--- license: cc-by-4.0 dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 104882523 num_examples: 10 download_size: 96333545 dataset_size: 104882523 configs: - config_name: default data_files: - split: train path: data/train-* size_categories: - n<1K language: - ar tags: - Audio - Podcast - Arabic - Clear - Raw - Benchmark - Optimization --- **Dataset Description:** This dataset is a **large-scale collection of raw Arabic 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, 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**. **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: Arabic -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: 6024 hours **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** 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** ```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" } } ``` **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](https://infobay.ai/) or contact us directly. -Ph: (91) 8303174762 -Email: vipul@infobay.ai