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https://github.com/k2-fsa/icefall.git
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5aafbb970e
* initial commit for SPGISpeech recipe * add decoding * add spgispeech transducer * remove conformer ctc; minor fixes in RNN-T * add results * add tensorboard * add pretrained model to HF * remove unused scripts and soft link common scripts * remove duplicate files * pre commit hooks * remove change in librispeech * pre commit hook * add CER numbers
80 lines
2.5 KiB
Python
Executable File
80 lines
2.5 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2022 Johns Hopkins University (authors: Desh Raj)
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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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"""
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This file splits the training set into train and dev sets.
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"""
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import logging
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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 lhotse.recipes.utils import read_manifests_if_cached
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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 split_spgispeech_train():
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src_dir = Path("data/manifests")
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manifests = read_manifests_if_cached(
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dataset_parts=["train", "val"],
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output_dir=src_dir,
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prefix="spgispeech",
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suffix="jsonl.gz",
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lazy=True,
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)
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assert manifests is not None
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train_dev_cuts = CutSet.from_manifests(
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recordings=manifests["train"]["recordings"],
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supervisions=manifests["train"]["supervisions"],
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)
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dev_cuts = train_dev_cuts.subset(first=4000)
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train_cuts = train_dev_cuts.filter(lambda c: c not in dev_cuts)
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# Add speed perturbation
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train_cuts = (
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train_cuts + train_cuts.perturb_speed(0.9) + train_cuts.perturb_speed(1.1)
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)
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# Write the manifests to disk.
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train_cuts.to_file(src_dir / "cuts_train_raw.jsonl.gz")
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dev_cuts.to_file(src_dir / "cuts_dev_raw.jsonl.gz")
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# Also write the val set to disk.
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val_cuts = CutSet.from_manifests(
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recordings=manifests["val"]["recordings"],
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supervisions=manifests["val"]["supervisions"],
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)
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val_cuts.to_file(src_dir / "cuts_val_raw.jsonl.gz")
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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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split_spgispeech_train()
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