---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- audio-to-audio
language:
- as
- bn
- brx
- doi
- gu
- hi
- kn
- ks
- kok
- mai
- ml
- mni
- mr
- ne
- or
- pa
- sa
- sat
- sd
- ta
- te
- ur
pretty_name: Indic DiarBench
size_categories:
- 1K<n<10K
tags:
- diarization
- indic
- speech
- multi-speaker
- annotation
- speaker-attributed-asr
- code-mixing
configs:
- config_name: Assamese
  data_files:
  - split: test
    path: Assamese/test-*
- config_name: Bengali
  data_files:
  - split: test
    path: Bengali/test-*
- config_name: Bodo
  data_files:
  - split: test
    path: Bodo/test-*
- config_name: Dogri
  data_files:
  - split: test
    path: Dogri/test-*
- config_name: Gujarati
  data_files:
  - split: test
    path: Gujarati/test-*
- config_name: Hindi
  data_files:
  - split: test
    path: Hindi/test-*
- config_name: Kannada
  data_files:
  - split: test
    path: Kannada/test-*
- config_name: Kashmiri
  data_files:
  - split: test
    path: Kashmiri/test-*
- config_name: Konkani
  data_files:
  - split: test
    path: Konkani/test-*
- config_name: Maithili
  data_files:
  - split: test
    path: Maithili/test-*
- config_name: Malayalam
  data_files:
  - split: test
    path: Malayalam/test-*
- config_name: Manipuri
  data_files:
  - split: test
    path: Manipuri/test-*
- config_name: Marathi
  data_files:
  - split: test
    path: Marathi/test-*
- config_name: Nepali
  data_files:
  - split: test
    path: Nepali/test-*
- config_name: Odia
  data_files:
  - split: test
    path: Odia/test-*
- config_name: Punjabi
  data_files:
  - split: test
    path: Punjabi/test-*
- config_name: Sanskrit
  data_files:
  - split: test
    path: Sanskrit/test-*
- config_name: Santali
  data_files:
  - split: test
    path: Santali/test-*
- config_name: Sindhi
  data_files:
  - split: test
    path: Sindhi/test-*
- config_name: Tamil
  data_files:
  - split: test
    path: Tamil/test-*
- config_name: Telugu
  data_files:
  - split: test
    path: Telugu/test-*
- config_name: Urdu
  data_files:
  - split: test
    path: Urdu/test-*
---

# 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](https://arxiv.org/abs/2607.23808) (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

```python
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:

```bibtex
@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](https://creativecommons.org/licenses/by/4.0/) license.