Batch Container
API Guide
Parallel Processing

Ability to run a container with multiple cores

For customers who are looking to improve job turnaround time and who are able to assign sufficient resources, it is possible to pass a parallel transcription parameter to the container to take advantage of multiple CPUs. The parameter is called parallel and the following example shows how it can be used. In this case to use 4 cores to process the audio you would run the container like this:

docker run -i -rm -v ~/tmp/shipping-forecast.wav:/input.audio \
  -v ~/tmp/config.json:/config.json \
  batch-asr-transcriber-en:9.0.1 \

Depending on your hardware, you may need to experiment to find the optimum performance. We've noticed significant improvement in turnaround time for jobs by using this approach.

If you limit or are limited on the number of CPUs you can use (for example your platform places restrictions on the number of cores you can use, or you use the --cpu flag in your docker run command), then you should ensure that you do not set the parallel value to be more than the number of available cores. If you attempt to use a setting in excess of your free resources, then the container will only use the available cores.

If you simply increase the parallel setting to a large number you will see diminishing returns. Moreover, because files are split into 5 minute chunks for parallel processing, if your files are shorter than 5 minutes then you will see no parallelization (in general the longer your audio files the more speedup you will see by using parallel processing).

If you are running the container on a shared resource you may experience different results depending on what other processes are running at the same time.

The optimum number of cores is N/5, where N is the length of the audio in minutes. Values higher than this will deliver little to no value, as there will be more cores than chunks of work. A typical approach will be to increment the parallel setting to a point where performance plateaus, and leave it at that (all else being equal).

For large files and large numbers of cores, the time taken by the first and last stages of processing (which cannot be parallelized) will start to dominate, with diminishing returns.