Update README.md

README.md

@@ -1,3 +1,119 @@
----
-license: cc0-1.0
----
+---
+license: cc0-1.0
+language:
+- zom
+pretty_name: Zomi ASR
+tags:
+- automatic-speech-recognition
+- audio
+- zomi
+- kuki-chin
+- burmese
+- myanmar
+- webdataset
+- public-domain
+task_categories:
+- automatic-speech-recognition
+- audio-to-audio
+- audio-classification
+language_creators:
+  - found
+source_datasets:
+  - original
+---
+**This is the first public Zomi language ASR dataset in AI history.**
+
+# Zomi ASR
+
+This dataset contains audio recordings and aligned metadata in the **Zomi** language — a collective ethnolinguistic identity adopted by some Kuki-Chin language-speaking communities in Myanmar and India. The term **Zomi** means "Zo people", derived from the root word **Zo** (ancestral identity) and **mi** meaning "people." While originally coined to encompass all Zo-related communities, usage of the term varies regionally and politically.
+
+All audio segments in this dataset were sourced from publicly available news broadcasts by **Zoland Voice TV**, an ethnic-language news channel affiliated with the **National Unity Government (NUG)** of Myanmar. These broadcasts promote information access in minority languages, including Zomi.
+
+The dataset includes over **18.99 hours** of segmented and labeled audio, prepared in [WebDataset](https://github.com/webdataset/webdataset) format, with paired `.audio` and `.json` files suitable for training automatic speech recognition (ASR) systems.
+
+### Acknowledgments
+
+Special thanks to:
+
+- **Zoland Voice TV and PVTV** for producing and releasing multilingual content freely
+- **National Unity Government (NUG)** for supporting inclusive language outreach
+- Volunteers and researchers advancing low-resource ASR for ethnic languages
+
+## Dataset Structure & Format
+
+This dataset follows the [WebDataset](https://github.com/webdataset/webdataset) format. Each training sample consists of two paired files inside a tar archive:
+
+- `XXXX.audio` — the audio chunk (in MP3 format)
+- `XXXX.json` — the corresponding metadata (UTF-8 JSON)
+
+🟢 Minimum chunk duration: 2.04 sec  
+🔴 Maximum chunk duration: 15.05 sec
+
+Each `.json` file contains the following fields:
+
+```json
+{
+  "file_name": "XXXX.audio",
+  "video_id": "YouTubeVideoID",
+  "title": "Original broadcast title from Zoland Voice TV",
+  "url": "https://www.youtube.com/watch?v=YouTubeVideoID",
+  "duration": 13.24
+}
+```
+## Usage Example
+
+You can load and stream this dataset using the Hugging Face `datasets` library:
+```
+    from datasets import load_dataset
+
+    dataset = load_dataset(
+        "freococo/zomi_asr",
+        split="train",
+        streaming=True
+    )
+
+    for sample in dataset:
+        print(sample["audio"])        # Audio object
+        print(sample["file_name"])    # Chunk filename
+        print(sample["duration"])     # Duration in seconds
+        print(sample["title"])        # Broadcast title
+        print(sample["url"])          # YouTube source URL
+```
+Each sample includes:
+- `audio`: the audio chunk (stored as `.audio`, typically MP3 format)
+- `file_name`: filename of the chunk
+- `title`: broadcast title in Zomi or Burmese
+- `url`: original YouTube video link
+- `video_id`: YouTube video ID
+- `duration`: duration of the audio in seconds
+
+## Known Limitations
+
+This dataset was segmented automatically from broadcast videos using pause-based or fixed-length chunking. As such:
+
+- **No transcriptions** are included.
+- Some chunks may contain **background music**, **news jingles**, or **non-speech segments**.
+- No **speaker labels**, **noise filtering**, or **speech-vs-music tagging** is applied.
+- Audio quality varies depending on the original broadcast conditions.
+
+Despite these limitations, this dataset is the most comprehensive public resource available for developing ASR and pretraining models in the Zomi language.
+
+## Licensing & Use
+
+All content is released under the **Creative Commons Zero (CC0 1.0 Universal)** public domain dedication.
+
+You are free to:
+```
+- Use, adapt, and remix the data
+- Train both open and commercial models
+- Publish derivative works, applications, and papers
+```
+We ask users to respect the dignity and intent of the original community broadcasts.
+
+## 📚 Citation
+
+> **Freococo (2025).**  
+> *Zomi ASR*  
+> [https://huggingface.co/datasets/freococo/zomi_asr](https://huggingface.co/datasets/freococo/zomi_asr)  
+> Dataset compiled from Zoland Voice TV ethnic news broadcasts in the Zomi language.  
+> Released under CC0 1.0 (Public Domain).