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)