Exam Professional Machine Learning Engineer topic 1 question 282 discussion

I went with D. "following Google-recommended best practices" https://cloud.google.com/speech-to-text/docs/optimizing-audio-files-for-speech-to-text#:~:text=We%20recommend%20a%20sample%20rate%20of%20at%20least%2016%20kHz%20in%20the%20audio%20files%20that%20you%20use%20for%20transcription%20with%20Speech%2Dto%2DText

We have longer than minute, 8KHz recordings. https://cloud.google.com/speech-to-text/docs/best-practices-provide-speech-data "avoid re-sampling. For example, in telephony the native rate is commonly 8000 Hz, which is the rate that should be sent to the service." -> 8KHz https://cloud.google.com/speech-to-text/docs/sync-recognize "Synchronous speech recognition returns the recognized text for short audio (less than 60 seconds). To process a speech recognition request for audio longer than 60 seconds, use Asynchronous Speech Recognition." -> asynchronous So, the correct answer is B.

According to the documentation: If possible, set the sampling rate of the audio source to 16000 Hz. Otherwise, set the sample_rate_hertz to match the native sample rate of the audio source (instead of re-sampling). https://cloud.google.com/speech-to-text/docs/best-practices-provide-speech-data

Lower sampling rates may reduce accuracy. However, avoid re-sampling. For example, in telephony the native rate is commonly 8000 Hz, which is the rate that should be sent to the service. https://cloud.google.com/speech-to-text/docs/best-practices-provide-speech-data

While you can use the original 8 kHz sample rate, upsampling to 16 kHz is likely to improve transcription accuracy.

The correct answer is D because the Google Cloud Speech-to-Text API recommends a sample rate of 16 kHz for optimal performance. While it can handle 8 kHz, the accuracy will be significantly lower. Synchronous recognition means the API waits for the entire audio file to be processed before returning a result. This is fine for short audio clips, but for recordings longer than a minute (as specified), it's highly inefficient and could lead to timeouts or delays in the application. Asynchronous recognition allows the API to process the audio in the background, returning a notification when the transcription is complete. This is much better suited for longer audio files and doesn't block the application.

Agree on B. If you read carefuly the documentation pointed will come to the conclusion that there is no need to upsample voice

B) Use original sampling rate and use asynchronous recognition... "If possible, set the sampling rate of the audio source to 16000 Hz. Otherwise, set the sample_rate_hertz to match the native sample rate of the audio source (instead of re-sampling)." https://cloud.google.com/speech-to-text/docs/best-practices-provide-speech-data#sampling_rate

According to google recommandation on Sampling rate: "If possible, set the sampling rate of the audio source to 16000 Hz. Otherwise, set the sample_rate_hertz to match the native sample rate of the audio source (instead of re-sampling)." So we should match the native sample (8kHz) in the question.

https://cloud.google.com/speech-to-text/docs/best-practices-provide-speech-data: Capture audio with a sampling rate of 16,000 Hz or higher. Lower sampling rates may reduce accuracy. However, avoid re-sampling. For example, in telephony the native rate is commonly 8000 Hz, which is the rate that should be sent to the service. https://cloud.google.com/speech-to-text/docs/optimizing-audio-files-for-speech-to-text#sample_rate_frequency_range: It's possible to convert from one sample rate to another. However, there's no benefit to up-sampling the audio, because the frequency range information is limited by the lower sample rate and can't be recovered by converting to a higher sample rate. -----> B, not D

According to the documentation, it's best to have 16 KHz sample rate, however one should avoid up-sampling and rather use the native sample rate

Following best practices, the easiest choice is B

Upsample to 16 kHz and Use Asynchronous Speech-to-Text Recognition

Upsampling to 16 kHz: The Speech-to-Text API recommends an audio sample rate of 16 kHz for optimal transcription accuracy. Upsampling the 8 kHz recordings to 16 kHz will improve the quality of the transcription. Asynchronous Recognition: Asynchronous recognition is suitable for longer audio recordings (more than one minute). It allows you to submit the audio file and receive the transcription results later, which is more efficient for batch processing. https://cloud.google.com/speech-to-text/docs/best-practices-provide-speech-data

My Answer: B - Not necessary upsampling (exclude C and D) - Asynchronous means executing different tasks with no sequential order. Therefore, is preferred over synchronous recognition for longer audio recordings as it allows for more efficient processing, especially when dealing with larger volumes of data.

My Answer: B - Not necessary upsampling (exclude C and D) - Asynchronous means executing different tasks with no sequential order. Therefore, is preferred over synchronous recognition for longer audio recordings as it allows for more efficient processing, especially when dealing with larger volumes of data.

B https://cloud.google.com/speech-to-text/docs/speech-to-text-requests#:~:text=Synchronous%20recognition%20requests%20are%20limited,periodically%20poll%20for%20recognition%20results.