# modelscope/3D-Speaker

A Repository for Single- and Multi-modal Speaker Verification, Speaker Recognition and Speaker Diarization

Repository: https://github.com/modelscope/3D-Speaker
Canonical: https://ross.abutalabs.com/products/3d-speaker
Language: Python
License: Apache-2.0
License Family: permissive
Topics: campplus, speaker-diarization, speaker-verification, voxceleb, 3d-speaker, eres2net, language-identification, modelscope, cnceleb, sdpn
Last push: 2025-12-08T07:20:17+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 56, release rhythm 35, longevity 91
- inputs: {"age_days": 1276, "days_push": 268, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3121, forks 268 (observed 2026-08-28T04:07:44.275561+00:00)

## What it is
3D-Speaker is an open-source Python toolkit for single- and multi-modal speaker verification, speaker recognition, and speaker diarization, with pretrained models hosted on ModelScope. It includes recipes and benchmarks for models like CAM++, ERes2Net, ECAPA-TDNN, and SDPN, plus a large-scale speech corpus for speech representation research.

## Use cases
- verify speaker identity from voice recordings
- diarize multi-speaker meeting audio to see who spoke when
- extract speaker embeddings from audio
- train speaker verification models on VoxCeleb or CNCeleb
- identify spoken language from audio
- run multi-modal speaker recognition combining audio and video

## When to choose
- you need state-of-the-art speaker verification or diarization with pretrained models
- you want reproducible recipes and benchmarks on VoxCeleb, CNCeleb, or 3D-Speaker datasets
- you work in Python/PyTorch and want ModelScope model integration

## When to avoid
- you need general speech-to-text transcription rather than speaker analysis
- you need a production-ready plug-and-play service without training infrastructure
- you work outside Linux/PyTorch environments

## Facets
- artifact type: library
- maturity: active
- function: speech-recognition, machine-learning, audio-processing, benchmarking, sdk
- domain: speech-processing, machine-learning, deep-learning
- platform: python
- tags: speaker-verification, speaker-diarization, speaker-recognition, voice-embeddings, pytorch, pretrained-models, language-identification, voxceleb, audio, linux, gpu

## Member repositories
- modelscope/3D-Speaker (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:44.275561+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:26:37.074510+00:00, confidence not recorded.
  - readme: https://github.com/modelscope/3D-Speaker (fetched 2026-08-28T04:07:44.275561+00:00, sha fcb41ae4723e)
- Data as of 2026-08-30T08:39:29.467469+00:00.