Fix README: canonical repo id, arXiv citation, changelog wording
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by deovratmehendale - opened
README.md
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A multilingual joint diarization and ASR benchmark for Indian languages, spanning all **22 scheduled languages** of India with approximately **108 hours** of natural multi-speaker audio.
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## Dataset Summary
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Indic DiarBench is a conversational speech benchmark designed to evaluate speaker-attributed ASR in realistic multi-speaker settings for Indian languages. All annotations are human-corrected with time-aligned, speaker-attributed transcriptions. The dataset captures conversational nuances prevalent in Indian speech, such as English code-mixing, dialectal variation, and frequent speaker overlap.
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from datasets import load_dataset
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# Load a specific language
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ds = load_dataset("
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sample = ds[0]
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print(f"Sample: {sample['sample_id']}")
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| Multimodal LLMs | GPT-4o | 36.2 | 83.1 | 40.4 |
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| | Gemini 3 Pro | 74.0 | 58.9 | 33.0 |
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##
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1. **`language` is now the full language name everywhere.**
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`
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2. **`file_name` replaced by `recording_id`.** The
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the source filename, which for in-the-wild samples was the YouTube video
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it was also duplicated in `audio.path`. Both are now anonymised, with
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`audio.path` set to `<sample_id>.wav`. `recording_id` retains the one piece
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of information that `file_name` carried and users need: which clips came from
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the same source recording.
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## Citation
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```bibtex
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@
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title={Indic DiarBench: A Multilingual Joint Diarization and ASR Benchmark for Indian Languages},
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author={
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year={2026}
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}
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```
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A multilingual joint diarization and ASR benchmark for Indian languages, spanning all **22 scheduled languages** of India with approximately **108 hours** of natural multi-speaker audio.
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**Paper:** [Indic DiarBench: A Multilingual Joint Diarization and ASR Benchmark for Indian Languages](https://arxiv.org/abs/2607.23808) (Interspeech 2026)
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## Dataset Summary
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Indic DiarBench is a conversational speech benchmark designed to evaluate speaker-attributed ASR in realistic multi-speaker settings for Indian languages. All annotations are human-corrected with time-aligned, speaker-attributed transcriptions. The dataset captures conversational nuances prevalent in Indian speech, such as English code-mixing, dialectal variation, and frequent speaker overlap.
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from datasets import load_dataset
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# Load a specific language
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ds = load_dataset("sarvamai/indic-diarbench", "Hindi", split="test")
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sample = ds[0]
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print(f"Sample: {sample['sample_id']}")
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| Multimodal LLMs | GPT-4o | 36.2 | 83.1 | 40.4 |
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| | Gemini 3 Pro | 74.0 | 58.9 | 33.0 |
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## Changelog (August 2026)
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The August 2026 release of [`sarvamai/indic-diarbench`](https://huggingface.co/datasets/sarvamai/indic-diarbench)
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updated the schema. Audio bytes and all annotations, timings, and counts are
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unchanged.
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1. **`language` is now the full language name everywhere.** Previously, the
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210 in-the-wild samples used ISO codes (`bn`, `gu`, `hi`, `kn`, `ml`, `mr`,
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`or`, `pa`, `ta`, `te`) while the 954 near- and far-field samples used full
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names, so `language` was not comparable across `dataset_type`.
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2. **`file_name` replaced by `recording_id`.** The former `file_name` column
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held the source filename, which for in-the-wild samples was the YouTube video
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id; it was also duplicated in `audio.path`. Both are now anonymised, with
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`audio.path` set to `<sample_id>.wav`. `recording_id` retains the one piece
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of information that `file_name` carried and users need: which clips came from
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the same source recording.
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## Citation
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If you use this dataset, please cite:
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```bibtex
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@misc{mehendale2026indicdiarbenchmultilingualjoint,
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title={Indic DiarBench: A Multilingual Joint Diarization and ASR Benchmark for Indian Languages},
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author={Deovrat Mehendale and Aditya Mehndiratta and Dhruv Rathi and Kaushal Bhogale and Mitesh M. Khapra},
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year={2026},
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eprint={2607.23808},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2607.23808},
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}
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```
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