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Agent/eval/0000.parquet

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README.md

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----
-license: apache-2.0
----
+---
+language:
+- zh
+license: apache-2.0
+task_categories:
+- automatic-speech-recognition
+viewer: true
+dataset_info:
+- config_name: User
+  features:
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: text
+    dtype: string
+  - name: duration
+    dtype: float64
+  - name: chat_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 889743413.000
+    num_examples: 1008
+  - name: eval
+    num_bytes: 235780103.000
+    num_examples: 253
+- config_name: Agent
+  features:
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: text
+    dtype: string
+  - name: duration
+    dtype: float64
+  - name: chat_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3749359214.000
+    num_examples: 4552
+  - name: eval
+    num_bytes: 930931836.000
+    num_examples: 1138
+configs:
+- config_name: User
+  data_files:
+  - split: train
+    path: User/train/*
+  - split: eval
+    path: User/eval/*
+- config_name: Agent
+  data_files:
+  - split: train
+    path: Agent/train/*
+  - split: eval
+    path: Agent/eval/*
+tags:
+- medical
+---
+
+## MMedFD: A Real-World Healthcare Benchmark for Multi-Turn Full-Duplex Automatic Speech Recognition
+
+### 📄 **Preprint**: [MMedFD](https://arxiv.org/abs/2509.19817) — For the complete benchmark construction pipeline, evaluation methodology, dataset specifications, and additional implementation details, please refer to the preprint.
+
+
+### ⚠️Data Availability
+Full access requires internal approval and a research-only data use agreement. 
+🚫 Non-Commercial Use
+This dataset is provided **for non-commercial research and education only**. **Commercial use is prohibited.**  
+Researchers who wish to request full access may contact yangxiao.wxy@antgroup.com with a brief description of their affiliation, project goals, intended use, and data protection plan. Only de-identified data may be shared, and redistribution is prohibited.
+
+## 🗂️ Data Release & Access
+
+- **Public release (partial subset)**: We release **only a portion of the data used for this benchmark’s training and evaluation**. This Lite subset **differs in amount and coverage** from our internal full dataset and is **not** a drop-in replacement for the complete data.
+- **What’s included**: A **reduced selection** of dialogues/audio/text sufficient to reproduce the reported benchmark protocol at a smaller scale.
+- **Not included**: Additional sessions, higher-fidelity artifacts, and full validation/test coverage remain internal.
+
+## 🔒 Privacy, Safety & Redaction
+
+- **Privacy-preserving audio**: To protect speaker privacy, all audio has been **re-synthesized via TTS** (privacy-preserving re-encoding). This process **obfuscates speaker identity and acoustic biomarkers** while preserving task-relevant linguistic content for modeling.
+- **Real-world dialogs**: The **dialogue content originates from real-world collections**. However, **sensitive spans** (e.g., direct identifiers, highly specific personal details) are **automatically redacted by an LLM-based filter** before release.
+- **Residual risk**: Despite these protections, **re-identification attempts are prohibited**. Please do not try to recover original identities or link samples to outside sources.
+
+
+## 📑 How to Cite
+
+If this code or our benchmark is useful for your research, please consider citing our paper:
+```bibtex
+@misc{chen2025mmedfdrealworldhealthcarebenchmark,
+      title={MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition}, 
+      author={Hongzhao Chen and XiaoYang Wang and Jing Lan and Hexiao Ding and Yufeng Jiang MingHui Yang and DanHui Xu and Jun Luo and Nga-Chun Ng and Gerald W. Y. Cheng and Yunlin Mao and Jung Sun Yoo},
+      year={2025},
+      eprint={2509.19817},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS},
+      url={https://arxiv.org/abs/2509.19817}, 
+}
+```

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