# Datasheet for IN22-Legal

Following the template of [*Datasheets for Datasets*](https://arxiv.org/abs/1803.09010) (Gebru et al., CACM 2021). The structure mirrors the seven sections of the original paper. Where a question is not applicable to this benchmark, it is marked *N/A*.

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## 1. Motivation

**For what purpose was the dataset created?**
IN22-Legal is a small, test-only **out-of-distribution (OOD) benchmark** for Indic ASR, designed to surface failure modes that general-domain benchmarks (FLEURS, IndicVoices) do not exercise: legal terminology, statute and section references, witness designations, and rich-orthographic numerals (dates, monetary amounts, ranges). It is released alongside the [SCRIBE evaluation framework](https://github.com/adalat-ai-tech/scribe-eval) so practitioners can diagnose whether formatting conventions learned on conversational corpora generalize to high-stakes specialized vocabulary.

**Who created the dataset and on behalf of which entity?**
[Adalat AI](https://www.adalat.ai/) data and ML Team.

**Who funded the creation of the dataset?**
Self-funded by Adalat AI as part of the SCRIBE release.

**Any other comments?**
The benchmark deliberately re-uses an existing, openly-licensed text corpus rather than commissioning new legal text, so that the *audio* layer is the new contribution.

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## 2. Composition

**What do the instances that comprise the dataset represent?**
Each instance is a single read-speech audio recording of one Indic-language legal passage paired with a verbatim, rich-orthographic reference transcription. Instances span three languages (Malayalam, Hindi, Kannada), released as three configs of the same Hugging Face dataset (`ml`, `hi`, `kn`).

**How many instances are there in total?**
240 total — exactly 80 per language. The 80 passages correspond to the **entire legal-domain subset of [`ai4bharat/IN22-Gen`](https://huggingface.co/datasets/ai4bharat/IN22-Gen)** (the n-way multilingual translation corpus); IN22-Legal is therefore a saturating audio realization of that subset rather than a sample.

**Does the dataset contain all possible instances or is it a sample?**
All instances of the IN22-Gen legal subset are included; no sub-sampling.

**What data does each instance consist of?**
- `audio` — 48 kHz mono WAV, 16-bit PCM (read-speech recording).
- `transcription` — string; reference transcript in *rich orthography* (grammatical punctuation, ASCII digits, formatted dates such as `DD.MM.YYYY`, merged acronyms such as `ಬಿಎಸ್ಎನ್ಎಲ್`).
- `speaker_id` — pseudonymized identifier of the form `speaker_N`.
- `duration` — clip length in seconds (float).
- `language` — `Malayalam` / `Hindi` / `Kannada`.

**Is there a label or target associated with each instance?**
Yes — `transcription` is the ASR target.

**Is any information missing from individual instances?**
No fields are missing. Speaker demographics beyond `speaker_id` are not released.

**Are relationships between individual instances made explicit?**
Yes — `language` and `speaker_id` group instances. Because the source text is parallel across the three languages (IN22 is n-way aligned), instances with the same row index across configs share the same source passage (translation-aligned).

**Are there recommended data splits?**
`test` only. The dataset is **eval-only** — it is not intended for training, fine-tuning, or model selection.

**Are there any errors, sources of noise, or redundancies?**
- Recordings used smartphone or laptop built-in microphones in quiet rooms (not studio-grade), so background noise floor and frequency response vary across speakers.
- A small number of transcripts in earlier releases carried Indic-script-digit forms or dotted abbreviations; the current release has been normalized to ASCII digits and merged acronyms by domain annotators.
- Mean clip durations vary by language (ml ≈ 16.1 s, hi ≈ 25.3 s, kn ≈ 15.1 s), reflecting differences in passage length across IN22-Gen.

**Is the dataset self-contained, or does it link to external resources?**
Self-contained — audio bytes are embedded in the parquet shards. Only the upstream text corpus ([`ai4bharat/IN22-Gen`](https://huggingface.co/datasets/ai4bharat/IN22-Gen)) is an external dependency, and only for citation purposes.

**Does the dataset contain data that might be considered confidential?**
No. Source text is from a publicly released translation corpus; no confidential, attorney–client, or court-restricted material.

**Does the dataset contain data that might be offensive, insulting, threatening, or otherwise cause anxiety?**
The text discusses legal topics including criminal cases, court procedure, and statute references; consumers should expect domain-typical content (e.g. references to offences, parties, allegations). The text is taken verbatim from IN22-Gen and has not been altered for sensitivity.

