| --- |
| license: cc-by-4.0 |
| size_categories: |
| - 10M<n<100M |
| --- |
| # Dataset Description |
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| **This dataset is a large-scale collection of 4,162 hours of processed Marathi 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.** |
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| 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 and scalable AI systems. |
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| Additionally, this dataset can be used in data pipelines for **Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) workflows.** |
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| ## Audio Processing & Refinement Pipeline |
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| To ensure enterprise-grade quality and usability, the dataset undergoes a comprehensive **4-step audio refining and processing pipeline** before final delivery: |
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| * **Duplicate Asset Elimination** |
| Removal of duplicate or repeated recordings to maintain dataset uniqueness, consistency, and high-quality training data. |
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| * **Low-Activity Voice Removal** |
| Filtering of silent, low-volume, inactive, or low-quality audio samples to improve overall dataset reliability. |
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| * **PII Detection & Muting** |
| Automatic detection and redaction/muting of personally identifiable information (PII) to support privacy compliance and safe AI training. |
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| * **Background Noise Removal** |
| Application of advanced noise-reduction and audio-cleaning techniques to enhance speech clarity and improve model performance. |
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| This processing pipeline ensures that the dataset is clean, scalable, production-ready, and optimized for speech AI, conversational AI, ASR, SFT, and RLHF workflows. |
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| ## Dataset Specification |
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| * Duration: 4162 hours |
| * Language: Marathi |
| * Type: Processed |
| * Channel Format: Dual-Channel |
| * Audio Conditions: Real-world environments (including noise and variability) |
| * Format: .wav, .mp3, .ogg, etc. |
| * Sampling Rate: 8000 Hz |
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| ## Key Use Cases |
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| * 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 |
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| ## Value of Dual-Channel Dataset |
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| * 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 |
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| **Audio Quality Analysis** |
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| Signal Quality Analysis (Signal QA) |
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| To ensure robust signal-level integrity and consistency, the dataset was evaluated using multiple acoustic and signal-processing metrics. |
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| | 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 | |
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| DNSMOS Evaluation |
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| To ensure production-level reliability, the dataset was evaluated using DNSMOS (Deep Noise Suppression Mean Opinion Score) out of 5 is. |
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| | 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 | |
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| **SQUIM-Based Audio Quality Analysis** |
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| To further assess perceptual and signal characteristics, the dataset was evaluated using SQUIM-based metrics. |
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| | 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 | |
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| Key Insight |
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| The dataset maintains strong acoustic quality despite real-world conditions, making it suitable for production-grade AI systems, LLM pipelines, and speech understanding models. |
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| **Dataset Validation via End-to-End Model Training** |
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| To validate dataset effectiveness, a complete speech-to-NLP training pipeline was built and executed using InfoBay.AI Audio dataset |
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| **Full Pipeline** |
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| → OpenAI Whisper transcription |
| → Sentiment labeling |
| → DistilBERT training (from scratch) |
| → 3-class sentiment classification |
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| Validation Insight |
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| 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. |
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| **Sentiment Classification Task** |
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| The dataset supports supervised learning for sentiment understanding across three classes: |
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| Negative (Class 0) |
| Neutral (Class 1) |
| Positive (Class 2) |
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| The dataset contains naturally occurring emotional and contextual variation, making it highly suitable for: |
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| RLHF preference modeling |
| Emotion-aware conversational agents |
| Human-aligned response generation systems |
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| **Model Performance (From-Scratch Training) From our Dataset** |
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| 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 |
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| Classification Report |
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| | 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 | |
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| **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" |
| } |
| } |
| ``` |
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| **Data Creation** |
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| Procured through formal agreements and generated in the ordinary course of business. |
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| **Considerations** |
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| 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. |
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| -Ph: (91) 8303174762 |
| -Email: datareq@infobay.ai |
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