Datasets:
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. 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).
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 करोPROPER 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.