README.md

metadata

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)

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)