Introducing Open Source DAW Plugin for Eclipsa Audio

Eclipsa Audio is the brand name for a new, open-source 3D spatial audio technology. It's built upon the Immersive Audio Model and Formats (IAMF) specification, developed as a collaborative effort from the Alliance for Open Media (AOMedia). IAMF technology is available under a royalty free license from AOMedia.

An open source Eclipsa Audio plugin is now available for Digital Audio Workstations (DAWs) and Non-Linear Editing (NLE) software :

• Open source code is available at: https://github.com/google/eclipsa-audio-plugin
• Pre-compiled plugin binaries are made available to download from www.eclipsaapp.com

IAMF: A New Era for Immersive Audio

IAMF is a new open-source audio container specification poised to revolutionize how we experience sound. Developed by AOMedia, with significant contributions from industry, including Google and Samsung, IAMF aims to deliver truly immersive, three-dimensional audio across a wide array of applications, platforms, and devices.

At its core, IAMF is designed to deliver a realistic and engaging 3D soundscape. IAMF allows audio to be anywhere in space, including above, below, and behind the listener, creating a vivid three dimensional sphere of sound. This creates a more lifelike "3D audio" experience.

IAMF is designed as a versatile and open-source audio container format with several key technical characteristics to enable immersive and interactive audio experiences:

• Codec-Agnostic Container: IAMF itself is not a codec but a container format. This means it can carry audio data compressed by various existing and future codecs, such as Opus, PCM, AAC, and FLAC.
• Support for Multiple Audio Types: IAMF can handle different types of audio presentations, also called Audio Elements in the IAMF specification:
  • Channel-based audio: Such as 5.1.2 and 7.1.4, according to the Rec. ITU-R BS.2051-3
  • Scene-based audio: Full ambisonics spherical soundfield
• 3D Spatial Audio Rendering: Open source based rendering to loudspeakers and binaurally for headphones.
• Metadata for Rendering and Customization: IAMF includes Mix Presentation metadata that specifies how to render, process and mix one or more Audio Elements:
  • Creators can make user selectable Mix Presentations, for example enabling users to adjust dialog channel volume.
• Open Source Reference Software: AOMedia provides various open-source tools for developers:
  • libiamf open source reference decoder
  • iamf-tools for processing IAMF files
• Integration with Standard Media Containers: IAMF is designed to be integrated into common media container formats like MP4 (ISO-BMFF) for delivery with video content.

The IAMF specification includes a definition for profiles which determine how many audio elements and audio channels a corresponding IAMF file can include. The table below summarizes the profile requirements for the current IAMF specifications.

Feature	IAMF v1.0		IAMF v1.1
Profile	Simple	Base	Base Enhanced
Audio codec	Opus, AAC, FLAC, PCM	Opus, AAC, FLAC, PCM	Opus, AAC, FLAC, PCM
Max # of Audio Elements	1	2	28
Max # of audio channels	16	18	28

Eclipsa Audio support in YouTube

Since January 2025, YouTube now accepts files with Eclipsa Audio (IAMF v1.0) and consumers can now play the content on a growing range of compatible devices, including Samsung's 2025 TV and soundbar lineup.

Eclipsa Audio playback in a YouTube TV app can be verified with two different ways (see the screenshot below):

• "Eclipsa Audio" should be visible in the Settings menu
• "Stats for nerds" view should show the "iamf.001.001.Opus" string in the Codecs section

Here's an example of Eclipsa Audio content on YouTube. The actual audio track in this video consists of 3rd order ambisonics and stereo, thus it includes two audio elements and in total 18 channels of audio. Ambient sounds are all in the 3rd order ambisonics track (16 channels) and narrative parts in the stereo track (2 channels). YouTube uses the Opus open source codec for compressing the audio channel data.

Eclipsa Audio Plugins for Sound Design

The Eclipsa Audio plugin consists of two parts:

• Eclipsa Audio renderer plugin: central hub for monitoring, configuration and export
• Eclipsa Audio element plugin: connects your audio elements (channels) to the renderer plugin, with optional basic panning functionality

First release of the Eclipsa Audio plugin is available for Avid Pro Tools with macOS support. While downloading the plugin binaries from www.eclipsaapp.com, you can sign up to receive updates on the upcoming new releases.

The Eclipsa Audio Renderer Plugin manages the overall 3D audio mix, enabling you to configure speaker setups, monitor your mix, and export the final mix in the IAMF format. Additionally, it's used to create audio elements and configure mix presentations, both of which are required for playback.

The Eclipsa Audio Renderer Plugin provides comprehensive export options to ensure your 3D audio mix is correctly formatted and optimized for immersive playback systems. Once the final mix is ready for export, you can also select a video track to be muxed with the IAMF audio track. The final MP4 file after export is ready to be uploaded to YouTube.

The Eclipsa Audio Element plugin should be added on every track you want to spatialize. This setup ensures each sound source is routed to the correct audio element and fully integrated into the 3D mix. To reduce the number of panners needed, Pro Tools' buses can also be used to route multiple tracks through an Audio Element plugin instance before routing the audio to the Eclipsa Audio Renderer Plugin. Pro Tools includes a great selection of built-in panning tools so it is recommended to use these tools for the actual sound mixing and use the pass-through option in the Audio Element plugin.

