Name: speaker diarization community 1
Author: pyannote

Overview

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.

Highlights

Accurately partitions audio by speaker identity
Ideal for multi-speaker interview transcription
Seamlessly integrates into ASR metadata pipelines
Permissive CC-BY-4.0 license for broad use

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("pyannote/speaker-diarization-community-1")
tokenizer = AutoTokenizer.from_pretrained("pyannote/speaker-diarization-community-1")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download pyannote/speaker-diarization-community-1

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download pyannote/speaker-diarization-community-1 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('pyannote/speaker-diarization-community-1')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/pyannote/speaker-diarization-community-1

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/pyannote/speaker-diarization-community-1

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('pyannote/speaker-diarization-community-1')
tokenizer = AutoTokenizer.from_pretrained('pyannote/speaker-diarization-community-1')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model pyannote/speaker-diarization-community-1

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model pyannote/speaker-diarization-community-1 README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('pyannote/speaker-diarization-community-1')

Git Download

Make sure git-lfs is installed first

Git Download

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

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/pyannote/speaker-diarization-community-1.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'pyannote/speaker-diarization-community-1')