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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import sys
import numpy as np
from sklearn.cluster import MiniBatchKMeans
import joblib
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
logger = logging.getLogger("learn_kmeans")
def get_km_model(
n_clusters,
init,
max_iter,
batch_size,
tol,
max_no_improvement,
n_init,
reassignment_ratio,
):
return MiniBatchKMeans(
n_clusters=n_clusters,
init=init,
max_iter=max_iter,
batch_size=batch_size,
verbose=1,
compute_labels=False,
tol=tol,
max_no_improvement=max_no_improvement,
init_size=None,
n_init=n_init,
reassignment_ratio=reassignment_ratio,
)
def load_feature_shard(feat_dir, split, nshard, rank, percent):
feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy"
leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len"
with open(leng_path, "r") as f:
lengs = [int(line.rstrip()) for line in f]
offsets = [0] + np.cumsum(lengs[:-1]).tolist()
if percent < 0:
return np.load(feat_path, mmap_mode="r")
else:
nsample = int(np.ceil(len(lengs) * percent))
indices = np.random.choice(len(lengs), nsample, replace=False)
feat = np.load(feat_path, mmap_mode="r")
sampled_feat = np.concatenate(
[feat[offsets[i]: offsets[i] + lengs[i]] for i in indices], axis=0
)
logger.info(
(
f"sampled {nsample} utterances, {len(sampled_feat)} frames "
f"from shard {rank}/{nshard}"
)
)
return sampled_feat
def load_feature(feat_dir, split, nshard, seed, percent):
assert percent <= 1.0
feat = np.concatenate(
[
load_feature_shard(feat_dir, split, nshard, r, percent)
for r in range(nshard)
],
axis=0,
)
logging.info(f"loaded feature with dimension {feat.shape}")
return feat
def learn_kmeans(
feat_dir,
split,
nshard,
km_path,
n_clusters,
seed,
percent,
init,
max_iter,
batch_size,
tol,
n_init,
reassignment_ratio,
max_no_improvement,
):
np.random.seed(seed)
feat = load_feature(feat_dir, split, nshard, seed, percent)
km_model = get_km_model(
n_clusters,
init,
max_iter,
batch_size,
tol,
max_no_improvement,
n_init,
reassignment_ratio,
)
km_model.fit(feat)
joblib.dump(km_model, km_path)
inertia = -km_model.score(feat) / len(feat)
logger.info("total intertia: %.5f", inertia)
logger.info("finished successfully")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("feat_dir", type=str)
parser.add_argument("split", type=str)
parser.add_argument("nshard", type=int)
parser.add_argument("km_path", type=str)
parser.add_argument("n_clusters", type=int)
parser.add_argument("--seed", default=0, type=int)
parser.add_argument(
"--percent", default=-1, type=float, help="sample a subset; -1 for all"
)
parser.add_argument("--init", default="k-means++")
parser.add_argument("--max_iter", default=100, type=int)
parser.add_argument("--batch_size", default=10000, type=int)
parser.add_argument("--tol", default=0.0, type=float)
parser.add_argument("--max_no_improvement", default=100, type=int)
parser.add_argument("--n_init", default=20, type=int)
parser.add_argument("--reassignment_ratio", default=0.0, type=float)
args = parser.parse_args()
logging.info(str(args))
learn_kmeans(**vars(args))
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