Skip to content

Latest commit

 

History

History
88 lines (66 loc) · 5.63 KB

README.md

File metadata and controls

88 lines (66 loc) · 5.63 KB
Raw

SLAM-AAC

SLAM-AAC is a LLM-based model for Automated Audio Captioning (AAC) task. Inspired by techniques in machine translation and ASR, the model enhances audio captioning by incorporating paraphrasing augmentation and a plug-and-play CLAP-Refine strategy.

Model Architecture

SLAM-AAC uses EAT as the audio encoder and Vicuna-7B as the LLM decoder. During training, only the Linear Projector and LoRA modules are trainable. For inference, multiple candidates are generated using different beam sizes, which are then refined using the CLAP-Refine strategy.

Performance and checkpoints

We have released the pre-trained checkpoint of SLAM-AAC, as well as the fine-tuned checkpoints for the Clotho and AudioCaps datasets. The provided checkpoints include the model's Linear Projector and LoRA modules. Please note that when using each component, be sure to set up the corresponding environments according to the instructions provided in the respective repositories (e.g., for EAT).

Pre-training

SLAM-AAC was pre-trained on a combination of AudioCaps, Clotho, WavCaps, and MACS datasets. For more information on these datasets, you can refer to this repository. Additionally, the Clotho dataset was augmented using a back-translation-based paraphrasing technique.

Audio Encoder LLM Checkpoint Pre-training Dataset
EAT-base (fine-tuned) vicuna-7b-v1.5 link AudioCaps, Clotho, WavCaps, MACS

Fine-tuning

We fine-tuned the pre-trained model on the Clotho and AudioCaps datasets, respectively. The final evaluation was conducted using audio captions generated with the CLAP-Refine decoding strategy.

Dataset Audio Encoder LLM Checkpoint METEOR CIDEr SPICE SPIDEr SPIDEr-FL FENSE
Clotho EAT-base (fine-tuned) vicuna-7b-v1.5 link 19.7 51.5 14.8 33.2 33.0 54.0
AudioCaps EAT-base (fine-tuned) vicuna-7b-v1.5 link 26.8 84.1 19.4 51.8 51.5 66.8

Data preparation

Ensure your jsonl data follows the structure outlined below:

{"key": "Y7fmOlUlwoNg_1", "source": "/root/data/AudioCaps/waveforms/test/Y7fmOlUlwoNg.wav", "target": "Constant rattling noise and sharp vibrations"}
{"key": "Y6BJ455B1aAs_1", "source": "/root/data/AudioCaps/waveforms/test/Y6BJ455B1aAs.wav", "target": "A rocket flies by followed by a loud explosion and fire crackling as a truck engine runs idle"}

In addition, you can refer to the manifest file we've provided, which includes the Clotho dataset enhanced with paraphrasing augmentation as bonus.

Model Training

To pre-train the SLAM-AAC model with pre-training data, you can run the following command:

# Pre-train the model
bash scripts/pretrain.sh

You can fine-tune the model on the AudioCaps or Clotho datasets using the provided checkpoint or your own pre-trained model by running the following commands:

# Fine-tune on AudioCaps
bash scripts/finetune_audiocaps.sh

# Fine-tune on Clotho
bash scripts/finetune_clotho.sh

You can also fine-tune the model without loading any pre-trained weights, though this may result in reduced performance.

Note

  • In the current version of SLAM-LLM, the peft_ckpt parameter is no longer required. However, if you are using the checkpoint provided by us, which was trained with an earlier version, please keep the peft_ckpt parameter in your configuration to ensure compatibility.
  • Due to differences in dependency versions, there may be slight variations in the performance of the SLAM-AAC model.

Inference

To perform inference with the trained models, you can use the following commands to decode using the common beam search method:

# Inference on AudioCaps (Beam Search)
bash scripts/inference_audiocaps_bs.sh

# Inference on Clotho (Beam Search)
bash scripts/inference_clotho_bs.sh

For improved inference results, you can use the CLAP-Refine strategy, which utilizes multiple beam search decoding. To use this method, you need to download and use our pre-trained CLAP model. Note that CLAP-Refine may take longer to run, but it can provide better quality outputs. You can execute the following commands:

# Inference on AudioCaps (CLAP-Refine)
bash scripts/inference_audiocaps_CLAP_Refine.sh

# Inference on Clotho (CLAP-Refine)
bash scripts/inference_clotho_CLAP_Refine.sh

If you already have the generated candidates and want to directly refine them using the CLAP-Refine strategy, you can run the following command:

bash scripts/clap_refine.sh