A Whisper transformer for audio captioning trained with synthetic captions and transfer learning
About
The field of audio captioning has seen significant advancements in recent years, driven by the availability of large-scale audio datasets and advancements in deep learning techniques. In this technical report, we present our approach to audio captioning, focusing on the use of a pretrained speech-to-text Whisper model and pretraining on synthetic captions. We discuss our training procedures and present our experiments' results, which include model size variations, dataset mixtures, and other hyperparameters. Our findings demonstrate the impact of different training strategies on the performance of the audio captioning model. Our code and trained models are publicly available on GitHub and Hugging Face Hub.
Marek Kadl\v{c}\'ik, Adam H\'ajek, J\"urgen Kieslich, Rados{\l}aw Winiecki• 2023
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Automated Audio Captioning | Clotho DCASE 2023 Task 6A (eval) | METEOR17.2 | 5 |
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