sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7e345c55ba2f337627dd0e5874e1f4bdbfec781e895a5a684bc587c5e74a6cbc | Shell | 9,903 | 201 | #!/usr/bin/env bash
# Prerequisite checks for import-carla-ue58-walker (CARLA on UE 5.8). Read-only, no sudo, fast —
# the MCP check_prerequisites(name) tool must never hang.
# Hard blockers: no checkout, wrong engine major.minor, no engine binary, no cmake.
# On-camera banner: every skill run announces itself, so a te... |
4b506564adba2d495f293db1e599007b0981e09efdaa8334467590b0c40ad26f | Shell | 9,904 | 256 | #!/bin/bash
# if only type in runner_power, or runner_power-h, the usage is printed
Help()
{
echo "This is the master runner script for SSAML.
From here, you will define where your data is. You will also specify what data type you have
and various parameters of SSAML to run.
A master runner script like this can be mos... |
e7f69f2f558aeb9b2a784e89149b3e471657bdd56fdbe2c7c8b41dcfb3b27a7d | Shell | 9,904 | 256 | #!/usr/bin/env bash
## This is taken from mini_librispeech, but the proportional-shrink value was
# tuned for this corpus (hub4-spanish)
# steps/info/chain_dir_info.pl exp/chain/tdnn1a_sp
# exp/chain/tdnn1a_sp: num-iters=102 nj=2..5 num-params=7.0M dim=40+100->2334 combine=-0.082->-0.075 xent:train/valid[67,101,final... |
c7c4d58167029123b6e4de94de58c055dd519a1f2f0edffafafbc662a799df27 | Shell | 9,911 | 280 | #!/usr/bin/env bash
#
# Based mostly on the TED-LIUM and Switchboard recipe
#
# Copyright 2017 Johns Hopkins University (Author: Shinji Watanabe and Yenda Trmal)
# Apache 2.0
#
# Begin configuration section.
nj=96
decode_nj=20
stage=0
nnet_stage=-10
decode_stage=1
decode_only=false
num_data_reps=4
foreground_snrs="2... |
1bd2aa711e3534fd2ea08715afd6788c8b6f755b6123dc8988f7dc140df0d5be | Shell | 10,055 | 254 | #!/usr/bin/env bash
# Copyright 2016 University of Sheffield (Jon Barker, Ricard Marxer)
# Inria (Emmanuel Vincent)
# Mitsubishi Electric Research Labs (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
# This script is made from the kaldi recipe of the 2nd CHi... |
1a68522af1efbc1b06dad92ee4c83b22e4de361b33a04f963c722a0c12702599 | Shell | 10,095 | 223 | #!/usr/bin/env bash
# by default, with cleanup
# please note that the language(s) was not selected for any particular reason (other to represent the various sizes of babel datasets)
# 304-lithuanian | %WER 40.8 | 20041 61492 | 61.9 27.9 10.2 2.7 40.8 29.0 | -0.313 | exp/chain_cleaned/tdnn_lstm_sp/decode_dev10h.pem/... |
a602cf0f947997338e0a5267f90e7586f114fb2d2b8eb34ed0a82b5c72c65803 | Shell | 10,114 | 265 | #!/usr/bin/env bash
# Copyright 2012 Johns Hopkins University (Author: Guoguo Chen, Yenda Trmal)
# Apache 2.0.
#Fail at any unhandled non-zero error code
set -e
set -o pipefail
help_message="$0: create subset of the input directory (specified as the first directory).
The subset is specified by the ... |
665ad6a970ebb75664f0aad2d06f603dc8bd951c46e8d8118c8382e454bb228b | Shell | 10,118 | 289 | #!/usr/bin/env bash
# Copyright 2021 Behavox (author: Hossein Hadian)
# Apache 2.0
# This script fine tunes a model on new data using Learning Without
# Forgetting (LWF) regularization which is a Continual learning (CL) method.
# This recipe does not add new layers or freeze the
# weights. The alignments come from t... |
690cd583229d60ed3e0e1e56c276875153c02983662547050b726e5522367b50 | Shell | 10,132 | 265 | #!/usr/bin/env bash
# Copyright 2012 Johns Hopkins University (Author: Guoguo Chen, Yenda Trmal)
# Apache 2.0.
#Fail at any unhandled non-zero error code
set -e
set -o pipefail
help_message="$0: create subset of the input directory (specified as the first directory).
