mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
15c1a4a441
* workspace setup * csj prepare done * Change compute_fbank_musan.py t soft link * add description * change lhotse prepare csj command * split train-dev here * Add header * remove debug * save manifest_statistics * generate transcript in Lhotse * update comments in config file
183 lines
3.9 KiB
Python
183 lines
3.9 KiB
Python
#!/usr/bin/env python3
|
|
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
|
|
# 2022 The University of Electro-Communications (author: Teo Wen Shen) # noqa
|
|
#
|
|
# 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.
|
|
|
|
import argparse
|
|
from pathlib import Path
|
|
|
|
from lhotse import CutSet, load_manifest
|
|
|
|
ARGPARSE_DESCRIPTION = """
|
|
This file displays duration statistics of utterances in a manifest.
|
|
You can use the displayed value to choose minimum/maximum duration
|
|
to remove short and long utterances during the training.
|
|
|
|
See the function `remove_short_and_long_utt()` in
|
|
pruned_transducer_stateless5/train.py for usage.
|
|
"""
|
|
|
|
|
|
def get_parser():
|
|
parser = argparse.ArgumentParser(
|
|
description=ARGPARSE_DESCRIPTION,
|
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--manifest-dir", type=Path, help="Path to cutset manifests"
|
|
)
|
|
|
|
return parser.parse_args()
|
|
|
|
|
|
def main():
|
|
args = get_parser()
|
|
|
|
for path in args.manifest_dir.glob("csj_cuts_*.jsonl.gz"):
|
|
|
|
cuts: CutSet = load_manifest(path)
|
|
|
|
print("\n---------------------------------\n")
|
|
print(path.name + ":")
|
|
cuts.describe()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
|
|
"""
|
|
## eval1
|
|
Cuts count: 1272
|
|
Total duration (hh:mm:ss): 01:50:07
|
|
Speech duration (hh:mm:ss): 01:50:07 (100.0%)
|
|
Duration statistics (seconds):
|
|
mean 5.2
|
|
std 3.9
|
|
min 0.2
|
|
25% 1.9
|
|
50% 4.0
|
|
75% 8.1
|
|
99% 14.3
|
|
99.5% 14.7
|
|
99.9% 16.0
|
|
max 16.9
|
|
Recordings available: 1272
|
|
Features available: 1272
|
|
Supervisions available: 1272
|
|
SUPERVISION custom fields:
|
|
- fluent (in 1272 cuts)
|
|
- disfluent (in 1272 cuts)
|
|
- number (in 1272 cuts)
|
|
- symbol (in 1272 cuts)
|
|
|
|
## eval2
|
|
Cuts count: 1292
|
|
Total duration (hh:mm:ss): 01:56:50
|
|
Speech duration (hh:mm:ss): 01:56:50 (100.0%)
|
|
Duration statistics (seconds):
|
|
mean 5.4
|
|
std 3.9
|
|
min 0.1
|
|
25% 2.1
|
|
50% 4.6
|
|
75% 8.6
|
|
99% 14.1
|
|
99.5% 15.2
|
|
99.9% 16.1
|
|
max 16.9
|
|
Recordings available: 1292
|
|
Features available: 1292
|
|
Supervisions available: 1292
|
|
SUPERVISION custom fields:
|
|
- fluent (in 1292 cuts)
|
|
- number (in 1292 cuts)
|
|
- symbol (in 1292 cuts)
|
|
- disfluent (in 1292 cuts)
|
|
|
|
## eval3
|
|
Cuts count: 1385
|
|
Total duration (hh:mm:ss): 01:19:21
|
|
Speech duration (hh:mm:ss): 01:19:21 (100.0%)
|
|
Duration statistics (seconds):
|
|
mean 3.4
|
|
std 3.0
|
|
min 0.2
|
|
25% 1.2
|
|
50% 2.5
|
|
75% 4.6
|
|
99% 12.7
|
|
99.5% 13.7
|
|
99.9% 15.0
|
|
max 15.9
|
|
Recordings available: 1385
|
|
Features available: 1385
|
|
Supervisions available: 1385
|
|
SUPERVISION custom fields:
|
|
- number (in 1385 cuts)
|
|
- symbol (in 1385 cuts)
|
|
- fluent (in 1385 cuts)
|
|
- disfluent (in 1385 cuts)
|
|
|
|
## valid
|
|
Cuts count: 4000
|
|
Total duration (hh:mm:ss): 05:08:09
|
|
Speech duration (hh:mm:ss): 05:08:09 (100.0%)
|
|
Duration statistics (seconds):
|
|
mean 4.6
|
|
std 3.8
|
|
min 0.1
|
|
25% 1.5
|
|
50% 3.4
|
|
75% 7.0
|
|
99% 13.8
|
|
99.5% 14.8
|
|
99.9% 16.0
|
|
max 17.3
|
|
Recordings available: 4000
|
|
Features available: 4000
|
|
Supervisions available: 4000
|
|
SUPERVISION custom fields:
|
|
- fluent (in 4000 cuts)
|
|
- symbol (in 4000 cuts)
|
|
- disfluent (in 4000 cuts)
|
|
- number (in 4000 cuts)
|
|
|
|
## train
|
|
Cuts count: 1291134
|
|
Total duration (hh:mm:ss): 1596:37:27
|
|
Speech duration (hh:mm:ss): 1596:37:27 (100.0%)
|
|
Duration statistics (seconds):
|
|
mean 4.5
|
|
std 3.6
|
|
min 0.0
|
|
25% 1.6
|
|
50% 3.3
|
|
75% 6.4
|
|
99% 14.0
|
|
99.5% 14.8
|
|
99.9% 16.6
|
|
max 27.8
|
|
Recordings available: 1291134
|
|
Features available: 1291134
|
|
Supervisions available: 1291134
|
|
SUPERVISION custom fields:
|
|
- disfluent (in 1291134 cuts)
|
|
- fluent (in 1291134 cuts)
|
|
- symbol (in 1291134 cuts)
|
|
- number (in 1291134 cuts)
|
|
"""
|