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https://github.com/k2-fsa/icefall.git
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41b16d7838
* merge upstream * add SURT model and training * add libricss decoding * add chunk width randomization * decode SURT with libricss * initial commit for zipformer_ctc * remove unwanted changes * remove changes to other recipe * fix zipformer softlink * fix for JIT export * add missing file * fix symbolic links * update results * clean commit for SURT recipe * training libricss surt model * remove unwanted files * remove unwanted changes * remove changes in librispeech * change some files to symlinks * remove unwanted changes in utils * add export script * add README * minor fix in README * add assets for README * replace some files with symlinks * remove unused decoding methods * initial commit for SURT AMI recipe * fix symlink * add train + decode scripts * add missing symlink * change files to symlink * change file type
68 lines
2.2 KiB
Python
Executable File
68 lines
2.2 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 creates ICSI train segments.
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"""
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import logging
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from pathlib import Path
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from lhotse import load_manifest_lazy
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from prepare_ami_train_cuts import cut_into_windows
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def prepare_train_cuts():
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src_dir = Path("data/manifests")
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logging.info("Loading the manifests")
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train_cuts_ihm = load_manifest_lazy(
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src_dir / "cuts_icsi-ihm-mix_train.jsonl.gz"
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).map(lambda c: c.with_id(f"{c.id}_ihm-mix"))
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train_cuts_sdm = load_manifest_lazy(src_dir / "cuts_icsi-sdm_train.jsonl.gz").map(
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lambda c: c.with_id(f"{c.id}_sdm")
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)
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# Combine all cuts into one CutSet
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train_cuts = train_cuts_ihm + train_cuts_sdm
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train_cuts_1 = train_cuts.trim_to_supervision_groups(max_pause=0.5)
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train_cuts_2 = train_cuts.trim_to_supervision_groups(max_pause=0.0)
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# Combine the two segmentations
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train_all = train_cuts_1 + train_cuts_2
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# At this point, some of the cuts may be very long. We will cut them into windows of
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# roughly 30 seconds.
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logging.info("Cutting the segments into windows of 30 seconds")
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train_all_30 = cut_into_windows(train_all, duration=30.0)
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logging.info(f"Number of cuts after cutting into windows: {len(train_all_30)}")
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# Show statistics
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train_all.describe(full=True)
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# Save the cuts
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logging.info("Saving the cuts")
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train_all.to_file(src_dir / "cuts_train_icsi.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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prepare_train_cuts()
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