Research lab
AI4Bharat
IIT Madras
Kashmiri is one of the 22 languages in AI4Bharat's open stack, so much of what Kashmiri systems are trained and tested on comes from here: the BPCC parallel corpus, the IN22 benchmarks, IndicTrans2, IndicVoices speech and the Sangraha corpus.
- 8
- Datasets
- 13
- Models
Focus
Datasets 8
Rasa — Expressive TTS
Expressive multilingual text-to-speech dataset for Indian languages, targeting at least 20 hours per speaker with a female and a male voice per language. Kashmiri is included. Funded by Bhashini (MeitY).
IndicVoices — Kashmiri
AI4Bharat's natural and spontaneous speech corpus for 22 Indian languages (23.7K hours in total), with a Kashmiri split for speech recognition.
IndicVoices-R — Kashmiri
Speech-synthesis-oriented release derived from IndicVoices, with a Kashmiri split of mostly spontaneous recordings.
Sangraha — Kashmiri subset
AI4Bharat's large pretraining corpus for Indian languages; the 'verified' Kashmiri subset is about 0.8 MB.
BPCC — Bharat Parallel Corpus Collection (Kashmiri)
AI4Bharat's parallel corpus for 22 Indian languages and the training data of IndicTrans2. The Kashmiri (Perso-Arabic) part has several subsets: seed-latest (98,929 pairs), seed-v2 (88,381), wiki/seed-v1 (15,512), daily (4,311), nllb_seed (6,193) and nllb_filtered (124,917).
IN22 Benchmark (IndicTrans2)
India-specific multi-domain evaluation benchmark (IN22-Gen + IN22-Conv) for 22 scheduled Indian languages. The standard for evaluating Indian MT models.
Aksharantar
Large transliteration dataset for 20 Indic languages: 26M Indic–English word pairs, with a Kashmiri subset.
IndicNLP Catalog (incl. NLLB-Seed)
Comprehensive catalog of NLP resources for Indian languages including Kashmiri. Tracks datasets like NLLB-Seed parallel data and connects to FLORES-200 benchmarks.
Models 13
Indic Parler-TTS
Multilingual text-to-speech model covering Indian languages with reported support for Kashmiri. Provides a general-purpose TTS baseline that Kashmiri-specific systems such as Bolbosh are measured against.
Sarvam-Translate
Document-level translation model for the 22 official Indian languages including Kashmiri, built on Gemma3-4B-IT, designed for long inputs and varied content formats.
IndicLID — Language Identification for Indian Languages
Language identifier for Indian languages that recognises Kashmiri in Perso-Arabic (kas_Arab), Devanagari (kas_Deva) and Latin (kas_Latn) script.
IndicTrans2 (En→Indic 1B)
State-of-the-art multilingual NMT model supporting 22 scheduled Indian languages including Kashmiri. Uses script unification for cross-lingual transfer. The top-performing model for Indian MT.
IndicTrans2 (En→Indic, distilled 200M)
Distilled 200M English→Indic IndicTrans2 model covering the 22 scheduled languages including Kashmiri.
IndicTrans2 (Indic→En 1B)
Reverse-direction model of IndicTrans2 for translating from Indian languages (including Kashmiri) to English. Essential for bidirectional MT research.
IndicTrans2 (Indic→En, distilled 200M)
The distilled 200M-parameter version of IndicTrans2 for Indian languages into English, including Kashmiri.
IndicTrans2 (Indic→Indic 1B)
IndicTrans2 Indic→Indic 1B model, built by stitching the Indic→En and En→Indic 1B models, for direct translation between Indian languages including Kashmiri.
IndicTrans2 (Indic→Indic, distilled 320M)
Distilled 320M Indic→Indic IndicTrans2 model, stitched from the distilled 200M Indic→En and En→Indic models.
IndicXlit — Transliteration for 21 Indic Languages
Transliteration models between Roman and native scripts for 21 Indic languages, including Kashmiri.
IndicBARTSS
Indic-focused variant of mBART, specifically designed for Indian language generation and translation tasks including Kashmiri. Optimized for Indic script families.
IndicBERTv2 (ai4bharat)
Multilingual BERT model pretrained on Indian languages including Kashmiri. Used as embedding backbone in news classification benchmarks (F1=0.98 with GRU).
IndicConformer ASR — Kashmiri
Conformer-Large (120M params) ASR model with hybrid CTC-RNNT decoder specifically for Kashmiri speech. Requires 16kHz mono WAV input. Uses AI4Bharat NeMo framework.
Profile 1
Every work above is a record in the Kashmiri NLP resource hub, credited as published; profile details come from ai4bharat.iitm.ac.in. Something missing or wrong? Submit it.