Building capable language models for India requires scalable multilingual data and robust evaluation. We introduce Nemotron-Pretraining-Indic-v1, a synthetic pretraining corpus of approximately 2.83 trillion tokens covering all 22 scheduled Indian languages in native script and twelve in romanized form. It is the large...
Abhinav Khattar, Aishwarya Chhabra, A. Velingker et al.· 0 citations
Evaluation benchmarks for Indian language automatic speech recognition (ASR) suffer from two systematic biases: optimistic scores from clean, controlled audio conditions, and pessimistic scores from overly rigid transcription standards that penalize valid linguistic variations. We introduce Vimarsha, a 100-hour benchma...
K. Bhogale, Srija Anand, Sadakopa Ramakrishnan Thothathiri et al.· 0 citations
The WMT 2020–2024 shared-task lineage with an extended English–Malayalam resource is consolidated into INDICQE-APE, with up to four label types aligned on the same segment, a direct assessment, a human post-edit, word-level OK/BAD tags and an error explanation, and a test set stratified over four difficulty axes.
Diptesh Kanojia, Archchana Sindhujan, S. Deoghare et al.· 0 citations
In this work, we introduce Indic DiarBench, a speaker diarization and ASR benchmark dataset spanning all 22 scheduled languages of India. This corpus comprises approximately 108 hours of natural multi-speaker audio from near-field meetings, far-field recordings, and in-the-wild audios. All annotations are human-correct...
The WMT 2020-2024 shared-task lineage with an extended English-Malayalam resource is consolidated into IndicQE-APE, with up to four label types aligned on the same segment, a direct assessment, a human post-edit, word-level tags and an error explanation, and a test set stratified over four difficulty axes.
Diptesh Kanojia, Archchana Sindhujan, S. Deoghare et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
