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YAML Metadata Warning:The task_categories "speaker-diarization" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Sortformer Diarization Test Set
100 real speech samples extracted from LibriSpeech test-clean for speaker diarization testing and benchmarking with NVIDIA Sortformer 4spk-v2 ONNX models.
Usage with Sortformer ONNX
from huggingface_hub import snapshot_download
import soundfile as sf
# Download the test set
dataset_path = snapshot_download("DimQ1/sortformer-diarization-test-set")
# Load audio
audio, sr = sf.read(f"{dataset_path}/audio/ls_real_000.wav")
Diarization Models
Compatible ONNX models available on HuggingFace:
| Model | Size | Speed | Repo |
|---|---|---|---|
| Sortformer FP32 | 470 MB | 16× real-time | DimQ1/sortformer-4spk-v2-onnx-fp32-cpu |
| Sortformer INT8 | 129 MB | 29× real-time | DimQ1/sortformer-4spk-v2-onnx-int8-cpu |
| Sortformer INT4 | 73 MB | 33× real-time | DimQ1/sortformer-4spk-v2-onnx-int4-cpu |
Ground Truth RTTM
Speaker diarization ground truth annotations are provided in rttm/ directory (NIST RTTM format).
Similar Datasets
For larger-scale diarization training and evaluation:
| Dataset | Description | Source |
|---|---|---|
| LibriSpeech | 1000h English read speech | openslr.org/12 |
| VoxCeleb 1&2 | 7000+ celebrity speakers | robots.ox.ac.uk/~vgg/data/voxceleb |
| AMI Corpus | 100h meeting recordings | groups.inf.ed.ac.uk/ami/corpus |
| CALLHOME | Multilingual telephone speech | catalog.ldc.upenn.edu/LDC97S42 |
| DIHARD III | Challenging diarization benchmark | dihardchallenge.github.io/dihard3 |
| MUSAN | Music/speech/noise for augmentation | openslr.org/17 |
License
Derived from LibriSpeech (CC BY 4.0). See LibriSpeech license for details.
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