| --- |
| license: cc-by-4.0 |
| language: [hi, ta, te, kn, ml, bn, mr, gu, pa, as] |
| task_categories: [text-to-speech] |
| tags: [short-utterance, indic, tts, transcripts, text-only] |
| size_categories: [10K<n<100K] |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
| # Indic Short-Utterance Transcripts (1–3 words), 10 languages |
|
|
| **39,140 transcripts** of 1–3 word utterances across 10 Indic languages, filtered from |
| [`ai4bharat/Rasa`](https://huggingface.co/datasets/ai4bharat/Rasa). **Text only — no audio.** |
|
|
| Built to answer a specific question: *which short phrases exist in open Indic speech corpora, |
| and how many are in the register a voice assistant actually speaks?* TTS models trained on |
| sentence-level read speech degrade on very short inputs because such corpora contain almost no |
| short utterances. This is the text-side map of what is available. |
|
|
| For the audio, see the per-language companion sets (Hindi: |
| [`hindi-short-utterance-1to3w`](https://huggingface.co/datasets/OmS1ngh/hindi-short-utterance-1to3w)). |
|
|
| ## Per language |
|
|
| | Language | Code | Rows | In-register | share | |
| |---|---|---:|---:|---:| |
| | Malayalam | ml | 5,344 | **1,457** | 27.3% | |
| | Kannada | kn | 4,965 | 771 | 15.5% | |
| | Marathi | mr | 4,425 | 421 | 9.5% | |
| | Telugu | te | 4,328 | 764 | 17.7% | |
| | Punjabi | pa | 4,237 | 263 | 6.2% | |
| | Assamese | as | 3,728 | 404 | 10.8% | |
| | Hindi | hi | 3,669 | 371 | 10.1% | |
| | Tamil | ta | 3,240 | **1,010** | 31.2% | |
| | Gujarati | gu | 2,842 | 339 | 11.9% | |
| | Bengali | bn | 2,362 | 568 | 24.0% | |
| | **Total** | | **39,140** | **6,368** | 16.3% | |
|
|
| ## Columns |
|
|
| | Column | Meaning | |
| |---|---| |
| | `language`, `lang_code` | e.g. `malayalam` / `ml` | |
| | `transcript` | the utterance, native script | |
| | `word_count` | 1, 2, or 3 | |
| | `duration` | seconds of the source clip (audio not included) | |
| | `style` | **Rasa's own register label**, carried through unchanged | |
| | `register` | `in-register` if `style` ∈ {CONV, ALEXA}, else `out-of-register` | |
| | `tag` | secondary seed-word-list heuristic — see caveat | |
| | `gender` | speaker gender of the source clip | |
|
|
| ## The `register` column is the point |
|
|
| Raw short-utterance counts are misleading. Most short rows in these corpora are **proper nouns** |
| — monuments, cities, festivals, company names — not the conversational phrases an assistant |
| needs. `style` separates them: |
|
|
| - `CONV` — conversational speech: `ठीक है, सर।` · `जी नहीं।` · `बिलकुल!` |
| - `ALEXA` — voice-assistant commands: `अलार्म जोड़ें` · `थोड़ी आवाज़ बढ़ाओ` · `लाइट बंद करो` |
| - `PROPER NOUN` — `ताज महल` · `कुतुब मीनार` · `गोमती एक्सप्रेस` |
|
|
| **Malayalam and Tamil are 3–4× richer in in-register short speech than Hindi**, despite similar |
| raw counts. If you are choosing a language to start with, that matters more than total rows. |
|
|
| **Caveat on `tag`.** Derived from a hand-written Hindi seed word list, kept only for |
| cross-checking. It undercounts the in-register set ~2.5× (151 vs 371 on Hindi) and cannot |
| recognise the `ALEXA` register at all. It is meaningless for non-Hindi rows. **Use `register`.** |
|
|
| **Caveat on `style` for non-Hindi.** `style` values were spot-checked against actual transcripts |
| for Hindi only. The other nine languages assume `CONV`/`ALEXA` mean the same thing there. Verify |
| on a sample before relying on the per-language figures. |
|
|
| ## Convenience text files |
|
|
| `text/<language>_all.txt` and `text/<language>_in_register.txt` — deduplicated, one phrase per |
| line, frequency-ordered. Handy for picking a recording script or building a test set without |
| touching Parquet. |
|
|
| ## Selection criteria |
|
|
| Word count 1–3 after NFC normalization, stripping Unicode `P*`/`S*` categories while |
| **preserving combining marks** — stripping via `[^\w\s]` shatters Indic conjuncts at the virama |
| and inflates word counts roughly 3×, which silently reduced measured yield 4× during |
| development. Plus: ≥90% of letters in the language's own script; not empty, digits-only, or |
| single-character; source duration 0.2–4.0 s; ≤20 rows per unique phrase. |
|
|
| ## License and attribution |
|
|
| CC-BY-4.0, inherited from the source. Derived from **AI4Bharat's Rasa**: |
|
|
| > Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-resource |
| > Settings. AI4Bharat. https://huggingface.co/datasets/ai4bharat/Rasa |
|
|
| The source is access-gated on the Hub; please accept its terms there if you use the original. |
|
|
