sarvamai/indic-diarbench

Indic DiarBench

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.

Paper: Indic DiarBench: A Multilingual Joint Diarization and ASR Benchmark for Indian Languages (Interspeech 2026)

Dataset Summary

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.

Attribute	Value
Total samples	1,164
Total duration	~108 hours
Languages	22 scheduled Indian languages
Language families	4 (Indo-Aryan, Dravidian, Sino-Tibetan, Austroasiatic)
Unique speakers	485 (meetings) + ~750 (in-the-wild)
Districts represented	189
Average overlap ratio	12.8%

Recording Conditions

The corpus includes three acoustic conditions designed to capture diverse real-world scenarios:

Condition	Label	Hours	Description
Near-field	Near field	~53	Recorded using one close-proximity microphone per speaker in virtual meetings. Participants joined via an online meeting platform, enabling accurate speaker turns by combining individual microphone streams. Covers all 22 languages.
Far-field	Far field	~27	Recorded using distant microphones, introducing reverberation, background noise, and variable speaker-to-microphone distances. Covers the top 8 languages by native-speaker population.
In-the-wild	In the wild	~28	Curated from publicly available YouTube videos to capture unconstrained acoustic environments. Covers the 10 most widely spoken Indian languages.

Per-Language Statistics

Durations are in hours. NF = near-field, FF = far-field, ITW = in-the-wild.

Language	Family	NF	FF	ITW	Total	Overlap %
Assamese	Indo-Aryan	1.5	–	–	1.5	13.9
Bengali	Indo-Aryan	4.4	4.1	4.1	12.6	7.8
Bodo	Sino-Tibetan	1.6	–	–	1.6	15.2
Dogri	Indo-Aryan	1.4	–	–	1.4	24.2
Gujarati	Indo-Aryan	4.1	4.2	2.8	11.1	7.6
Hindi	Indo-Aryan	4.2	4.0	2.5	10.7	16.6
Kannada	Dravidian	3.7	1.4	3.3	8.5	15.2
Kashmiri	Indo-Aryan	1.1	–	–	1.1	21.2
Konkani	Indo-Aryan	1.6	–	–	1.6	14.0
Maithili	Indo-Aryan	1.3	–	–	1.3	24.7
Malayalam	Dravidian	1.3	–	2.4	3.7	12.9
Manipuri	Sino-Tibetan	1.5	–	–	1.5	20.6
Marathi	Indo-Aryan	4.2	3.5	2.7	10.4	11.3
Nepali	Indo-Aryan	1.3	–	–	1.3	22.9
Odia	Indo-Aryan	1.5	–	1.6	3.1	11.1
Punjabi	Indo-Aryan	4.3	4.0	2.4	10.6	6.1
Sanskrit	Indo-Aryan	1.6	–	–	1.6	21.4
Santali	Austroasiatic	1.6	–	–	1.6	6.5
Sindhi	Indo-Aryan	1.5	–	–	1.5	16.0
Tamil	Dravidian	4.2	2.5	3.2	10.0	12.4
Telugu	Dravidian	3.8	3.0	2.5	9.3	20.4
Urdu	Indo-Aryan	1.6	–	–	1.6	12.5
Total	4 families	53.2	26.8	27.6	~108	12.8

Annotation Pipeline

All recordings are annotated using a unified human-in-the-loop pipeline:

1. Bootstrap Transcription — Initial transcripts generated using multiple independent ASR systems, presented to annotators as editable drafts.
2. Human Transcription & Speaker Attribution — Professional annotators produce time-aligned, speaker-attributed transcriptions. No machine-generated annotation is retained without human validation.
3. Code-Mixed Transcription — Annotators produce two transcription formats: native-script (all text in Indic script) and normalized (English words in Roman script, numerals in Arabic digits).
4. Quality Control — Dedicated quality checkers (2–3 per language) verify transcription consistency, code-mixing conventions, speaker timestamps, and labels. Overlapping speech segments require multiple review rounds.
5. Expert Review — In-house language-specific experts perform final quality checks.

Dataset Fields

Field	Type	Description
audio	Audio	Audio waveform (WAV, 16kHz mono)
recording_id	string	Anonymised source-recording identifier (e.g. hindi_nf_003). Clips cut from the same source recording share one recording_id.
language	string	Full language name (e.g. Hindi), consistent across all recording conditions
annotated_transcript	list	Speaker-attributed segments: {speaker_id, transcript, start_time, end_time}
dataset_type	string	Recording condition: Near field, Far field, or In the wild
sample_id	string	Unique sample identifier (e.g. hindi_001), unique across the whole benchmark
num_speakers	int	Number of distinct speakers in the recording
num_segments	int	Number of transcript segments
duration_seconds	float	Audio duration in seconds

Identifying samples

sample_id is unique across the entire benchmark (all 1,164 samples), so it is the key to use when reporting or joining per-sample results.

recording_id exists because the 1,164 clips come from only 590 distinct source recordings — 47 recordings contribute more than one clip, and one contributes 21. Clips from the same recording are not independent samples: they share speakers, channel and acoustic conditions. Group by recording_id when splitting data or aggregating metrics, otherwise a handful of recordings will dominate the average.

recording_id is deliberately anonymised and carries no information about the source medium. Its form is <language>_<condition>_<nnn>, where condition is nf (near-field), ff (far-field) or itw (in-the-wild).

Usage

from datasets import load_dataset

# Load a specific language
ds = load_dataset("sarvamai/indic-diarbench", "Hindi", split="test")
sample = ds[0]

print(f"Sample:    {sample['sample_id']}")
print(f"Recording: {sample['recording_id']}")
print(f"Language:  {sample['language']}")
print(f"Duration:  {sample['duration_seconds']:.1f}s")
print(f"Speakers:  {sample['num_speakers']}")
print(f"Condition: {sample['dataset_type']}")

# Access speaker-attributed transcript
for seg in sample['annotated_transcript'][:5]:
    print(f"  [{seg['start_time']:.1f}-{seg['end_time']:.1f}] {seg['speaker_id']}: {seg['transcript']}")

Baseline Results

Duration-weighted aggregate metrics across all three acoustic conditions:

Category	Model	DER (%)	cpWER (%)	WDER (%)
Indic-specialized	Sarvam	16.0	38.8	33.1
Commercial APIs	AWS Transcribe	23.5	43.7	34.3
	ElevenLabs Scribe	35.0	58.3	40.7
	Azure STT	34.8	60.8	39.5
	Deepgram Nova-3	32.0	63.2	39.3
	AssemblyAI	40.5	88.6	43.7
Multimodal LLMs	GPT-4o	36.2	83.1	40.4
	Gemini 3 Pro	74.0	58.9	33.0

Citation

If you use this dataset, please cite:

@misc{mehendale2026indicdiarbenchmultilingualjoint,
  title={Indic DiarBench: A Multilingual Joint Diarization and ASR Benchmark for Indian Languages},
  author={Deovrat Mehendale and Aditya Mehndiratta and Dhruv Rathi and Kaushal Bhogale and Mitesh M. Khapra},
  year={2026},
  eprint={2607.23808},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2607.23808},
}

License

This dataset is released under the CC BY 4.0 license.