Metrics for evaluating Automated Audio Captioning systems, designed for PyTorch.
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Updated
Oct 18, 2024 - Python
Metrics for evaluating Automated Audio Captioning systems, designed for PyTorch.
text-only training or language-free training for multimodal tasks (image/audio/video caption, retrieval, text2image)
Workshop on Detection and Classification of Acoustic Scenes and Events
Audio Captioning datasets for PyTorch.
CoNeTTE: An efficient Audio Captioning system leveraging multiple datasets with Task Embedding
Reading list for research topics in Sound AI
Code for ICASSP 2024 Paper: RECAP: Retrieval-Augmented Audio Captioning
Song Describer is a data collection platform for annotating music with textual descriptions.
DCASE2024 Challenge Task 6 baseline system (Automated Audio Captioning)
PyTorch implementation of the ICASSP-24 paper: "Improving Audio Captioning Models with Fine-grained Audio Features, Text Embedding Supervision, and LLM Mix-up Augmentation"
Official Implementation of "Prefix tuning for Automated Audio Captioning(ICASSP 2023)"
[NeurIPS 2023 - ML for Audio Workshop (Oral)] Zero-shot audio captioning with audio-language model guidance and audio context keywords
Audio captioning baseline system for DCASE 2020 challenge.
2nd place solution for 2020 DCASE challenge task 6 audio captioning. http://dcase.community/challenge2020/task-automatic-audio-captioning-results#wuyusong2020_t6
This reporsitory code form Weakly Supervised Automaed Audio Captioning via Text Only Training
Using pretrained encoder and language models to generate captions from multimedia inputs.
Fluency ENhanced Sentence-bert Evaluation (FENSE), metric for audio caption evaluation. And Benchmark dataset AudioCaps-Eval, Clotho-Eval.
Tracking states of the arts and recent results (bibliography) on sound tasks.
6-th task solution of DCASE2020
Source code for "MusCaps: Generating Captions for Music Audio" (IJCNN 2021)
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