| --- |
| license: cc0-1.0 |
| language: |
| - my |
| tags: |
| - text-to-speech |
| - tts |
| - burmese |
| - myanmar |
| - stabletts |
| - from-scratch |
| pipeline_tag: text-to-speech |
| --- |
| |
| # MyanmarTTS |
|
|
| A from-scratch Burmese (Myanmar) text-to-speech model. 31M parameters, ~63 MB (fp16). |
|
|
| - **Language**: Burmese (မြန်မာဘာသာ) |
| - **Training data**: ~1.34M samples (news + real-world audio) |
| - **Training steps**: 81,000 |
| - **Training hardware**: A100-80GB (Colab Pro+), ~13 hours, ~88 compute units |
| - **Inference hardware**: Free Colab T4, any modern GPU, or CPU (slower) |
| - **Architecture**: StableTTS (DiT + flow matching) + Vocos vocoder |
| - **License**: **CC0 1.0** (public domain, no attribution required) |
|
|
| --- |
|
|
| ## Sample Output |
|
|
| All samples generated with default settings (`euler` solver, 12 steps). |
|
|
| **Sample 0** |
|
|
| **Text:** "မြန်မာလူမျိုးများဟာ အလွန် ယဥ်ကျေးသိမ်မွေ့ပြီး ဧည့်သည်များကို ပျူပျူငှာငှာ လှိုက်လှိုက်လှဲလှဲနဲ့ ကြိုဆိုကြပါတယ်" |
|
|
| <audio controls src="https://huggingface.co/freococo/MyanmarTTS/resolve/main/samples/sample_0.wav"></audio> |
|
|
| **Sample 1** |
|
|
| **Text:** "မင်္ဂလာပါရှင် ကျွန်မကတော့ မြန်မာလူမျိုး ကရင်တိုင်းရင်းသူ အမျိုးသမီးလေး တစ်ဦး ဖြစ်ပါတယ်" |
|
|
| <audio controls src="https://huggingface.co/freococo/MyanmarTTS/resolve/main/samples/sample_1.wav"></audio> |
|
|
| **Sample 2** |
|
|
| **Text:** "ဒီနေ့ ကျွန်မတို့ရဲ့ တီတီအက်စ် စနစ်သစ်လေး မော်ဒယ်အသစ်လေးတစ်ခုကို အောင်မြင်စွာ လေ့ကျင့် သင်ကြားနိုင်ခဲ့ပါတယ်" |
|
|
| <audio controls src="https://huggingface.co/freococo/MyanmarTTS/resolve/main/samples/sample_2.wav"></audio> |
|
|
| **Sample 3** |
|
|
| **Text:** "လူသားတိုင်း လူသားတိုင်း ကိုယ်စိတ်နှစ်ဖြာ ကျန်းမာရွှင်လန်းပြီး စီးပွားလာဘ်လာဘတွေ ဒီရေအလား ကြီးပွား တိုးတက်နိုင်ကြပါစေ" |
|
|
| <audio controls src="https://huggingface.co/freococo/MyanmarTTS/resolve/main/samples/sample_3.wav"></audio> |
|
|
| **Sample 4** |
|
|
| **Text:** "ဒီအသံထုတ်စနစ်လေးကို အသုံးပြုသူတိုင်း ကျန်းမာချမ်းသာပြီး လိုရာဆန္ဒတွေ တလုံးတဝတည်း ပြည့်စုံနိုင်ကြပါစေ" |
|
|
| <audio controls src="https://huggingface.co/freococo/MyanmarTTS/resolve/main/samples/sample_4.wav"></audio> |
|
|
| **Sample 5** |
|
|
| **Text:** "ဟယ်လို... ဒါလင်... မတွေ့ရတာ... ကြာပီနော်" |
|
|
| <audio controls src="https://huggingface.co/freococo/MyanmarTTS/resolve/main/samples/sample_5.wav"></audio> |
|
|
| ## Reference Audio |
|
|
| The reference voice used for voice cloning during inference: |
|
|
| <audio controls src="https://huggingface.co/freococo/MyanmarTTS/resolve/main/samples/sample_0.wav"></audio> |
|
|
| # Quick Start |
|
|
| ```bash |
| pip install myanmartts |
| |
| import soundfile as sf |
| from myanmar_tts import MyanmarTTS |
| |
| # Load model (auto-downloads from HuggingFace on first run) |
| tts = MyanmarTTS(device="cuda") |
| |
| # Inference |
| text = "လူသားတွေ အားလုံးကို အရမ်း ချစ်ပါတယ်ရှင့်" |
