wenet-e2e/wespeaker

Health v2 · maintenance only

Adoption not part of the score

1392 stars · 201 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

WeSpeaker is a research and production-oriented toolkit for speaker embedding learning, supporting speaker verification, recognition, and diarization. It provides pretrained models (xvector, ECAPA-TDNN, ResNet, CAM++, and more), a Python API, and command-line tools built on PyTorch.

Use cases

• extract speaker embeddings from audio files
• verify whether two voice recordings belong to the same speaker
• perform speaker diarization on a single audio file
• train custom speaker verification models on VoxCeleb or CN-Celeb
• deploy speaker recognition models to production with runtime support
• compare speaker similarity between two wav files
• batch extract embeddings for a list of utterances

When to choose

• you need state-of-the-art speaker verification or diarization with pretrained models
• you want both research training pipelines and production deployment runtimes
• you prefer a simple Python API or CLI over building models from scratch

When to avoid

• you need general speech recognition (ASR) or text transcription rather than speaker identity
• you work outside Python/PyTorch ecosystems and need a pure C++ or mobile-first solution
• you only need lightweight voice activity detection without speaker modeling

Facets

library · maturity active

machine-learning audio-processing speech-recognition cli sdk speech-processing machine-learning developer-tools python cli speaker-verification speaker-embedding speaker-diarization pytorch xvector ecapa-tdnn resnet voxceleb pretrained-models self-supervised-learning audio linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="wenet-e2e/wespeaker")