Myanmar ASR MODEL
An end-to-end Burmese (Myanmar) speech recognition model. Trained from scratch on 1.66M utterances of Myanmar speech.
23.8M parameters. 9.42% SER on held-out data.
Highlights
- Small & fast — 23.8M parameters. Runs efficiently on CPU and consumer GPUs.
- End-to-end — raw audio in, Burmese syllables out.
- Trained from scratch — no fine-tuning from an English or multilingual base.
- Robust — trained on mixed-domain audio: news, conversation, reading, and aggressively augmented variants.
- Open — Apache 2.0, weights + code + configs all published.
Model Architecture
| Feature | Specification |
|---|---|
| Parameters | 23,753,868 |
| Architecture | 12-layer Conformer encoder, joint CTC + attention |
| Encoder width | 256 |
| Attention heads | 4 (RoPE positional encoding) |
| Conv kernel | 31 |
| Vocabulary | 2,566 Burmese syllable tokens |
| Input | 16 kHz mono, 80-dim log-mel spectrogram |
| Output | Space-separated Burmese syllables |
Training Details
| Parameter | Value |
|---|---|
| Corpus | ~1.66M utterances of Burmese speech |
| Epochs | 25 |
| Optimizer steps | 178,325 |
| Optimizer | AdamW, cosine decay, warmup 1000 |
| Batch size | 180 s / 90 clips per micro-step, grad_accum=4 |
| Peak LR | 4e-4 |
| Hardware | 1× NVIDIA RTX 3060 (12 GB) |
| Wall time | ~42 hours |
Results
| Split | Size | SER |
|---|---|---|
| Held-out validation | 4,997 | 9.42% |
| Independent web crawl | 18,896 | 12.30% |
Metric — Syllable Error Rate (SER): Syllables are the natural phonological unit of Burmese. WER is not well-defined for Burmese (no orthographic word boundaries); CER is biased by variable code-point counts per syllable. SER gives a consistent, linguistically grounded error rate.
Usage
1. Installation
pip install -r requirements.txt
2. Command-Line Inference
# Transcribe a single file on CPU or GPU
python transcribe.py example.mp3 --device cpu
python transcribe.py input.wav --device cuda
# Batch transcribe multiple files to a text file
python transcribe.py *.wav --output results.txt
3. Python API
from transcribe import BurmeseASR
asr = BurmeseASR(device="cuda") # or device="cpu"
text = asr.transcribe("example.mp3")
print(text)
Example Output
Input : example.mp3 (3.13 s Burmese news clip)
Output: စစ် ကောင် စီ အ တွက် လေ ယာဉ် ဆီ ရောင်း ချ ဖြန့် ဖြူး ပေး နေ တဲ့ လူ
Release Files
| File | Purpose |
|---|---|
model.safetensors | Bit-identical FP32 model weights (95 MB) |
model.py | Standalone PyTorch Conformer ASR architecture |
transcribe.py | Production inference script (CLI + Python API) |
config.json | Model configuration and hyperparameter specifications |
vocab.json | Token-to-ID mapping (2,566 tokens) |
cmvn.json | Mel-spectrogram normalization statistics (mean/std) |
preprocessor_config.json | Audio feature extraction parameters |
example.mp3 | Verification sample audio |
requirements.txt | Python runtime dependencies |
Notes
- Audio preprocessing: Audio must be 16 kHz mono.
transcribe.pyhandles resampling and downmixing automatically for common formats (wav, mp3, flac, m4a). - Tokenization: The output consists of space-separated Burmese syllables matching the training vocabulary.
- Decoding: The joint CTC/Attention architecture includes the attention decoder in the weights, while the CTC head is used for fast streaming/greedy transcription.
Dedication & Acknowledgements
This model is dedicated to the preservation and accessibility of the Burmese language for future generations.
AI Engineering Partners
- DeepSeek AI: Primary co-engineering partner through every stage — architecture design, memory optimization, training pipeline, debugging, and evaluation. Lead technical collaborator for this project.
- Gemini AI (Google): Secondary review partner for code audits, metric analysis, and independent verification of results.
Data Contributors This model would not exist without the voices and archives of:
- National Unity Government (NUG) of Myanmar
- PVTV broadcasters and journalists
- Myanmar Celebrity Voices contributors
- Media Queen Entertainment archives
- Sunday Journal news team
- Khit Thit Media news team
- MRTV and MRTV-4 broadcast archives
- Google Myanmar ASR open corpus
- Myanmar Bible Speech corpus contributors
- Original Myanmar Voices open contributors
- Independent Myanmar journalists, broadcasters, and creators whose voices form the backbone of this language-preservation effort
To the voices of a nation — thank you. This model exists because you spoke, and your speech deserves to be understood.
Statement of Independence
This model was built independently — no funding, no sponsorship, no instructions from any organisation, government, company, or individual. It is a gift to the Burmese language community.
Every dataset used in this project is publicly available through this account.
The weights, code, and configurations are released under Apache 2.0. Use them however you want. No restrictions. The only goal is benefit for Myanmar and its language.
If this model produces incorrect or unexpected output, that responsibility is mine alone — freococo. Not the original creators of the source data, whose voices and archives made this work possible. All credit for the data belongs to them. All errors in the model belong to me.
Citation
@misc{myanmar_asr_2026,
title = {myanmar_asr: End-to-end Burmese Speech Recognition},
author = {freococo},
year = {2026},
url = {https://huggingface.co/freococo/myanmar_asr}
}