---
license: other
language: [da]
task_categories: [automatic-speech-recognition]
---
# danish-asr-verified

ALL rows of [`syvai/danish-asr-unified`](https://huggingface.co/datasets/syvai/danish-asr-unified) transcribed by the
**syv-transcribe ensemble** (hviske-v5.3 + hviske-v5, confidence-weighted ROVER),
each annotated with:

- `verified` — True when the ensemble independently reproduced the reference
  exactly (compared after lowercasing, punctuation-strip, whitespace-collapse).
  Two independent witnesses agree => near-certain label.
- `wer_teacher_vs_ref` / `cer_teacher_vs_ref` — word/character error rate
  between normalized teacher output and normalized reference; use for curriculum
  staging (e.g. noisy rows first, `verified` last) or custom quality thresholds.
  CER is the better signal for Danish (compound words make WER harsh).

**Audio is not duplicated here.** Each row points at its source clip:

- `src_file` — parquet path inside `syvai/danish-asr-unified` (revision pinned below)
- `src_row` — row index inside that file
- `key` — `dau:<file_idx>:<row_idx>` (same key joins the teacher K=128
  logprob dumps used for distillation)

Source revision pin: `5a3a49ee981baab6e1e37ddd2c45f9943c27d08f`

```python
from huggingface_hub import hf_hub_download
import pyarrow.parquet as pq

def load_audio_bytes(row):
    p = hf_hub_download("syvai/danish-asr-unified", row["src_file"], repo_type="dataset", revision="5a3a49ee981baab6e1e37ddd2c45f9943c27d08f")
    return pq.read_table(p, columns=["audio"])["audio"][row["src_row"]].as_py()["bytes"]
```

Fields: `text` (reference), `teacher_text` (ensemble output, differs only in
casing/punctuation), `source` (origin corpus), `duration_s`,
`teacher_avg_logprob` (teacher confidence).