The subset is specified by the ... |
95217a437f00ed08f836309eda649906f7a60509035c6c1d64ffae7cb16a5505 | Shell | 10,134 | 222 | #!/usr/bin/env bash
# by default, with cleanup
# please note that the language(s) was not selected for any particular reason (other to represent the various sizes of babel datasets)
# 304-lithuanian | %WER 41.3 | 20041 61492 | 61.7 28.9 9.4 3.0 41.3 28.9 | -0.300 | exp/chain_cleaned/tdnn_lstm_bab3_sp/decode_dev10h.... |
a4d53c097bcace797d207bbed36a5117d227b08cc9094e245c18f456615023e0 | Shell | 10,139 | 221 | #!/usr/bin/env bash
# by default, with cleanup
# please note that the language(s) was not selected for any particular reason (other to represent the various sizes of babel datasets)
# 304-lithuanian | %WER 40.8 | 20041 61492 | 61.4 27.0 11.6 2.2 40.8 28.8 | -0.172 | exp/chain_cleaned/tdnn_lstm_bab1_sp/decode_dev10h... |
80415d3879f9436b2e79d48d2d39fc31f0880e8dfbc8bfee3394a617ef67472d | Shell | 10,140 | 221 | #!/usr/bin/env bash
# by default, with cleanup
# please note that the language(s) was not selected for any particular reason (other to represent the various sizes of babel datasets)
# 304-lithuanian | %WER 41.7 | 20041 61492 | 61.4 29.0 9.6 3.1 41.7 29.0 | -0.414 | exp/chain_cleaned/tdnn_lstm_bab2_sp/decode_dev10h.... |
c0eb0c8c2110deaf1dfce2f0d123d1ce3c06b4cc347c625ea437646e51746994 | Shell | 10,140 | 222 | #!/usr/bin/env bash
# by default, with cleanup
# please note that the language(s) was not selected for any particular reason (other to represent the various sizes of babel datasets)
# 304-lithuanian | %WER 40.8 | 20041 61492 | 61.9 28.4 9.8 2.7 40.8 28.7 | -0.335 | exp/chain_cleaned/tdnn_lstm_bab6_sp/decode_dev10h.... |
55b86327c62a61291b66a493d6f4a9a2b8b19792e08ece6be8b4a46089da15b5 | Shell | 10,150 | 222 | #!/usr/bin/env bash
# by default, with cleanup
# please note that the language(s) was not selected for any particular reason (other to represent the various sizes of babel datasets)
# 304-lithuanian | %WER 40.7 | 20041 61492 | 61.9 27.9 10.2 2.6 40.7 28.8 | -0.283 | exp/chain_cleaned/tdnn_lstm_bab5_sp//decode_dev10... |
5ad2655ed0839c90e8cae445eea630e5fd44115078940bae8971b848c82aeca6 | Shell | 10,150 | 222 | #!/usr/bin/env bash
# by default, with cleanup
# please note that the language(s) was not selected for any particular reason (other to represent the various sizes of babel datasets)
# 304-lithuanian | %WER 40.5 | 20041 61492 | 62.1 28.1 9.8 2.7 40.5 29.0 | -0.307 | exp/chain_cleaned/tdnn_lstm_bab4_sp/decode_dev10h.... |
272a38e0d087ba34c2ee9c0cbc19bd59af09bf06faf6043ebe2ab62d9ed36614 | Shell | 10,208 | 255 | #!/usr/bin/env bash
# same as 1b but with shorter minibatches
set -e -o pipefail
# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
stage=0
mic=ihm
nj=30
min_seg_len=1.55
use_ihm_ali=false
train_set=train_cleaned
gmm=tri3_cleaned # the gmm f... |
b6e0f51761622a5e96358f191e74d1ce4f6d37f5015e47cc8f69057375f5bd8d | Shell | 10,251 | 244 | #!/usr/bin/env bash
