dodohow1011/SpeechAdvReprogram

A Study of Low-Resource Speech Commands Recognition Based on Adversarial Reprogramming ## Adversarial Reprogramming on Speech Command Recognition ### Environment Tensorflow 2.2 (CUDA=10.0) and Kapre 0.2.0. - option 1 (from yml) ```shell conda env create -f repr-scr.yml source activate repr-scr ``` - option 2 (from clean python 3.6) ```shell pip install tensorflow-gpu==2.1.0 pip install kapre==0.2.0 pip install h5py==2.10.0 ``` ### Dataset Arabic Speech Commands dataset - Please download the Arabic Speech Commands dataset here. ```shell ./prepare_ar_data.sh ``` Lithuanian Speech Commands dataset - Please download the Lithuanian Speech Commands dataset here. ```shell ./prepare_lt_data.sh ``` Dysarthric Speech Commands dataset - Please download the Lithuanian Speech Commands dataset here. ```shell ./prepare_dm_data.sh ``` ### Training For training and evaluating the three speech command recognition results. ```shell ./run_ar.sh ./run_lt.sh ./run_dm.sh ``` For more details please refer to AR-SCR, LT-SCR and DM-SCR (**Optional**) Note that in our default setting we use the random mapping strategy. To enable the similarity mapping, please modify the code at utils.py as followed: ```python def multi_mapping(prob, source_num, mapping_num, target_num): similarity_mapping = True ``` And choose lable_map according to your task. You can also see and check mapping results for each task by running the following command: ```sh python AR-SCR/source_target_pairing.py python LT-SCR/source_target_pairing.py python DM-SCR/source_target_pairing.py ``` #### Please consider to cite this work if you use the provided code or find the idea related to your research. Thank you! - A Study of Low-Resource Speech Commands Recognition Based on Adversarial Reprogramming Paper ```bib @article{yen2023neural, title={Neural model reprogramming with similarity based mapping for low-resource spoken command classification}, author={Yen, Hao and Ku, Pin-Jui and Yang, Chao-Han Huck and Hu, Hu and Siniscalchi, Sabato Marco and Chen, Pin-Yu and Tsao, Yu}, journal={Pr …