MODEL PROFILE

Nemotron-3-Diarization

NVIDIA · Nemotron-3-Diarization

**Nemotron 3 Diarization** is an open-weight speaker diarization model designed to determine "who spoke when" in real-world audio. It supports both streaming and offline inference and handles up to eight speakers. Following Sortformer [\[1\]](#ref-1), the model resolves speaker permutation by ordering its output channels according to each speaker's first arrival in the input audio. For streaming inference, the model adopts the Arrival-Order Speaker Cache (AOSC) and FIFO queue introduced in **Streaming Sortformer** [\[2\]](#ref-2). The AOSC retains speaker information from earlier chunks to preserve speaker identities over time, while the FIFO queue provides recent frame context for each processing step. A single checkpoint supports input buffer latency as low as 80 ms for latency-critical applications, although the lowest recommended configuration is 0.32 s; the offline-style configuration uses a 30.4 s input buffer. The output frame resolution is configurable in multiples of 10 ms. With chunked inference, the maximum audio duration is not limited.

license:openmdw-1.1
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/10/29 16:45:49
OVERVIEW

Model overview

**Nemotron 3 Diarization** is an open-weight speaker diarization model designed to determine "who spoke when" in real-world audio. It supports both streaming and offline inference and handles up to eight speakers. Following Sortformer [\[1\]](#ref-1), the model resolves speaker permutation by ordering its output channels according to each speaker's first arrival in the input audio. For streaming inference, the model adopts the Arrival-Order Speaker Cache (AOSC) and FIFO queue introduced in **Streaming Sortformer** [\[2\]](#ref-2). The AOSC retains speaker information from earlier chunks to preserve speaker identities over time, while the FIFO queue provides recent frame context for each processing step. A single checkpoint supports input buffer latency as low as 80 ms for latency-critical applications, although the lowest recommended configuration is 0.32 s; the offline-style configuration uses a 30.4 s input buffer. The output frame resolution is configurable in multiples of 10 ms. With chunked inference, the maximum audio duration is not limited.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:openmdw-1.1。模型介绍与第三方评测均以原来源的具体版本和方法为准。