# This factorized TDNN (TDNN-F) script is adapted from SWBD recipe 7q.
# It uses resnet-style skip connections.
# For details, refer to the paper:
# "Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks", Daniel Povey, Gaofeng Cheng, Yiming Wang, Ke Li, Hainan Xu, Mahsa Yarmohamad... |
9ca14a59c4f04f8e521ab50de45855e0609f60f8f50c7aa7e5d93244b05c37d5 | Shell | 10,252 | 244 | #!/usr/bin/env bash
# This factorized TDNN (TDNN-F) script is adapted from SWBD recipe 7q.
# It uses resnet-style skip connections.
# For details, refer to the paper:
# "Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks", Daniel Povey, Gaofeng Cheng, Yiming Wang, Ke Li, Hainan Xu, Mahsa Yarmohamad... |
64722d565631dd1b8a09bfb5fc20f0f98a4e12858d921041e9718245e6862ddd | Shell | 10,257 | 266 | #!/usr/bin/env bash
# Set -e here so that we catch if any executable fails immediately
set -euo pipefail
# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
stage=0
nj=96
train_set=train_worn_u100k
test_sets="dev_worn dev_beamformit_ref"
gmm=tr... |
8aa80640b0568aa54dd1a4b526100865a2d8ecee12fad7a1e00cc3a0c64a924d | Shell | 10,265 | 224 | #!/usr/bin/env bash
# by default, with cleanup
# please note that the language(s) was not selected for any particular reason (other to represent the various sizes of babel datasets)
# 304-lithuanian | %WER 39.9 | 20041 61492 | 62.7 27.3 10.0 2.6 39.9 28.6 | -0.268 | exp/chain_cleaned/tdnn_lstm_bab7_sp/decode_dev10h... |
e0eba9953e81362ed6f026b38dd4773da7bd6366498fcd357c437886f6f060b6 | Shell | 10,276 | 275 |
# This runs the complete expression_normalization processing pipeline
set -e
set -u
#### Only lines that should be changed ####
# TMPDIR for writing files
TMPDIR=
# original expression table
expression_file=
# Directory to write output to
output_dir=
# Directory to write configuration files to (will be put in $proje... |
d78ff622dc43be4d498be35e99efc760df9f2a919fec6c5543ff001452684377 | Shell | 10,317 | 247 | #!/usr/bin/env bash
# Wrappers around the leaderboard's route authoring scripts, plus an offline
# structural check they do not provide.
#
# bash route_tools.sh check --file routes.xml (offline, no server)
# bash route_tools.sh summary --file routes.xml
# bash route_tools.sh display --file routes.... |
7ac360c83e11fd2ba21e87b7feb34d5c394bf4cd06cd019529752d92874c898f | Shell | 10,384 | 226 | #!/usr/bin/env bash
# by default, with cleanup
# please note that the language(s) was not selected for any particular reason (other to represent the various sizes of babel datasets)
# 304-lithuanian | %WER 39.9 | 20041 61492 | 62.7 27.3 10.0 2.6 39.9 28.6 | -0.268 | exp/chain_cleaned/tdnn_lstm_bab7_sp/decode_dev10h... |
8cb758b3540fbebc830a407b481bffc7cae3d567bb0cd7ca0ca61de50c46b9cc | Shell | 10,403 | 247 | #!/usr/bin/env bash
# Copyright 2017 Vimal Manohar
# Apache 2.0
set -e
set -o pipefail
# This is fisher chain recipe for training a model on a subset of around
# 100-300 hours of supervised data.
# This system uses phone LM to model UNK.
# local/semisup/run_50k.sh and local/semisup/run_100k.sh show how to call this... |
9c11c7921e3bc28f89d9f41fbbc89985bea44f44dc14cb44596d716069c9242e | Shell | 10,403 | 246 | #!/usr/bin/env bash
