Praat++: Multimedia Annotation System for Speech and Vocalization

Accepted at ACL 2026 System Demonstrations.
Paper: https://aclanthology.org/2026.acl-demo.80/

Praat++ is a browser-based multimedia annotation system for speech and vocalization data. It supports time-aligned audio and video annotation with waveform, spectrogram, pitch, intensity, synchronized video playback, TextGrid import/export, file-pool management, and AI-assisted pre-annotation.

The system is inspired by Praat and extends Praat-style annotation workflows to a collaborative web-based environment.

Example

Key Features

User and File Management

• Role-based user management with regular annotators and superusers.

• Personal file pool for uploading, deleting, organizing, and tracking annotation files.

• Folder-based task management for different annotation tasks or groups.

• File metadata display, including file type, annotation status, duration, size, and last saved time.

• File locking with heartbeat updates to prevent simultaneous editing conflicts.

Multimodal Annotation Workspace

• Browser-based annotation for audio/video files.

• Synchronized waveform, spectrogram, pitch, intensity, and video playback.

• Adjustable acoustic views, including configurable spectrogram.

• Real-time acoustic feedback when hovering over the timeline, including time, pitch, intensity, and waveform amplitude.

• Region-based annotation with drag-to-create, resize, move, playback, label editing, and deletion.

• Tier-based annotation with custom label categories and region-level confidence control for different information layers, such as emotion, behavior, or vocalization type.

TextGrid Support

• Import TextGrid files together with matching media files or for media files already in the file pool.

• Export annotations to Praat-compatible .TextGrid files.

• Batch export of annotation data from the file pool.

AI-Assisted Pre-Annotation

• PANNs-based pre-annotation for generating candidate annotation regions.

• Configurable event label, threshold, minimum duration, and target tier.

• Human-in-the-loop review and refinement of AI-generated annotations.

Auto-Save and Statistics

• Automatic saving and loading of annotation progress.

• Preliminary statistics page for user-level file counts, folder-level progress, and custom category summaries.

For a system walkthrough, please refer to the Praat++ demo video.

Annotation Storage

Praat++ stores uploaded media files, annotations, and generated processing files separately for each user and folder-based task.

The general storage structure is:

static/videos/pool/{user}/{folder_name}/{file_name}

Each uploaded file is saved under the logged-in username and selected folder. Annotation progress is stored independently for each user, folder, and media file.

Try It Out

You can run Praat++ locally by following these steps:

1. Clone the Repository

git clone https://github.com/UTA-ACL2/PraatPlusPlus.git
cd PraatPlusPlus

2. Create and Activate Virtual Environment

python -m venv venv
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

3. Install Required Packages

pip install -r requirements.txt

4. Set Up External Tools and Models

• Install ffmpeg and ensure it is added to your system PATH.
• For PANNs-based pre-annotation, prepare the PANNs pretrained weights and place them under:

app/ai/panns/
├── Cnn14_DecisionLevelMax.pth
└── Cnn14_mAP=0.431.pth

The model weight files are not included in this repository and should be prepared separately.

5. Configure User Accounts

Open app/routes/login_routes.py and define allowed usernames and roles in the user account configuration.

6. Run the Flask App

python run.py

7. Open in Browser

Visit http://127.0.0.1:5000 to use Praat++ locally.

Paper and Citation

Praat++ is described in our ACL 2026 System Demonstrations paper:

Praat++: Multimedia Annotation System for Speech and Vocalization

Paper: https://aclanthology.org/2026.acl-demo.80/

If you use Praat++ in your research, please cite:

@inproceedings{zhang-zhu-2026-praat,
    title = "Praat++: Multimedia Annotation System for Speech and Vocalization",
    author = "Zhang, Weiran  and
      Zhu, Kenny Q.",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-demo.80/",
    doi = "10.18653/v1/2026.acl-demo.80",
    pages = "812--818",
    ISBN = "979-8-89176-392-0",
    abstract = "High-quality time-aligned annotation is fundamental to speech processing and animal vocalization research, yet precise boundary localization and consistent labeling remain challenging in collaborative settings. We present Praat++, a web-based multimedia annotation system designed for collaborative, video-informed, and AI-assisted timeline labeling of audio and video data. The system tightly synchronizes waveform, spectrogram, pitch, intensity, and time-aligned video playback with fine-grained region-based editing, enabling precise boundary refinement and improved label accuracy within a unified interface. Praat++ further incorporates role-aware workflow management and human-in-the-loop AI-assisted pre-annotation to enhance inter-annotator consistency and reduce labeling time. Through real-world multimodal speech and animal vocalization annotation scenarios, we demonstrate that Praat++ provides an integrated infrastructure for improving annotation quality and efficiency in dataset construction workflows. The demo video (https://www.youtube.com/watch?v=YboCoBRF5lg), website (https://redgiant.uta.edu/praat) and source code (https://github.com/UTA-ACL2/PraatPlusPlus) are now publicly available."
}

Contact

Peter (Weiran Zhang)
Email: wxz9630@mavs.uta.edu

Acknowledgments

Praat++ was inspired by the design of Praat on the Web (Domínguez et al., 2016).
We gratefully acknowledge their contribution to web-based speech annotation platforms.

Praat++ is developed by the ACL Lab at the University of Texas at Arlington (UTA).