**Does the dataset identify any subpopulations?**
Only by language. Speaker gender, age, region, caste, religion, and other demographic attributes are not recorded or released.

**Is it possible to identify individuals from the dataset?**
Speakers are pseudonymized as `speaker_1`…`speaker_4`. Voice biometric re-identification is theoretically possible if an adversary already has a labelled enrolment for a particular speaker, but no name-to-`speaker_id` mapping is released. See §3 on consent.

**Does the dataset contain data that might be considered sensitive in any way?**
No PII; no protected-class attributes. Voice itself is biometric data and is treated as such (see §3).

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## 3. Collection process

**How was the data associated with each instance acquired?**
- *Text*: directly inherited from [`ai4bharat/IN22-Gen`](https://huggingface.co/datasets/ai4bharat/IN22-Gen)'s legal-domain subset ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)).
- *Audio*: newly recorded for this release. Speakers read each passage aloud once in a quiet room using a smartphone or laptop built-in microphone.
- *Transcripts*: prepared directly in rich-orthographic format and manually corrected on a per-sample basis by legal-domain annotators (the source IN22-Gen text was used as a starting point and updated to reflect rich-transcription conventions).

**What mechanisms or procedures were used to collect the data?**
Self-recording via a shared script template; collected files were renamed to `sample_NNNNNNNN.wav` and aggregated centrally. No physical sensor calibration was performed beyond per-speaker level-checks.

**If the dataset is a sample from a larger set, what was the sampling strategy?**
*N/A* — the dataset covers the entire legal-domain subset of IN22-Gen.

**Who was involved in the data collection process and how were they compensated?**
A mix of (a) Adalat AI in-house team members, who recorded as part of their work and received no separate per-session compensation, and (b) paid external voice talent contracted per session via standard vendor agreements. Annotator correction was performed by Adalat AI's legal-domain annotation team.

**Over what timeframe was the data collected?**
Recordings and initial annotation were carried out in early-to-mid 2025; transcripts were re-normalized to rich-orthographic conventions in May 2026 in preparation for the SCRIBE release.

**Were any ethical review processes conducted?**
No formal IRB / institutional ethics-board review was conducted (Adalat AI is an industry organization without an academic IRB). Speakers were verbally informed that their recordings would be released as a public benchmark for ASR research, and verbal consent was obtained from each speaker before recording. No written consent form was used.

**Does the dataset relate to people?**
Yes — every audio clip contains a human voice.

**Did you collect the data from the individuals in question directly, or obtain it via third parties?**
Directly from the speakers themselves.

**Were the individuals notified about the data collection?**
Yes — verbally, at the time of recording.

**Did the individuals consent to the collection and use of their data?**
Verbal consent only; no written release was signed. Consumers redistributing the dataset should treat this as the basis for the release.

**If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future?**
No formal revocation channel exists. Speakers may contact Adalat AI directly to request removal of their recordings.

**Has an analysis of the potential impact of the dataset and its use on data subjects been conducted?**
Informal — given the small number of speakers (2–4 per language), the absence of demographic labels, and the non-sensitive nature of the spoken text, the principal residual risk is voice-biometric re-identification. No formal data-protection impact assessment was conducted.

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## 4. Preprocessing / cleaning / labeling

**Was any preprocessing/cleaning/labeling of the data done?**
Yes:
- Audio: trimmed to passage boundaries; no resampling, denoising, or loudness normalization was applied. Native sample rate (48 kHz, mono, 16-bit PCM WAV) is preserved.
- Transcripts: source IN22-Gen text was edited per-sample by legal-domain annotators to reflect rich-transcription conventions — grammatical punctuation, ASCII digits, formatted dates (`DD.MM.YYYY`), merged Indic-script acronyms (e.g. `बीएसएनएल`, `ಬಿಎಸ್ಎನ್ಎಲ್`), and elimination of dotted abbreviation forms.

**Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data?**
The original IN22-Gen text remains publicly available at [`ai4bharat/IN22-Gen`](https://huggingface.co/datasets/ai4bharat/IN22-Gen). Earlier transcript snapshots are recoverable from the [Hugging Face commit history](https://huggingface.co/datasets/adalat-ai/in22-legal/commits/main) of `adalat-ai/in22-legal`. Raw uncut audio is not separately released — the released clips already correspond to the natural passage boundaries.

**Is the software used to preprocess/clean/label the instances available?**
The SCRIBE evaluation framework — including the rich-orthography normalization rules used during annotation — is open-sourced at <https://github.com/adalat-ai-tech/scribe-eval>.

