InfoBayAI/call_center_audio_dual_channel_so

Dataset Description:

This dataset is a large-scale collection of raw Somali call center audio (dual-channel), designed to support the development and training of advanced speech and AI systems.

It consists of real-world customer and agent speech recordings collected from call center environments. The dataset is organized in a dual-channel format, where corresponding customer and agent audio are available as separate recordings, enabling clear role-based modeling and analysis. The dataset captures authentic speech characteristics such as tone variation, pauses, silence patterns, and natural speaking behaviour commonly observed in customer service environments. This makes it highly valuable for building accurate, scalable, and production-ready AI systems for enterprise and customer support applications. 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

-Training Automatic Speech Recognition (ASR) systems for call center environments
-Call analytics and conversation intelligence
-Sentiment analysis and emotion detection
-Quality monitoring and compliance analysis
-Virtual assistants for customer support
-Speech-to-Text (STT) for structured call transcription

Dataset Specification

-Language: Somali
-Type: Raw, unprocessed call center audio
-Speech Style: Customer and agent speech from call center environments
-Audio Conditions: Real-world call center environments
-Domains: customer support, service calls, query resolution, etc.
-Channel Configuration: Dual-channel (separate customer and agent recordings)
-Format: .wav, .mp3, .ogg, etc.
-Sampling Rate: 8000 Hz
-Duration: Somali(SO) - 291 & Somali(UG) - 105   
 

Value of Dual Channel Dataset

-Clear separation of customer and agent speech for accurate modeling
-Supports role-based speech analysis and modeling
-Improves performance in real-world customer support scenarios
-Ideal for call center analytics and conversation intelligence systems
-Facilitates precise annotation and labeling workflows

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): 1,316,582

This dataset is part of a large multilingual podcast audio collection covering the following languages: Arabic, Arabic(EG), Arabic(UG), Assamese, Bengali, Chinese, English (India), English (UK), English (US), Filipino, French, Ganda, German, Gujarati, Hindi, Italian, Japanese, Kannada, Kinyarwanda, Korean, Luganda, Malay, Malayalam, Mandarin, Marathi, Mizo, Nepali, Oriya, Punjabi, Russian, Somali (SO), Somali (UG), Spanish (MX), Spanish (ES), Swahili (KE), Swahili (UG), Tamil, Telugu, Tigrinya, Urdu and Yoruba.

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