def transcribe_audio(audio_file, extension, model):
username = os.environ.get("BLUEMIX-STT-USERNAME")
password = os.environ.get("BLUEMIX-STT-PASSWORD")
speech_to_text = SpeechToText(
username = username,
password = password,
x_watson_learning_opt_out = False,
)
with open(audio_file, "rb") as audio:
try:
result = speech_to_text.recognize(
audio,
content_type = "audio/" + extension,
model = model
)
except Exception as ex:
print(ex)
raise
try:
transcripted_text = result["results"][0]["alternatives"][0]["transcript"]
except:
print("I'm sorry, the audio is blank! If youre sure that there was"
"an audio, it probably was below the microphone sensibility. Try "
"speaking louder.")
raise
return transcripted_text.rstrip()
def transcribe_audio(path_to_audio_file):
username = os.environ.get("BLUEMIX_USERNAME")
password = os.environ.get("BLUEMIX_PASSWORD")
speech_to_text = SpeechToText(username=username, password=password)
with open(join(dirname(__file__), path_to_audio_file), 'rb') as audio_file:
return speech_to_text.recognize(audio_file, content_type='audio/wav')
def transcribe_audio(path_to_audio_file):
username = config.SPEECH_TO_TEXT_USERNAME
password = config.SPEECH_TO_TEXT_PASSWORD
speech_to_text = SpeechToText(username=username, password=password)
with open(join(dirname(__file__), path_to_audio_file), 'rb') as audio_file:
return speech_to_text.recognize(audio_file, content_type='audio/wav')
def transcribe_audio(path_to_audio_file):
username = "******"
password = "******"
speech_to_text = SpeechToText(username=username, password=password)
with open(path_to_audio_file, 'rb') as audio_file:
return speech_to_text.recognize(audio_file, content_type='audio/wav')
def transcribe_audio(path_to_audio_file):
# enter your info here
username = ""
password = ""
speech_to_text = SpeechToText(username=username, password=password)
with open(path_to_audio_file, 'rb') as audio_file:
return speech_to_text.recognize(audio_file, content_type='audio/wav')
def transcribe_audio(path_to_audio_file):
username = os.environ.get("BLUEMIX_USERNAME")
password = os.environ.get("BLUEMIX_PASSWORD")
speech_to_text = SpeechToText(username=username,
password=password)
with open(join(dirname(__file__), path_to_audio_file), 'rb') as audio_file:
return speech_to_text.recognize(audio_file,
content_type='audio/wav')
def transcribe_audio(self, path_to_audio_file):
#username = os.environ.get("BLUEMIX_USERNAME")
#password = os.environ.get("BLUEMIX_PASSWORD")
username = "******"
password = "******"
speech_to_text = SpeechToText(username=username, password=password)
with open(path_to_audio_file, 'rb') as audio_file:
return speech_to_text.recognize(audio_file,
content_type='audio/wav')
def get_watson_stt_object(self):
"""create Watson speech-to-text object"""
stt = None
if self.api_key is not None:
stt = SpeechToTextV1(iam_apikey=self.api_key)
elif self.username is not None and self.password is not None:
stt = SpeechToTextV1(username=self.username,
password=self.password)
return stt
def initiate_watson():
"""Establishes connection with API
"""
watson_dict = {
'ibm_api': 'kzL_ZWnn4T0xxQ_A6bUTBdqdh7yvljTJsWO2qraw-nDa',
'ibm_url': 'https://stream.watsonplatform.net/speech-to-text/api',
'ibm_version': '2018-08-01',
