Merge pull request #170 from Andreas-Xi/lxq-drcap

Lxq drcap

examples/drcap_zeroshot_aac/README.md

@@ -1,7 +1,7 @@
 # DRCap_Zeroshot_Audio_Captioning
 
 ## Introduction
-DRCap is a data-efficient and flexible audio captioning system requiring text-only data for training and can quickly adapt to new domains without additional fine-tuning. 
+[DRCap](https://www.arxiv.org/abs/2410.09472) is a data-efficient and flexible audio captioning system requiring text-only data for training and can quickly adapt to new domains without additional fine-tuning. It uses projection decoding and retrieval-augmented generation to perform zero-shot audio captioning. 
 
 ![](assets/model.png)
 
@@ -14,7 +14,7 @@ You could download our pretrained CLAP model and linear mapping network through
 * LLM [vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5)
 
 ## Inference
-You could modify the variables `run_dir`, `audio_encoder_dir`, `output_dir`, `llm_path` in `scripts/inference_drcap.sh` to match the paths where the downloaded checkpoints are located. Additionally, update the `source` in `data/audiocaps_test.jsonl` to ensure the audio paths point to your audio files, and then run:
+You could modify the variables `run_dir`, `audio_encoder_dir`, `output_dir`, `llm_path` in `scripts/inference_drcap.sh` to match the paths where the downloaded checkpoints are located. Additionally, update the `source` in `data_examples/audiocaps_test.jsonl` to ensure the audio paths point to your audio files, and then run:
 
 ```shell
 bash scripts/inference_drcap.sh
@@ -24,10 +24,10 @@ bash scripts/inference_drcap.sh
 ## Data preparation
 Prepare your `jsonl` data file in the following format:
 ```json
-{"key": "Y7fmOlUlwoNg_1", "target": "Constant rattling noise and sharp vibrations", "text": "Constant rattling noise and sharp vibrations"}
-{"key": "Y6BJ455B1aAs_1", "target": "A rocket flies by followed by a loud explosion and fire crackling as a truck engine runs idle", "text": "A rocket flies by followed by a loud explosion and fire crackling as a truck engine runs idle"}
+{"key": "Y7fmOlUlwoNg_1", "target": "Constant rattling noise and sharp vibrations", "text": "Constant rattling noise and sharp vibrations", "similar_captions": ["The engine of a small machine pulling chains", "A market vendor is producing a rhythmic sound with metal forceps.", "A masonry machine is in operation at a fair."]}
+{"key": "Y6BJ455B1aAs_1", "target": "A rocket flies by followed by a loud explosion and fire crackling as a truck engine runs idle", "text": "A rocket flies by followed by a loud explosion and fire crackling as a truck engine runs idle", "similar_captions": ["An engine is revving, with fire and an explosion.", "An explosion is heard after an engine cuts out.", "A car speeding past with a large boom"]}
 ```
-Please note that only textual data is required for training. However, for zero-shot inference, audio files are also necessary. You could find an example of the jsonl file in `data/audiocaps_test.jsonl`
+Please note that only textual data is required for training. However, for zero-shot inference, audio files are also necessary. You could find an example of the jsonl file in `data_examples/audiocaps_test.jsonl`
 
 Run the following command to do the retrieval-augmentation and create the text embedding support for evaluation: 
 ```shell
@@ -42,4 +42,16 @@ bash scripts/finetune_drcap.sh
 For training only the linear layer (without using LoRA or other PEFT methods), you can set the following parameters: `use_peft=false` and `freeze_llm=true`. To turn off the RAG, you could set `use_arg=false` and `rag_first=false`
 
 ## Acknowledgement
-The code of training the CLAP model is based on the [WavCaps](https://github.com/XinhaoMei/WavCaps) repo, we thank the contributors for open-sourcing their work.
+The code of training the CLAP model is based on the [WavCaps](https://github.com/XinhaoMei/WavCaps) repo, we thank the contributors for open-sourcing their work.
+
+
+## Citation
+You can refer to our paper for more results
+```
+@article{li2024drcap,
+  title={DRCap: Decoding CLAP Latents with Retrieval-augmented Generation for Zero-shot Audio Captioning},
+  author={Li, Xiquan and Chen, Wenxi and Ma, Ziyang and Xu, Xuenan and Liang, Yuzhe and Zheng, Zhisheng and Kong, Qiuqiang and Chen, Xie},
+  journal={arXiv preprint arXiv:2410.09472},
+  year={2024}
+}
+```