File size: 2,128 Bytes
24303c9 | 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 | import math
import multiprocessing
import argparse
from random import shuffle
import torch.multiprocessing as mp
import torch
from glob import glob
from tqdm import tqdm
import utils
from data_conf import data_root
from module.cnhubert import get_model, get_content
import logging
logging.getLogger("numba").setLevel(logging.WARNING)
import librosa
def process_one(file_path, model):
# file_path16k = file_path.replace('genshin_data', 'genshin_data16k')
ssl_path = file_path.replace(".wav", ".ssl.pt").replace(".mp3", ".ssl.pt")
ssl_content = get_content(model, file_path)
assert not torch.isnan(ssl_content).any(), f"NaN in {file_path}"
torch.save(ssl_content.half().cpu(), ssl_path)
def process_batch(filenames):
print("Loading content model...")
rank = mp.current_process()._identity
rank = rank[0] if len(rank) > 0 else 0
gpu_id = rank % torch.cuda.device_count()
device = torch.device(f"cuda:{gpu_id}")
ssl_model = get_model()
ssl_model = ssl_model.to(device)
ssl_model.eval()
print("Loaded content model.")
for filename in tqdm(filenames):
try:
process_one(filename, ssl_model)
except Exception as e:
print(f"Error processing {filename}: {e}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--config", type=str, default="configs/vits.json", help="path to config"
)
args = parser.parse_args()
filenames = glob(f"{data_root}/**/*.wav", recursive=True) # [:10]
filenames += glob(f"{data_root}/**/*.mp3", recursive=True) # [:10]
hps = utils.get_hparams_from_file(args.config)
shuffle(filenames)
multiprocessing.set_start_method("spawn", force=True)
num_processes = 1
chunk_size = int(math.ceil(len(filenames) / num_processes))
chunks = [
filenames[i : i + chunk_size] for i in range(0, len(filenames), chunk_size)
]
print([len(c) for c in chunks])
processes = [
multiprocessing.Process(target=process_batch, args=(chunk, )) for chunk in chunks
]
for p in processes:
p.start() |