--- title: "Call Center Audio Dataset" description: "Call center dataset containing 13,000+ hours of real-world customer service calls from global call centers, featuring 90%+ unique speakers and time-stamped transcripts. The dataset includes…" url: "https://unidata.pro/datasets/call-center-audio/" date_modified: "2026-04-09T13:07:19+03:00" language: "en-US" --- Call center dataset containing 13,000+ hours of real-world customer service calls from global call centers, featuring 90%+ unique speakers and time-stamped transcripts. The dataset includes high-quality call center data with rich metadata and multilingual conversations, enabling businesses and researchers to analyze customer interactions, perform sentiment analysis, and improve customer support. ## Dataset Structure ### The Numbers Section **Numbered list:** - **Number:** 13,000+ — **Text:** Hours - **Number:** 90%+ — **Text:** Unique Speakers ### Tooltips Section **Tooltip items:** - **Name:** Speech Analysis - **Name:** ASR - **Name:** Machine Learning - **Name:** Audio Processing - **Name:** Voice Recognition ### Dataset Information **Table with data:** | Characteristic | Data | | --- | --- | | Description | Audio of real customer service calls | | Data types | Audio | | Tasks | Speech Recognition, Speaker Diarization | | Hours of audio | 13,000+ | | Language | English: 96%, Spanish: 2.5%, Hindi: 1%, Other languages: 0.5% | | Labeling | Metadata (id, company, category, device, OS, city, state, country, duration, wait time, transcription, AI summary) | **Media Slider:** - **Video on Slayder:** **Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1GaqchsG63rQZRZpBZhRxOTsZbGJMxY_u) ### Technical Specifications **Table with data:** | Characteristic | Data | | --- | --- | | Audio Format | OGG, FLAC | | Duration | Mean = ~770 seconds (~13 minutes) | **Source and data collection methodology:** Source and collection methodology: Data was collected by a partner of Unidata. ### Dataset Use Cases - Slider **Industry Cards:** - **Industry:** Customer Support & Contact Centers — **Title:** Training Conversational AI for Customer Service — **Text:** A large call center dataset with real customer conversations helps train conversational AI used in contact centers. The audio dataset contains phone calls, transcripts, and metadata that reflect real-world call scenarios. Models learn customer intent, agent responses, and dialogue structure, improving automated support tools, response suggestions, and overall customer service efficiency. - **Industry:** Speech Technology & AI Research — **Title:** Speech Recognition for Real Customer Calls — **Text:** Researchers use call center data to train speech recognition systems on natural conversations recorded in real environments. The dataset includes varied accents, interruptions, and background noise common in phone calls. These audio datasets support model training, transcription accuracy testing, speaker diarization, and development of robust speech processing tools. - **Industry:** Business Analytics & Customer Insights — **Title:** Customer Sentiment and Conversation Analysis — **Text:** A call center database containing thousands of real customer conversations enables detailed analysis of customer sentiment and service performance. Analysts apply natural language processing and sentiment analysis tools to transcripts and audio. The data helps identify recurring issues, evaluate agent interactions, and understand patterns affecting customer satisfaction and service outcomes. - **Industry:** Retail & E-commerce Platforms — **Title:** Improving Customer Support Automation — **Text:** Retail companies use customer service datasets to build systems that support automated customer interactions. Real-world call recordings allow models to recognize common product questions, delivery issues, and refund requests. Training on authentic call center data helps conversational systems respond accurately and assists agents with faster issue resolution and customer communication. ### Fact **FAQs Heading:** FAQs **List of Questions:** - **Question:** Can I request a sample of the call center dataset before purchasing? — **Answer:** Yes, a sample of the call center dataset can be requested for evaluation purposes. This allows teams to review the structure of the audio files, transcripts, and metadata before integrating the full customer service dataset into their training pipelines. - **Question:** What types of annotations are provided with the dataset? — **Answer:** Each audio file includes structured metadata such as company category, device, operating system, geographic location, call duration, and wait time. The dataset also contains transcriptions and AI-generated summaries. - **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model. Free samples are available for testing and validation, while full datasets containing large volumes of audio data are provided through purchase. - **Question:** Do Unidata datasets comply with GDPR and other privacy regulations? — **Answer:** Yes, all datasets are curated in compliance with GDPR and applicable data protection laws. Data is obtained from legally permissible sources to ensure ethical use in AI research and machine learning development. - **Question:** How are Unidata datasets stored? — **Answer:** All datasets are stored securely on AWS cloud infrastructure, ensuring reliability and scalability. Storage and management processes follow ISO 27001 and ISO 27701 standards, providing a secure environment for handling large audio datasets and associated metadata. - **Question:** How long does it take to receive the dataset? — **Answer:** After submitting a request, the Unidata team reviews the requirements and completes the necessary documentation. Once the agreement is signed and payment is processed, the dataset is typically delivered within 3–10 days. - **Question:** Why is real customer conversation data valuable for speech recognition models? — **Answer:** Real customer calls contain natural speech variations, spontaneous dialogue, and diverse communication styles. Training AI models on authentic conversations helps improve accuracy when processing real-world customer interactions. - **Question:** Is this dataset real-world or synthetic? — **Answer:** This dataset consists entirely of real-world customer service call recordings captured during actual interactions between customers and agents. The audio data is fully licensed, non-synthetic, and reflects communication patterns found in modern call centers. - **Question:** Why are transcripts and metadata important for conversational AI training? — **Answer:** Structured transcripts and metadata help AI systems connect spoken content with additional context, making it easier to train and evaluate models. This supports more accurate speech processing, conversation analysis, and automation workflows. [Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)