| import numpy as np |
| from pyannote.audio import Model |
| from pyannote.audio import Inference |
|
|
| import math |
| import multiprocessing |
| from random import shuffle |
| import torch.multiprocessing as mp |
|
|
| import torch |
| from glob import glob |
|
|
| from tqdm import tqdm |
|
|
| import logging |
|
|
| from data_conf import data_root |
|
|
| logging.getLogger("numba").setLevel(logging.WARNING) |
|
|
|
|
| def process_one(file_path, inference, device): |
| spk_emb_path = file_path.replace(".wav", ".spk.npy").replace(".mp3", ".spk.npy") |
| try: |
| np.load(spk_emb_path) |
| except: |
|
|
| embedding = inference(file_path) |
| np.save(spk_emb_path, embedding) |
| np.save(spk_emb_path, embedding) |
|
|
|
|
| def process_batch(filenames): |
| print("Loading models ...") |
| process_idx = mp.current_process()._identity |
| rank = process_idx[0] if len(process_idx) > 0 else 0 |
| gpu_id = rank % torch.cuda.device_count() |
| device = torch.device(f"cuda:{gpu_id}") |
| print(device) |
|
|
| model = Model.from_pretrained("pyannote/wespeaker-voxceleb-resnet34-LM") |
| model = model.to(device) |
| inference = Inference(model, window="whole") |
|
|
| print("Loaded .") |
| with torch.no_grad(): |
| for filename in tqdm(filenames): |
| process_one(filename, inference, device) |
|
|
|
|
| in_dir = data_root |
|
|
| if __name__ == "__main__": |
| filenames = glob(f"{in_dir}/**/*.wav", recursive=True) |
| filenames += glob(f"{in_dir}/**/*.mp3", recursive=True) |
| 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() |
|
|
| for p in processes: |
| p.join() |
|
|