Next Steps

The Eclipsa Audio plugins continue to evolve. As an open source project, we invite developers to join and contribute.

By Jani Huoponen, Felicia Lim, Jan Skoglund - Open Media Audio Team

Introducing New Open Source Documentation Resources

Today we're introducing two new open source documentation resources for open source software maintainers, a Docs Advisor guide and a set of Documentation Project Archetypes. These tools are intended to help maintainers make effective use of limited resources when it comes to planning and executing open source documentation work.

The Docs Advisor is a guide intended to demystify documentation work, including help picking a documentation approach, understanding your audience and available resources, and how to write, revise, evaluate, and maintain your documentation.

Documentation Project Archetypes are a set of thirteen project field guides. Each archetype represents a different type of documentation project, the problems it can solve, and how to bring the right collaborators together on the project to create great docs.

Origin story

More than 130 open source projects wrote 200+ case studies and project reports as a part of their participation in the Google Season of Docs program from 2019 to 2024. These case studies and project reports represent a variety of documentation projects from a wide range of open source groups. In these wrap-ups, project maintainers and technical writers describe how they approached their documentation projects, capturing many successes and more than a few challenges.

These reports are a treasure trove of lessons learned–but it's unrealistic to expect time-crunched open source maintainers to read through them all. So we got in touch with Daniel Beck and Erin Kissane to chat about ways to help organize and summarize some of these lessons learned.

These conversations turned into the Docs Advisor guide (‘like having an experienced technical writer hanging over your shoulder') and the thirteen Documentation Project Archetypes.

Our goal with these resources was to turn all of the hard-won experience of the Google Season of Docs participants into explicit documentation advice and guidance for open source maintainers.

More about the Docs Advisor

The Docs Advisor guide is intended to demystify the work of good documentation. It collects practices and processes from within technical writing and docs communities and from user experience, information architecture, and content strategy.

• In Part 1, you'll pick an overall approach that suits the needs of your project.
• In Part 2, you'll learn enough about your community and their needs to ensure that your hard work will be helping real people.
• In Part 3, you'll assess your existing resources and pull together everything you need to move quickly and confidently through the work of creating and revising your docs.
• In Part 4, you'll get to work writing and revising your docs and set yourself to successfully evaluate your work and maintain it.

The Docs Advisor guide also includes a docs plan template to help you accomplish your docs plan work, including:

• What approach will you take to your documentation work, as a whole?
• What risks do you need to mitigate?
• Are there any documents to make or steps to perform to increase your chances of success?

The Docs Advisor incorporates guidance from interviews with open source maintainers and technical writers as well as from the Google Season of Docs case studies, and integrates the Documentation Project Archetypes into the recommendations for maintainers planning docs work.

More about the Archetypes

Documentation Project Archetypes are meant to help you recognize common types of documentation work (whether you're writing a new user guide or replatforming your docs site), the situations in which they apply, and organize yourself to bring the work to completion.

The archetypes cover the following areas:

• Planning and evaluating your docs: Experiment and analysis archetypes support future docs work, by learning more about your existing docs, your audience, and your capacity to deliver meaningful change.
• Producing new docs: Creation archetypes make new docs that directly help your audience complete tasks and achieve their goals.
• Revising and transforming existing docs: Revision archetypes modify existing docs, to improve quality, reduce maintenance costs, and reach wider audiences.
• Equipping yourself with docs tools and process: Tool and process archetypes adopt new tools or practices that help you make more, better, or higher quality docs.

All of the archetypes are available on GitHub.

Doc tools in the wild

We are excited to share these tools and are looking forward to seeing how they are used and evolve.

Daniel demoed the concept and first completed archetype, The Migration, at FOSDEM 2025 in his talk Patterns for maintainer and tech writer collaboration. He also talked about the work on the API Resilience Podcast episode "Patterns in Documentation."

We hope to get valuable feedback during a proposed Doc Archetypes session at Open Source Summit Europe 2025 (acceptance pending).

We are also excited to be developing some Doc Archetype illustration cards with Heather Cummings — a few previews are already live on The Edit, The Audit, and The Factory.

If you have questions or suggestions, please raise an issue in the Open Docs repo.

By Elena Spitzer & Erin McKean, Google Open Source Programs Office

Transforming Kubernetes and GKE into the leading platform for AI/ML

The world is rapidly embracing the power of AI/ML, from training cutting-edge foundation models to deploying intelligent applications at scale. As these workloads become more sophisticated and demanding, the infrastructure required to support them must evolve. Kubernetes has emerged as the standard for container orchestration, but AI/ML introduces unique challenges that push traditional infrastructure to its limits.

AI training jobs often require massive scale, needing to coordinate thousands of specialized hardware like GPUs and TPUs. Reliability is critical, as failures can be costly for long running, large-scale training jobs. Efficient resource sharing across teams and workloads is essential given the expense of accelerators. Furthermore, deploying and scaling AI models for inference demands low latency and faster startup times for large container images and models.

