Spaces:
Running
Running
File size: 4,269 Bytes
2216a22 08cc398 2216a22 08cc398 2216a22 08cc398 2216a22 08cc398 2216a22 08cc398 2216a22 08cc398 2216a22 08cc398 2216a22 08cc398 2216a22 08cc398 2216a22 08cc398 2216a22 08cc398 2216a22 08cc398 2216a22 |
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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
import argparse
import pathlib
import tqdm
from torch.utils.data import Dataset, DataLoader
from score import loadWav, Score
import torch
import os
import warnings
warnings.filterwarnings("ignore")
def get_arg():
parser = argparse.ArgumentParser()
parser.add_argument("--mode", required=True, choices=["predict_file", "predict_dir"], type=str, help="predict mode")
parser.add_argument("--ckpt_path", required=False, default="voxsim_wavlm_ecapa.model", type=pathlib.Path, help="path to the model checkpoint")
parser.add_argument("--inp_dir", required=False, default=None, type=pathlib.Path, help="input directory when predict_dir mode")
parser.add_argument("--ref_dir", required=False, default=None, type=pathlib.Path, help="reference directory when predict_dir mode")
parser.add_argument("--inp_path", required=False, default=None, type=pathlib.Path, help="input file when predict_file mode")
parser.add_argument("--ref_path", required=False, default=None, type=pathlib.Path, help="reference file when predict_file mode")
parser.add_argument("--out_path", required=True, type=pathlib.Path, help="output path")
parser.add_argument("--num_workers", required=False, default=4, type=int, help="number of workers for dataloader")
return parser.parse_args()
class AudioDataset(Dataset):
def __init__(self, inp_dir_path: pathlib.Path, ref_dir_path: pathlib.Path, max_frames: int = 400):
self.inp_dir_path = inp_dir_path
self.ref_dir_path = ref_dir_path
self.inp_wavlist = [file for file in os.listdir(inp_dir_path) if file.endswith(".wav")]
inp_wavset = set(self.inp_wavlist)
ref_wavset = set([file for file in os.listdir(ref_dir_path) if file.endswith(".wav")])
diff = inp_wavset - ref_wavset
if diff:
diff = list(diff)
diff.sort()
raise ValueError(f"Files {diff} are in inp_dir but not in ref_dir.")
self.inp_wavlist.sort()
self.max_audio = max_frames * 160 + 240
def __len__(self):
return len(self.inp_wavlist)
def __getitem__(self, idx):
inp_wavs, inp_wav = loadWav(os.path.join(self.inp_dir_path, self.inp_wavlist[idx]))
ref_wavs, ref_wav = loadWav(os.path.join(self.ref_dir_path, self.inp_wavlist[idx]))
return inp_wavs, inp_wav, ref_wavs, ref_wav
def main():
args = get_arg()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.mode == "predict_file":
assert args.inp_path is not None, "inp_path is required when mode is predict_file."
assert args.ref_path is not None, "ref_path is required when mode is predict_file."
assert args.inp_path.exists()
assert args.ref_path.exists()
assert args.inp_path.is_file()
assert args.ref_path.is_file()
inp_wavs, inp_wav = loadWav(args.inp_path)
ref_wavs, ref_wav = loadWav(args.ref_path)
scorer = Score(ckpt_path=args.ckpt_path, device=device)
score = scorer.score(inp_wavs, inp_wav, ref_wavs, ref_wav)
print("VoxSIM score: ", score)
with open(args.out_path, "w") as fw:
fw.write(str(score))
else:
assert args.inp_dir is not None, "inp_dir is required when mode is predict_dir."
assert args.ref_dir is not None, "ref_dir is required when mode is predict_dir."
assert args.inp_dir.exists()
assert args.ref_dir.exists()
assert args.inp_dir.is_dir()
assert args.ref_dir.is_dir()
dataset = AudioDataset(args.inp_dir, args.ref_dir)
loader = DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=args.num_workers)
scorer = Score(ckpt_path=args.ckpt_path, device=device)
avg_score = []
with open(args.out_path, 'w') as fw:
for batch in tqdm.tqdm(loader):
inp_wavs, inp_wav, ref_wavs, ref_wav = batch
score = scorer.score(inp_wavs, inp_wav, ref_wavs, ref_wav)
avg_score.append(score)
fw.write(str(score) + "\n")
print("Average VoxSIM score: ", sum(avg_score)/len(avg_score))
print("save to ", args.out_path)
if __name__ == "__main__":
main() |