| import numpy as np |
| import torch,os,argparse |
| from tqdm import tqdm |
| from utils import save_dir_res |
| parser = argparse.ArgumentParser(description='Process prompt search agent arguments') |
| parser.add_argument('--data_path', |
| type=str, |
| default='data/ossi', |
| help='data_path') |
| parser.add_argument('--embd_path', |
| type=str, |
| help='embd_path') |
| parser.add_argument('--eval_mode', |
| type=str, |
| help='[small,medium,large]') |
| parser.add_argument('--spk_mode', |
| type=str, |
| help='[perspk1,perspk3,perspk5]') |
| parser.add_argument('--output_path', |
| type=str, |
| help='path to output results') |
| |
| if __name__ == '__main__': |
| args = parser.parse_args() |
| embd_dict = np.load(args.embd_path,allow_pickle=True).item() |
| gallery = os.path.join(args.data_path,args.eval_mode,args.spk_mode,'gallery') |
| probe = os.path.join(args.data_path,args.eval_mode,args.spk_mode,'probe') |
| Gfeat = torch.FloatTensor([]) |
| Glabel = torch.LongTensor([]) |
| for line in open(gallery): |
| [utt,label] = line.strip().split() |
| feat = torch.from_numpy(embd_dict[utt]) |
| label = torch.tensor([int(label)]) |
| Gfeat = torch.cat((Gfeat, feat), dim=0) |
| Glabel = torch.cat((Glabel, label), dim=0) |
| Pfeat = torch.FloatTensor([]) |
| Plabel = torch.LongTensor([]) |
| for line in tqdm(open(probe)): |
| [utt,label] = line.strip().split() |
| feat = torch.from_numpy(embd_dict[utt]) |
| label = torch.tensor([int(label)]) |
| Pfeat = torch.cat((Pfeat, feat), dim=0) |
| Plabel = torch.cat((Plabel, label), dim=0) |
| save_dir_res(Gfeat, Glabel, Pfeat, Plabel, |
| os.path.join(args.output_path,'res_fig.jpg'), |
| os.path.join(args.output_path,'res.txt'), |
| [0.1,0.01,0.001] |
| ) |
| |
|
|
