Datasets:

adjaysagar
/

nirantar

language:
  - bn
  - brx
  - doi
  - gu
  - hi
  - kn
  - kok
  - ks
  - mai
  - ml
  - mni
  - mr
  - ne
  - or
  - pa
  - sa
  - sat
  - sd
  - ta
  - te
  - ur
license: cc-by-4.0
task_categories:
  - automatic-speech-recognition
  - text-to-speech
size_categories:
  - 100K<n<1M

Nirantar

Nirantar speech dataset (22 Indian languages) in Hugging Face format. Source: AI4Bharat/Nirantar.

Downloading language-wise subsets

Each language is a separate configuration (subset), so you can load only one language (like ai4bharat/Rasa):

from datasets import load_dataset, get_dataset_config_names

# Single language (only that language's parquet is downloaded)
ds = load_dataset("adjaysagar/nirantar", "hi", trust_remote_code=True)  # Hindi
ds["train"]  # Dataset for that language

# List available language configs
get_dataset_config_names("adjaysagar/nirantar")  # e.g. ['as', 'bn', 'hi', ...]

# All languages (DatasetDict: config -> Dataset)
ds_all = load_dataset("adjaysagar/nirantar", trust_remote_code=True)

Dataset structure

Column	Description
`audio`	16 kHz audio (WAV/FLAC)
`text`	Normalized transcript
`language`	ISO language code
`speaker_id`	Speaker ID (if available)
`duration`	Duration in seconds (if available)
`gender`	Gender (if available)
`state` / `district`	Location metadata (if available)

Language and metadata statistics

Duration (h) = total audio duration per language. Config name (code) is used in load_dataset(..., "hi").

Language	Config	Utterances	Duration (h)	GB	Unique speakers	States	Districts
Bengali	bn	18,729	196.17	22.12	724	1	11
brx	brx	13,220	287.36	30.85	1,061	1	4
doi	doi	7,154	110.74	12.05	495	1	5
Gujarati	gu	2,174	19.27	2.07	72	1	4
Hindi	hi	13,396	135.50	14.55	490	4	12
Kannada	kn	8,293	94.71	10.16	530	1	13
Konkani	kok	10,604	100.15	10.75	245	1	4
Kashmiri	ks	10,247	103.57	11.17	514	1	10
mai	mai	21,207	241.91	25.98	726	1	9
Malayalam	ml	15,472	166.93	17.91	504	1	10
Manipuri	mni	4,133	40.85	4.38	166	1	3
Marathi	mr	13,943	116.15	12.46	447	1	10
Nepali	ne	26,003	249.03	26.72	780	1	4
Odia	or	15,370	121.79	13.07	473	1	9
Punjabi	pa	14,762	122.92	13.19	344	1	6
Sanskrit	sa	7,332	68.33	7.33	222	7	17
Santali	sat	13,503	161.29	17.30	433	1	8
Sindhi	sd	4,474	26.20	2.81	240	3	4
Tamil	ta	25,747	234.50	25.20	1,242	1	19
Telugu	te	13,490	217.69	23.36	767	2	28
Urdu	ur	12,219	122.05	13.10	564	4	10
Total	—	271,472	2937.11	316.52	—	—	—

Citation

@misc{nirantar2024,
  title={Nirantar: Continual Learning for Indian Languages},
  author={AI4Bharat},
  year={2024},
  url={https://github.com/AI4Bharat/Nirantar}
}