A Hindi speech instruction-following dataset designed for training speech-language models such as LLaMA-Omni. Each example pairs a spoken Hindi user question (audio) with a text assistant response.

Dataset Summary

Property	Value
Language	Hindi (hi)
Total examples	~110,718
Train split	~105,000 examples (batches 001–210)
Validation split	~5,500 examples (batches 211–222)
Audio format	FLAC, 16,000 Hz mono
Conversation type	Single-turn (one user question → one assistant response)

Data Sources

Text conversations are sourced from ai4bharat/indic-instruct-data-v0.1, using the following subsets:

Subset	Description
Anudesh	Human-annotated Hindi instruction-response pairs
LMSYS	Chat conversations translated/adapted to Hindi
HH-RLHF	Anthropic Helpful & Harmless RLHF data in Hindi
Flan v2	Flan collection prompts in Hindi

Only single-turn conversations were retained (one user question, one assistant response).

Audio Generation

User questions were converted to speech using:

TTS Model: facebook/mms-tts-hin (MMS Hindi TTS — VITS architecture)
Sampling rate: 16,000 Hz
Format: FLAC (lossless)

Each audio file corresponds to the user turn of a conversation and is named {id}-1_user.flac.

Dataset Structure

Parquet files (`data/`)

Each row in the parquet batches contains:

Column	Type	Description
`id`	string	Unique conversation ID
`user_text`	string	Original Hindi text of the user question
`assistant_text`	string	Hindi text of the assistant response
`audio`	Audio	FLAC audio of the user question (`bytes` + `path`)

JSON file (`dataset.json`)

A flat JSON array with the same conversations in message format:

{
  "id": "c01d4234-8d55-51f5-b84f-0ddfd8a271b0",
  "messages": [
    {"role": "user", "content": "न्यूयॉर्क में 3 दिवसीय यात्रा का कार्यक्रम बनाएं।"},
    {"role": "assistant", "content": "..."}
  ]
}

Usage

from datasets import load_dataset

ds = load_dataset("Pastaaaaa2003/hindi-llama-omni")

# Access train split
for example in ds["train"]:
    audio = example["audio"]          # dict with 'bytes' and 'path'
    question = example["user_text"]   # Hindi text
    answer = example["assistant_text"]
    print(question, "->", answer[:80])

License

Creative Commons Attribution 4.0 (CC BY 4.0)

Please also comply with the licenses of the original source datasets:

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Hindi LLaMA-Omni Instruct Dataset