Audio Annotation Output Formats

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Most virtual assistance, speech recognition models, virtual customer support operations, and so on are built on audio annotation. To train, we need a dataset that has been annotated by specially designed applications/tools. Let's look at a few audio annotation tools and their export formats.

Label studio

  • Label Studio is an open-source data labeling tool.
  • It is a multi-type data labeling tool that supports audio, text, image, and video data labeling.
  • The tool is available in any web browser and provides datasets with high precision.
  • For exporting completed audio labeling tasks, Label Studio supports JSON, CSV, ASR MANIFEST, and TSV formats.

Diffgram

  • Diffgram is an open-source platform divided into three sections: annotation, catalog, and workflow.
  • It has scalable Training Data (Annotation, Catalog, Workflow) for all Data Types (Image, Video, 3D, Text, Geo, Audio, and more).
  • It only supports.mp3,.wav, and .flac files for audio annotation.
  • Diffgram supports export format JSON.

Audacity

  • Audacity is free and open-source software.
  • It is a multi-track audio editor and recorder for Windows, macOS, GNU/Linux, and other operating systems.
  • It can import, export, and record a variety of file formats, including WAV, AIFF, and MP3.
  • The default export format is plain ASCII text.

VGG Image Annotator (VIA)

  • VIA is an open-source project built entirely on HTML, JavaScript, and CSS.
  • VGG Image Annotator is a standalone manual annotation software for image, audio, and video.
  • VGG Image Annotator supports CSV and JSON export formats.
  • It can also be run as an offline application in any HTML-capable browser.

Audino

  • Audino is another open-source audio annotation tool that includes features such as transcription and labeling, allowing annotation for tasks such as Voice Activity Detection (VAD), Diarization, Speaker Identification, Automated Speech Recognition, Emotion Recognition, and more.
  • The annotations are easily exportable in JSON format.

The other open-source audio annotation tools are EchoML, Pratt, Aubio, and audio-annotator. The project's requirements are met and the desired results are obtained by selecting the appropriate tools.

Frequently Asked Questions

JSON is the most widely supported format, exported by Label Studio, Diffgram, VGG Image Annotator and Audino. Label Studio additionally supports CSV, ASR MANIFEST and TSV, and VGG Image Annotator supports CSV. Audacity is the outlier: its default export format is plain ASCII text. Matching the export format to what your training pipeline expects is usually the deciding factor when choosing a tool.
JSON, CSV, ASR MANIFEST and TSV. Label Studio is an open-source, multi-type data labeling tool supporting audio, text, image and video, available in any web browser. Its ASR MANIFEST support makes it a natural fit when the output feeds an automatic speech recognition pipeline.
Audino. It is an open-source audio annotation tool that includes transcription and labeling, allowing annotation for Voice Activity Detection (VAD), diarization, speaker identification, automated speech recognition and emotion recognition. Its annotations are easily exportable in JSON.
It depends on the tool. Diffgram supports .mp3, .wav and .flac for audio annotation. Audacity can import, export and record a variety of formats including WAV, AIFF and MP3. Check a tool's supported input formats against your source recordings before committing, since converting a large corpus after the fact is avoidable work.
EchoML, Pratt, Aubio and audio-annotator are also available beyond the five covered here. There is no single best tool: the project's requirements determine which one delivers the results you need, and the main variables are the export format, the supported input formats and the annotation types required.