license: apache-2.0
pretty_name: BudgieScribe held-out evaluation sets
language:
- fr
- en
task_categories:
- text2text-generation
tags:
- asr
- speech-to-text
- text-normalization
- inverse-text-normalization
- dictation
- evaluation
size_categories:
- 1K<n<10K
configs:
- config_name: fr
data_files: fr/*.jsonl
- config_name: en
data_files: en/*.jsonl
BudgieScribe held-out evaluation sets
The synthetic held-out sets every BudgieScribe
build is scored on. Deterministic generators
(github.com/alexxxcoelho/budgie-scribe,
scribe/generateurs/gen_*.py, fixed seeds) start from the value, so the
expected output is known to the character and scoring is exact string
comparison — no judge. Each generator writes 8 % of what it produces into a
held-out slice, by family, never seen in training. These files are that slice.
| Language | File | Axis | Cases |
|---|---|---|---|
| fr | fr/heldout_forme.jsonl |
Formatting blocks (lists, emails, addresses) | 635 |
| fr | fr/heldout_correction.jsonl |
Speaker self-corrections | 476 |
| fr | fr/heldout_itn.jsonl |
Inverse text normalization (numbers, amounts, dates, times, phones) | 797 |
| fr | fr/heldout_compo.jsonl |
Composition: 2–3 adjacent numbers in one sentence | 319 |
| fr | fr/heldout_cor_itn.jsonl |
Self-correction × number in one sentence | 238 |
| en | en/heldout_forme.jsonl |
Formatting blocks | 634 |
| en | en/heldout_correction.jsonl |
Speaker self-corrections | 474 |
| en | en/heldout_itn.jsonl |
Inverse text normalization | 798 |
| en | en/heldout_compo.jsonl |
Composition | 319 |
| en | en/heldout_cor_itn.jsonl |
Self-correction × number | 237 |
Row format
One JSON object per line:
{"id": "itn-00005", "file": "itn-telephone", "source": "itn", "lang": "fr",
"held_out": true,
"styling": "semi-formal", "structure": "prose", "context": "general",
"control": "[Styling: semi-formal] [Structure: prose] [Context: general] [Lang: fr]",
"dirty": "tu peux m'appeler au zéro six douze trente-quatre cinquante-six soixante-dix-huit",
"clean": "Tu peux m'appeler au 06 12 34 56 78."}
dirty is what an ASR engine produces; clean is the expected output;
control is the line the model receives above the transcript (see
FORMAT.md).
file carries the family (itn-telephone, frm-liste, …) the benches
report by.
Scoring a build
hf download flowcorp-ch/BudgieScribe-eval --repo-type dataset --local-dir eval
export SCRIBE_LANG=fr SCRIBE_TRAVAIL=$PWD/eval
python scribe/bancs/eval_synth.py <checkpoint-dir | base | base:mini> eval/fr/heldout_forme.jsonl
or, on Hugging Face Jobs, hf/jobs/eval.py --model <repo> --lang fr from the
code repository, which runs every file of the language. Reported per axis:
exact matches, and for numbers wrong value separately from wrong
formatting.
Personal data
Nothing here comes from a recording or a person. Every string is generated
from templates and random values. Before publication the sets were passed
through the project's PII scanner (scribe/pipeline/pii_scan.py); the e-mail
domains the generators use (gmail.com, outlook.fr, free.fr, …) were
replaced on both sides of each pair by RFC 2606 reserved domains
(example.com, example.org, example.net) so that no plausible real
address is distributed. Phone numbers are random digit strings in the
national formats; any match with a real number is coincidental. The
published model tables were measured on the sets before the domain swap;
the swap changes the spoken domain only, and a re-run is expected to differ
by at most a few cases on the e-mail families.
License
Apache 2.0, like the generators that produce them.