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speaker-diarization-community-1

Speaker Diarization Community 1, powered by pyannote, is a specialized tool for the 'who spoke when' problem in audio processing. Unlike standard ASR that only transcribes text, this model partitions audio streams into segments based on speaker identity. It is particularly effective for multi-speaker environments such as podcasts, interviews, and meeting recordings where precise speaker attribution is required. For developers, it integrates well into speech-to-text pipelines to provide structured metadata, allowing for the creation of speaker-labeled transcripts. Compared to generic clustering methods, it offers a more robust framework for handling overlapping speech and varying acoustic conditions, operating under a permissive CC-BY-4.0 license for flexible deployment.

pyannoteautomatic speech recognition
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01 / MODEL CARD

Model card

Speaker Diarization Community 1, powered by pyannote, is a specialized tool for the 'who spoke when' problem in audio processing. Unlike standard ASR that only transcribes text, this model partitions audio streams into segments based on speaker identity. It is particularly effective for multi-speaker environments such as podcasts, interviews, and meeting recordings where precise speaker attribution is required. For developers, it integrates well into speech-to-text pipelines to provide structured metadata, allowing for the creation of speaker-labeled transcripts. Compared to generic clustering methods, it offers a more robust framework for handling overlapping speech and varying acoustic conditions, operating under a permissive CC-BY-4.0 license for flexible deployment.

Model typeautomatic speech recognition
Providerpyannote
Licensecc-by-4.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/pyannote/speaker-diarization-community-1
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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/speaker-diarization-community-1
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/speaker-diarization-community-1
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/speaker-diarization-community-1 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/speaker-diarization-community-1')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/pyannote/speaker-diarization-community-1.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/speaker-diarization-community-1.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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