Update README.md

README.md

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----
-license: cc0-1.0
----
+---
+datasets:
+- freococo/Google_Myanmar_ASR
+tags:
+- audio
+- asr
+- speech-recognition
+- webdataset
+- Myanmar
+license: cc0-1.0
+language:
+- my
+task_categories:
+- automatic-speech-recognition
+pretty_name: Google Myanmar ASR Dataset (WebDataset)
+size_categories:
+- 1K<n<10K
+---
+
+# Google Myanmar ASR Dataset (WebDataset Version)
+
+This repository provides a clean, user-friendly, and robust version of the **Google Myanmar ASR Dataset**, which is derived from the [OpenSLR-80 Burmese Speech Corpus](https://openslr.org/80/).
+
+This version has been carefully re-processed into the **WebDataset** format. Each sample consists of a `.wav` audio file and a clean `.json` metadata file, packaged into sharded `.tar` archives. This format is highly efficient for large-scale training of ASR models.
+
+---
+
+## Dataset Description
+
+This dataset consists of 16 kHz `.wav` audio files and their corresponding transcriptions, formatted for training and evaluating **automatic speech recognition (ASR)** models in the Burmese (Myanmar) language.
+
+### Key Highlights
+
+- **Language**: Myanmar (Burmese)
+- **Sample Rate**: 16,000 Hz
+- **Format**: WebDataset (`.tar` archives containing `.wav`, `.txt`, and `.json` files)
+- **Total Samples**: 2,530 examples
+- **Split**: All data is combined into a single `train` split for maximum flexibility.
+
+---
+
+## Dataset Structure
+
+Each sample within the WebDataset archives contains three components:
+
+1.  A `.wav` file with the audio data.
+2.  A `.txt` file with the transcription for easy access.
+3.  A `.json` file with all associated metadata.
+
+The JSON metadata for each sample has the following clean structure:
+
+| Field         | Description                                     | Data Type |
+|---------------|-------------------------------------------------|-----------|
+| `__key__`     | A unique identifier for the sample.             | `string`  |
+| `file_name`   | The name of the corresponding `.wav` file.      | `string`  |
+| `transcript`  | The transcription (space-separated syllables).  | `string`  |
+| `speaker`     | The identified speaker (`Female` / `Male`).     | `string`  |
+| `duration`    | The duration of the audio in seconds.           | `float`   |
+
+```json
+// Example of a clean .json file in the dataset
+{
+  "__key__": "bur_9762_9943594974",
+  "file_name": "bur_9762_9943594974.wav",
+  "transcript": "န မ့် ဆန် ထွက် လက် ဖက် ခြောက် များ ကို ငယ် ငယ် က တည်း က မြင် ဖူး ၏",
+  "speaker": "Female",
+  "duration": 5.12
+}
+```
+---
+
+## Preprocessing Details
+
+The dataset was re-processed with the following steps to ensure quality and usability:
+
+1.  **Data Consolidation**: Audio files from the original `train` and `test` splits were moved into a single collection.
+2.  **Metadata Extraction**: Metadata was extracted from the original `.parquet` files.
+3.  **Data Cleaning**:
+    - Fields containing `null` values (such as the original `transcript` and `gender` fields) were removed to prevent errors.
+    - The reliable `tokenized_transcription` was promoted to be the main `transcript`.
+    - A clean JSON file was generated for every corresponding audio file.
+4.  **WebDataset Packaging**: The validated `(wav, json)` pairs were packaged into sharded `.tar` archives using the WebDataset format for efficient, streaming access.
+
+---
+
+## How to Use
+
+You can easily stream this dataset using the Hugging Face `datasets` library. The library handles the WebDataset format automatically.
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+# The `streaming=True` mode is highly recommended for large datasets
+dataset = load_dataset("freococo/Google_Myanmar_ASR", split="train", streaming=True)
+
+# Iterate through the first few samples
+print("First 5 samples:")
+for i, sample in enumerate(dataset.take(5)):
+    print(f"\n--- Sample {i+1} ---")
+    print(f"Transcript: {sample['text']}")
+    # The audio is automatically decoded
+    print(f"Audio Sampling Rate: {sample['audio']['sampling_rate']}")
+    # Access other metadata from the flattened JSON
+    print(f"Speaker: {sample['json']['speaker']}")
+    print(f"Duration: {sample['json']['duration']}")
+```
+
+---
+
+## Attribution
+
+This dataset is derived from the original [OpenSLR Burmese Speech Corpus](https://openslr.org/80/), curated and published by Google.
+
+### Original Citation
+
+```
+@inproceedings{oo-etal-2020-burmese,
+  title     = {Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech},
+  author    = {Oo, Yin May and Wattanavekin, Theeraphol and Li, Chenfang and De Silva, Pasindu and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Jansche, Martin and Kjartansson, Oddur and Gutkin, Alexander},
+  booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)},
+  year      = {2020},
+  pages     = {6328--6339},
+  address   = {Marseille, France},
+  publisher = {European Language Resources Association (ELRA)},
+  url       = {https://www.aclweb.org/anthology/2020.lrec-1.777},
+  ISBN      = {979-10-95546-34-4}
+}
+```
+
+---
+
+## License
+
+This dataset is released under the **Creative Commons Zero (CC0 1.0 Universal)** license.
+
+> You may freely use, share, modify, and redistribute the dataset for any purpose, including commercial use, without attribution. However, attribution to the original source is encouraged when possible.