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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
108 lines
3.1 KiB
Python
Executable File
108 lines
3.1 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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import os
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from pathlib import Path
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from typing import Optional
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import torch
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from lhotse import CutSet
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from lhotse.recipes.utils import read_manifests_if_cached
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from icefall.utils import str2bool
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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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"--dataset",
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type=str,
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help="""Dataset parts to compute fbank. If None, we will use all""",
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)
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return parser.parse_args()
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def preprocess_librilight(
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dataset: Optional[str] = None,
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):
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src_dir = Path("data/manifests")
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output_dir = Path("data/kmeans")
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if dataset is None:
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dataset_parts = (
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"small",
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"medium",
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"large",
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)
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else:
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dataset_parts = dataset.split(" ", -1)
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prefix = "librilight"
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suffix = "jsonl.gz"
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manifests = read_manifests_if_cached(
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dataset_parts=dataset_parts,
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output_dir=src_dir,
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prefix=prefix,
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suffix=suffix,
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)
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assert manifests is not None
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assert len(manifests) == len(dataset_parts), (
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len(manifests),
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len(dataset_parts),
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list(manifests.keys()),
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dataset_parts,
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)
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for partition, m in manifests.items():
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cuts_filename = f"{prefix}_cuts_{partition}_raw.{suffix}"
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if (output_dir / cuts_filename).is_file():
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logging.info(f"{partition} already exists - skipping.")
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continue
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logging.info(f"Processing {partition}")
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cut_set = CutSet.from_manifests(
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recordings=m["recordings"],
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supervisions=m["supervisions"],
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)
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cut_set = cut_set.trim_to_supervisions(
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keep_overlapping=False, min_duration=None
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)
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logging.info(f"Saving to {output_dir / cuts_filename}")
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cut_set.to_file(output_dir / cuts_filename)
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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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preprocess_librilight(
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dataset=args.dataset,
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)
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