At Google, we are deeply invested in the AI/ML revolution. This is why we are doubling down on our commitment to advancing Kubernetes as the foundational open standard for these workloads. Our strategy centers on evolving the core Kubernetes platform to meet the needs of the "next trillion core hours," specifically focusing on batch and AI/ML. We then bring these advancements, alongside enterprise-grade management and optimizations, to users through Google Kubernetes Engine (GKE).

Here's how we are transforming Kubernetes and GKE:

Redefining Kubernetes' relationship with specialized hardware

Kubernetes was initially designed for more uniform CPU compute. The surge of AI/ML brought new requirements for seamless integration and efficient management of expensive, sparse, and diverse accelerators. To support these new demands, Google has been a key investor in upstream Kubernetes to offer robust support for a diverse portfolio of the latest accelerators, including multiple generations of TPUs and a wide range of NVIDIA GPUs.

A core Kubernetes enhancement driven by Google and the community to better support AI/ML workloads is Dynamic Resource Allocation (DRA). This framework, developed in the heart of Kubernetes, provides a more flexible and extensible way for workloads to request and consume specialized hardware resources beyond traditional CPU and memory, which is crucial for efficiently managing accelerators. Building on such foundational open-source capabilities, GKE can then offer features like Custom Compute Classes, which improve the obtainability of these resources through intelligent fallback priorities across different capacity types like reservations, on-demand, and Spot instances. Google's active contributions to advanced resource management and scheduling capabilities within the Kubernetes community ensure that the platform evolves to meet the sophisticated demands of AI/ML, making efficient use of these specialized hardware resources more broadly accessible.

Unlocking scale and reliability

AI/ML workloads demand unprecedented scale and have new failure modes compared to traditional applications. GKE is built to handle this, supporting up to 65,000 nodes in a single cluster. We've demonstrated the ability to run the largest publicly announced training jobs, coordinating 50,000 TPU chips with near-ideal scaling efficiency.

Critically, we are enhancing core Kubernetes capabilities to support the scale and reliability needed for AI/ML. For instance, to better manage distributed AI workloads like serving large models split across multiple hosts, Google has been instrumental in developing features like JobSet (emerging from earlier concepts like LeaderWorkerSet) within the Kubernetes community (SIG Apps). This provides robust orchestration for co-scheduled, interdependent groups of Pods. We are also actively working upstream to improve Kubernetes reliability and stability through initiatives like Production Readiness Reviews, promoting safer upgrade paths, and enhancing etcd stability for the benefit of all Kubernetes users.

Optimizing Kubernetes performance for efficient inference

Low-latency and cost-efficient inference is critical for AI applications. For serving, the GKE Inference Gateway routes requests based on model server metrics like KVCache utilization and pending queue length, reducing serving costs by up to 30% and tail latency by 60% compared to traditional load balancing. We've even achieved vLLM fungibility across TPUs and GPUs, allowing users to serve the same model on either accelerator without incremental effort.

To address slow startup times for large AI/ML container images (often 20GB+), GKE offers rapid scale-out features. Secondary boot disks allow preloading container images and data, resulting in up to 29x faster container mounting time. GCS FUSE enables streaming data directly from Cloud Storage, leading to faster model load times. Furthermore, GKE Inference Quickstart provides data-driven, optimized Kubernetes deployment configurations, saving extensive benchmarking effort and enabling up to 30% lower cost, 60% lower tail latency, and 40% higher throughput.

Simplifying the Kubernetes experience and enhancing observability for AI/ML

We understand that data scientists and ML researchers may not be Kubernetes experts. Google aims to simplify the setup and management of AI-optimized Kubernetes clusters. This includes contributions to Kubernetes usability efforts and SIG-Usability. Managed offerings like GKE provide multiple paths to set up AI-optimized environments, from default configurations to customizable blueprints. Offerings like GKE Autopilot further abstract away infrastructure management, aiming for the ease of use that benefits all users.
Ensuring visibility into AI/ML workloads is paramount. Google actively supports and contributes to the integration of standard open-source observability tools within the Kubernetes ecosystem, such as Prometheus, Grafana, and OpenTelemetry. Building on this open foundation, GKE then provides enhanced, out-of-the-box observability integrated with popular AI frameworks & tools, including specific insights into workload startup latency and end-to-end tracing.

Looking ahead: continued investment in Open Source Kubernetes for AI/ML

The transformation continues. Our roadmap includes exciting developments in upstream Kubernetes for easily deploying and managing large-scale clusters, support for new GPU & TPU generations integrated through open-source mechanisms, and continued community-driven innovations in fast startup, reliability, and ease of use for AI/ML workloads.

Google is committed to making Kubernetes the premier open-source platform for AI/ML, pushing the boundaries of scale, performance, and efficiency while maintaining stability and ease of use. By driving innovation in core Kubernetes and building powerful, deeply integrated capabilities in our managed offering, GKE, we are empowering organizations to accelerate their AI/ML initiatives and unlock the next generation of intelligent applications built on an open foundation.

Come explore the possibilities with Kubernetes and GKE for your AI/ML workloads!

By Francisco Cabrera & Federico Bongiovanni, GCP Google Kubernetes Engine