Add back original so-vits-svc

voice_conversion/so-vits-svc/preprocess_hubert_f0.py

@@ -0,0 +1,62 @@
+import math
+import multiprocessing
+import os
+import argparse
+from random import shuffle
+
+import torch
+from glob import glob
+from tqdm import tqdm
+
+import utils
+import logging
+logging.getLogger('numba').setLevel(logging.WARNING)
+import librosa
+import numpy as np
+
+hps = utils.get_hparams_from_file("configs/config.json")
+sampling_rate = hps.data.sampling_rate
+hop_length = hps.data.hop_length
+
+
+def process_one(filename, hmodel):
+    # print(filename)
+    wav, sr = librosa.load(filename, sr=sampling_rate)
+    soft_path = filename + ".soft.pt"
+    if not os.path.exists(soft_path):
+        devive = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
+        wav16k = torch.from_numpy(wav16k).to(devive)
+        c = utils.get_hubert_content(hmodel, wav_16k_tensor=wav16k)
+        torch.save(c.cpu(), soft_path)
+    f0_path = filename + ".f0.npy"
+    if not os.path.exists(f0_path):
+        f0 = utils.compute_f0_dio(wav, sampling_rate=sampling_rate, hop_length=hop_length)
+        np.save(f0_path, f0)
+
+
+def process_batch(filenames):
+    print("Loading hubert for content...")
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    hmodel = utils.get_hubert_model().to(device)
+    print("Loaded hubert.")
+    for filename in tqdm(filenames):
+        process_one(filename, hmodel)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--in_dir", type=str, default="dataset/44k", help="path to input dir")
+
+    args = parser.parse_args()
+    filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True)  # [:10]
+    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()