Create README.md

README.md

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+---
+language:
+- zh
+- en
+license: cc-by-nc-4.0
+task_categories:
+- automatic-speech-recognition
+tags:
+- code-switching
+dataset_info:
+  config_names:
+  - SECoMiCSC
+  - DevCECoMiCSC
+---
+# Robust Code-Switching ASR Benchmark
+
+## Dataset Summary
+This dataset is a **processed and cleaned derivative** of the open-source MagicData corpus, specifically optimized for our project **Code-Switched ASR robustness** (e.g., Whisper fine-tuning). 
+
+We addressed the "context fragmentation" issue in original long-form audio by applying a **Smart-Merge Strategy** (merging short segments into 5-15s chunks using ground-truth timestamps) and filtering out conversational fillers.
+
+## Original Data Sources
+This dataset is derived from the following open-source datasets released by **MagicData Technology**:
+
+* **Training Subset:** Derived from **ASR-SECoMiCSC**
+    * *Source:* [MagicData Open Source Community](https://magichub.com/datasets/chinese-english-code-mixing-conversational-speech-corpus/)
+* **Benchmark/Test Subset:** Derived from **ASR-DevCECoMiCSC**
+    * *Source:* [MagicData Open Source Community](https://magichub.com/datasets/dev-set-of-chinese-english-code-mixing-conversational-speech-corpus/)
+
+*> Note: This repository contains processed audio chunks and metadata only. Please refer to the original links for full datasets and license details.*
+
+## Processing Pipeline (Why this version?)
+1.  **Smart Segmentation:** Instead of random VAD cutting, we merged short utterances into **5s - 15s segments** based on speaker identity and time gaps. This provides better context for Transformer-based models.
+2.  **Noise Filtering:** Removed pure filler segments (e.g., "嗯", "啊", "[ENS]") to reduce hallucination during training.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# 1. Load Training Data (SECoMiCSC)
+dataset_train = load_dataset("1uckyan/code-switch_chunks", data_dir="SECoMiCSC", split="train")
+
+# 2. Load Benchmark Test Set (DevCECoMiCSC)
+dataset_test = load_dataset("1uckyan/code-switch_chunks", data_dir="DevCECoMiCSC", split="train")