STT...")
for flac_file in flac_files:
if "stadt_zuerich" in flac_file:
bin_print(verbosity, 2, "Processing " + flac_file)
try:
json = get_raw("gs://" + bucket_name + "/" + flac_file, client)
json_path = out_path + "/" + flac_file.replace(
".flac", "_google_output") + ".json"
bin_print(verbosity, 2, "Writing " + json_path)
f = open(json_path, "w")
f.write(json)
f.close()
except _OperationNotComplete:
bin_print(verbosity, 1, "Timeout for " + flac_file)
def legenda_gcs(gcs_uri):
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
audio = types.RecognitionAudio(uri=gcs_uri)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.FLAC,
sample_rate_hertz=44100,
language_code='pt-BR')
operation = client.long_running_recognize(config, audio)
print('Esperando a conclusao da operacao...')
response = operation.result(timeout=3000)
file = open('c:\legenda2.txt', 'w')
for result in response.results:
#print(u'Legenda: {}'.format(result.alternatives[0].transcript))
#print('Acuracia: {}'.format(result.alternatives[0].confidence))
file.write('{}\n'.format(result.alternatives[0].transcript))
file.close()
#ffmpeg.exe -i "flac1.flac" -ac 1 mono1.flac
#gsutil acl ch -u AllUsers:R "gs://legenda/mono02.flac" para deixar arquivo publico no Storage
#set GOOGLE_APPLICATION_CREDENTIALS = c:\MyProject.json
#export GOOGLE_APPLICATION_CREDENTIALS="/home/joao_sinieghi/chave.json"
##Ambiente de desenvolvimento do Python##
#cd your-project
#virtualenv --python python3 env
#.\env\Scripts\activate
#source env/bin/activate
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 transcribe_file(fileName):
"""Convert given audio file to single channel."""
monoFileName = uploader._safe_filename('mono.wav')
sound = AudioSegment.from_file('./' + fileName)
sound = sound.set_channels(1)
sound = sound.set_sample_width(2)
duration_in_milliseconds = len(sound)
sound.export(monoFileName, format='wav')
"""Transcribe the given audio file."""
client = speech.SpeechClient()
# [START migration_sync_request]
# [START migration_audio_config_file]
with io.open(monoFileName, 'rb') as audio_file:
content = audio_file.read()
gcs_uri = uploader.upload_file(content, monoFileName, 'audio/wav')
plotGraph(monoFileName, gcs_uri)
audio = types.cloud_speech_pb2.RecognitionAudio(content=content)
config = types.cloud_speech_pb2.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
language_code='en-IN')
# [END migration_audio_config_file]
# [START migration_sync_response]
response = client.recognize(config, audio)
# [END migration_sync_request]
# Each result is for a consecutive portion of the audio. Iterate through
# them to get the transcripts for the entire audio file.
text = ''
for result in response.results:
# The first alternative is the most likely one for this portion.
text += u'{}'.format(result.alternatives[0].transcript)
if (len(result.alternatives) > 0):
text += ' '
# [END migration_sync_response]
return [duration_in_milliseconds, text, gcs_uri]
def transcribe_gcs_with_word_time_offsets(gcs_uri):
"""Transcribe the given audio file asynchronously and output the word time
offsets."""
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
audio = types.RecognitionAudio(uri=gcs_uri)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.FLAC,
sample_rate_hertz=16000,
language_code='en-US',
enable_word_time_offsets=True)
operation = client.long_running_recognize(config, audio)
print('Waiting for operation to complete...')
result = operation.result(timeout=90)
for result in result.results:
alternative = result.alternatives[0]
print('Transcript: {}'.format(alternative.transcript))
print('Confidence: {}'.format(alternative.confidence))
for word_info in alternative.words:
word = word_info.word
print(word)
start_time = word_info.start_time
end_time = word_info.end_time
if word in bad_words_list_final1:
aList.append(word)
aList.append(start_time)
aList.append(end_time)
print('Word: {}, start_time: {}, end_time: {}'.format(
word, start_time.seconds + start_time.nanos * 1e-9,
end_time.seconds + end_time.nanos * 1e-9))
def transcribe_file(speech_file):
"""Transcribe the given audio file asynchronously."""
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
client = speech.SpeechClient()
# [START migration_async_request]
with io.open(speech_file, 'rb') as audio_file:
content = audio_file.read()
audio = types.RecognitionAudio(content=content)
config = types.RecognitionConfig(
encoding=enums.RecognitionConfig.AudioEncoding.FLAC,
sample_rate_hertz=16000,
language_code='en-US')
# [START migration_async_response]
operation = client.long_running_recognize(config, audio)
# [END migration_async_request]
print('Waiting for operation to complete...')
response = operation.result(timeout=90)
# Each result is for a consecutive portion of the audio. Iterate through
# them to get the transcripts for the entire audio file.
f = open("speech_file.html", "w+")
for result in response.results:
# The first alternative is the most likely one for this portion.
#print(u'Transcript: {}'.format(result.alternatives[0].transcript))
#print('Confidence: {}'.format(result.alternatives[0].confidence))
f.write(format(result.alternatives[0].transcript))
# [END migration_async_response]
f.close()
webbrowser.open(
'file:///media/spritle/ACER%20DATA/Health%20NLP/audiototext/speech_file.html'
)
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 main():
client = speech.SpeechClient()
config = speech.types.RecognitionConfig(
encoding=speech.enums.RecognitionConfig.AudioEncoding.LINEAR16,
sample_rate_hertz=SAMPLE_RATE,
language_code='en-US',
max_alternatives=1,
enable_word_time_offsets=True)
streaming_config = speech.types.StreamingRecognitionConfig(
config=config, interim_results=True)
mic_manager = ResumableMicrophoneStream(SAMPLE_RATE, CHUNK_SIZE)
print('Say "Quit" or "Exit" to terminate the program.')
with mic_manager as stream:
while not stream.closed:
audio_generator = stream.generator()
requests = (speech.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, stream)