90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
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import os
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from glob import glob
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from pathlib import Path
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import torch
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import logging
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import argparse
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import torch
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import numpy as np
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from sklearn.cluster import KMeans, MiniBatchKMeans
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import tqdm
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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import time
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import random
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def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False):
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logger.info(f"Loading features from {in_dir}")
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features = []
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nums = 0
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for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
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features.append(torch.load(path).squeeze(0).numpy().T)
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# print(features[-1].shape)
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features = np.concatenate(features, axis=0)
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print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype)
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features = features.astype(np.float32)
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logger.info(f"Clustering features of shape: {features.shape}")
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t = time.time()
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if use_minibatch:
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kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features)
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else:
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kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features)
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print(time.time()-t, "s")
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x = {
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"n_features_in_": kmeans.n_features_in_,
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"_n_threads": kmeans._n_threads,
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"cluster_centers_": kmeans.cluster_centers_,
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}
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print("end")
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return x
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--dataset', type=Path, default="./dataset/44k",
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help='path of training data directory')
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parser.add_argument('--output', type=Path, default="logs/44k",
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help='path of model output directory')
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args = parser.parse_args()
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checkpoint_dir = args.output
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dataset = args.dataset
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n_clusters = 10000
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ckpt = {}
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for spk in os.listdir(dataset):
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if os.path.isdir(dataset/spk):
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print(f"train kmeans for {spk}...")
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in_dir = dataset/spk
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x = train_cluster(in_dir, n_clusters, verbose=False)
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ckpt[spk] = x
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checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt"
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checkpoint_path.parent.mkdir(exist_ok=True, parents=True)
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torch.save(
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ckpt,
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checkpoint_path,
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)
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# import cluster
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# for spk in tqdm.tqdm(os.listdir("dataset")):
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# if os.path.isdir(f"dataset/{spk}"):
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# print(f"start kmeans inference for {spk}...")
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# for feature_path in tqdm.tqdm(glob(f"dataset/{spk}/*.discrete.npy", recursive=True)):
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# mel_path = feature_path.replace(".discrete.npy",".mel.npy")
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# mel_spectrogram = np.load(mel_path)
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# feature_len = mel_spectrogram.shape[-1]
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# c = np.load(feature_path)
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# c = utils.tools.repeat_expand_2d(torch.FloatTensor(c), feature_len).numpy()
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# feature = c.T
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# feature_class = cluster.get_cluster_result(feature, spk)
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# np.save(feature_path.replace(".discrete.npy", ".discrete_class.npy"), feature_class)
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