--- license: other license_name: custom license_link: LICENSE pretty_name: Media Queen Entertainment Voices language: - my tags: - audio - speech-recognition - asr - myanmar - burmese - low-resource - fair-use - tiktok - webdataset task_categories: - automatic-speech-recognition - audio-classification --- # Media Queen Entertainment Voices > Where the stars speak, and their stories come to life. **Media Queen Entertainment Voices** is a massive, large-scale collection of **190,013 short audio segments** (totaling approximately **125 hours** of speech) derived from public videos by **Media Queen Entertainment** — a prominent digital media channel in Myanmar focused on celebrity news, lifestyle content, and in-depth interviews. The source channel regularly features: - Interviews with artists, actors, creators, and public figures. - Behind-the-scenes content and event coverage. - Authentic, conversational discussions on culture and entertainment. These videos capture the natural, everyday voices of Myanmar's vibrant cultural scene, making this dataset an invaluable resource for building robust, real-world speech technology. --- ### ❤️ 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 mine, ours, Myanmar’s. > 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 - **`190,013`** audio-text chunks - **`~125 hours`** of real Burmese speech (125h 15m 23s) - Auto-transcribed captions with timestamps - Rich video metadata (title, views, likes, hashtags) - A collection of **20** WebDataset-ready `.tar.gz` shards for efficient streaming & training ## 📂 Dataset Structure This dataset is packaged as **20 sharded `.tar.gz` archives** containing paired audio, transcript, and metadata files in the WebDataset format. Each sample consists of three files, grouped by a unique key: - `.mp3` — a short audio chunk extracted from a video. - `.txt` — the aligned transcript for the audio chunk. - `.json` — rich metadata including the source video context. All files are named using UUIDs: ``` a3f1d9e671a44b88.mp3 a3f1d9e671a44b88.txt a3f1d9e671a44b88.json ``` The `.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 (also in the separate .txt file) | | `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 | | `channel` | Publisher name and description | | `upload_date` | In `YYYYMMDD` format | | `hashtags` | List of hashtags from the description | | `thumbnail` | URL to video thumbnail | | `source` | URL to the source TikTok channel | --- ## 🚀 How to Use This dataset is designed for modern, large-scale training pipelines and is compatible with 🤗 Hugging Face Datasets. ### ✅ Load using Hugging Face Datasets (streaming) This is the recommended way to use the dataset, as it avoids downloading all ~5.7 GB at once. ```python from datasets import load_dataset # The library automatically finds all .tar.gz shards in the repo ds = load_dataset( "freococo/media_queen_entertaiment_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 would not exist without the incredible creative and production efforts of: - 🌟 **Media Queen Entertainment** and its entire team. - 🎤 The journalists, hosts, and interviewers who guide the conversations. - 🗣️ The artists, creators, and public figures who generously shared their stories and perspectives. - 🎥 The producers, editors, and behind-the-scenes teams who bring these moments to light. These individuals are not just content creators — they are curators of culture. Their work provides a vibrant, authentic window into the heart of Myanmar's entertainment world. --- > 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 unprecedented scale for Burmese speech, it is not without imperfections. - **Auto-caption errors**: All transcripts were generated by an automated system (Whisper). A portion of segments may contain minor to moderate transcription errors. - **No human corrections**: No post-processing or human-in-the-loop editing was performed. This dataset reflects the raw, real-world performance of an automated pipeline. - **Audio quality**: While generally clear, some clips may include background noise, music overlays, or sound effects common in entertainment media. - **Representational Focus**: The dataset primarily features voices from the entertainment industry, using standard Burmese. It may not fully represent the linguistic diversity of all regional dialects in Myanmar. --- ### ✅ But Here's the Strength Even with a conservative estimate of data quality, you are likely left with over **150,000 to 170,000 high-quality speech chunks**. That alone makes this one of the **largest publicly available Burmese speech datasets** in the world. It is not perfect — but it is powerful. It is not corrected — but it is real. And real voices have always mattered more than perfect ones. ## 📄 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 **Media Queen Entertainment's** public channels. For any commercial inquiries, please contact the original content owner directly. For full details, see the `LICENSE` file in this repository. ## 📚 Citation ```bibtex @misc{freococo_2025_mediaqueen, author = {freococo}, title = {Media Queen Entertainment Voices: A Large-Scale Dataset for Burmese ASR}, year = {2025}, howpublished = {Hugging Face Datasets}, url = {https://huggingface.co/datasets/freococo/media_queen_entertaiment_voices} } ``` ```