Update README.md

README.md

@@ -1,3 +1,51 @@
----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+language:
+- en
+size_categories:
+- 10K<n<100K
+---
+
+This dataset includes **91,706 high-quality transcriptions** corresponding to approximately **10,500 hours** of **real-world call center conversations** in **English**, collected across various industries and global regions. The dataset features both **inbound and outbound** calls and spans multiple accents, including **Indian**, **American**, and **Filipino** English. All transcripts have been **carefully redacted for PII** and enriched with **word-level timestamps** and **ASR confidence scores**, making it ideal for training robust speech and language models in real-world scenarios.
+
+* 🗣️ **Language & Accents**: English (Indian, American, Filipino)
+* 📞 **Call Types**: Inbound and outbound customer service conversations
+* 🏢 **Source**: Sourced via partnerships with BPO centers across a range of industries
+* 🔊 **Audio Length**: 10,500+ hours of corresponding real-world audio (not included in this release)
+* 📄 **Transcripts**: 91,706 JSON-formatted files with:
+
+  * Word-level timestamps
+  * ASR confidence scores
+  * Categorized by domain, topic, and accent
+  * Redacted for privacy
+
+🔧 **Processing Pipeline**:
+
+1. Raw, uncompressed audio was downloaded directly from BPO partners to maintain acoustic integrity.
+
+2. Calls were tagged by **domain**, **accent**, and **topic** (inbound vs outbound).
+
+3. Transcription was done using **AssemblyAI’s paid ASR model**.
+
+4. Transcripts and audios were **redacted for PII** based on the following list:
+
+   ```
+   account_number, banking_information, blood_type, credit_card_number, credit_card_expiration, 
+   credit_card_cvv, date, date_interval, date_of_birth, drivers_license, drug, duration, 
+   email_address, event, filename, gender_sexuality, healthcare_number, injury, ip_address, 
+   language, location, marital_status, medical_condition, medical_process, money_amount, 
+   nationality, number_sequence, occupation, organization, passport_number, password, person_age, 
+   person_name, phone_number, physical_attribute, political_affiliation, religion, statistics, 
+   time, url, us_social_security_number, username, vehicle_id, zodiac_sign
+   ```
+
+5. A manually QA’d subset was used to calculate **word error rate (WER)**, with the overall transcription accuracy estimated at **96.131%**.
+
+6. Final output is provided in **JSON format**, with cleaned and standardized fields.
+
+📜 **Paper Coming Soon**: A detailed paper describing the full pipeline, challenges, and benchmarks will be released on **arXiv**.
+📣 **Want Updates?** Drop a comment in the **community section** to be notified when the paper goes live.
+
+🔐 **License**: Provided **strictly for research and AI model development**. **Commercial use, resale, or redistribution is prohibited.**
+
+🎓 **Brought to you by AIxBlock** – *a decentralized platform for AI development and workflow automations, with a commitment to enabling the development of fair, accurate, and responsible AI systems through high-quality open datasets.*