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md-asr-v0-report
greenadntan/md-asr-v0-report
md-asr-v0-report is a machine learning model from greenadntan. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Meta AI Research Intern Report | Chișinău, April 2026
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- Access
- Public
- Created
- Apr 29, 2026
- Updated
- Apr 29, 2026
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From the Hugging Face model README
🇲🇩 md-ASR-v0 — Moldova-Specific ASR: Evaluation & Prototype Path
Meta AI Research Intern Report | Chișinău, April 2026
Overview
This repository contains the complete evaluation and prototype design for a Moldova-specific Automatic Speech Recognition (ASR) system that handles:
- 🇷🇴 Romanian/Moldovan with local accent and correct diacritics (ă â î ș ț)
- 🇷🇺 Russian as spoken in Moldova
- 🔄 Code-switched ro↔ru speech (the primary challenge)
- 🟡 Gagauz and Ukrainian (best-effort)
Key Findings
| Finding | Detail |
|---|---|
| Best zero-shot bilingual model | Whisper large-v3 (8.24% WER ro, 4.17% WER ru on FLEURS) |
| Best Romanian-only model | Parakeet Ro 110M (3.29% WER on CV-21, not publicly released) |
| Critical data gap | 0h of open ro↔ru code-switched speech; 0h of Gagauz audio |
| Recommendation | Fine-tune Whisper small + LoRA adapters + language-ID routing |
| Projected deployment | ~107MB on-device, ~250ms latency on Android |
| Projected improvement | >55% WER reduction vs Whisper small on code-switched Moldovan audio |
Repository Contents
| File | Description |
|---|---|
report/md_asr_v0_report.md | 6-page comprehensive report with tables, benchmarks, and recommendations |
report/eval_toolkit.py | Evaluation toolkit: WER, CER, Diacritics F1, Code-Switch Accuracy, NE-F1, md-ASR Score |
report/colab_demo.py | Colab demo: upload 30s audio, get side-by-side comparison with language tags |
Candidate Models Evaluated
| Model | FLEURS-ro | FLEURS-ru | Size | On-Device |
|---|---|---|---|---|
| Whisper large-v3 | 8.24% | 4.17% | 3GB | ❌ |
| SeamlessM4T-v2 | 8.87% | 7.43% | 4.6GB | ❌ |
| Canary-1B-v2 | 6.61% | 6.90% | 2GB | ❌ |
| MMS-1B-all | 27.9% | 30.3% | 1.9GB | ❌ |
| md-ASR-v0 (projected) | ~12% | ~10% | 107MB | ✅ |
Quick Start — Evaluation Toolkit
from eval_toolkit import compute_wer, compute_diacritics_f1, compute_md_asr_score
# Romanian WER with diacritics-preserving normalization
result = compute_wer("Am mers la Primăria Chișinău", "Am mers la Primaria Chisinau", lang="ro")
print(f"WER: {result['wer']:.2%}") # Catches diacritics errors
# Diacritics F1
diac = compute_diacritics_f1("Primăria Chișinău", "Primaria Chisinau")
print(f"Diacritics F1: {diac['diacritics_f1']:.2%}")
# Composite md-ASR Score
score = compute_md_asr_score(wer_ro=0.12, wer_ru=0.10, cs_accuracy=0.65, diacritics_f1=0.92, ne_f1=0.60)
print(f"md-ASR Score: {score['md_asr_score']:.4f}")
Citation
@misc{md-asr-v0,
title={md-ASR-v0: Moldova-Specific Automatic Speech Recognition — Evaluation & Prototype Path},
author={Meta AI Moldova Research},
year={2026},
note={Internal research prototype report}
}
License
Apache 2.0
- Created
- Apr 29, 2026
- Updated
- Apr 29, 2026