'ibm_api_ta': 'I5yNi3pszexgabF6cCc4BIM1_QS5Fm63jAwnnDGl7j-j',
'ibm_url_ta': 'https://gateway.watsonplatform.net/tone-analyzer/api',
'ibm_version_ta': '2016-05-19', 'ibm_api_per': 'VwVF3pMl1j_OWcKbKTB_teau5OA4hSj3KSizWYTXIrRQ',
'ibm_url_per': 'https://gateway.watsonplatform.net/personality-insights/api',
'ibm_version_per': '2017-10-13'}
speech_to_text = SpeechToTextV1(iam_apikey=watson_dict['ibm_api'])
tone_analyzer = ToneAnalyzerV3(
url = watson_dict['ibm_url_ta'],
version = watson_dict['ibm_version_ta'],
iam_apikey = watson_dict['ibm_api_ta'])
personality_analyzer = PersonalityInsightsV3(
version = watson_dict['ibm_version_per'],
url = watson_dict['ibm_url_per'],
iam_apikey = watson_dict['ibm_api_per'])
tone_analyzer.set_detailed_response(True)
return speech_to_text, tone_analyzer, personality_analyzer
def stt(speech_file):
sst_api_key = 'e_jevakkc6QsQcCmA41mMtKE2sJl1Ug0OEoKEi8oLIb1'
sst_url = 'https://stream.watsonplatform.net/speech-to-text/api'
speech_to_text = SpeechToTextV1(iam_apikey=sst_api_key, url=sst_url)
class MyRecognizeCallback(RecognizeCallback):
def __init__(self):
RecognizeCallback.__init__(self)
def on_data(self, data):
print(json.dumps(data, indent=2))
def on_error(self, error):
print('Error received: {}'.format(error))
def on_inactivity_timeout(self, error):
print('Inactivity timeout: {}'.format(error))
myRecognizeCallback = MyRecognizeCallback()
with open(join(dirname(__file__), './.', speech_file), 'rb') as audio_file:
audio_source = AudioSource(audio_file)
speech_to_text.recognize_using_websocket(
audio=audio_source,
interim_results=True,
content_type='audio/wav',
recognize_callback=myRecognizeCallback,
keywords=[
'ticket', 'speeding', 'limit', 'cell phone', 'cellphone',
'seatbelt', 'tailgating'
],
keywords_threshold=0.8)
def convertSpeechToText(self, audioFile):
# Read IBM watson SpeechToText service username and password from config file
username = self.configObject.get('ibm_speech_to_text_service_username')
password = self.configObject.get('ibm_speech_to_text_service_password')
# Create SpeechToText service object
stt = SpeechToTextV1(username=username, password=password)
audio_file = open(audioFile, "rb")
audio_data = json.dumps(stt.recognize(audio_file,
content_type="audio/wav",
model='en-US_NarrowbandModel',
continuous=True),
indent=2)
print(audio_data)
audio_text = ""
try:
audio_json_data = json.loads(audio_data)
print(" Here is the audio text :::")
if (len(audio_json_data["results"]) != 0):
audio_text = audio_json_data["results"][0]["alternatives"][0][
"transcript"]
return (audio_text)
else:
return (None)
except Exception as e:
print("Error was: ", e)
def getTextFromSpeech():
print('at speech to text')
tts_kwargs = {
'username': speechToTextUser,
'password': speechToTextPassword,
'iam_apikey': speechToTextIAMKey,
'url': speechToTextUrl
}
sttService = SpeechToTextV1(**tts_kwargs)
response = sttService.recognize(audio=request.get_data(cache=False),
content_type='audio/wav',
timestamps=True,
word_confidence=True,
smart_formatting=True).get_result()
# Ask user to repeat if STT can't transcribe the speech
if len(response['results']) < 1:
return Response(mimetype='plain/text',
response="Sorry, didn't get that. please try again!")
