Whisper-Base-ASR

Whisper-Base-ASR ( Burmese )

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

Model Overview

• Architecture: Whisper (Base)
• Parameters: ~74M
• 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

Evaluation Results

Usage

import torch
from transformers import pipeline

pipe = pipeline(
    'automatic-speech-recognition',
    model='thantzinphyo/Whisper-Base-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