freococo/khit_thit_news_voices

Khit Thit News Voices

In the fight for truth, these are the voices that refuse to be silenced.

Khit Thit News Voices is a focused collection of 15,841 audio segments (≈14.7 hours total) from Khit Thit News, one of Myanmar's most vital and trusted independent media outlets. Founded by renowned journalist Mr. Thar Lun Zaung Htet, Khit Thit News stands as a pillar of reliable information and a primary voice for democratic forces within the country.

This dataset primarily features the clear, articulate voices of its two main news announcers (one male, one female), whose delivery has become synonymous with courage and journalistic integrity.

The source channel provides:

• Urgent, factual reporting on the state of the nation
• Unfiltered news bulletins and analysis
• A platform for voices suppressed elsewhere
• Daily updates that are a critical source of information for millions on Facebook and beyond

These recordings capture the sound of contemporary Burmese history as it unfolds, spoken with the formal precision and emotional gravity that the moment demands.

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❤️ Why I Built This

• Myanmar (Burmese) is often labeled a “low-resource language” in the AI world.
• I don’t reject that label because it’s false — I reject it because it reflects global neglect.
• I built this dataset to show what’s possible — to give Myanmar speech the visibility, respect, and technical foundation it deserves.

I care about languages. I care about people being heard. And if AI is going to learn from voices — I want it to hear ours.

If you want your voice to be heard — you must first teach the machines to listen.

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🕊️ Why It Matters to Me

We will come, and we will go. But if your voice is integrated into AI technology — it will go on. Forever.

I cannot build you a pyramid like the ancient Egyptians did. But I can build something more accessible, more global: A living archive — of your beautiful, strong, and clear voices.

Maybe, just maybe — AI will speak our beautiful Myanmar language through your voice. And I believe it will. I truly do. 🙂

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🔍 What's Included

• 15,841 audio-text chunks
• ~14.7 hours of real Burmese speech (14h 44m 4s)
• Auto-transcribed captions with timestamps
• Rich video metadata (title, views, likes, hashtags)
• WebDataset-ready .tar archives for streaming & training

📂 Dataset Structure

This dataset is packaged as two .tar archives containing paired audio and metadata files in WebDataset format.

Each audio-text pair consists of:

• .mp3 — a short audio chunk extracted from a video
• .json — aligned metadata including the transcript and contextual information

All files are named using UUIDs:

a3f1d9e671a44b88.mp3
a3f1d9e671a44b88.json

Each .json file contains the following fields:

Field	Description
file_name	Name of the chunked audio file
original_file	Source video’s .mp3 filename
transcript	Burmese caption from the source
duration	Duration of the chunk (in seconds)
video_url	Link to the original source video
language	Always "my" (Myanmar)
title	Title of the video
description	Full video description
view_count	View count at the time of download
like_count	Like count
comment_count	Comment count
repost_count	Repost/share count
channel	Publisher name
upload_date	In YYYYMMDD format
hashtags	List of hashtags from the description
thumbnail	URL to video thumbnail
source	Always: Khit Thit News

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All audio-text pairs are organized flatly inside the .tar archives.

The dataset can be streamed or loaded using PyTorch WebDataset, Hugging Face 🤗 Datasets, or custom Python loaders.

🚀 How to Use

This dataset is stored as .tar archives and is compatible with both 🤗 Hugging Face Datasets and 🧠 WebDataset training pipelines.

---

✅ Load using Hugging Face Datasets (streaming)

from datasets import load_dataset

# The library automatically finds the .tar shards in the repo
ds = load_dataset(
    "freococo/khit_thit_news_voices",
    split="train",
    streaming=True
)

# Iterate through the first 5 samples
for sample in ds.take(5):
    # The transcript is a top-level feature for easy access
    print(f"🎙️ Transcript: {sample['txt']}")

    # The audio data is in the 'mp3' column
    audio_data = sample['mp3']
    audio_array = audio_data['array']
    sampling_rate = audio_data['sampling_rate']
    print(f"🎧 Audio loaded with shape {audio_array.shape} and rate {sampling_rate} Hz")

    # The 'json' column is ALREADY a Python dictionary
    metadata = sample['json']
    print(f"📺 Channel: {metadata.get('channel')}")
    print(f"🎥 Video URL: {metadata.get('video_url')}")
    print("---")

🙏 Special Thanks

This dataset is a tribute to the unwavering courage of independent journalism in Myanmar. It would not exist without the dedication of:

• 📰 Khit Thit News, its founder Mr. Thar Lun Zaung Htet, and the entire editorial team.
• 🎤 The two primary news announcers (male and female) whose professional, steady voices form the heart of this dataset.
• 🗣️ The sources, citizens, and activists who trust Khit Thit to share their stories.
• 🎥 The producers, editors, and behind-the-scenes teams who ensure the truth reaches the public every single day.

These individuals are not just content creators — they are the chroniclers of a nation's struggle and resilience.

They prove that in the darkest times, a clear voice is not just data—it is a beacon. And in the hands of the right generation, that beacon can become a tool for a better future.

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Thank you for giving us your voices. Now, they may echo in the machines we build — not to replace you, but to remember you.

🫡🇲🇲🧠📣

⚠️ Limitations

While this dataset offers high-quality access to formal Burmese news speech, users should be aware of its limitations:

• Auto-caption errors: All transcripts were likely generated using an automated system. A portion of segments may contain minor transcription errors (missing particles, spelling issues, etc.).
• No human corrections (yet): This dataset reflects the raw output of automated systems. No human-in-the-loop correction has been performed.
• Audio quality: While generally very clear, some clips may include news-related sound effects, brief musical intros/outros, or minimal background noise.
• Limited Scope: The dataset is focused on formal news delivery. It does not represent the full diversity of regional dialects, accents, or informal conversational speech in Myanmar.

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✅ But Here's the Strength

Even with potential imperfections, you are left with over 10,000 to 12,000 high-quality speech chunks.

That makes this one of the most significant, publicly available Burmese speech datasets for its specific domain: clear, formal news reporting.

It is not perfect — but it is powerful. It is not corrected — but it is real. And in this context, real voices have always mattered more than perfect ones.

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📌 Recommendation

For training high-performance ASR models, it is recommended to incorporate human-in-the-loop correction, quality filtering, or use semi-supervised fine-tuning techniques.

📄 License

This dataset is released under a Fair Use / Research-Only License.

It is intended for:

• ✅ Non-commercial research
• ✅ Educational use
• ✅ Language preservation
• ✅ Open AI development for Burmese (Myanmar) speech

All content was sourced from Khit Thit News's public channels. For any commercial inquiries, please contact the original content owner directly.

For full details, see the LICENSE file.

📚 Citation

@misc{freococo2025khitthitvoices,
  title        = {Khit Thit News Voices: A WebDataset for Burmese ASR and Speech Research},
  author       = {freococo},
  year         = {2025},
  howpublished = {Hugging Face Datasets},
  url          = {https://huggingface.co/datasets/freococo/khit_thit_news_voices}
}