Whisper Small Myanmar Partial Freezing
This model is a fine-tuned version of openai/whisper-small (244 Million parameters) dedicated for Myanmar (Burmese) speech recognition.
Whisper Small Myanmar Partial Freezing Benchmark Results
| Metric | Baseline (Zero-Shot) | Fine-Tuned (Partial-Freezing) | Net Improvement |
|---|---|---|---|
| CER (Character Error Rate) | 159.97% | 17.94% | -142.03% |
| WER (Word Error Rate) | 317.49% | 56.31% | -261.18% |
| Sentence Exact Match | 0.00% | 2.06% | +2.06% |
| Evaluation Loss | 3.3531 | 1.5270 | -1.8261 |
Model & Training Architecture
- Base Architecture:
openai/whisper-small(244M parameters, 12 encoder layers, 12 decoder layers) - Strategy: Partial Encoder Freezing (Layers 0-7 frozen, Layers 8-11 + all 12 decoder layers trainable)
- Tonal Language Safeguards: Phase-vocoder speed perturbation (0.9x, 1.0x, 1.1x) + F0-protected SpecAugment (protecting 80-300Hz fundamental tone frequencies)
- Optimization: AdamW + Cosine Annealing Learning Rate Scheduler (Peak LR: 3.75e-5, 1000 Steps)
- Language Force Token: Burmese (
<|my|>) withtask='transcribe'and Unicode NFC normalization
Usage (Inference Pipeline)
import torch
from transformers import pipeline
# Load pipeline directly from HuggingFace
pipe = pipeline(
"automatic-speech-recognition",
model="thantzinphyo/Whisper-Small-Myanmar-Partial-Freezing",
torch_dtype=torch.float16,
device="cuda:0" if torch.cuda.is_available() else "cpu"
)
# Transcribe Burmese audio (16kHz WAV)
result = pipe("your_burmese_audio.wav", generate_kwargs={"language": "my", "task": "transcribe"})
print("Transcription:", result["text"])