mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 18:12:19 +00:00
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
|
|
# Copyright 2022 Johns Hopkins University (authors: Desh Raj)
|
|
#
|
|
# 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.
|
|
|
|
|
|
"""
|
|
This file creates ICSI train segments.
|
|
"""
|
|
import logging
|
|
from pathlib import Path
|
|
|
|
from lhotse import load_manifest_lazy
|
|
from prepare_ami_train_cuts import cut_into_windows
|
|
|
|
|
|
def prepare_train_cuts():
|
|
src_dir = Path("data/manifests")
|
|
|
|
logging.info("Loading the manifests")
|
|
train_cuts_ihm = load_manifest_lazy(
|
|
src_dir / "cuts_icsi-ihm-mix_train.jsonl.gz"
|
|
).map(lambda c: c.with_id(f"{c.id}_ihm-mix"))
|
|
train_cuts_sdm = load_manifest_lazy(src_dir / "cuts_icsi-sdm_train.jsonl.gz").map(
|
|
lambda c: c.with_id(f"{c.id}_sdm")
|
|
)
|
|
|
|
# Combine all cuts into one CutSet
|
|
train_cuts = train_cuts_ihm + train_cuts_sdm
|
|
|
|
train_cuts_1 = train_cuts.trim_to_supervision_groups(max_pause=0.5)
|
|
train_cuts_2 = train_cuts.trim_to_supervision_groups(max_pause=0.0)
|
|
|
|
# Combine the two segmentations
|
|
train_all = train_cuts_1 + train_cuts_2
|
|
|
|
# At this point, some of the cuts may be very long. We will cut them into windows of
|
|
# roughly 30 seconds.
|
|
logging.info("Cutting the segments into windows of 30 seconds")
|
|
train_all_30 = cut_into_windows(train_all, duration=30.0)
|
|
logging.info(f"Number of cuts after cutting into windows: {len(train_all_30)}")
|
|
|
|
# Show statistics
|
|
train_all.describe(full=True)
|
|
|
|
# Save the cuts
|
|
logging.info("Saving the cuts")
|
|
train_all.to_file(src_dir / "cuts_train_icsi.jsonl.gz")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
|
logging.basicConfig(format=formatter, level=logging.INFO)
|
|
|
|
prepare_train_cuts()
|