text_output = response['results'][0]['alternatives'][0]['transcript']
text_output = text_output.strip()
return Response(response=text_output, mimetype='plain/text')
def __init__(self):
self.STT = SpeechToText(
username='******',
password='******')
self.TTS = TextToSpeech(
username='******',
password='******')
def main(app):
if request.method == 'POST':
status = {}
if 'file' not in request.files:
status = {'valid': False, 'text': 'No file part'}
file = request.files['file']
if file.filename == '':
status = {'valid': False, 'text': 'No selected file'}
if file and allowed_file(file.filename,
allowed_extensions=set(['wav', 'mp3'])):
file.save(
'/home/sripravan/Projects/sadhana-mega/app/static/audio/test.wav'
)
with io.open(
'/home/sripravan/Projects/sadhana-mega/app/static/audio/test.wav',
'rb') as audio_file:
speech_to_text = SpeechToTextV1(
username='******',
password='******')
speech_recognition_results = speech_to_text.recognize(
audio=audio_file,
content_type="audio/wav",
timestamps=True,
smart_formatting=True).get_result()
status = {
'valid': True,
'transcript': speech_recognition_results
}
return jsonify(status)
def __init__(self, uname, pword):
self.user_name = uname
self.url = 'https://stream.watsonplatform.net/speech-to-text/api'
self.password = pword
self.speech_to_text = SpeechToTextV1(username=self.user_name,
password=self.password,
url=self.url)
def call_speech2text(audioFileLocation):
Config=configparser.ConfigParser()
Config.read("watson.ini")
userid=ConfigSectionMap(Config,"Speech to Text-RAI")['username']
pwd=ConfigSectionMap(Config,"Speech to Text-RAI")['password']
speech_to_text = SpeechToTextV1(username=userid,password=pwd,x_watson_learning_opt_out=False)
#status = downloadFileandConvert(audioFileLocation)
status=ffmpegconvert(audioFileLocation,'wav') # Convert to WAV file
if status == "Not Ok":
print("Error in File Conversion - In Watson")
return "Error in File Conversion - In Watson"
#audioFileLocation=join(dirname(__file__), audioFile)
#audioFileLocation = audioFile
speech_to_text.get_model('en-US_NarrowbandModel')
#speech_to_text.get_custom_model('9c1d00a0-330c-11e7-94ad-3b2269260fbc')
with open(status,'rb') as audio_file:
returnedJSON = json.dumps(speech_to_text.recognize(audio_file, content_type='audio/wav', timestamps=True,word_confidence=True,model='en-US_NarrowbandModel',continuous=True),indent=2)
#print(returnedJSON)
#Deserialize
returnedJSONStr = json.loads(returnedJSON)
print(returnedJSONStr)
try:
returnMsg=returnedJSONStr['results'][0]['alternatives'][0]['transcript']
print(returnedJSONStr['results'][0]['alternatives'][0]['transcript'])
return returnMsg
except:
return "Can't Convert Speech2Text"
def speech_to_text(usr, password):
try:
return SpeechToTextV1(username=usr,
password=password,
x_watson_learning_opt_out=False)
except:
return []
def diagraph(path):
speech_to_text = SpeechToTextV1(
username='******',
password='******',
x_watson_learning_opt_out=False)
with open(join(dirname(__file__), path),'rb') as audio_file:
x = speech_to_text.recognize(
audio_file, content_type='audio/wav', timestamps=True,speaker_labels=True,
word_confidence=True)
speakers_stamp = x['speaker_labels']
checks = x['results']
speakers = {0:[],
1:[]}
c = 0
for r in range(len(checks)):
each_check = checks[r]["alternatives"]
for i in range(len(each_check)):
ex1 = checks[r]["alternatives"][i]["timestamps"]
for w in ex1:
speakers[int(speakers_stamp[c]["speaker"])].append(w[0])
c+=1
return speakers
def recognize_speech(username, password, audio_file_path,
forced_mime_type,
buffer_size=_4K,
audio_model=None,
inactivity_timeout=None,
extra_options=None,
progress_callback=None):
stt = SpeechToTextV1(username=username, password=password)
content_type = guess_mime_type(audio_file_path, forced_mime_type)
kwargs = {
'content_type': content_type,
'continuous': True,
'timestamps': False,
'max_alternatives': 1
}
default_options = build_default_options(
audio_model=audio_model,
inactivity_timeout=inactivity_timeout)
kwargs.update(default_options)
kwargs.update(extra_options or {})
return stt.recognize(
chunked_upload(audio_file_path, buffer_size, progress_callback),
**kwargs)
def getTextFromSpeech():
tts_kwargs = {
'username': speechToTextUser,
'password': speechToTextPassword,
'iam_apikey': speechToTextIAMKey,
'url': speechToTextUrl
}
sttService = SpeechToTextV1(**tts_kwargs)
response = sttService.recognize(audio=request.get_data(cache=False),
content_type='audio/wav',
model='ja-JP_BroadbandModel',
timestamps=True,
word_confidence=True,
smart_formatting=True).get_result()
# Ask user to repeat if STT can't transcribe the speech
if len(response['results']) < 1:
return Response(mimetype='plain/text',
response="聞こえなかったので、もう一度お願いします!")
