google/uis-rnn

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1588 stars · 320 forks observed · 2026-08-28

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

A Python library implementing the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm for segmenting and clustering sequential data, originally published in the paper 'Fully Supervised Speaker Diarization'. It models each speaker with a parameter-sharing RNN integrated with a distance-dependent Chinese Restaurant Process to handle an unknown number of speakers.

Use cases

• diarize who spoke when in a multi-speaker audio recording
• cluster speaker embeddings into individual speakers
• segment and label sequential data with an unknown number of classes
• train a supervised clustering model from labeled speaker examples
• perform online speaker diarization on d-vector embeddings

When to choose

• you have speaker embeddings (e.g., d-vectors) and need to assign them to speakers
• you need online/real-time diarization rather than offline spectral clustering
• the number of speakers is unknown in advance
• you have labeled training data for supervised clustering

When to avoid

• you need a complete end-to-end diarization pipeline including speech detection and embedding extraction
• you want an actively developed tool with frequent updates
• you need offline-only clustering and prefer simpler methods like spectral clustering
• you cannot provide labeled training data

Facets

library · maturity maintenance

machine-learning audio-processing speech-recognition machine-learning speech-processing python cross-platform speaker-diarization uis-rnn clustering pytorch supervised-learning sequence-modeling audio

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markdown · JSON · MCP: product_card(name="google/uis-rnn")