---
license: cc-by-4.0
language:
- en
- pa
task_categories:
- automatic-speech-recognition
- audio-classification
tags:
- speech
- audio
- podcast
- asr
- speech-recognition
- stt
- conversational-ai
- speech-ai
- gujarati
- multilingual
- ssl
- rlhf
pretty_name: Punjabi-audio-dataset
size_categories:
- 10M<n<100M
---
**Dataset Description:**

**This dataset is a large-scale collection of 4,840 hours of processed Punjabi podcast audio recordings, containing 57,569 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 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, 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:

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

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

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

4. **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: 4840 hours
    -Language: Punjabi
    -Type: Processed
    -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



**Value of Single Channel Dataset**

    -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**


Signal Quality Analysis (Signal QA)

To ensure robust signal-level integrity and consistency, the dataset was evaluated using multiple acoustic and signal-processing metrics.

| Metric                    | Value      | Interpretation                                                                  |
| ------------------------- | ---------- | ------------------------------------------------------------------------------- |
| **Average SNR (dB)**      | **50.03**  | High signal-to-noise ratio indicating clean audio with minimal background noise |
| **Average RMS Energy**    | **0.089**  | Stable signal energy level, suitable for speech processing tasks                |
| **Silence Ratio**         | **0.448**  | reflects natural conversational pauses                                          |
| **Clipping Ratio**        | **0.0**    | No clipping detected, ensuring distortion-free audio                            |
| **Loudness (LUFS)**       | **-22.12** | Well-balanced loudness within acceptable range for speech datasets              |
| **Overall Quality Score** | **70.83**  | Good signal quality, appropriate for training and evaluation pipelines          |



 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 |


**SQUIM-Based Audio Quality Analysis**

To further assess perceptual and signal characteristics, the dataset was evaluated using SQUIM-based metrics.


| Metric                       | Value     | Interpretation                                                              |
| ---------------------------- | --------- | --------------------------------------------------------------------------- |
| **Average Energy**           | **0.003** | Low energy level, indicating controlled signal amplitude without distortion |
| **Spectral Flatness**        | **0.052** | Low flatness suggests speech-dominant signal (not noise-like)               |
| **Zero Crossing Rate (ZCR)** | **0.062** | Low ZCR, consistent with voiced speech and minimal high-frequency noise     |
| **Dynamic Range**            | **1.683** | Moderate variation in amplitude, capturing natural speech dynamics          |
| **SI-SDR Proxy**             | **15.0**  | Good signal-to-distortion ratio, indicating clear and well-separated speech |
| **SQUIM Score**              | **62.59** | Solid perceptual quality, suitable for real-world speech applications       |


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**

    → 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**
```json
{
"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"
}
}
```

**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](https://infobay.ai/) or contact us directly.
  
    -Ph: (91) 8303174762
    -Email: datareq@infobay.ai