---

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)

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Sample Output

Sample 0

Text: "မြန်မာလူမျိုးများဟာ အလွန် ယဥ်ကျေးသိမ်မွေ့ပြီး ဧည့်သည်များကို ပျူပျူငှာငှာ လှိုက်လှိုက်လှဲလှဲနဲ့ ကြိုဆိုကြပါတယ်"

Sample 1

Text: "မင်္ဂလာပါရှင် ကျွန်မကတော့ မြန်မာလူမျိုး ကရင်တိုင်းရင်းသူ အမျိုးသမီးလေး တစ်ဦး ဖြစ်ပါတယ်"

Sample 2

Text: "ဒီနေ့ ကျွန်မတို့ရဲ့ တီတီအက်စ် စနစ်သစ်လေး မော်ဒယ်အသစ်လေးတစ်ခုကို အောင်မြင်စွာ လေ့ကျင့် သင်ကြားနိုင်ခဲ့ပါတယ်"

Sample 3

Text: "လူသားတိုင်း လူသားတိုင်း ကိုယ်စိတ်နှစ်ဖြာ ကျန်းမာရွှင်လန်းပြီး စီးပွားလာဘ်လာဘတွေ ဒီရေအလား ကြီးပွား တိုးတက်နိုင်ကြပါစေ"

Sample 4

Text: "ဒီအသံထုတ်စနစ်လေးကို အသုံးပြုသူတိုင်း ကျန်းမာချမ်းသာပြီး လိုရာဆန္ဒတွေ တလုံးတဝတည်း ပြည့်စုံနိုင်ကြပါစေ"

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Reference Audio

The reference voice used for voice cloning during inference:

Quick Start

import torch, soundfile as sf
from huggingface_hub import hf_hub_download
from api import StableTTSAPI
from burmese import burmese_to_ipa2

# Download model assets
model_path = hf_hub_download("freococo/MyanmarTTS", "model_fp16.pt")
vocos_path = hf_hub_download("freococo/MyanmarTTS", "vocos.pt")
ref_path   = hf_hub_download("freococo/MyanmarTTS", "samples/sample_0.wav")

# Initialize model
model = StableTTSAPI(model_path, vocos_path, "vocos").to("cuda")
model.g2p_mapping["burmese"] = burmese_to_ipa2

# Inference
text = "လူသားတွေ အားလုံးကို အရမ်း ချစ်ပါတယ်ရှင့်"
audio, _ = model.inference(text, ref_path, "burmese", step=32, solver="dopri5", cfg=3.0)
sf.write("output.wav", audio.squeeze(0).cpu().numpy(), 44100)

Translation: "I love all human beings very much." (female polite form)

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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	--

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Training Progression

See checkpoint_comparison for sample audio generated at steps 8k, 33k, 44k, 55k, and 81k.

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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.

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License

• Model weights and generated audio: CC0 1.0 Universal (Public Domain).
• Supporting code: Adapted from KdaiP/StableTTS (MIT).
• Vocoder: From KdaiP/StableTTS1.1 (MIT).

See NOTICE for additional details.