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thantzinphyo/Whisper-Tiny-ASR

sourceHugging Faceapache-2.0updated 1d agoView on Hugging Face

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Whisper-Tiny-ASR ( Burmese )

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

Model Overview

—Architecture: Whisper (Tiny)
—Parameters: ~39M
—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 (16 per device x 4 gradient accumulation)
Peak Learning Rate	1.5e-4
Learning Rate Scheduler	Cosine
Warmup Steps	150
Total Steps	1,500 (~9.26 Epochs)
Mixed Precision	BF16
Weight Decay	0.01
Gradient Checkpointing	Enabled
Augmentation	None

Evaluation Results

Stage / Evaluation	Step	Train Loss	Val Loss	WER (%)	CER (%)	SER (%)	DER (%)	IER (%)	chrF
Baseline (Untrained)	0	-	-	104.62	130.83	100.00	41.54	4.62	0.00
Validation Set	250	0.9590	0.1098	60.79	19.93	97.61	10.94	5.76	69.02
Validation Set	500	0.3188	0.0551	38.17	8.25	84.78	8.45	2.90	86.15
Validation Set	750	0.1719	0.0428	30.01	5.98	75.13	5.39	3.62	90.26
Validation Set	1000	0.0447	0.0442	26.92	5.17	69.13	5.18	3.08	91.85
Validation Set	1250	0.0119	0.0492	25.66	4.57	66.83	4.57	3.45	92.78
Validation Set	1500	0.0026	0.0508	25.21	4.47	65.52	4.47	3.42	92.91
UNSEEN TEST (Final)	Final	-	-	42.29	11.69	94.17	7.99	3.47	79.82

Usage

python

import torch
from transformers import pipeline

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

output = pipe("audio.wav", generate_kwargs={"language": "burmese", "task": "transcribe"})
print(output["text"])

License

Apache-2.0