# Copyright 2016-17 Vimal Manohar
# 2017 Nagendra Kumar Goel
# Apache 2.0.
# This script does nnet3-based speech activity detection given an input
# kaldi data directory and outputs a segmented kaldi data directory.
# This script can also do music detection and other similar segmen... |
6141c8ee3f0ef4159ea81773cb5833a7890c31dca0bd5f5f868c2899f89260da | Shell | 10,405 | 247 | #!/usr/bin/env bash
# Copyright 2017-2018 David Snyder
# 2017-2018 Matthew Maciejewski
# Apache 2.0.
#
# This is still a work in progress, but implements something similar to
# Greg Sell's and Daniel Garcia-Romero's iVector-based diarization system
# in 'Speaker Diarization With PLDA I-Vector Scoring And Un... |
1756a94913455293b720d2c7d1b0b5a8932c9a04b59259e9bfaae290a50e7908 | Shell | 10,412 | 270 | #!/usr/bin/env bash
# Set -e here so that we catch if any executable fails immediately
set -euo pipefail
# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
stage=0
nj=96
train_set=train_worn_u100k
test_sets="dev_worn dev_beamformit_ref"
gmm=tr... |
d41ec585edf36659253e7d2553cee5a29fa4247f78b4849a12dd09a6fb69fb62 | Shell | 10,465 | 247 | #!/usr/bin/env bash
# This script is based on run_tdnn_7p.sh in swbd chain recipe.
# Results
# local/chain/compare_wer.sh --online exp/chain/tdnn_7h_chain_2b_sp
# Model tdnn_7h_chain_2b_sp
# CER(%) 23.67
# CER(%)[online] 23.69
# CER(%)[per-utt] 24.67
# Final trai... |
953c146268a02575236c9663530f2647346723cfdfbc200c757f8f7ce6f2692c | Shell | 10,475 | 300 | #!/bin/bash
# This runs the complete GeneNetwork processing pipeline
# TODO: because after PCA and smaple removal this has to be rerun, the removing duplicate
# samples is in double for the second round. Can organize this better
set -e
set -u
module load Python
#### Only lines that should be changed ####
# TM... |
e9c9c576492c81ee8637f881e15a956379c337e7f1a204a5d4599dfefbd11a71 | Shell | 10,503 | 282 | #!/usr/bin/env bash
# Copyright 2021 Xiaomi Corporation (Author: Yongqing Wang)
# Seasalt AI, Inc (Author: Guoguo Chen)
# Apache 2.0
set -e -o pipefail
stage=0
# GigaSpeech configurations.
gigaspeech_root=/data/GigaSpeech_Data/
gigaspeech_train_subset=XL
gigaspeech_test_sets="gigaspeech_dev gigaspee... |
575c89c8d662e8a6bdada07de4d511568248288ac16b4322b16bf98c65b78c5c | Shell | 10,512 | 297 | #!/usr/bin/env bash
#
# Based mostly on the TED-LIUM and Switchboard recipe
#
# Copyright 2017 Johns Hopkins University (Author: Shinji Watanabe and Yenda Trmal)
# Apache 2.0
#
# Begin configuration section.
nj=96
decode_nj=20
stage=0
nnet_stage=-10
num_data_reps=4
snrs="20:10:15:5:0"
foreground_snrs="20:10:15:5:0"
... |
008246f3bd8c4ad3a0ebd2ecadcd36b49953ee3b4e0dd8a601fddfa9e4fe795f | Shell | 10,518 | 201 | #!/usr/bin/env bash
# Launch a CARLA RPC server on this host. You point it at ONE path and it detects
# what that is (see carla_detect_target in env.sh):
#
# package <path>/CarlaUE4.sh — a downloaded/extracted release. Renders for
# real, needs no UE4 and no content checkout. Headless via
# ... |
0fa1f01d24271983a73e92e70f9aa1497a7fe654c00f2405fb62f7b31c6250f3 | Shell | 10,651 | 309 | #!/usr/bin/env bash
#
# Based mostly on the TED-LIUM and Switchboard recipe
# This recipe is for chime6 track1 (ASR only), it recognise
# a given evaluation utterance given ground truth
# diarization information
#
# Copyright 2017 Johns Hopkins University (Author: Shinji Watanabe and Yenda Trmal)
# 2020 ... |
6c554f8e3f3a43fe9223a999ed6e5f995827be29c10ce18b4662542a7d4a342e | Shell | 10,654 | 254 | #!/usr/bin/env bash
# Copyright 2012 Arnab Ghoshal
# Copyright 2010-2011 Microsoft Corporation
# 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.... |
76813f97cc9daf1c0511ddcc4bf8b241e97e26737ed5438bb17093647e08b80e | Shell | 10,664 | 254 | #!/usr/bin/env bash
