Datasets:
license: mit
language:
- en
task_categories:
- text-generation
tags:
- dictation
- speech-to-text
- text-cleanup
- on-device
- synthetic
pretty_name: MacWispr Polish Training Data
size_categories:
- 1K<n<10K
MacWispr Polish — training & eval datasets
The complete open dataset behind MacWispr's on-device dictation polish model (Qwen3.5-0.8B post-trained to turn raw speech-to-text into clean, structured writing). Training pipeline and verifier live in the MacWispr repo.
Contents
| Path | Rows | What it is |
|---|---|---|
sft/train.jsonl (+valid/test) |
3,011 / 276 / 173 | Main SFT pool. {"text": "### Input:\n<raw>\n\n### Output:\n<gold>"} |
synthetic/synth_hard.jsonl |
311 | Synthetic hard-category examples (multi-list, numbered, mixed styles, checklist) generated with Grok, validated by the rule-based polish_verifier, deduped vs all other pools. {"raw", "gold", "tags", "source"} |
eval/ood_eval_set.jsonl |
40 | Out-of-distribution eval suite (held out from all training). {"id", "raw", "tags", ...} |
dpo/dpo_prompts.jsonl |
220 | Prompts + golds used to build DPO preference pairs |
results/ |
— | Benchmark vs Claude Sonnet (same suite, same scorer), incl. per-case outputs |
Task
Input: raw ASR transcript with disfluencies and a spoken formatting request.
Output: cleaned text with the requested structure (bullets / 1. numbered /
- [ ] checklists / multiple labelled lists / email), fillers removed,
self-corrections applied, questions preserved as questions (never answered).
Benchmark snapshot (2026-07-21)
40-case OOD suite, shared rule-based scorer:
| System | Pass | Mean latency |
|---|---|---|
| MacWispr local 0.8B (4-bit, on-device) | 23/40 (57.5%) | 191 ms |
| Claude Sonnet (cloud) | 25/40 (62.5%) | 4,407 ms |
Provenance & license
Synthetic and curated data created for this project (no user dictations — MacWispr never collects transcripts). Grok was used to draft the synthetic examples; every gold is machine-validated by the open verifier. MIT.