Speech to Text with File in Python

This sample demonstrates how to use Azure Cognitive Services to perform speech recognition on an audio file in a Python application.

main.py

How to generate this sample

Command

ai dev new speech-to-text-with-file --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-file' in 'speech-to-text-with-file-py' (2 files)...

main.py
requirements.txt

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

main.py

STEP 1: Read the configuration settings from environment variables:

main.py

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>"
input_file = sys.argv[1] if len(sys.argv) == 2 else "audio.wav"

STEP 2: Check if the input file exists:

main.py

if not os.path.exists(input_file):
    print("ERROR: Cannot find audio input file: {}".format(input_file))
    sys.exit(1)

STEP 3: Create instances of a speech config and audio config:

main.py

speech_config = SpeechConfig(subscription=speech_key, region=service_region, speech_recognition_language="en-US")
audio_config = AudioConfig(filename=input_file)

STEP 4: Create the speech recognizer from the above configuration information:

main.py

speech_recognizer = SpeechRecognizer(speech_config=speech_config, audio_config=audio_config)

STEP 5: Subscribe to the recognizing and recognized events:

main.py

def recognizing(args):
    print("RECOGNIZING: {}".format(args.result.text))

def recognized(args):
    if args.result.reason.name == "RecognizedSpeech" and args.result.text != "":
        print("RECOGNIZED: {}\n".format(args.result.text))
    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 6: Create a future to wait for the session to stop:

main.py

session_stopped_no_error = Future()

STEP 7: Subscribe to session_started and session_stopped events:

main.py

def session_started(args):
    print("SESSION STARTED: {}\n".format(args.session_id))

def session_stopped(args):
    print("SESSION STOPPED: {}".format(args.session_id))
    session_stopped_no_error.set_result(True)

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

STEP 8: Subscribe to the canceled event:

main.py

def canceled(args):
    print("CANCELED: Reason={}".format(args.cancellation_details.reason))
    if args.cancellation_details.reason == CancellationReason.EndOfStream:
        print("CANCELED: End of the audio stream was reached.")
    elif args.cancellation_details.reason == CancellationReason.Error:
        print("CANCELED: ErrorDetails={}".format(args.cancellation_details.error_details))
        print("CANCELED: Did you update the subscription info?")
    session_stopped_no_error.set_result(args.cancellation_details.reason != CancellationReason.Error)

speech_recognizer.canceled.connect(canceled)

STEP 9: Allow the user to press ENTER to stop recognition:

main.py

threading.Thread(target=lambda: (
    input(""),
    speech_recognizer.stop_continuous_recognition())
).start()

STEP 10: Start speech recognition:

main.py

speech_recognizer.start_continuous_recognition()
print("Listening, press ENTER to stop...")

STEP 11: Wait for the session to stop:

main.py

exit_code = 0 if session_stopped_no_error.result() == True else 1
os._exit(exit_code)

requirements.txt

azure-cognitiveservices-speech>=1.35.0