Vartalaap is a corpus of synthetic Indian customer-support calls whose speaker timbres are cloned from consented research recordings. Access is reviewed individually.

By requesting access you agree to use this dataset for non-commercial research only, to make no attempt to identify or re-synthesise the speakers whose voices informed it, and not to redistribute it.

Vartalaap — full-duplex Hindi/English conversational speech

Dual-channel synthetic Indian customer-support calls for training full-duplex speech-to-speech models.

74,638 calls · 1,715.5 hours · 1983 shards (last updated 2026-09-30 12:09 IST)

Audio layout

Each row's audio is a stereo FLAC at 24000 Hz:

channel	content
0 (LEFT)	agent — pristine, TTS speech and silence only
1 (RIGHT)	user — the caller

from datasets import load_dataset
ds = load_dataset("kapturecx/Vartalaap", split="train", streaming=True)
row = next(iter(ds))
agent = row["audio"]["array"][:, 0]     # left
user   = row["audio"]["array"][:, 1]    # right

Text conventions

Hindi is written in Devanagari, English in Latin, in the same string — "आपका order अभी out for delivery है". Romanized Hindi is rejected by a QA gate. Everything the TTS spoke is spelled out: no digits (₹4,500 → चार हज़ार पाँच सौ रुपये), abbreviations letter-by-letter in Devanagari (OTP → ओ टी पी), phone numbers in two-digit batches (अट्ठानवे चालीस पैंतालीस बावन तिरासी). Backchannels are deliberately Latin (ji, hmm ji, haan ji).

Fields

call_id, audio, duration_s, sample_rate, channel_layout
domain, scenario, outcome, register, language
agent_voice / agent_gender / agent_speaker_key, same for user_*, plus the full speakers list with per-speaker rate, pitch and target F0
transcript — list of turns: role, kind, start, end, text, emotion, truncated
words_agent / words_user — word-level timings (absolute seconds)
events — duplex annotations: barge_in, agent_yield, user_yield, backchannel, overlap, latency, silence, hold, hangup
n_interruptions, n_backchannels, overlap_ratio, silence_ratio
augmented / augmentations_json — acoustic degradation applied, if any

What makes it full-duplex

Barge-ins land at word onsets and the interrupted side stops within ~60-200 ms, with transcript[i].truncated and the shortened text marking exactly what had been said when the floor was surrendered. Backchannels overlap the speaker without taking the floor. Response latency is drawn per role.

Caveats

Synthetic speech carries TTS artefacts; mix with real audio. Speaker identity is a small set of cloned voices with pitch/rate variation, so treat speakers as acoustic conditions rather than distinct people. augmented=false rows are clean by design — the acoustic degradation pass is separate.

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