mlsanityData to 7 Oct 2026

A Whisper transformer for audio captioning trained with synthetic captions and transfer learning

arXiv 2305.09690 ↗First posted 15 May 2023cs.SD cs.LG eess.AScited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 5 of its 9 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

5 of 9 references.

References · 5

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2019Clotho: An Audio Captioning Datasetcited by 354
  3. 2020FSD50K: An Open Dataset of Human-Labeled Sound Eventscited by 339
  4. 2023WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Researchcited by 266
  5. 2021The Benefit Of Temporally-Strong Labels In Audio Event Classificationcited by 83

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Citing papers per year: 2023: 4; 2024: 4; 2025: 1.

9 papers.