---
language:
  - en
  - he
license: cc-by-4.0
pretty_name: Noisy Voice Notes
size_categories:
  - n<1K
task_categories:
  - automatic-speech-recognition
  - audio-classification
  - text-classification
tags:
  - speech
  - voice-notes
  - noise
  - background-noise
  - asr-evaluation
  - dnsmos
  - personal-recordings
---

# Noisy Voice Notes

A small, hand-curated set of real-world voice notes recorded over roughly a year by a single speaker (me — Daniel Rosehill), captured in everyday environments rather than a studio. Most clips contain meaningful background noise: traffic, café chatter, kitchens, public transport, wind, kids, etc. They are deliberately **not** clean.

The dataset is intended as a small evaluation / probing set, not a training corpus. Each clip ships with the original transcript plus a layer of personal annotations.

## What it is useful for

Three use cases drove the curation:

1. **Background-noise / denoising evaluation** — clips with realistic, varied noise floors and known DNSMOS-style quality scores, to probe how denoisers and noise-suppression models behave on consumer-grade voice notes.
2. **ASR evaluation on imperfect material** — speech-to-text systems are typically benchmarked on relatively clean speech. These clips let you measure degradation on the kind of audio people actually capture on phones, with a human transcript already in place for reference.
3. **Voice-note classification** — the speaker's own annotation of *what each note is* (to-do list, note-to-self, diary entry, email draft, blog idea, podcast prompt, outline, etc.) is the basis for a personal classification project. The same labels can be used by anyone exploring few-shot or fine-tuned classifiers over short spoken-intent audio.

## Source

All recordings come from my own personal archive on [voicenotes.com](https://voicenotes.com), a voice-note app I've been using and recommend. Transcripts in this dataset are the ones generated by voicenotes.com's pipeline (ElevenLabs Scribe under the hood, at the time of capture) — they have **not** been hand-corrected. They reflect what a strong commercial ASR system produced on this audio, which is itself part of what makes the dataset useful for ASR comparisons.

Audio is released as MP3 in its original form (no denoising, no normalisation, no trimming).

## Layout

```
audio/         # <id>.mp3
transcripts/   # <id>.md   (original voicenotes.com transcript)
metadata.csv   # one row per clip
```

Each row in `metadata.csv` joins to `audio/<id>.mp3` and `transcripts/<id>.md` via `audio_relpath` / `transcript_relpath`.

## Schema (selected columns)

The full column-by-column reference lives in [`DATA_DICTIONARY.md`](./DATA_DICTIONARY.md). The summary below is a quick orientation.

| Column | Meaning |
|---|---|
| `id`, `uuid` | stable identifier |
| `title`, `speaker`, `recorded_at`, `duration_s` | basic metadata (speaker is always Daniel Rosehill) |
| `BAK`, `SIG`, `OVRL` | DNSMOS P.835 scores (1–5; lower BAK = noisier background) |
| `noise_level` | bucketed BAK |
| `audio_quality_rating` | 1–5 star human rating |
| `audio_defects` | list (clipping, wind, distortion, etc.) |
| `languages` | spoken languages present |
| `hebrew_usage`, `background_languages` | code-switch / ambient-language flags |
| `non_intended_audio` | TV, music, other speakers, etc. |
| `note_types_multi`, `note_categories_multi` | speaker-assigned type/category labels |
| `subject_matter` | short topic descriptor |
| `mwp_prompt` | boolean — speaker's "Morning Writing Prompt" tag |
| `transcription_quality` | human judgement of the source transcript |
| `microphone`, `capture_location` | capture context (location supports freehand entries) |
| acoustic features | `rms_dbfs`, `peak_dbfs`, `crest_factor_db`, `clipping_ratio`, `silence_ratio`, `speech_ratio`, `snr_db_estimate`, spectral & ZCR features, HNR proxy |
| transcript stats | `transcript_chars`, `transcript_words`, `wpm`, `active_wpm` |

DNSMOS is reference-free, run from the Microsoft ONNX models locally; it is a *proxy* for perceived quality, not ground truth.

## Annotation status

Annotation is in two stages, and the dataset card will be updated as the schema stabilises:

1. **Release validation (current).** Each candidate clip is reviewed against my voicenotes.com archive to confirm it is OK to release publicly. Clips that touch on private people, sensitive topics, or PII flagged by an automated screen are withheld. The corpus on disk is therefore deliberately a subset of what I've recorded.
2. **Background-noise triggers (next).** A second annotation pass will tag the *type* of background noise present (traffic, café, kitchen, wind, HVAC, music, other speakers, etc.) so the dataset can be sliced by acoustic environment. The schema for those tags is still firming up.

PII screening combines Microsoft Presidio + spaCy NER with keyword heuristics for medical / financial / relationship / credential mentions. It is conservative — false positives are dropped from the public set rather than risk releasing something I shouldn't.

## What this is *not*

- Not a clean-speech corpus.
- Not multi-speaker — it's one speaker, one accent, one set of recording habits.
- Not a hand-corrected transcript benchmark — the transcripts are the ASR system's output, kept as-is.
- Not large. It is a probing / evaluation set; treat it accordingly.

## Citation / attribution

If you use this dataset, please cite or link back to the Hugging Face page (`danielrosehill/Noisy-Voice-Notes`). Released under CC-BY-4.0.

## Contact

Issues, corrections, or "please remove this clip" requests: open an issue on the Hugging Face dataset page or contact daniel@danielrosehill.co.il.