---
license: cc-by-4.0
task_categories:
  - automatic-speech-recognition
  - audio-classification
tags:
  - speech
  - audio
  - podcast
  - asr
  - speech-recognition
  - stt
  - conversational-ai
  - speech-ai
  - Malayalam
  - multilingual
  - ssl
  - rlhf
pretty_name: Malayalam Podcast Audio Dataset Dual Channel
size_categories:
  - 10M<n<100M
---
 # Dataset Description

**This dataset is a large-scale collection of 3,956 hours of processed Malayalam dual-channel 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 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

```
-Language: Malayalam
-Type: Processed
-Channel Format: Dual-Channel
-Audio Conditions: Real-world environments (including noise and variability)
-Format: .wav, .mp3, .ogg, etc.
-Sampling Rate: 8000 Hz
-Duration: 3956 hours
```

## 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
```

    
**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