Add paper link, project page and GitHub repository

Hi! This PR improves the dataset card for Voices-in-the-Wild-2M by:
- Linking the dataset to the [Mega-ASR paper](https://huggingface.co/papers/2605.19833).
- Adding the GitHub repository and project page links.
- Setting the license to `apache-2.0` in the metadata as specified in the source documentation.
- Updating the loading snippet to use the Hub dataset ID.
- Adding the BibTeX citation.

README.md

@@ -2,15 +2,16 @@
 language:
 - en
 - zh
+license: apache-2.0
 task_categories:
 - automatic-speech-recognition
+pretty_name: Voices in the Wild
 tags:
 - audio
 - speech
 - asr
 - robustness
 - noisy-speech
-pretty_name: Voices in the Wild
 configs:
 - config_name: default
   data_files:
@@ -144,174 +145,15 @@ dataset_info:
     dtype: string
   - name: index
     dtype: int64
-  splits:
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-    num_examples: 47333
-  - name: distortion_dropout
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-  - name: distortion_dropout_noise
-    num_bytes: 628069045
-    num_examples: 2017
-  - name: distortion_noise
-    num_bytes: 599319574
-    num_examples: 1998
-  - name: dropout
-    num_bytes: 14396982676
-    num_examples: 47040
-  - name: echo
-    num_bytes: 14511837194
-    num_examples: 47450
-  - name: echo_distortion
-    num_bytes: 630980880
-    num_examples: 2072
-  - name: echo_distortion_dropout
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-    num_examples: 2108
-  - name: echo_distortion_dropout_noise
-    num_bytes: 645544366
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-  - name: echo_dropout
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-  - name: echo_noise
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-  - name: echo_recording_dropout_noise
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-  - name: far_field
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-    num_examples: 2020
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-  - name: obstructed
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-  - name: recording
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-  - name: recording_distortion_dropout_noise
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-    num_bytes: 637848939
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-  - name: recording_dropout
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-    num_examples: 1961
 ---
 
 # Voices in the Wild
 
-Voices in the Wild is an automatic speech recognition dataset for robustness training and evaluation under diverse acoustic conditions. Audio files use public sequential names and are grouped only by normalized acoustic subset.
+[**Project Page**](https://xzf-thu.github.io/Mega-ASR/) | [**Paper**](https://huggingface.co/papers/2605.19833) | [**GitHub**](https://github.com/xzf-thu/Mega-ASR)
+
+Voices in the Wild (Voices-in-the-Wild-2M) is a large-scale automatic speech recognition (ASR) dataset designed for robustness training and evaluation under diverse, real-world acoustic conditions. It covers 7 classic acoustic phenomena (including noise, far-field speech, obstruction, echo/reverberation, recording artifacts, electronic distortion, and transmission dropout) and 54 physically plausible compound scenarios.
+
+The dataset was introduced as part of the **Mega-ASR** framework to address the "acoustic robustness bottleneck" where models produce omissions or hallucinations under severe compositional distortions.
 
 ## Data Fields
 
@@ -327,15 +169,32 @@ Voices in the Wild is an automatic speech recognition dataset for robustness tra
 
 ## Dataset Size
 
-- Total examples: 645925
-- Subset categories: 54
+- **Total examples**: 645,925
+- **Subset categories**: 54
 
 ## Loading
 
 ```python
 from datasets import load_dataset, Audio
 
-ds = load_dataset("audiofolder", data_dir="/path/to/Voices-in-the-Wild")
+# Load the dataset from the Hub
+ds = load_dataset("zhifeixie/Voices-in-the-Wild-2M")
 ds = ds.cast_column("audio", Audio())
-print(ds["train"][0])
+
+# Access an example
+print(ds["far_field"][0])
 ```
+
+## Citation
+
+```bibtex
+@misc{xie2026megaasrinthewild2speechrecognition,
+      title={Mega-ASR: Towards In-the-wild^2 Speech Recognition via Scaling up Real-world Acoustic Simulation},
+      author={Zhifei Xie and Kaiyu Pang and Haobin Zhang and Deheng Ye and Xiaobin Hu and Shuicheng Yan and Chunyan Miao},
+      year={2026},
+      eprint={2605.19833},
+      archivePrefix={arXiv},
+      primaryClass={cs.SD},
+      url={https://arxiv.org/abs/2605.19833},
+}
+```