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Google Exam Professional Machine Learning Engineer Topic 5 Question 86 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 86
Topic #: 5
[All Professional Machine Learning Engineer Questions]

You work at an organization that maintains a cloud-based communication platform that integrates conventional chat, voice, and video conferencing into one platform. The audio recordings are stored in Cloud Storage. All recordings have an 8 kHz sample rate and are more than one minute long. You need to implement a new feature in the platform that will automatically transcribe voice call recordings into a text for future applications, such as call summarization and sentiment analysis. How should you implement the voice call transcription feature following Google-recommended best practices?

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Suggested Answer: D

Contribute your Thoughts:

Galen
1 months ago
I see your point, Alba. Let's go with the original rate for now.
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Erasmo
1 months ago
I'm with the crowd on this one. Option D is the way to go. Though I do wonder if the audio quality will be as good as a human transcriptionist. Maybe we should hire some really bored linguists instead?
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Lanie
13 days ago
I agree, using asynchronous recognition with the Speech-to-Text API will also speed up the transcription process.
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Katheryn
19 days ago
Option D is definitely the best choice. Upsampling the audio to 16 kHz will improve the transcription accuracy.
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Alba
1 months ago
That's a good point, but I think using the original rate is more efficient.
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Reta
1 months ago
But wouldn't upsampling to 16 kHz improve the transcription accuracy?
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Margurite
1 months ago
Haha, I'm just imagining the poor interns having to listen to all those long, boring call recordings. Good thing they've got the Speech-to-Text API to do the dirty work!
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Frederick
6 days ago
D) Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.
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Sueann
7 days ago
C) Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
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Maile
9 days ago
B) Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.
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Alyce
12 days ago
A) Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
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Galen
1 months ago
I agree with Alba, using the original rate seems like the best option.
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Curt
1 months ago
Option D all the way! 16 kHz audio and async recognition - that's the way to go. Gotta love those Google best practices, am I right?
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Lai
1 months ago
I agree with Antonio. The Google-recommended best practices suggest using asynchronous recognition for longer audio files, and upsampling to 16 kHz will improve the accuracy of the transcription.
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Antonio
2 months ago
Option D seems like the best choice here. Upsampling to 16 kHz and using asynchronous recognition will likely give us the best results for long audio recordings.
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Elli
10 days ago
Definitely, implementing this new feature will enhance the platform's capabilities for users.
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Dalene
16 days ago
It's important to follow best practices to ensure accurate transcriptions for future applications.
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Denny
18 days ago
Agreed, asynchronous recognition will also help with processing longer audio recordings more efficiently.
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Daniel
1 months ago
Great, let's go with option D then. It seems like the most effective approach for implementing the voice call transcription feature.
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Carmen
1 months ago
I agree, using asynchronous recognition will also help with processing longer audio recordings more efficiently.
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Kirk
1 months ago
I think option D is the way to go. Upsampling to 16 kHz should improve the transcription accuracy.
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Blossom
1 months ago
I think option D is the way to go. Upsampling to 16 kHz should improve transcription accuracy.
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Alba
2 months ago
I think we should use the original audio sampling rate for transcription.
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