Use the Speech to Text API

Azure Speech in Foundry Tools supports speech recognition through the Speech to text* API. While the specific details vary, depending on the SDK being used (Python, C#, and so on); there's a consistent pattern for using the Speech to text API:

1. Use a SpeechConfig object to encapsulate the information required to connect to your Foundry resource. Specifically, its endpoint (or region) and key.
2. Optionally, use an AudioConfig to define the input source for the audio to be transcribed. By default, this is the default system microphone, but you can also specify an audio file.
3. Use the SpeechConfig and AudioConfig to create a SpeechRecognizer object. This object is a proxy client for the Speech to text API.
4. Use the methods of the SpeechRecognizer object to call the underlying API functions. For example, the RecognizeOnceAsync() method uses the Azure Speech service to asynchronously transcribe a single spoken utterance.
5. Process the response. In the case of the RecognizeOnceAsync() method, the result is a SpeechRecognitionResult object that includes the following properties:
   • Duration
   • OffsetInTicks
   • Properties
   • Reason
   • ResultId
   • Text

If the operation was successful, the Reason property has the enumerated value RecognizedSpeech, and the Text property contains the transcription. Other possible values for Result include NoMatch (indicating that the audio was successfully parsed but no speech was recognized) or Canceled, indicating that an error occurred (in which case, you can check the Properties collection for the CancellationReason property to determine what went wrong).

Example - Transcribing an audio file

The following Python example uses Azure Speech in Foundry Tools to transcribe speech in an audio file.

import azure.cognitiveservices.speech as speech_sdk

# Speech config encapsulates the connection to the resource
speech_config = speech_sdk.SpeechConfig(subscription="YOUR_FOUNDRY_KEY",
                                       endpoint="YOUR_FOUNDRY_ENDPOINT")

# Audio config determines the audio stream source (defaults to system mic)
file_path = "audio.wav"
audio_config = speech_sdk.audio.AudioConfig(filename=file_path)

# Use a speech recognizer to transcribe the audio
speech_recognizer = speech_sdk.SpeechRecognizer(speech_config=speech_config,
                                               audio_config=audio_config)

result = speech_recognizer.recognize_once_async().get()

# Did it succeeed
if result.reason == speech_sdk.ResultReason.RecognizedSpeech:
    # Yes!
    print(f"Transcription:\n{result.text}")
else:
    # No. Try to determine why.
    print("Error transcribing message: {}".format(result.reason))