Topic 1 Question 193
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?
- Upload the audio files to Cloud Storage
- Call the speech:longrunningrecognize API endpoint to generate transcriptions
- Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions.
- Upload the audio files to Cloud Storage.
- Call the speech:longrunningrecognize API endpoint to generate transcriptions
- Create a Cloud Function that calls the Natural Language API by using the analyzeSentiment method
- Iterate over your local files in Python
- Use the Speech-to-Text Python library to create a speech.RecognitionAudio object, and set the content to the audio file data
- Call the speech:recognize API endpoint to generate transcriptions
- Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions.
- Iterate over your local files in Python
- Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object and set the content to the audio file data
- Call the speech:longrunningrecognize API endpoint to generate transcriptions.
- Call the Natural Language API by using the analyzeSentiment method
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コメント(1)
- 正解だと思う選択肢: A
Efficient audio processing: speech:longrunningrecognize is specifically designed for handling large audio files, offering asynchronous processing and optimized performance. Scalability: Cloud Storage and Vertex AI AutoML scale seamlessly to handle large volumes of data and model inferences. Cost-effectiveness: Separating transcription and sentiment analysis allows for potential cost optimization by using different pricing models for each service.
👍 2pikachu0072024/01/12
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