"Can I use the Azure Speech-to-Text fast transcription REST API for short audio to perform pronunciation assessment? How do I use it?

My problem is that the mode right now is too expensive for my work, which is $1.3 per hour. I want to try to use fast transcription mode to to perform pronunciation assessment,which may finally cost $0.66 per hour. Can I?

Here is the example code from the VScode plugin Azure AI Speech Toolkit ,please tell me how to switch to it.

def pronunciation_assessment_with_rest_api():

def pronunciation_assessment_with_rest_api():
    """Performs pronunciation assessment asynchronously with REST API for a short audio file.
    See more information at https://learn.microsoft.com/azure/ai-services/speech-service/rest-speech-to-text-short
    """

    # A generator which reads audio data chunk by chunk.
    # The audio_source can be any audio input stream which provides read() method,
    # e.g. audio file, microphone, memory stream, etc.
    def get_chunk(audio_source, chunk_size=1024):
        yield WaveHeader16K16BitMono
        while True:
            time.sleep(chunk_size / 32000)  # to simulate human speaking rate
            chunk = audio_source.read(chunk_size)
            if not chunk:
                global upload_finish_time
                upload_finish_time = time.time()
                break
            yield chunk

    # Build pronunciation assessment parameters
    locale = "en-US"
    audio_file = open(AUDIO_PCM_FILE, "rb")
    reference_text = "Good morning."
    enable_prosody_assessment = True
    phoneme_alphabet = "SAPI"  # IPA or SAPI
    enable_miscue = True
    nbest_phoneme_count = 5
    pron_assessment_params_json = (
        '{"GradingSystem":"HundredMark","Dimension":"Comprehensive","ReferenceText":"%s",'
        '"EnableProsodyAssessment":"%s","PhonemeAlphabet":"%s","EnableMiscue":"%s",'
        '"NBestPhonemeCount":"%s"}'
        % (reference_text, enable_prosody_assessment, phoneme_alphabet, enable_miscue, nbest_phoneme_count)
    )
    pron_assessment_params_base64 = base64.b64encode(bytes(pron_assessment_params_json, "utf-8"))
    pron_assessment_params = str(pron_assessment_params_base64, "utf-8")

    # https://learn.microsoft.com/azure/ai-services/speech-service/how-to-get-speech-session-id#provide-session-id-using-rest-api-for-short-audio
    session_id = uuid.uuid4().hex

    # Build request
    url = f"https://{service_region}.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1"
    url = f"{url}?format=detailed&language={locale}&X-ConnectionId={session_id}"
    headers = {
        "Accept": "application/json;text/xml",
        "Connection": "Keep-Alive",
        "Content-Type": "audio/wav; codecs=audio/pcm; samplerate=16000",
        "Ocp-Apim-Subscription-Key": speech_key,
        "Pronunciation-Assessment": pron_assessment_params,
        "Transfer-Encoding": "chunked",
        "Expect": "100-continue",
    }

    print(f"II URL: {url}")
    print(f"II Config: {pron_assessment_params_json}")

    # Send request with chunked data
    response = requests.post(url=url, data=get_chunk(audio_file), headers=headers)
    get_response_time = time.time()
    audio_file.close()

    # Show Session ID
    print(f"II Session ID: {session_id}")

    if response.status_code != 200:
        print(f"EE Error code: {response.status_code}")
        print(f"EE Error message: {response.text}")
        exit()
    else:
        print(f"II Response: {response.json()}")

    latency = get_response_time - upload_finish_time
    print(f"II Latency: {int(latency * 1000)}ms")