Delete egs/librilight/SSL/local/analyze_codebook.py

egs/librilight/SSL/local/analyze_codebook.py

@@ -1,88 +0,0 @@
-#!/usr/bin/env python3
-# Copyright    2024  Xiaomi Corp.        (authors: Yifan Yang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import logging
-from collections import Counter
-from pathlib import Path
-
-import torch
-from lhotse import CutSet
-from tqdm import tqdm
-
-# Torch's multithreaded behavior needs to be disabled or
-# it wastes a lot of CPU and slow things down.
-# Do this outside of main() in case it needs to take effect
-# even when we are not invoking the main (e.g. when spawning subprocesses).
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--cuts-path",
-        type=str,
-        default="data/kmeans/librispeech_cuts_dev-clean.jsonl.gz",
-    )
-
-    parser.add_argument(
-        "--num-clusters",
-        type=int,
-        default=500,
-    )
-
-    return parser.parse_args()
-
-
-def analyze_codebook(args):
-    cuts_path = Path(args.cuts_path)
-    assert cuts_path.is_file(), f"{cuts_path} does not exist"
-
-    logging.info(f"Loading {cuts_path}")
-    cut_set = CutSet.from_file(cuts_path)
-
-    cluster_counts = Counter()
-    logging.info("Analyzing codebook")
-    for cut in tqdm(cut_set):
-        kmeans = map(int, cut.custom["kmeans"].split())
-        cluster_counts.update(kmeans)
-
-    utilized_clusters = len(cluster_counts)
-
-    total_count = sum(cluster_counts.values())
-    counts = torch.tensor([cluster_counts[i] for i in range(args.num_clusters)])
-    normalized_counts = (counts / total_count).clamp(min=1e-10)
-    codebook_entropy = (
-        -(normalized_counts * normalized_counts.log()).sum()
-        * torch.log2(torch.tensor(torch.e))
-    ).item()
-
-    logging.info(
-        f"Codebook utilization rate: {utilized_clusters / args.num_clusters:%}"
-    )
-    logging.info(f"Codebook entropy: {codebook_entropy}")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    args = get_args()
-    logging.info(vars(args))
-    analyze_codebook(args)