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## 5. Uses

**Has the dataset been used for any tasks already?**
Yes — it is the OOD legal-domain test set in the SCRIBE benchmark (*SCRIBE: Diagnostic Evaluation and Rich Transcription Models for Indic ASR*, Interspeech 2026, under review). It is used to compute language-stratified WER / NER / PER / LER / TER metrics against several Whisper-derived models.

**Is there a repository that links to any or all papers or systems that use the dataset?**
The dataset card maintains a "Related" section with current sister benchmarks ([`adalat-ai/fleurs-ro`](https://huggingface.co/datasets/adalat-ai/fleurs-ro)). External uses can be tracked via Hugging Face's repo backlinks.

**What (other) tasks could the dataset be used for?**
- Diagnostic evaluation of legal-domain ASR systems (any architecture).
- Evaluation of post-ASR text-normalization / inverse-text-normalization systems on Indic legal text.
- Stress-testing punctuation/numeral models on a rich-orthography target.

**Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?**
- The 240-clip total is too small for training; users should not fine-tune on it.
- Speaker pool is 2–4 per language, so absolute WER is sensitive to per-speaker idiosyncrasies and *relative* model rankings are more meaningful than absolute scores.
- Recording quality varies (smartphone/laptop mics); models trained exclusively on studio-grade audio may show artifacts that reflect channel rather than language modelling.
- Source text is from IN22-Gen, which is *translation* corpus material — sentences are well-formed but not always representative of natural spontaneous legal speech (e.g. courtroom dictation).

**Are there tasks for which the dataset should not be used?**
- Training, fine-tuning, or model selection.
- Saturation-level evaluation of legal ASR (the 80-passage scope is diagnostic, not exhaustive).
- Speaker identification or voice biometric work (intended speakers are pseudonymized and have not consented to such use).
- Any downstream legal-decision application (the dataset is a research artifact, not a legal reference).

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## 6. Distribution

**Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created?**
Yes — publicly released on the Hugging Face Hub.

**How will the dataset be distributed?**
As a Hugging Face dataset repository at <https://huggingface.co/datasets/adalat-ai/in22-legal>, with three language configs (`ml`, `hi`, `kn`) loadable via `datasets.load_dataset`.

**When will the dataset be distributed?**
Released alongside the SCRIBE paper (Interspeech 2026 timeline). Earlier private revisions exist in the repository commit history.

**Will the dataset be distributed under a copyright or other intellectual property license, or under applicable terms of use?**
[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/), inherited from the upstream [`ai4bharat/IN22-Gen`](https://huggingface.co/datasets/ai4bharat/IN22-Gen) text corpus. Audio recordings produced by Adalat AI are released under the same license.

**Have any third parties imposed IP-based or other restrictions?**
No restrictions beyond [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) attribution.

**Do any export controls or other regulatory restrictions apply?**
None known.

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## 7. Maintenance

**Who will be supporting/hosting/maintaining the dataset?**
Adalat AI hosts the dataset on the Hugging Face Hub and is responsible for maintenance.

**How can the owner/curator/manager of the dataset be contacted?**
- Via the Hugging Face repository's Community tab: <https://huggingface.co/datasets/adalat-ai/in22-legal/discussions>
- General contact: <https://www.adalat.ai/>

**Is there an erratum?**
None at time of release. Material corrections will be recorded in the [Hugging Face commit history](https://huggingface.co/datasets/adalat-ai/in22-legal/commits/main).

**Will the dataset be updated?**
Yes — transcript-level corrections are expected as annotation review continues. Historical revisions remain accessible via Hugging Face commit history.

**If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances?**
No formal retention limit. Speakers may request removal of their recordings (see §3).

**Will older versions of the dataset continue to be supported/hosted/maintained?**
Older parquet revisions remain accessible via the commit history. The "current" `main` branch is the supported reference.

**If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?**
Pull requests and discussions are welcome via the Hugging Face Community tab. Adalat AI will review and, where appropriate, integrate community contributions while preserving the test-only diagnostic intent of the benchmark.

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## Citation

If you use IN22-Legal, please cite:

- Source text passages: Gala et al., [*IndicTrans2 / IN22-Gen*](https://arxiv.org/abs/2305.16307), 2023 ([`ai4bharat/IN22-Gen`](https://huggingface.co/datasets/ai4bharat/IN22-Gen)).
- Recording, rich-transcription annotation, and benchmark release: *SCRIBE: Diagnostic Evaluation and Rich Transcription Models for Indic ASR*, Interspeech 2026 (under review).