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