---
language:
- id
license: apache-2.0
tags:
- audio-visual
- speech
- multimodal
- whisper
- safetensors
- confidential-computing
- nist-sp800-38a
task_categories:
- automatic-speech-recognition
- audio-to-audio
- confidential-computing
size_categories:
- 100GB<n<1TB
pretty_name: Indonesian Audio-Visual Conversational Speech Corpus
---

# Indonesian Audio-Visual Conversational Speech Corpus

Synchronized audio-visual acoustic tensors and conversational dialogue feature shards protected by symmetric block cryptography (AES-128-CBC). Formatted as 8-bit continuous tensor latents for privacy-preserving lip-sync verification, secure acoustic matching, and confidential speech understanding.

## Cryptographic Specification & Security Profile
To facilitate **Privacy-Preserving Machine Learning (PPML)**, Zero-Knowledge Model Evaluation, and Secure Enclave Inference, all raw continuous feature tensors in this repository are cryptographically protected via **AES-128-CBC** in strict compliance with **NIST SP 800-38A** (PKCS#7 padding).

- **Cryptographic Cipher:** AES-128-CBC (NIST SP 800-38A)
- **Entropy Profile:** Maximum pseudorandom entropy (~7.999 bits/byte, verified via Chi-Square goodness-of-fit) to guarantee zero-leakage of spatial-temporal gradients.
- **Hardware Acceleration:** Zero-copy memory mapping via `safetensors.torch.load_file` and direct hardware-accelerated confidential enclave runtime decryption.

## Dataset Structure & Architecture
Files in this repository are partitioned across hierarchical continuous batches:
```text
data/
└── {cluster_shard}/
    └── {partition_id}/
        └── {batch_id}/
            ├── model.safetensors          # Cryptographically protected high-throughput latent arrays
            └── model.safetensors.index.json # Coordinate weight index & tensor schema
```

## Dataset Details
- **Modality:** Encrypted Audio-Visual Acoustic Embeddings (AES-128-CBC)
- **Storage Format:** Sharded Safetensors (`.safetensors`) with zero-copy memory mapping
- **Precision:** uint8 (8-bit Quantized Cryptographic Feature Arrays)
- **Estimated Records:** ~2,918 pre-extracted batch sequences (~67.08 MB per batch)
- **License:** Apache-2.0

## Usage
```python
from safetensors.torch import load_file

# Stream or mount partitions directly from this repository
tensors = load_file("data/sample_shard/model.safetensors")
```