Optimizing AWS Transcribe Batch for Quick Arabic Audio (2-3 sec)

Select Language:

If you’re using AWS Transcribe Batch through boto3 in a Lambda function to transcribe short student recordings stored in S3, you might notice that even very brief audio files—around 2 to 3 seconds—take about 30 to 40 seconds to finish transcribing. If you’re wondering whether this is normal or if there’s a way to make it faster, here’s what you need to know.

First, based on your setup, you’re starting transcriptions with code similar to this:

python
transcribe_client.start_transcription_job(
TranscriptionJobName=job_name,
Media={‘MediaFileUri’: s3_uri},
MediaFormat=’mp3′,
LanguageCode=’ar-SA’,
OutputBucketName=bucket,
)

After starting a job, you’re checking its status every second:

python
while True:
job = transcribe_client.get_transcription_job(TranscriptionJobName=job_name)
if job[‘TranscriptionJob’][‘TranscriptionJobStatus’] == ‘COMPLETED’:
break
time.sleep(1)

What you’ve observed is that even for short clips lasting just a few seconds, the total process still takes around 40 seconds. This delay seems to occur within the AWS Transcribe service itself, not during your Lambda function or S3 access.

You also noticed that the input audio file has an .mp3 extension, but in the Transcribe console, the format sometimes appears as WAV. This mismatch could potentially cause a processing delay.

Now, to address your questions:

Is a 30–40 second delay normal for very short audio files?
Yes, this is generally expected. AWS Transcribe Batch isn’t optimized for rapid turnaround of small files; it’s designed for larger jobs where the setup overhead is negligible compared to the overall processing time.

Does AWS Transcribe Batch queue jobs before processing?
Indeed, Transcribe may place your jobs in a queue if there are many requests or if resources are busy, which can add to the total delay.

Can polling every second impact performance or cause throttling?
Polling every second is usually fine, but excessive requests could contribute to throttling if you’re executing many jobs simultaneously. Keep an eye on your account’s request limits.

Could mismatched audio formats affect processing time?
Yes, if the format you specify doesn’t match the actual audio file, or if there’s a format mismatch in the console display, it might cause delays. Make sure the format you specify (mp3, wav, etc.) matches the actual file.

What’s the best way to reduce latency for short recordings?
For small files like student reading responses, consider switching to real-time transcription using Amazon Transcribe Streaming instead of batch jobs. Streaming allows for near-immediate results and is more suitable for short clips. Alternatively, if batch is preferred, consider compressing the audio or processing longer segments at once to maximize efficiency.

In summary, expecting about 30–40 seconds for small files is normal with batch processing. To speed things up, exploring streaming transcription or combining multiple short responses into a single longer file before processing can help minimize delays and improve user experience.

Hope this helps you optimize your transcription workflow!