InfoBayAI/Hindi_Podcast_Audio_Dataset_Dual_Channel

Dataset Description

This dataset is a large-scale collection of 11,607 hours of processed Hindi dual-channel podcast audio recordings, containing 57,568 hours of processed podcast audio recordings across 12 languages, designed to support the development and training of advanced speech AI and conversational AI systems.

It captures real-world podcast conversations across diverse topics and formats. The dataset is organized in a dual-channel format, where corresponding speaker audio streams are separated into individual channels, enabling clear speaker attribution and enhanced conversational analysis. It preserves natural speech patterns, speaker variability, turn-taking behavior, and authentic podcast environments, making it highly valuable for building robust, scalable, and production-ready AI systems.

Additionally, this dataset can be used in data pipelines for Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) workflows.

Audio Processing & Refinement Pipeline

To ensure enterprise-grade quality and usability, the dataset undergoes a comprehensive 4-step audio refining and processing pipeline before final delivery:

• Duplicate Asset Elimination Removal of duplicate or repeated recordings to maintain dataset uniqueness, consistency, and high-quality training data.

• Low-Activity Voice Removal Filtering of silent, low-volume, inactive, or low-quality audio samples to improve overall dataset reliability.

• PII Detection & Muting Automatic detection and redaction/muting of personally identifiable information (PII) to support privacy compliance and safe AI training.

• Background Noise Removal Application of advanced noise-reduction and audio-cleaning techniques to enhance speech clarity and improve model performance.

This processing pipeline ensures that the dataset is clean, scalable, production-ready, and optimized for speech AI, conversational AI, ASR, SFT, and RLHF workflows.

Dataset Specification

• Duration: 11607 hours
• Language: Hindi
• Type: Processed
• Channel Format: Dual-Channel
• Audio Conditions: Real-world environments (including noise and variability)
• Format: .wav, .mp3, .ogg, etc.
• Sampling Rate: 8000 Hz

Key Use Cases

• Pretraining Automatic Speech Recognition (ASR) systems
• Speech-to-Text (STT) systems
• Self-supervised learning (SSL) for speech models
• Large Language Models (LLMs) with audio understanding capabilities
• Speech representation learning
• Noise-robust and real-world voice applications
• Speaker diarization and speaker separation systems
• Conversational AI and dialogue modeling
• Multi-speaker interaction analysis

Value of Dual-Channel Dataset

• Clear separation of speakers for accurate speaker attribution
• Improved speaker diarization and speaker recognition performance
• Better modeling of conversational dynamics and turn-taking behavior
• Enhanced training for dialogue systems and conversational AI
• Reduced speaker overlap ambiguity during model training
• More accurate transcription and conversation analytics
• Improved performance in multi-speaker and real-world audio environments
• Flexible preprocessing and custom annotation pipelines tailored to specific business needs