Papers › MEDIBENG WHISPER TINY: A FINE-TUNED CODE-SWITCHED BENGALI-ENGLISH TRANSLATOR FOR...

MEDIBENG WHISPER TINY: A FINE-TUNED CODE-SWITCHED BENGALI-ENGLISH TRANSLATOR FOR CLINICAL APPLICATIONS

25 Apr 2025medRxiv 2025 4archive 2025-07-28

Promila Ghosh, Sunipun Talukder

Code-switching in multilingual healthcare settings challenges automated transcription systems as facilities adopt AI documentation tools. To tackle this issue, we developed a cost-effective solution using the MediBeng Whisper Tiny model, a fine-tuned version of the Whisper Tiny model specifically designed for code-switched Bengali-English conversations in healthcare contexts. The model was fine-tuned on MediBeng, a synthetic dataset created to simulate the types of bilingual interactions often found in healthcare environments. The fine-tuning process involved using just 20% of the dataset, making it highly efficient in terms of computational resources and data usage. Despite the limited data, our model achieved an exceptional 0.01 Word Error Rate (WER), reflecting near-perfect transcription accuracy. Additionally, the model attained a 0.98 BLEU score, indicating its ability to accurately translate mixed-language input into English. These impressive results demonstrate that even with minimal data and resources, the model can handle complex code-switching tasks effectively. This model improves information processing for healthcare professionals, allowing doctors to save time on paperwork and focus more on patient care. It also ensures patient records are more accurate and accessible, aiding better healthcare decision-making.

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Code

pr0mila/MediBeng-Whisper-Tiny mentioned in paperpytorch report

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Tasks

Clinical Language Translation Machine Translation Speech-to-Text Speech-to-Text Translation Synthetic Data Generation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Speech-to-Text Translation	MediBeng	MediBeng Whisper Tiny	Bleu	0.98	#1 of 2	Archive leaderboard	report
Speech-to-Text Translation	MediBeng	Whisper Tiny	Bleu	0.30	#2 of 2	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

ADOPT Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Focus Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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