InfoBayAI/Urdu-Podcast-ASR-Dataset

Dataset Description:

This dataset is a large-scale collection of raw Urdu podcast audio, specifically designed to support the development and pretraining of speech and language models.

It captures real-world interactions across diverse topics and formats. The dataset preserves natural speech patterns, speaker variability, and authentic podcast environments, making it highly valuable for building robust and scalable AI systems. Additionally, this dataset can be used in data pipelines for Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) workflows.

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

Dataset Specification

-Language: Urdu
-Type: Raw, unprocessed podcast audio, Single Channel  
-Speech Style: Natural, conversational, unscripted
-Audio Conditions: Real-world environments (including noise and variability)
-Domains: discussions, storytelling, interviews, etc.
-Format: .wav, .mp3, .ogg, etc.
-Sampling Rate: 8000 Hz
-Duration: 2297 hours

Value of Raw Single Channel Data

-Training models that can handle real-world conversational complexity
-Improved performance in noisy and uncontrolled environments
-Development of accurate speaker diarization systems
-Better generalization across accents, tones, and speaking styles
-Flexible preprocessing and custom annotation pipelines tailored to specific business needs

**Audio Quality Analysis**

DNSMOS Evaluation

To ensure production-level reliability, the dataset was evaluated using DNSMOS (Deep Noise Suppression Mean Opinion Score) out of 5 is.

Metric	Score	Interpretation
Speech Quality (SIG)	3.89	Clear and intelligible conversational speech
Background Noise (BAK)	4.01	Strong noise suppression with stable acoustic clarity
Overall MOS (OVR)	3.81	High-quality real-world audio suitable for model training

Key Insight

The dataset maintains strong acoustic quality despite real-world conditions, making it suitable for production-grade AI systems, LLM pipelines, and speech understanding models.

Dataset Validation via End-to-End Model Training

To validate dataset effectiveness, a complete speech-to-NLP training pipeline was built and executed using InfoBay.AI Audio dataset

Full Pipeline

Raw podcast audio → OpenAI Whisper transcription → Sentiment labeling → DistilBERT training (from scratch) → 3-class sentiment classification

Validation Insight

This end-to-end workflow demonstrates that the dataset is not only large-scale but also self-sufficient for training downstream AI models without reliance on external pretrained datasets.

Sentiment Classification Task

The dataset supports supervised learning for sentiment understanding across three classes:

Negative (Class 0)
Neutral (Class 1)
Positive (Class 2)

The dataset contains naturally occurring emotional and contextual variation, making it highly suitable for:

RLHF preference modeling
Emotion-aware conversational agents
Human-aligned response generation systems

Model Performance (From-Scratch Training) From our Dataset

A DistilBERT-based model trained from scratch achieved strong performance on this dataset:

Accuracy: ~98%
Macro F1-score: ~0.98
Weighted F1-score: ~0.99

Classification Report

Class	Sentiment	Precision	Recall	F1-score	Support
0	Negative	0.97	0.96	0.96	1,128
1	Neutral	0.99	0.99	0.99	7,865
2	Positive	0.98	0.98	0.98	2,658

Basic JSON Schema

{
  "id": "string",
  "audio_filepath": "string",
  "duration": "float",
  "language": "string",
  "sample_rate": "integer",
  "format": "string",
  "num_speakers": "integer",
  "domain": "string",
  "metadata": {
    "source": "string",
    "recording_condition": "string"
  }
}

Full Dataset Overview

Total Duration (in hours): 57,568

This dataset is part of a large multilingual podcast audio collection covering the following languages: Arabic, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Punjabi, Tamil, Telugu, and Urdu.

Data Creation

Procured through formal agreements and generated in the ordinary course of business.

Considerations

This dataset is provided for research and educational purposes only. It contains only sample data. For access to the full dataset and enterprise licensing options, please visit our website InfoBay AI or contact us directly.

-Ph: (91)8303174762
-Email: vipul@infobay.ai