Label Studio Revs Up Audio Labeling Performance with Version 1.7 of Popular Open Source ML/AI Data Labeling Platform

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Support for larger audio files, millisecond-level controls and an advanced rendering engine among new features that put Label Studio at the forefront in data labeling for audio files.

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This release puts Label Studio at the forefront of audio labeling platforms in terms of usability, functionality and extensibility.

Data science teams gain powerful new features for annotating audio files with the availability of Label Studio v1.7, the most popular open source data labeling platform to support all data types—video, image, text and hypertext, time-series and audio. The latest release also adds support for Terraform and improvements to Helm charts for Kubernetes to ease the deployment and management of Label Studio.

New audio labeling features in version 1.7 include:

  • An advanced rendering engine that displays audio waveforms with a highly responsive and performant interface
  • Granular, up-to-the-millisecond controls for fine-grained annotation control
  • New, highly configurable UI that improves audio labeling efficiency and ergonomics

Along with these updated features and configurable UI, Label Studio v1.7 now leverages the new HTML Audio tag to provide full coverage for a wider range of audio labeling use cases, including:

  • Automatic speech recognition
  • Sound event detection
  • Multiple speaker segmentation
  • Overlapping speech detection
  • Signal quality detection
  • Dialogue analysis
  • Intent classification
  • Voice activity detection

This release also includes two major updates to simplify the deployment and management of the Label Studio application:

  • Scripted, full infrastructure provisioning with Terraform
  • Scalable service management on Kubernetes with Helm chart deployment

“This release puts Label Studio at the forefront of audio labeling platforms in terms of usability, functionality and extensibility,” said Chris Hoge, head of community at Heartex, creators of Label Studio. “We’re also acting on feedback from our user community in the latest survey and support forums to ease deployments and management of the application with new options like Terraform. And for the growing segment of users deploying Label Studio at enterprise scale, the addition of Terraform support and Helm charts will simplify deployment automation and make it even easier to integrate Label Studio as a central platform for data-centric ML/AI operations.”

Label Studio is the most popular data labeling platform with more than 150,000 users worldwide, 95 million+ annotations created, and over 11,000 stars on GitHub. The 1.7 release is ready to install or upgrade today, and new features are available in the Label Studio Enterprise SaaS or supported on-prem offering from Heartex. Read the release notes and get started with Label Studio here:

The Label Studio Community released its first user community survey last week. It highlights how data science teams are shifting their focus from model development to dataset development and dives into popular technology choices in the migration to data-centric AI. Find more details in the full report.

About Heartex
Heartex is the company behind Label Studio, the most popular open source data labeling platform for Machine Learning and Artificial Intelligence. Founded in 2019 by data scientists and engineers who faced common challenges with model accuracy due to poor quality training data, the team believed the only viable solution was to enable internal teams with domain expertise to annotate and curate training data. They created Label Studio with a focus on usability, flexibility and collaborative workflows that support internal data labeling operations at scale and increase the accuracy of ML/AI models.

Today, more than 150,000 people around the world have used Label Studio to label nearly 100 million pieces of data, including production ML/AI initiatives for enterprises like Bombora, Geberit, Outreach, Wyze, Yext and more. For more information, visit


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