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MODEL Listed

voice-activity-detection

Pyannote's Voice Activity Detection (VAD) is a specialized tool designed to distinguish human speech from silence or background noise in audio streams. For developers building speech-to-text pipelines or voice assistants, this model serves as a critical preprocessing layer to reduce computational overhead by filtering out non-speech segments before they hit heavier ASR engines. Unlike simple energy-based thresholds, this model handles complex acoustic environments more robustly, making it ideal for long-form audio transcription and speaker diarization workflows. It integrates easily into Python-based stacks and is released under the permissive MIT license, allowing for flexible commercial deployment and modification.

pyannoteautomatic speech recognition
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01 / MODEL CARD

Model card

Pyannote's Voice Activity Detection (VAD) is a specialized tool designed to distinguish human speech from silence or background noise in audio streams. For developers building speech-to-text pipelines or voice assistants, this model serves as a critical preprocessing layer to reduce computational overhead by filtering out non-speech segments before they hit heavier ASR engines. Unlike simple energy-based thresholds, this model handles complex acoustic environments more robustly, making it ideal for long-form audio transcription and speaker diarization workflows. It integrates easily into Python-based stacks and is released under the permissive MIT license, allowing for flexible commercial deployment and modification.

Model typeautomatic speech recognition
Providerpyannote
Licensemit
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/pyannote/voice-activity-detection
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Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: pyannote/voice-activity-detection
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model pyannote/voice-activity-detection
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model pyannote/voice-activity-detection README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('pyannote/voice-activity-detection')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/pyannote/voice-activity-detection.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/pyannote/voice-activity-detection.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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