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--- |
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language: |
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- zh |
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- en |
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license: cc-by-nc-4.0 |
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task_categories: |
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- automatic-speech-recognition |
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tags: |
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- code-switching |
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dataset_info: |
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config_names: |
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- SECoMiCSC |
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- DevCECoMiCSC |
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--- |
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# Robust Code-Switching ASR Benchmark |
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## Dataset Summary |
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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). |
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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. |
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## Original Data Sources |
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This dataset is derived from the following open-source datasets released by **MagicData Technology**: |
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* **Training Subset:** Derived from **ASR-SECoMiCSC** |
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* *Source:* [MagicData Open Source Community](https://magichub.com/datasets/chinese-english-code-mixing-conversational-speech-corpus/) |
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* **Benchmark/Test Subset:** Derived from **ASR-DevCECoMiCSC** |
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* *Source:* [MagicData Open Source Community](https://magichub.com/datasets/dev-set-of-chinese-english-code-mixing-conversational-speech-corpus/) |
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*> Note: This repository contains processed audio chunks and metadata only. Please refer to the original links for full datasets and license details.* |
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## Processing Pipeline (Why this version?) |
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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. |
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2. **Noise Filtering:** Removed pure filler segments (e.g., "嗯", "啊", "[ENS]") to reduce hallucination during training. |
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## Usage |
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```python |
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from datasets import load_dataset |
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# 1. Load Training Data (SECoMiCSC) |
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dataset_train = load_dataset("1uckyan/code-switch_chunks", data_dir="SECoMiCSC", split="train") |
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# 2. Load Benchmark Test Set (DevCECoMiCSC) |
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dataset_test = load_dataset("1uckyan/code-switch_chunks", data_dir="DevCECoMiCSC", split="train") |
