Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API You want to use the most efficient approach. What should you do?

A. 1. Upload the audio files to Cloud Storage
2. Call the speech:longrunningrecognize API endpoint to generate transcriptions
3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions. B. 1. Upload the audio files to Cloud Storage.
2. Call the speech:longrunningrecognize API endpoint to generate transcriptions
3. Create a Cloud Function that calls the Natural Language API by using the analyzeSentiment method C. 1. Iterate over your local files in Python
2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object, and set the content to the audio file data
3. Call the speech:recognize API endpoint to generate transcriptions
4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions. D. 1. Iterate over your local files in Python
2. Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object and set the content to the audio file data
3. Call the speech:longrunningrecognize API endpoint to generate transcriptions.
4. Call the Natural Language API by using the analyzeSentiment method