text_output = response['results'][0]['alternatives'][0]['transcript']
text_output = text_output.strip()
return Response(response=text_output, mimetype='plain/text')
def save_audio():
#ACESSA API DE RECONHECIMENTO DE AUDIO
speechtotext = SpeechToTextV1(
username='******',
password='******')
#SALVANDO AUDIO
Audio.rec.stop()
url_voz = MEDIA_ROOT + "/usuario" + str(
datetime.datetime.now()) + ".wav"
Audio.rec.save(url_voz)
vetor_audio = open(url_voz, 'rb').read()
try:
recognized_audio = speechtotext.recognize(
audio=vetor_audio,
content_type='audio/wav',
model='pt-BR_BroadbandModel',
interim_results=False,
keywords=['conta', 'cooperativa', 'valor', 'transferir'],
keywords_threshold=0.3,
max_alternatives=3)
print(recognized_audio)
VozUsuario.objects.cria_voz(vozusuario_padrao=url_voz)
return {'url_voz': url_voz, 'recognized_audio': recognized_audio}
except WatsonApiException as ex:
Audio.rec.delete(url_voz)
print("Código de erro " + str(ex.code) + ": " + ex.message)
def speech_to_text(file_name, model_id):
"""Use Watson Speech to Text to convert audio file to text."""
# create Watson Speech to Text client
stt = SpeechToTextV1(iam_apikey=keys.speech_to_text_key)
# open the audio file
with open(file_name, 'rb') as audio_file:
# pass the file to Watson for transcription
result = stt.recognize(audio=audio_file,
content_type='audio/wav',
model=model_id).get_result()
# Get the 'results' list. This may contain intermediate and final
# results, depending on method recognize's arguments. We asked
# for only final results, so this list contains one element.
results_list = result['results']
# Get the final speech recognition result--the list's only element.
speech_recognition_result = results_list[0]
# Get the 'alternatives' list. This may contain multiple alternative
# transcriptions, depending on method recognize's arguments. We did
# not ask for alternatives, so this list contains one element.
alternatives_list = speech_recognition_result['alternatives']
# Get the only alternative transcription from alternatives_list.
first_alternative = alternatives_list[0]
# Get the 'transcript' key's value, which contains the audio's
# text transcription.
transcript = first_alternative['transcript']
return transcript # return the audio's text transcription
def data_input(request):
if request.method == "POST":
image_file_name = request.FILES["image"].name
audio_file_name = request.FILES["audio"].name
image_file_binary = request.FILES["image"].read()
audio_file_binary = request.FILES["audio"].read()
speed = request.POST.get("speed")
# Image Analysis
dbx = dropbox.Dropbox(DROPBOX_ACCESS_TOKEN)
image_path = "/images/" + str(uuid.uuid1()) + "__" + image_file_name
dbx.files_upload(image_file_binary, image_path, mute=True)
client = Algorithmia.client(ALGO_ACCESS_KEY)
algo = client.algo(ALGO_EMOTION_API)
params = {}
params["image"] = "dropbox://" + image_path
params["numResults"] = 7
image_analysis = algo.pipe(params).result
# speech Analysis
speech_to_text = SpeechToTextV1(username=WATSON_SPT_SERVICE_USERNAME,
password=WATSON_SPT_SERVICE_PASSWORD,
x_watson_learning_opt_out=False)
models = speech_to_text.models()
us_model = speech_to_text.get_model('en-US_BroadbandModel')
results = speech_to_text.recognize(audio_file_binary,
content_type='audio/wav',
timestamps=True,
word_confidence=True,
speaker_labels=True)
transcripts = get_transcripts(json.dumps(results))
transcripts_str = ". ".join(transcripts)
tone_analysis = call_to_watson_tone_analysis_api(transcripts_str)
response = format(image_analysis, tone_analysis)
response.update({"speed": float(speed), "weather": 0})
(score, (msg, aloc)) = decision_engine.decide(response)
# a = decision_engine.decide(response)
#
# if a:
# score = a[0]
# msg = a[1][0]
# aloc = a[1][1]
#import ipdb; ipdb.set_trace()
return render(request, 'results.html', {
"score": score,
"msg": msg,
"score_breakup": response
})
#return render(request, 'results.html')
