gs224/whisper-tiny-polish

Model Card

Model Details

Model Description

This model is a fine-tuned version of OpenAI's Whisper-Tiny ASR model, optimized for transcribing Polish voice commands. The fine-tuning process utilized the MASSIVE Speech dataset to enhance the model's performance on Polish utterances. The Whisper-Tiny model is a transformer-based encoder-decoder architecture, pre-trained on 680,000 hours of labeled speech data.

• Developed by: gs224
• Language(s) (NLP): Polish
• Finetuned from model: Whisper-tiny

Uses

The model can be used for automatic transcription of Polish speech-to-text tasks, including voice command recognition.

Out-of-Scope Use

The model may not perform well on languages or domains it was not fine-tuned for, and it is not suitable for sensitive applications requiring very high accuracy.

Bias, Risks, and Limitations

The fine-tuning was performed on a relatively small subset of Polish voice data with limited epochs, leading to potential underperformance in certain dialects or accents. The presence of capital letters and punctuation in the ground-truth transcription may affect the Word Error Rate (WER) score.

Recommendations

Future improvements could include training on larger datasets, more diverse utterances, and addressing case sensitivity and punctuation in ground-truth labels.

Training Details

Training Data

https://huggingface.co/datasets/FBK-MT/Speech-MASSIVE-test

Evaluation

Word Error Rate (WER)

Testing Data, Factors & Metrics

Metrics

WER, a typical metrics for ASR.

Results

WER of fine-tuned model: 0.3216