Datasets:
File size: 2,182 Bytes
a39a7d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | ---
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](https://macwispr.lintware.com)'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](https://github.com/vasanthsreeram/macwispr/tree/main/bench/polish_posttrain).
## 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.
|