thantzinphyo/whisper-tiny-myanmar-shopvoice

Whisper Tiny Myanmar — ShopVoice ASR

A fine-tuned OpenAI Whisper Tiny model for Myanmar-language ShopVoice applications.

Based on chuuhtetnaing/whisper-tiny-myanmar and trained with the author’s Myanmar ShopVoice data.

Dataset

Dataset used to train: thantzinphyo/myanmar-shopvoice

The publicly available dataset is provided for demonstration purposes; however, the definitive model was trained using a moderately sized ShopVoice dataset compiled by the authors

Currently, the model has been trained on 237 distinct dish categories sourced from traditional Myanmar eateries.

Training Results

Step	Eval Loss	WER	CER	Quantity Acc.	Exact Sentence Acc.	Product Name Acc.	Catalog Top-1 Acc.
100	0.3237	48.18%	14.34%	86.13%	23.36%	27.01%	29.20%
200	0.0509	31.39%	8.77%	94.16%	46.72%	48.54%	51.82%
300	0.0360	25.73%	7.10%	96.72%	55.84%	57.30%	60.58%
400	0.0311	24.27%	6.25%	96.35%	58.76%	60.95%	64.23%
500	0.0289	22.99%	6.00%	97.08%	60.58%	62.04%	64.23%
600	0.0279	20.62%	5.49%	97.45%	63.50%	64.96%	67.88%
700	0.0272	19.34%	5.18%	97.45%	65.69%	66.79%	69.34%
800	0.0272	19.53%	5.39%	97.45%	65.33%	66.42%	69.34%
900	0.0272	19.53%	5.30%	97.08%	65.33%	66.79%	69.71%
1000	0.0272	19.34%	5.18%	97.45%	65.69%	66.79%	69.71%

Usage

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="thantzinphyo/whisper-tiny-myanmar-shopvoice",
)

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

For best results, use 16 kHz mono audio.

Attribution

• Model: thantzinphyo/whisper-tiny-myanmar-shopvoice
• Base model: chuuhtetnaing/whisper-tiny-myanmar
• Dataset: thantzinphyo/myanmar-shopvoice
• Author: Thant Zin Phyo