Speech Recognition with Keyword Spotting in Python

This page was automatically generated by AI; not yet reviewed for accuracy...

The content and code samples on this page were generated by using the ai CLI with customized prompts in this repository.

It's cool, but, it's experimental. 😁

Please review the content and code before using it in your application.

This sample demonstrates how to use the Azure Cognitive Services Speech SDK for speech recognition with keyword spotting in a Python console application.

main.py

How to generate this sample

Command

ai dev new speech-to-text-with-keyword --python

Output

AI - Azure AI CLI, Version 1.0.0
Copyright (c) 2024 Microsoft Corporation. All Rights Reserved.

This PUBLIC PREVIEW version may change at any time.
See: https://aka.ms/azure-ai-cli-public-preview

Generating 'speech-to-text-with-keyword' in 'speech-to-text-with-keyword-py' (2 files)...

main.py
requirements.txt

Generating 'speech-to-text-with-keyword' in 'speech-to-text-with-keyword-py' (2 files)... DONE!

main.py

STEP 1: Import necessary modules and set up configuration.

main.py

from concurrent.futures import Future
from azure.cognitiveservices.speech import SpeechConfig, SpeechRecognizer, AudioConfig, KeywordRecognitionModel, CancellationReason
import threading
import os
import sys

# Connection and configuration details required
speech_key = os.environ.get('AZURE_AI_SPEECH_KEY') or '<insert your Speech Service API key here>'
service_region = os.environ.get('AZURE_AI_SPEECH_REGION') or '<insert your Speech Service region here>'
speech_language = 'en-US'
input_file = sys.argv[1] if len(sys.argv) == 2 else None
keyword_file = 'keyword.table'

STEP 2: Check for input and keyword files, and create configurations.

main.py

# Check to see if the input file exists
if input_file is not None and not os.path.exists(input_file):
    print(f'ERROR: Cannot find audio input file: {input_file}')
    sys.exit(1)

# Check to see if the keyword file exists
if not os.path.exists(keyword_file):
    print(f'ERROR: Cannot find keyword file: {keyword_file}')
    sys.exit(1)

# Create instances of a speech config and audio config
speech_config = SpeechConfig(subscription=speech_key, region=service_region, speech_recognition_language=speech_language)
audio_config = AudioConfig(filename=input_file) if input_file is not None else AudioConfig(use_default_microphone=True)

STEP 3: Initialize the speech recognizer and connect event handlers.

main.py

# Create the speech recognizer from the above configuration information
speech_recognizer = SpeechRecognizer(speech_config=speech_config, audio_config=audio_config)

# Subscribe to the recognizing and recognized events. As the user speaks individual
# utterances, intermediate recognition results are sent to the recognizing event,
# and the final recognition results are sent to the recognized event.
def recognizing(args):
    print(f'RECOGNIZING: {args.result.text}')

def recognized(args):
    if args.result.reason.name == 'RecognizedKeyword' and args.result.text != '':
        print(f'RECOGNIZED: {args.result.text}\n')
    elif args.result.reason.name == 'RecognizedSpeech' and args.result.text != '':
        print(f'RECOGNIZED: {args.result.text}\n')
    elif args.result.reason.name == 'NoMatch':
        print('NOMATCH: Speech could not be recognized.\n')

speech_recognizer.recognizing.connect(recognizing)
speech_recognizer.recognized.connect(recognized)

STEP 4: Handle session events and start keyword recognition.

main.py

# Create a future to wait for the session to stop. This is needed in console apps to
# prevent the main thread from terminating while the recognition is running
# asynchronously on a separate background thread.
session_stopped_no_error = Future()

# Subscribe to session_started and session_stopped events. These events are useful for
# logging the start and end of a speech recognition session. In console apps, this is
# used to allow the application to block the main thread until recognition is complete.
def session_started(args):
    print(f'SESSION STARTED: {args.session_id}\n')

def session_stopped(args):
    print(f'SESSION STOPPED: {args.session_id}')

speech_recognizer.session_started.connect(session_started)
speech_recognizer.session_stopped.connect(session_stopped)

# Subscribe to the canceled event, which indicates that the recognition operation
# was stopped/canceled, likely due to an error of some kind.
def canceled(args):
    print(f'CANCELED: Reason={args.cancellation_details.reason}')

    # Check the CancellationReason for more detailed information.
    if args.cancellation_details.reason == CancellationReason.EndOfStream:
        print('CANCELED: End of the audio stream was reached.')
    elif args.cancellation_details.reason == CancellationReason.Error:
        print(f'CANCELED: ErrorDetails={args.cancellation_details.error_details}')
        print('CANCELED: Did you update the subscription info?')

    # Set the future's result so the main thread can exit
    session_stopped_no_error.set_result(args.cancellation_details.reason != CancellationReason.Error)

speech_recognizer.canceled.connect(canceled)

# Allow the user to press ENTER to stop recognition
threading.Thread(target=lambda: (
    input(''),
    speech_recognizer.stop_continuous_recognition())
).start()

# Start keyword recognition
keyword_model = KeywordRecognitionModel(filename=keyword_file)
speech_recognizer.start_keyword_recognition(keyword_model)
print('Listening for keyword; press ENTER to stop ...')

# Wait for the session to stop. result() will not return until the recognition
# session stops, and the result will indicate whether the session completed
# or was canceled.
exit_code = 0 if session_stopped_no_error.result() == True else 1
os._exit(exit_code)