Whisper-Tiny-Myanmar-Partial-Freezing

Whisper-Tiny-Myanmar-Partial-Freezing

Fine-tuned version of openai/whisper-tiny on SLR80: Crowdsourced High-Quality Burmese Speech Dataset ( https://www.openslr.org/80 ).

Best Model Selection Notice:

This repository contains the best checkpoint (Step 1,000), selected for its lowest CER of 19.60%.

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How to Use (Python Inference)

import torch
from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="thantzinphyo/Whisper-Tiny-Myanmar-Partial-Freezing",
    device=0 if torch.cuda.is_available() else -1
)

result = pipe("your_burmese_audio.wav", generate_kwargs={"language": "my", "task": "transcribe"})
print("Transcribed Text:", result["text"]) # အခု ကတော့ သဘောတရားလေး ပဲ ပြောပြတာပါ

Complete Training & Evaluation History (Steps 0 to 1,500)

Evaluated on the isolated validation set (252 clips / ~0.42 hrs) and formatted in Percentage (%):

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Initial Training Configuration

• Base Architecture: openai/whisper-tiny (37.8M parameters)
• Training Strategy: Phase 1 Partial Encoder Freeze — Layers 0–1 frozen, while Layers 2–3 and the Decoder were trainable (33.1M parameters / 87.66%)
• Effective Batch Size: 64 (16 per device × 4 gradient accumulation)
• Learning Rate: 1.5e-4 with a Cosine Scheduler and 150 warmup steps
• Dataset: 2,277 training clips and 252 validation clips using 100% OpenSLR-80 NFC-canonical transcriptions