---
license: cc-by-nc-4.0
language:
- da
task_categories:
- automatic-speech-recognition
tags:
- speaker-diarization
- synthetic
- danish
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test.parquet
---

# Danish Diarization Benchmark (Synthetic) — v2

A 3996-row synthetic speaker-diarization benchmark in Danish, built by mixing
single-speaker utterances from
[syvai/danish-asr-unified](https://huggingface.co/datasets/syvai/danish-asr-unified)
into multi-speaker recordings.

## What changed in v2 (2026-05-18)

- **Per-segment text** — each entry in `segments` now carries its `text` field directly. The redundant parallel `texts` column has been removed. Old consumers that joined `segments[i]` with `texts[i]` should switch to `segments[i]["text"]`.
- **Silent padding trimmed.** Audio is now trimmed to `[first_segment_start - 0.3 s, last_segment_end + 0.3 s]` and segment timestamps are rewritten relative to the new start. The previous version intentionally added 1-5 s of leading/trailing silence to K=1 clips, but this caused autoregressive diarization models that emit speech every time they're asked to transcribe to hallucinate filler tokens (e.g. "Ja", "Mh") into the silent regions. Baselines below are re-measured on v2 audio.
- **Schema:** `texts` column removed, everything else unchanged. `segments` JSON is now `[{start, end, speaker, text}, ...]`.

## Baselines (DER, lower is better)

Evaluated on a 100-row sample with collar=0.0 s (no leniency), using
`pyannote.metrics.DiarizationErrorRate`, audio capped at 30 s.

| Model | Overall DER | K=1 | K=2 | K=3 | K=4 |
|---|---:|---:|---:|---:|---:|
| [syvai/hviske-v5.1-diarize-ts](https://huggingface.co/syvai/hviske-v5.1-diarize-ts) | **15.19%** | **6.20%** | **11.44%** | **10.38%** | 25.99% |

Reference-only model — hviske-v5.1-diarize-ts produces speaker tokens and 100 ms timestamps in a single auto-regressive decoder pass. The full prior pyannote / DiariZen baseline grid was measured on v1 audio; numbers will shift on v2 because most clips no longer have the 1-5 s silent padding.

**Timestamp accuracy (matched segments, IoU ≥ 0.05):**
- `|Δstart|` median **49 ms** · p90 819 ms
- `|Δend|` median **71 ms** · p90 1134 ms
- Segment IoU (matched) median **0.965**

## Composition

| | |
|---|---|
| Rows | 3996 |
| Speakers per row | 1 - 8 (~500 rows per K) |
| Audio | 16 kHz mono WAV, trimmed to labeled region ±0.3 s |
| Total speech | ~34 h |
| Source parquets | 14 distinct from danish-asr-unified |

## Fields

| Field | Type | Description |
|---|---|---|
| id | string | Sample identifier (`sample_NNNNN`) |
| audio | audio | 16 kHz mono WAV bytes, trimmed |
| num_speakers | int32 | Number of distinct speakers (1..8) |
| duration | float | Length in seconds (after trim) |
| segments | string (JSON) | List of `{start, end, speaker, text}` |
| sources | string (JSON) | Per-speaker source dataset (`voxpopuli`, `nst_da`, ...) |

## Usage

```python
import json
from datasets import load_dataset

ds = load_dataset("syvai/danish-diarization-bench", split="test")
ex = ds[0]
segments = json.loads(ex["segments"])
print(ex["num_speakers"], ex["duration"])
for s in segments:
    print(f"  [{s['start']:.2f} - {s['end']:.2f}]  {s['speaker']}  {s['text'][:80]!r}")
```

## Build provenance

For each synthetic row, K speakers were sampled from K distinct parquet files in
`syvai/danish-asr-unified` (when K <= pool size = 14). Each speaker contributes
one 4-12 s random crop of its utterance, mixed in time with a 40% overlap
probability per transition (resulting overlap rate ~7.9%). Originally built
2026-05-15. v2 rebuilt 2026-05-18 with text inlined and silent padding trimmed.