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audio audioduration (s) 4.17 6.16 |
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license: cc0-1.0 task_categories: - audio-classification language: - en tags: - speaker-diarization - test-dataset size_categories: - n<1K
ZantOS Golden Test Set
Human-recorded meeting audio with ground truth speaker annotations for acceptance testing.
Dataset Details
- Version: 1.0.0
- Clips: 3 meetings (2-4 minutes each)
- Speakers: 2-4 per clip
- Format: 16kHz mono WAV
- Annotation: RTTM format (Rich Transcription Time Marked)
Structure
v1.0.0/ ├── metadata.json # Dataset version info ├── audio/ │ ├── meeting_001.wav # 3 min, 2 speakers, clear audio │ ├── meeting_002.wav # 4 min, 3 speakers, some cross-talk │ └── meeting_003.wav # 2 min, 4 speakers, challenging └── reference/ ├── meeting_001.rttm # Ground truth annotations ├── meeting_002.rttm └── meeting_003.rttm
RTTM Format
Each line represents a speaker segment: SPEAKER meeting_001 1 0.00 5.32 speaker_0 SPEAKER meeting_001 1 5.32 12.15 speaker_1
Fields: type file channel start duration _ _ speaker_id _ _
Usage
Download via Python:
from datasets import load_dataset
dataset = load_dataset("zant-os/zant-echo-golden", split="v1.0.0")
Or clone directly:
git clone https://huggingface.co/datasets/zant-os/zant-echo-golden
Quality Targets
- DER (Diarization Error Rate): ≤18%
- Speaker detection accuracy: ≥95%
- Overlap handling: Graceful degradation
License
CC0 1.0 Universal (Public Domain)
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