Thank you for the impressive work on LLM2Fx!
I am currently trying to reproduce the evaluation results (MMD scores) as described in the paper, usingllm2fx/evaluation/text2fx/llm_eval.pyand upperbound.py.
I noticed that these scripts rely on Ground Truth (GT) embeddings, which are currently pointing to a hardcoded local path in the code:
Could you please clarify the standard workflow to obtain these GT embedding files?
Are these pre-computed embeddings available for direct download? Or is the intended method to manually re-synthesize the SocialFX audio using the fx-processor repository and then run llm2fx/evaluation/embeds/fx_encoder.py to generate the .npy files locally?
Any guidance on how to properly set up the gt_embeds_dir to run the evaluation scripts would be greatly appreciated.
Thanks!
Thank you for the impressive work on LLM2Fx!
I am currently trying to reproduce the evaluation results (MMD scores) as described in the paper, using
llm2fx/evaluation/text2fx/llm_eval.pyandupperbound.py.I noticed that these scripts rely on Ground Truth (GT) embeddings, which are currently pointing to a hardcoded local path in the code:
In upperbound.py:
EMB_DIR = "/data3/seungheon/fx_embedding_new"In llm_eval.py: It requires the argument
args.gt_embeds_dir.Could you please clarify the standard workflow to obtain these GT embedding files?
Are these pre-computed embeddings available for direct download? Or is the intended method to manually re-synthesize the SocialFX audio using the
fx-processorrepository and then runllm2fx/evaluation/embeds/fx_encoder.pyto generate the .npy files locally?Any guidance on how to properly set up the
gt_embeds_dirto run the evaluation scripts would be greatly appreciated.Thanks!