juanmc2005/diart

Health v2 · maintenance only

Adoption not part of the score

2041 stars · 168 forks observed · 2026-10-19

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

Diart is a Python framework for building AI-powered real-time audio applications, best known for state-of-the-art streaming speaker diarization. It combines speaker segmentation and embedding models with incremental clustering, and supports custom pipelines, hyper-parameter tuning, benchmarking, and serving over websockets.

Use cases

• identify who is speaking in a live audio stream
• real-time speaker diarization for meetings or calls
• stream voice activity detection over websockets
• build custom real-time speech AI pipelines
• benchmark and tune online diarization hyper-parameters
• live captioning with speaker labels

When to choose

• you need low-latency, streaming speaker diarization in Python
• you want to prototype real-time audio AI pipelines with pre-trained models
• you need to serve audio AI models over websockets

When to avoid

• you only need offline (batch) diarization of recorded files
• you need production transcription, which is still marked as coming soon
• you work outside the Python/PyTorch ecosystem

Facets

framework · maturity active

audio-processing speech-recognition machine-learning deep-learning streaming benchmarking speech-processing machine-learning artificial-intelligence python cross-platform speaker-diarization streaming-audio voice-activity-detection speaker-embedding websockets real-time-speech audio real-time

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markdown · JSON · MCP: product_card(name="juanmc2005/diart")