def request_priv_info():
PRIVATE_FILE = "private.json"
path = os.path.join(os.getcwd(), PRIVATE_FILE)
key, endpoint = get_private(path, "Text Analytics")
return key, endpoint
if ver(azver) < ver("0.6.0"):
sys.exit(
f"""*** WARNING *** Currently you have installed version {azver} of the
Azure Cognitives Services Computer Vision library. This might have
been installed automatically as part of the *configure* of the
package. Some incompatible changes have emerged in recent
upgrades. Please upgrade to the latest version of that library using:
pip3 install --upgrade azure-cognitiveservices-vision-computervision
""")
# ----------------------------------------------------------------------
# Request subscription key and endpoint from user.
# ----------------------------------------------------------------------
key, endpoint = get_private()
mlask()
# Set credentials.
credentials = CognitiveServicesCredentials(key)
# Create client.
client = ComputerVisionClient(endpoint, credentials)
url0 = "https://upload.wikimedia.org/"
url1 = "wikipedia/commons/thumb/1/12/Broadway_and_Times_Square_by_night.jpg/"
url2 = "450px-Broadway_and_Times_Square_by_night.jpg"
url = url0 + url1 + url2
from translate import translate_speech_to_text
import azure.cognitiveservices.speech as speechsdk
import os
import sys
mlcat("Speech Services", """\
Welcome to a demo of the pre-built models for Speech provided
through Azure's Cognitive Services. The Speech cloud service
supports speech to text, text to speech, speech translation and
Speaker Recognition capabilities.
""")
# ----------------------------------------------------------------------
# Request subscription key and location from user.
# ----------------------------------------------------------------------
key, location = get_private()
# Recognition is experimental and is only available at present
# 20210428 from the westus data centre.
RECOGNISE_FLAG = (location == "westus")
# -----------------------------------------------------------------------
# Set up a speech recognizer and synthesizer.
# -----------------------------------------------------------------------
# Following is the code that does the actual work, creating an
# instance of a speech config with the specified subscription key and
# service region, then creating a recognizer with the given settings,
# and then performing recognition. recognize_once() returns when the
# first utterance has been recognized, so it is suitable only for
# ----------------------------------------------------------------------
# Parse command line arguments
# ----------------------------------------------------------------------
option_parser = argparse.ArgumentParser(add_help=False)
option_parser.add_argument('path', help='path or url to image')
args = option_parser.parse_args()
# ----------------------------------------------------------------------
# Request subscription key and endpoint from user.
# ----------------------------------------------------------------------
subscription_key, endpoint = get_private()
# Set credentials.
credentials = CognitiveServicesCredentials(subscription_key)
# Create client.
client = ComputerVisionClient(endpoint, credentials)
# Check the URL supplied. Also want to support local file.
# Send image to azure to identify landmark
# url = "https://images.pexels.com/photos/338515/pexels-photo-338515.jpeg"
error = response["errorDetails"]
errors = " ".join(error)
if "query: This parameter is missing or invalid." in errors:
sys.exit("The address parameter is required. ")
elif "Access was denied" in errors:
sys.exit(errors +
"\nPlease run 'ml configure bing' to update the key. ")
else:
sys.exit(errors)
return result
if __name__ == "__main__":
key = get_private()[0]
# Private file stores the Bing Maps key required by the geocoding
# function.
parser = argparse.ArgumentParser(description='Bing Maps')
parser.add_argument('address',
type=str,
nargs='*',
help='location to geocode')
parser.add_argument('--neighbourhood',
'-n',
action="store_true",
help='include neighbourhood of the address.')
js = intent_result.intent_json
js = json.loads(js)
score = js["topScoringIntent"]["score"]
entities = ""
sep = ""
for item in js["entities"]:
entities += sep + item["entity"]
sep = ", "
print("Recognized: \"{}\" with intent id `{}`. The score: {}, and entities: {}".
format(intent_result.text, intent_result.intent_id, str(score), entities))
elif intent_result.reason == speechsdk.ResultReason.RecognizedSpeech:
print("Recognized: {}".format(intent_result.text))
elif intent_result.reason == speechsdk.ResultReason.NoMatch:
print("No speech could be recognized: {}".format(intent_result.no_match_details))
elif intent_result.reason == speechsdk.ResultReason.Canceled:
print("Intent recognition canceled: {}".format(intent_result.cancellation_details.reason))
if intent_result.cancellation_details.reason == speechsdk.CancellationReason.Error:
print("Error details: {}".format(intent_result.cancellation_details.error_details))
if __name__ == "__main__":
# ----------------------------------------------------------------------
# Request subscription key and location from user.
# ----------------------------------------------------------------------
key, location, location, app_id = get_private()
intent_config = speechsdk.SpeechConfig(subscription=key, region=location)
intent(intent_config, app_id)
an operation to be performed on specific entities.
""")
mlask(end="\n")
# Import the required libraries.
import sys
import requests
from time import sleep
from mlhub.pkg import get_private
# ----------------------------------------------------------------------
# Request subscription key, endpoint and App ID from user.
# ----------------------------------------------------------------------
subscription_key, endpoint, location, id = get_private()
mlcat(
"", """\
LUIS includes a set of prebuilt intents from a number of prebuilt domains
for quickly adding common intents and utterances to conversational client
apps. These include Camera, Music, HomeAutomation, and many more. We will
begin with a demonstration of Home Automation. Do note that typically you
will need to train the LUIS model with your speceific intents.
Below we will demonstrate a series of commands and identify the intent and
the entities, together with a confidence score.
""")
mlask()
headers = {'Ocp-Apim-Subscription-Key': subscription_key}
option_parser.add_argument('--original',
"-f",
default="en-US",
help='original language')
option_parser.add_argument('--target', "-t", help='target language')
option_parser.add_argument(
'--output',
"-o",
help='path to an audio file to save. The file type should be wav')
args = option_parser.parse_args()
from_language = args.original
to_language = args.target
if args.original:
pass
else:
args.original = "en-US"
# ----------------------------------------------------------------------
# Request subscription key and location from user.
# ----------------------------------------------------------------------
key, region = get_private()
translate_speech_to_text(from_language, to_language, True, args.output,
key, region)