# This is based on tdnn_1d_sp, but adding cnn as the front-end.
# The cnn-tdnn-f (tdnn_cnn_1a_sp) outperforms the tdnn-f (tdnn_1d_sp).
# bash local/chain/compare_wer.sh exp/chain_cleaned/tdnn_1d_sp exp/chain_cleaned/tdnn_cnn_1a_sp/
# System tdnn_1d_sp tdnn_cnn_1a_sp
# WER ... |
faa3f69f22846d57d18ada4c4c85695bd56925effabafd6c203a2c2a9a89d495 | Shell | 10,698 | 261 | #!/usr/bin/env bash
# Copyright 2015 Tokyo Institute of Technology
# (Authors: Takafumi Moriya, Tomohiro Tanaka and Takahiro Shinozaki)
# 2015 Mitsubishi Electric Research Laboratories (Author: Shinji Watanabe)
# Apache 2.0
# Acknowledgement This work was supported by JSPS KAKENHI Grant Nu... |
54cf2a8cfb70604708aacc5ae047a9d6f8458af553023d71959d7e94faf8f029 | Shell | 10,718 | 240 | #!/usr/bin/env bash
# Copyright 2017 Johns Hopkins University (Author: Daniel Garcia-Romero)
# 2017 Johns Hopkins University (Author: Daniel Povey)
# 2017-2018 David Snyder
# 2018 Ewald Enzinger
# 2018 Zili Huang
# Apache 2.0.
#
# See ../README.txt for more info on... |
fd6d5d02b76139c2c0a77b658c34bcf3fc3799356bd0c9a0b1ad086dc01b0db6 | Shell | 10,722 | 290 | #!/bin/bash
# Hierarchical HotNet is parallelizable, but this script runs each Hierarchical
# HotNet sequentially. Please see the example_commands_parallel.sh script for a
# parallelized example.
# Compile Fortran module.
#cd ../src
#f2py -c fortran_module.f95 -m fortran_module > /dev/null
#cd ..
##################... |
e6e49006265778578188ca70fa2d2ce9f0e253edc5e46eb45560a0ff200002a4 | Shell | 10,748 | 249 | #!/usr/bin/env bash
# This factorized TDNN (TDNN-F) script is adapted from SWBD recipe 7q.
# It uses resnet-style skip connections.
# For details, refer to the paper:
# "Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks", Daniel Povey, Gaofeng Cheng, Yiming Wang, Ke Li, Hainan Xu, Mahsa Yarmohamad... |
c35efbe516ddc956cb6d9bd63bbaea79d72411a7697885dedbc2a71a6cea5466 | Shell | 10,757 | 265 | #!/usr/bin/env bash
# same as 1b but uses batchnorm components instead of renorm
# Results on 03/27/2017:
# local/chain/compare_wer_general.sh ihm tdnn1b_sp_bi tdnn1e_sp_bi
# System tdnn1b_sp_bi tdnn1e_sp_bi
# WER on dev 21.9 21.4
# WER on eval 22.2 21.5
# ... |
33d70da6be013c64dbc280b5ec2afa9656147c35147fb3081660675a73211fa4 | Shell | 10,759 | 204 | #!/usr/bin/env bash
export LC_ALL=C
words_file=
train_text=
dev_text=
oov_symbol="<UNK>"
echo "$0 $@"
[ -f path.sh ] && . ./path.sh
. ./utils/parse_options.sh || exit 1
echo "-------------------------------------"
echo "Building an SRILM language model "
echo "-------------------------------------"
if [ $# -n... |
dc7f97cb4f294c91195af6f8abccc5f2beabd29ccc51d6fc9a752232c2791a79 | Shell | 10,790 | 267 | #!/usr/bin/env bash
