Spaces:
Running
Running
File size: 2,044 Bytes
9791162 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
import os
import torch
import argparse
import numpy as np
from tqdm import tqdm
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("dataset_speaker", type=str)
parser.add_argument("dataset_singer", type=str)
data_speaker = parser.parse_args().dataset_speaker
data_singer = parser.parse_args().dataset_singer
os.makedirs(data_singer, exist_ok=True)
for speaker in os.listdir(data_speaker):
subfile_num = 0
speaker_ave = 0
for file in tqdm(os.listdir(os.path.join(data_speaker, speaker)), desc=f"average {speaker}"):
if not file.endswith(".npy"):
continue
source_embed = np.load(os.path.join(data_speaker, speaker, file))
source_embed = source_embed.astype(np.float32)
speaker_ave = speaker_ave + source_embed
subfile_num = subfile_num + 1
if subfile_num == 0:
continue
speaker_ave = speaker_ave / subfile_num
np.save(os.path.join(data_singer, f"{speaker}.spk.npy"),
speaker_ave, allow_pickle=False)
# rewrite timbre code by average, if similarity is larger than cmp_val
rewrite_timbre_code = False
if not rewrite_timbre_code:
continue
cmp_src = torch.FloatTensor(speaker_ave)
cmp_num = 0
cmp_val = 0.85
for file in tqdm(os.listdir(os.path.join(data_speaker, speaker)), desc=f"rewrite {speaker}"):
if not file.endswith(".npy"):
continue
cmp_tmp = np.load(os.path.join(data_speaker, speaker, file))
cmp_tmp = cmp_tmp.astype(np.float32)
cmp_tmp = torch.FloatTensor(cmp_tmp)
cmp_cos = torch.cosine_similarity(cmp_src, cmp_tmp, dim=0)
if (cmp_cos > cmp_val):
cmp_num += 1
np.save(os.path.join(data_speaker, speaker, file),
speaker_ave, allow_pickle=False)
print(f"rewrite timbre for {speaker} with :", cmp_num)
|