Datasets:
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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") |