| audio = tts.tts(text, solver="euler", step=32, cfg=3.0) |
| |
| sf.write("output.wav", audio, 44100) |
| ``` |
| - *Translation: "I love all human beings very much." (female polite form)* |
|
|
| # Advanced |
|
|
| *Faster (slightly lower quality)* |
| audio = tts.tts(text, step=8) |
|
|
| *Higher quality* |
| audio = tts.tts(text, step=24) |
|
|
| --- |
|
|
| ### Default Settings |
|
|
| - **Solver**: `euler` (fast, ~16× faster than `dopri5`) |
| - **Steps**: `12` (RTF ~0.08 on T4 GPU - 12× faster than real-time) |
| - **CFG**: `3.0` |
|
|
| Override per call: |
|
|
| ```python |
| audio = tts.tts("ဟယ်လို ဒါလင် မတွေ့ရတာ ကြာပီ", step=8) # fastest |
| audio = tts.tts("ဟယ်လို ဒါလင် မတွေ့ရတာ ကြာပီ", step=24) # higher quality |
| audio = tts.tts("ဟယ်လို ဒါလင် မတွေ့ရတာ ကြာပီ", solver="dopri5", step=32) # max quality |
| ``` |
|
|
| ## Files |
|
|
| | File | Size | Purpose | License | |
| | :--- | :--- | :--- | :--- | |
| | `model_fp16.pt` | 63 MB | Default model weights | CC0 | |
| | `model_fp32.pt` | 126 MB | Full precision weights | CC0 | |
| | `vocos.pt` | 57 MB | Mel-to-waveform vocoder | MIT (KdaiP) | |
| | `vocab.txt` | 98 tokens | Burmese character-level vocab | CC0 | |
| | `config.json` | -- | Mel + model configuration | CC0 | |
| | `symbols.py`, `burmese.py` | -- | Text frontend & G2P | MIT (adapted) | |
| | `api.py` | -- | Inference wrapper API | MIT (adapted) | |
| | `samples/` | -- | Demo audio WAV files | CC0 | |
| | `transcripts.json` | -- | Sample text/audio mapping | CC0 | |
| | `NOTICE` | -- | Full license summary | -- | |
|
|
| --- |
|
|
| ## Training Progression |
|
|
| See [`checkpoint_comparison`](checkpoint_comparison) for sample audio generated at steps **8k**, **33k**, **44k**, **55k**, and **81k**. |
|
|
| --- |
|
|
| ## Acknowledgments |
|
|
| This work would not exist without the generous open-source community and AI assistance: |
|
|
| - **StableTTS by KdaiP** — The DiT + flow-matching architecture and training code (MIT). |
| - **Vocos** — Pretrained mel-to-wav vocoder (MIT). |
| - **DeepSeek AI** — Provided AI pair-programming and engineering assistance throughout the project. From data pipeline design and architecture choices to resolving CUDA OOM bottlenecks, DeepSeek's guidance was instrumental at every stage. |
| - **The Burmese open-data community** — For providing the audio corpora that made training possible. |
|
|
| ### Special Thanks |
| To **DeepSeek AI** — a true engineering partner from the first line of code to the final deployment. This model exists because of that collaboration. |
|
|
| --- |
|
|
| ## License |
|
|
| - **Model weights and generated audio**: [CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/) (Public Domain). |
| - **Supporting code**: Adapted from [KdaiP/StableTTS](https://github.com/KdaiP/StableTTS) (MIT). |
| - **Vocoder**: From KdaiP/StableTTS1.1 (MIT). |
|
|
| *See `NOTICE` for additional details.* |
