| 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): |
|
|
| |
|
|
| 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) |
| filenames += glob(f"{data_root}/**/*.mp3", recursive=True) |
| 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() |