#return HttpResponseRedirect("/emoDrive/analyze/" + upload_path)
else:
return render(request, 'upload.html')
def post_audio():
content = request.get_json(silent=True)
print(content)
state = load_obj()
objectList = ['refrigerator', 'printer', 'coffee']
commandList = ['status', 'buy', 'purchase', 'order', 'add', 'cart']
wordsList = []
jsonWords = []
finalList = []
sentence = ""
speech_to_text = SpeechToTextV1(
username='******',
password='******',
x_watson_learning_opt_out=False)
print(json.dumps(speech_to_text.models(), indent=2))
print(
json.dumps(speech_to_text.get_model('en-US_BroadbandModel'), indent=2))
path = '/Users/andreaskarinam/Developer/React/Hermes/samples/response2.wav'
if state["firstCommand"] == True:
path = '/Users/andreaskarinam/Developer/React/Hermes/samples/response3.wav'
with open(join(dirname(__file__), path), 'rb') as audio_file:
text_dict = speech_to_text.recognize(audio_file,
content_type='audio/wav',
timestamps=True,
word_confidence=True)
wordsList = text_dict['results'][0]['alternatives'][0][
'word_confidence']
for lists in wordsList:
jsonWords.append(lists[0])
print(jsonWords)
for word in jsonWords:
if (word in commandList):
print(word)
for word in jsonWords:
if (word in objectList):
print(word)
print(state["firstCommand"])
if state["firstCommand"] == True:
state["firstCommand"] = False
state["shoppingcart"].append({
"name": "Maui Coffee",
"quantity": 1,
"price": "$12.99"
})
else:
data = state["devices"][0]["data"]
state["shoppingcart"] = []
save_obj(state)
return json.dumps({"response": 200})
def transcribe_audio(path_to_audio_file):
speech_to_text = SpeechToTextV1(
username='******',
password='******',
x_watson_learning_opt_out=False)
with open(join(dirname(__file__), path_to_audio_file), "rb") as audio_file:
return speech_to_text.recognize(audio_file,
content_type="audio/wav",
word_confidence=True)
def connect_speechtext():
speech_to_text = SpeechToTextV1(
username="******",
password='******',
x_watson_learning_opt_out=False)
# print(json.dumps(speech_to_text.models(), indent=2))
# print(json.dumps(speech_to_text.get_model('en-US_BroadbandModel'), indent=2))
return speech_to_text
def transcribe_audio():
username = "******"
path_to_audio_file = "file.wav"
password = "******"
speech_to_text = SpeechToTextV1(
username='******',
password="******")
with open(path_to_audio_file, "rb") as audio_file:
return (speech_to_text.recognize(audio_file, content_type="audio/wav"))
def __init__(self):
self.url = "https://stream.watsonplatform.net/speech-to-text/api"
self.username = "******"
self.password = "******"
self.speech_to_text = SpeechToTextV1(
username = self.username,
password = self.password,
url = self.url
)
def __init__(self,debug_mode=False):
self.debug_mode=debug_mode
f = open("key.txt", "r")
f1 = f.read().splitlines()
f.close()
self.speech_to_text = SpeechToTextV1(
iam_apikey=f[14],
url=f[15]
)
def convert_speech_to_text(audio):
speech_to_text = SpeechToTextV1(
username=os.environ["WATSON_TRANSCRIPTION_USERNAME"],
password=os.environ["WATSON_TRANSCRIPTION_PASSWORD"],
)
return speech_to_text.recognize(
audio, content_type="audio/mp3", timestamps=False, word_confidence=False
)
def __init__(self):
self.config = SpeechToTextV1(
# iam_apikey='6YzHFvvaDU6XJfvovrrYXxNrCfhI8Ee1enkGS-Crjouw',
iam_apikey='z2T7SNluJPGEGStI2etKxLgBpj56kR_M6mOmYbyCilH4',
url='https://stream.watsonplatform.net/speech-to-text/api')
self.threshold = 500
self.chunk_size = 1024
self.format = pyaudio.paInt16
self.rate = 16000
import json
from os.path import join, dirname
from watson_developer_cloud import SpeechToTextV1 as SpeechToText
speech_to_text = SpeechToText(username='******',
password='******')
print(json.dumps(speech_to_text.models(), indent=2))
with open(join(dirname(__file__), '../resources/speech.wav'), 'rb') as audio_file:
print(json.dumps(speech_to_text.recognize(audio_file,
content_type='audio/wav'), indent=2))