---
license: cc-by-4.0
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- ne
- or
- pa
- ta
- te
- ur
task_categories:
- automatic-speech-recognition
- audio-classification
tags:
- speech
- conversational
- multilingual
- indian-languages
- diarization
- asr
- tts
pretty_name: Multilingual Indian Conversational Speech
configs:
- config_name: default
  data_files:
  - split: train
    path: metadata.jsonl
---

# Multilingual Indian Conversational Speech

A dataset of **naturalistic, spontaneous two-speaker conversations** across
**13 Indian languages**, with segment-level transcripts, speaker profiles,
timestamps, and recording metadata. Designed for ASR, TTS, speaker
diarization, and conversational speech research.

## Languages (13)

Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali,
Odia, Punjabi, Tamil, Telugu, Urdu.

## Content

Conversations resemble real-world interactions across multiple domains:
Technology / customer support, Financial services, Healthcare, Food &
delivery, and Retail & commerce. Speech is conversational and spontaneous —
natural turn-taking, code-switching (English mixed with the local language),
interruptions, and expressive prosody.

## Structure

```
audio/                    full conversation recordings (WAV), one per language
metadata.jsonl            segment-level annotations referencing the recordings
```

Each row is one **utterance segment** (mostly 1–20 s) that references its
source recording in `audio/` with start/end timestamps.

## Schema

| Field               | Type   | Description                                              |
|---------------------|--------|----------------------------------------------------------|
| `segment_id`        | string | Segment identifier within a recording (`SEG-001`).       |
| `recording_id`      | string | Source recording ID (`REC-ASM-HLT-011`).                 |
| `audio_file`        | string | Relative path to the full recording in `audio/`.         |
| `language`          | string | Language of the conversation.                            |
| `speaker_id`        | string | Speaker label (`SPK_01`, `SPK_02`).                      |
| `speaker_role`      | string | Conversational role (Customer, Agent, Pharmacist, ...).  |
| `speaker_gender`    | string | Speaker gender (from speaker profile).                   |
| `speaker_age`       | string | Age bracket (e.g. `Adult (18+)`).                        |
| `speaker_region`    | string | Speaker region/location.                                 |
| `accent_dialect`    | string | Accent or dialect description.                           |
| `start_time`        | string | Segment start (`HH:MM:SS.mmm`).                          |
| `end_time`          | string | Segment end (`HH:MM:SS.mmm`).                            |
| `start_seconds`     | float  | Segment start in seconds.                                |
| `end_seconds`       | float  | Segment end in seconds.                                  |
| `duration_seconds`  | float  | Segment duration in seconds.                             |
| `transcript`        | string | Verbatim transcript in the native script.                |
| `domain`            | string | Conversation domain.                                     |
| `collection_method` | string | How the audio was collected.                             |
| `environment_type`  | string | Recording environment description.                       |
| `recording_date`    | string | Date of recording.                                       |
| `sample_rate`       | int    | Audio sample rate (Hz).                                  |
| `channels`          | int    | Number of audio channels.                                |
| `bit_depth`         | int    | Audio bit depth.                                         |
| `source_recording`  | string | Original recording filename.                             |

## Audio

- Uncompressed WAV, mostly 48 kHz / 16-bit (some 44.1 kHz and 24-bit).
- One full conversation recording per language; segment rows reference offsets
  within it, so clips can be extracted on demand from `start_seconds` /
  `end_seconds`.

## Notes

- Transcripts include natural English code-switching, common in Indian
  conversational speech.
- Timestamps normalized to a consistent `HH:MM:SS.mmm` format; a few segments
  with source timestamp inconsistencies have a null duration.
- Speaker attributes (role, gender, age, region, accent) come from the
  per-recording speaker profile block in the source annotations.

## Extracting a segment clip (example)

```python
import soundfile as sf, json
row = json.loads(open("metadata.jsonl").readline())
data, sr = sf.read(row["audio_file"])
clip = data[int(row["start_seconds"]*sr):int(row["end_seconds"]*sr)]
sf.write("segment.wav", clip, sr)
```