speaker diarization precision 2

Providerpyannote
Categoryvoice-activity-detection
LicenseApache-2.0
Downloads115
Stars0

Overview

Speaker Diarization Precision 2, powered by pyannote, is a specialized voice-activity-detection tool designed to solve the 'who spoke when' problem in multi-speaker audio streams. Unlike general speech-to-text models, this model focuses on high-precision temporal segmentation and speaker identity clustering. It is particularly effective for developers building automated meeting minutes, podcast indexing, or forensic audio analysis where distinguishing between overlapping voices is critical. Built on an open Apache-2.0 license, it integrates easily into Python-based ML pipelines, offering a robust alternative to proprietary cloud APIs by allowing for local deployment and fine-tuning on domain-specific acoustic environments.

Highlights

  • High-precision speaker segmentation and identity clustering
  • Optimized for multi-speaker audio stream analysis
  • Permissive Apache-2.0 license for commercial deployment
  • Seamless integration with Python-based ML workflows
  • Local execution reduces latency and data privacy risks

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-precision-2")
tokenizer = AutoTokenizer.from_pretrained("pyannote/speaker-diarization-precision-2")

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

Download the full repository 
huggingface-cli download pyannote/speaker-diarization-precision-2

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

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download pyannote/speaker-diarization-precision-2 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('pyannote/speaker-diarization-precision-2')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/pyannote/speaker-diarization-precision-2

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/pyannote/speaker-diarization-precision-2

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

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('pyannote/speaker-diarization-precision-2')
tokenizer = AutoTokenizer.from_pretrained('pyannote/speaker-diarization-precision-2')

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

Download the full repository 
modelscope download --model pyannote/speaker-diarization-precision-2

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

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model pyannote/speaker-diarization-precision-2 README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('pyannote/speaker-diarization-precision-2')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/pyannote/speaker-diarization-precision-2.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/pyannote/speaker-diarization-precision-2.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

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

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'pyannote/speaker-diarization-precision-2')

Full Documentation

来源: HuggingFace

---
tags:
- pyannote
- pyannote-audio
- pyannote-audio-pipeline
- audio
- voice
- speech
- speaker
- speaker-diarization
- speaker-change-detection
- voice-activity-detection
- overlapped-speech-detection
---

Precision-2 speaker diarization

This pipeline runs Precision-2 speaker diarization on pyannoteAI cloud.
Read the announcement blog post.

This pipeline is a stripped down version of pyannoteAI SDK that provides much more features:

  • speaker diarization optimized for speech-to-text

  • speaker voiceprinting and identification

  • confidence scores

  • and more...

A self-hosted version of Precision-2 is also available for enterprise customers.

Setup

1. pip install pyannote.audio
2. Create an API key on pyannoteAI dashboard (free credits included)

Usage

python
# initialize speaker diarization pipeline
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained(
    'pyannote/speaker-diarization-precision-2', 
    token="{pyannoteAI-api-key}")

run speaker diarization on pyannoteAI cloud

output = pipeline("/path/to/audio.wav")

enjoy state-of-the-art speaker diarization

for turn, speaker in output.speaker_diarization:
    print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}")
View official source
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