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README.md

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----
-license: other
-license_name: custom
-license_link: LICENSE
----
+---
+license: other
+license_name: custom
+license_link: LICENSE
+pretty_name: Khit Thit News Voices
+language:
+  - my
+tags:
+  - speech
+  - audio
+  - asr
+  - myanmar
+  - low-resource
+  - fair-use
+  - tiktok
+  - webdataset
+task_categories:
+  - automatic-speech-recognition
+  - audio-classification
+---
+
+# 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.
+
+---
+
+### ❤️ 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.
+
+---
+
+### 🕊️ 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. 🙂
+
+---
+
+### 🔍 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*
+
+---
+
+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)
+
+```python
+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.
+
+---
+
+> 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.
+
+---
+
+### ✅ 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.
+
+---
+
+### 📌 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
+
+```bibtex
+@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}
+}
+```
+```