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import sys,os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import numpy as np
import argparse
import torch
import librosa
from tqdm import tqdm
from hubert import hubert_model
def load_audio(file: str, sr: int = 16000):
x, sr = librosa.load(file, sr=sr)
return x
def load_model(path, device):
model = hubert_model.hubert_soft(path)
model.eval()
model.half()
model.to(device)
return model
def pred_vec(model, wavPath, vecPath, device):
feats = load_audio(wavPath)
feats = torch.from_numpy(feats).to(device)
feats = feats[None, None, :].half()
with torch.no_grad():
vec = model.units(feats).squeeze().data.cpu().float().numpy()
# print(vec.shape) # [length, dim=256] hop=320
np.save(vecPath, vec, allow_pickle=False)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-w", "--wav", help="wav", dest="wav", required=True)
parser.add_argument("-v", "--vec", help="vec", dest="vec", required=True)
args = parser.parse_args()
print(args.wav)
print(args.vec)
os.makedirs(args.vec, exist_ok=True)
wavPath = args.wav
vecPath = args.vec
device = "cuda" if torch.cuda.is_available() else "cpu"
hubert = load_model(os.path.join("hubert_pretrain", "hubert-soft-0d54a1f4.pt"), device)
for spks in os.listdir(wavPath):
if os.path.isdir(f"./{wavPath}/{spks}"):
os.makedirs(f"./{vecPath}/{spks}", exist_ok=True)
files = [f for f in os.listdir(f"./{wavPath}/{spks}") if f.endswith(".wav")]
for file in tqdm(files, desc=f'Processing vec {spks}'):
file = file[:-4]
pred_vec(hubert, f"{wavPath}/{spks}/{file}.wav", f"{vecPath}/{spks}/{file}.vec", device)
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