Klay Beam is a toolkit for running Apache Beam jobs that process audio data. It can be used for parallelizing audio transformations and feature extraction. The basic workflow:

1. Write an audio transformation or feature-extraction pipeline
2. Run and test the pipeline locally
3. Launch a job with the GCP Dataflow runner, scaling up execution to hundreds or thousands of concurrent processes.

This repository bundles:

• The klay_beam python package with basic utilities for reading, transforming, and writing audio in beam pipelines
• Docker image build processes for images that can be used in pipelines executed on the Dataflow Beam Runner on GCP. Core images include support for a python versions 3.9, 3.10, 3.11, and PyTorch (optionally with CUDA support). (See: hub.docker.com/r/klaymusic/klay-beam/)
• Examples of simple pipeline launch scripts for running jobs locally and on GCP Dataflow.

See the python package readme in klay_beam for more information.