mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-02-03 16:53:55 +03:00
82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
|
import os
|
||
|
|
from pathlib import Path
|
||
|
|
import torch
|
||
|
|
import logging
|
||
|
|
import argparse
|
||
|
|
import numpy as np
|
||
|
|
from sklearn.cluster import KMeans, MiniBatchKMeans
|
||
|
|
import tqdm
|
||
|
|
|
||
|
|
logging.basicConfig(level=logging.INFO)
|
||
|
|
logger = logging.getLogger(__name__)
|
||
|
|
import time
|
||
|
|
|
||
|
|
|
||
|
|
def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False):
|
||
|
|
logger.info(f"Loading features from {in_dir}")
|
||
|
|
features = []
|
||
|
|
nums = 0
|
||
|
|
for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
|
||
|
|
features.append(torch.load(path).squeeze(0).numpy().T)
|
||
|
|
# print(features[-1].shape)
|
||
|
|
features = np.concatenate(features, axis=0)
|
||
|
|
print(nums, features.nbytes / 1024**2, "MB , shape:", features.shape, features.dtype)
|
||
|
|
features = features.astype(np.float32)
|
||
|
|
logger.info(f"Clustering features of shape: {features.shape}")
|
||
|
|
t = time.time()
|
||
|
|
if use_minibatch:
|
||
|
|
kmeans = MiniBatchKMeans(n_clusters=n_clusters, verbose=verbose, batch_size=4096, max_iter=80).fit(features)
|
||
|
|
else:
|
||
|
|
kmeans = KMeans(n_clusters=n_clusters, verbose=verbose).fit(features)
|
||
|
|
print(time.time() - t, "s")
|
||
|
|
|
||
|
|
x = {
|
||
|
|
"n_features_in_": kmeans.n_features_in_,
|
||
|
|
"_n_threads": kmeans._n_threads,
|
||
|
|
"cluster_centers_": kmeans.cluster_centers_,
|
||
|
|
}
|
||
|
|
print("end")
|
||
|
|
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
parser = argparse.ArgumentParser()
|
||
|
|
parser.add_argument("--dataset", type=Path, default="./dataset/44k", help="path of training data directory")
|
||
|
|
parser.add_argument("--output", type=Path, default="logs/44k", help="path of model output directory")
|
||
|
|
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
checkpoint_dir = args.output
|
||
|
|
dataset = args.dataset
|
||
|
|
n_clusters = 10000
|
||
|
|
|
||
|
|
ckpt = {}
|
||
|
|
for spk in os.listdir(dataset):
|
||
|
|
if os.path.isdir(dataset / spk):
|
||
|
|
print(f"train kmeans for {spk}...")
|
||
|
|
in_dir = dataset / spk
|
||
|
|
x = train_cluster(in_dir, n_clusters, verbose=False)
|
||
|
|
ckpt[spk] = x
|
||
|
|
|
||
|
|
checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt"
|
||
|
|
checkpoint_path.parent.mkdir(exist_ok=True, parents=True)
|
||
|
|
torch.save(
|
||
|
|
ckpt,
|
||
|
|
checkpoint_path,
|
||
|
|
)
|
||
|
|
|
||
|
|
# import cluster
|
||
|
|
# for spk in tqdm.tqdm(os.listdir("dataset")):
|
||
|
|
# if os.path.isdir(f"dataset/{spk}"):
|
||
|
|
# print(f"start kmeans inference for {spk}...")
|
||
|
|
# for feature_path in tqdm.tqdm(glob(f"dataset/{spk}/*.discrete.npy", recursive=True)):
|
||
|
|
# mel_path = feature_path.replace(".discrete.npy",".mel.npy")
|
||
|
|
# mel_spectrogram = np.load(mel_path)
|
||
|
|
# feature_len = mel_spectrogram.shape[-1]
|
||
|
|
# c = np.load(feature_path)
|
||
|
|
# c = utils.tools.repeat_expand_2d(torch.FloatTensor(c), feature_len).numpy()
|
||
|
|
# feature = c.T
|
||
|
|
# feature_class = cluster.get_cluster_result(feature, spk)
|
||
|
|
# np.save(feature_path.replace(".discrete.npy", ".discrete_class.npy"), feature_class)
|