CoolFace
Datasetpublic

ansh-rohilla/verbalyze-dialogues

Verbalyze: Indic Voice Telephony Dialogue Corpus (16,370 Conversations) Verbalyze Dialogues is an enterprise-grade multi-turn conversational voice dataset in 12 Indian languages specifically engineered for training low-latency telephony Voice Agents and Small Language Models (SLMs). Unlike standard text-chat datasets, Verbalyze dialogues replicate the dynamics of real telephone calls: Short, natural spoken sentences (1-2 sentences per turn) Conversational fillers ("haan", "hmm"… See the full description on the dataset page: https://huggingface.co/datasets/ansh-rohilla/verbalyze-dialogues.

sourceHugging Facemitupdated 14d agoView on Hugging Face
1likes114downloads
Dataset Card

Verbalyze: Indic Voice Telephony Dialogue Corpus (16,370 Conversations)

Verbalyze Dialogues is an enterprise-grade multi-turn conversational voice dataset in 12 Indian languages specifically engineered for training low-latency telephony Voice Agents and Small Language Models (SLMs).

Unlike standard text-chat datasets, Verbalyze dialogues replicate the dynamics of real telephone calls:

  • —Short, natural spoken sentences (1-2 sentences per turn)
  • —Conversational fillers ("haan", "hmm", "acha", "okay")
  • —Real-time customer emotions (frustration, salary delays, payment objections)
  • —User interruptions and mid-turn corrections
  • —Native tool-calling (disconnect_tool) for telephony hang-up state machines

Languages Covered

LanguageCodeConversations
Assameseas1,300
Bengalibn1,300
Englishen1,377
Gujaratigu1,377
Hindihi1,377
Kannadakn1,377
Malayalamml1,377
Marathimr1,377
Odiaor1,377
Punjabipa1,377
Tamilta1,377
Telugute1,377
Total1216,370

Domain Focus

  • —Muthoot Fincorp EMI Recovery & Loan Verification: Authentic outbound debt recovery calls balancing politeness with firm collection milestones.
  • —Banking Customer Care: KYC verification, UPI transaction failure disputes, and net banking support.

Usage with TRL / SFTTrainer

python
from datasets import load_dataset
from trl import SFTTrainer

dataset = load_dataset("json", data_files="data/dialogues/train.jsonl")
# Feed directly into SFTTrainer with chat_template