Whisper-Small-ASR ( Burmese )

This model is a fine-tuned version of openai/whisper-small for Automatic Speech Recognition (ASR) in Burmese .

Model Overview

Architecture: Whisper Small
Parameters: ~244M
Task: Automatic Speech Recognition (ASR)
Language: Burmese (my)
Sampling Rate: 16,000 Hz (Mono)

Dataset Details

The model was trained on a standardized, high-quality Burmese speech corpus:

Total Duration: ~22 Hours of audio
Total Utterances: 24,560 WAV files (16 kHz, 16-bit PCM, Mono)
Total Speakers: 13 Synthetic Burmese Speakers
Speakers: ပီယ ၊ ဝါစာ ၊ သီရိ ၊ ဒီပ ၊ အက္ခရာ ၊ သဒ္ဒါ ၊ သရ ၊ တာရာ ၊ ကဝိ ၊ သုတ ၊ ပသာဒ ၊ ဂီတ ၊ နန္ဒ
Training & Validation: 11 speakers (20,699 train utterances, 2,300 validation utterances)
Test Set (Held-Out): 2 unseen speakers ( ဂီတ & နန္ဒ , 1,561 utterances )
Text Preprocessing: Standardized Myanmar Unicode (\u1000-\u109F) with unified word segmentation and punctuation removal. The held-out test split evaluates zero-shot acoustic generalization across completely unseen voices.

Training Configuration

Parameter	Value
Effective Batch Size	64 (8 per device x 4 gradient accumulation x 2 GPUs)
Peak Learning Rate	2.5e-5
Learning Rate Scheduler	Cosine
Warmup Steps	200
Total Steps	2,000 (~6 Epochs)
Mixed Precision	FP16
Gradient Checkpointing	Enabled
Augmentation	None

Evaluation Results

Stage / Evaluation	Step	Train Loss	Val Loss	WER (%)	CER (%)	SER (%)	DER (%)	IER (%)	chrF
Baseline (Untrained)	0	-	-	610.77	441.11	100.00	44.62	510.77	0.00
Validation Set	250	0.9354	0.1031	60.56	14.25	97.70	18.50	1.39	74.65
Validation Set	500	0.3638	0.0493	33.98	6.46	82.16	4.85	4.22	88.13
Validation Set	750	0.1874	0.0387	28.06	5.14	71.61	4.94	3.03	91.09
Validation Set	1000	0.0869	0.0338	23.82	3.90	65.54	3.88	3.27	93.54
Validation Set	1250	0.0842	0.0314	23.44	3.70	64.97	5.46	2.16	94.00
Validation Set	1500	0.0319	0.0339	21.29	3.37	60.33	3.31	3.47	94.58
Validation Set	1750	0.0080	0.0372	20.96	3.25	58.98	3.87	2.91	94.80
Validation Set	2000	0.0043	0.0375	20.70	3.21	58.59	3.87	2.78	94.86
UNSEEN TEST (Final)	Final	-	-	28.22	5.74	84.38	6.34	2.47	89.93

Usage

import torch
from transformers import pipeline

pipe = pipeline(
    'automatic-speech-recognition',
    model='thantzinphyo/Whisper-Small-ASR',
    device='cuda:0' if torch.cuda.is_available() else 'cpu',
)

output = pipe('audio.wav', generate_kwargs={"language": "my", "task": "transcribe"})
print(output['text']) # ငါ့ အချစ်ရေးကျတော့ ဘာလို့ မကောင်းတာလဲ ဆိုတာ ဘယ်သူမှ မပြောနိုင်ဘူး