Duplicate from AIxBlock/92k-real-world-call-center-scripts-english

Co-authored-by: AIxBlock <AIxBlock@users.noreply.huggingface.co>

(re-uploaded)PII_Redacted_Transcripts_aixblock-automotive-stereo-inbound-104h.zip

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(reupload)PII_redacted_auto_insurance_script.zip

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README.md

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+---
+license: cc-by-nc-4.0
+language:
+- en
+size_categories:
+- 10K<n<100K
+---
+
+[ArXiv Paper Publication Here: "Real-World En Call Center Transcripts Dataset with PII Redaction"](https://arxiv.org/abs/2507.02958)
+
+
+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 is now published here: https://arxiv.org/abs/2507.02958
+📣 **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 and 3 independent researchers: Gaurav Chawla, Raghu Banda, Caleb DeLeeuw ** 

auto_insurance_customer_service_inbound.zip

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automotive_and_healthcare_insurance_inbound.zip

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automotive_inbound.zip

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customer_service_general_inbound.zip

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home_ervice_inbound&telecom _outbound.zip

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home_service_inbound.zip

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insurance_outbound.zip

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medical_equipment_outbound.zip

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medicare_inbound.zip

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