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mago-ai/
ultra_diar_streaming_sortformer_8spk_v1

Name: ultra_diar_streaming_sortformer_8spk_v1
Availability: InStock

nvidia/diar_streaming_sortformer_4spk-v2.1

finetune

apache-2.0

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Ultra Diar Streaming Sortformer (8-Speaker)

This model extends NVIDIA Streaming Sortformer speaker diarization from 4 speakers to 8 speakers. The original diar_streaming_sortformer_4spk-v2.1 supports up to 4 speakers; this model expands the capability to handle up to 8 speakers through fine-tuning and architectural modifications.

Model Details

Base model: nvidia/diar_streaming_sortformer_4spk-v2.1
Extension: 4spk → 8spk
Framework: NeMo (NVIDIA)
Version: 1.0
GitHub: github.com/LilDevsy0117/Ultra-Sortformer

Code & Training

Extension scripts, NeMo patches for split-head / split-LR training, synthetic data tooling, and documentation: Ultra-Sortformer (GitHub).

Training

Hardware: 2× NVIDIA H100 GPUs

Usage

This model requires the NVIDIA NeMo toolkit to train, fine-tune, or perform diarization. Install NeMo after installing Cython and the latest PyTorch.

Install NeMo

bash

apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython packaging
pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]

Quick Start: Run Diarization

python

from nemo.collections.asr.models import SortformerEncLabelModel

# Load model from Hugging Face
diar_model = SortformerEncLabelModel.from_pretrained("devsy0117/ultra_diar_streaming_sortformer_8spk_v1")
diar_model.eval()

# Streaming parameters (recommended for best performance)
diar_model.sortformer_modules.chunk_len = 340
diar_model.sortformer_modules.chunk_right_context = 40
diar_model.sortformer_modules.fifo_len = 40
diar_model.sortformer_modules.spkcache_update_period = 300

# Run diarization
predicted_segments = diar_model.diarize(audio=["/path/to/your/audio.wav"], batch_size=1)

for segment in predicted_segments[0]:
    print(segment)

Loading the Model

python

from nemo.collections.asr.models import SortformerEncLabelModel

# Option 1: Load directly from Hugging Face
diar_model = SortformerEncLabelModel.from_pretrained("devsy0117/ultra_diar_streaming_sortformer_8spk_v1")

# Option 2: Load from a downloaded .nemo file
diar_model = SortformerEncLabelModel.restore_from(
    restore_path="/path/to/ultra_diar_streaming_sortformer_8spk_v1.nemo",
    map_location="cuda",
    strict=False,
)

diar_model.eval()

Input Format

Single audio file: audio_input="/path/to/multispeaker_audio.wav"
Multiple files: audio_input=["/path/to/audio1.wav", "/path/to/audio2.wav"]

Note: The base model is limited to 4 speakers. Extending to 8 speakers changes speaker-count behavior on short or low-speaker sessions; interpret Spk_Count_Acc together with DER. This release prioritizes strong DER on challenging multi-speaker settings.

License

This repository’s model weights and documentation are released under the Apache License 2.0.

The upstream base model may be subject to separate terms; see nvidia/diar_streaming_sortformer_4spk-v2.1 for its license and attribution requirements.

mago-ai/ultra_diar_streaming_sortformer_8spk_v1