---
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.