# same as 1b but uses PCA instead of
# LDA features for the ivector extractor.
# Results on 03/27/2017:
# local/chain/compare_wer_general.sh ihm tdnn1b_sp_bi tdnn1d_sp_bi
# System tdnn1b_sp_bi tdnn1d_sp_bi
# WER on dev 22.0 21.9
# WER on eval 22... |
0b334d026e13841335a01cb83dc10e2ac4396adf934af4bd54912f164a174d26 | Shell | 10,805 | 256 | #!/usr/bin/env bash
# _1b is as _1a, but with pitch feats, i-vector and dropout schedule added, referenced from wsj
# basic info:
# steps/info/chain_dir_info.pl exp/chain/tdnn_1f_nopitch_ivec_sp/exp/chain/tdnn_1f_nopitch_ivec_sp/: num-iters=578 nj=2..8 num-params=19.3M dim=43+100->4520 combine=-0.082->-0.081 (over 6)... |
d0c15459ea57e2680b0619bfcdf0d9ac71ea2011f1e80f574fa63c66a997378a | Shell | 10,841 | 273 | #!/usr/bin/env bash
## This is taken from mini_librispeech
# local/chain/compare_wer.sh exp/chain/tdnn1a_sp exp/chain/tdnn1b_sp
# System tdnn1a_sp tdnn1b_sp
#WER test 14.19 13.89
# [online:] 14.26 14.02
# Final train prob -0.0707 -0.0941
# Final va... |
0affc21c0b135567916a1464a6a6715b69be46aaeabbda1098a4bf7f64bb1678 | Shell | 10,859 | 407 | #!/bin/bash
# call variants with Mutect2 (2.1 part of GATK 4)
# script filename
script_path="${BASH_SOURCE[0]}"
script_name=$(basename "$script_path")
segment_name=${script_name/%.sh/}
echo -e "\n ========== SEGMENT: $segment_name ========== \n" >&2
# check for correct number of arguments
if [ ! $# == 6 ] ; then
... |
0a50af6919514c47b4221e3732559196c4160725f12ba9ada3ac923c095f487a | Shell | 10,869 | 268 | #!/usr/bin/env bash
# This is a chain-training script with TDNN neural networks.
# Please see RESULTS_* for examples of command lines invoking this script.
# local/nnet3/run_tdnn.sh --stage 8 --use-ihm-ali true --mic sdm1 # rerunning with biphone
# local/nnet3/run_tdnn.sh --stage 8 --use-ihm-ali false --mic sdm1
# ... |
63e6df8c37f88fb45e3d156e12b7651330fcc0e8e6f9a42f8466dc82cbfddc4d | Shell | 10,874 | 288 | #!/usr/bin/env bash
# Adapted from egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1e.sh
# local/chain/compare_wer.sh exp/chain/tdnn1a_sp exp/chain/tdnn1a_sp_online
# System tdnn1a_sp tdnn1a_sp_online
#WER valid_dev 4.82 4.88
#WER valid_test 4.44 4.27
# Final train prob ... |
4371bc7ba32930ab75a777ac95adc58a596c8a1a37bf1c074115a0940c9e4dfb | Shell | 10,921 | 405 | #!/bin/bash
# GATK realignment and recalibration
# script filename
script_path="${BASH_SOURCE[0]}"
script_name=$(basename "$script_path")
segment_name=${script_name/%.sh/}
echo -e "\n ========== SEGMENT: $segment_name ========== \n" >&2
# check for correct number of arguments
if [ ! $# == 4 ] ; then
echo -e "\n $... |
bc21d72f7c49e7ead33123f9d28f03d3815f3ebdf0368956b00d7e63cbcb1f6b | Shell | 10,928 | 319 | #!/usr/bin/env bash
# Copyright 2016 LeSpeech (Author: Xingyu Na)
# prepare dictionary for HKUST
# it is done for English and Chinese separately,
# For English, we use CMU dictionary, and Sequitur G2P
# for OOVs, while all englist phone set will concert to Chinese
# phone set at the end. For Chinese, we use an online ... |
836212fc3e593278d9d3b371fa28050bb31cfd9af381897ae22bc2415391ba78 | Shell | 10,931 | 283 | #!/usr/bin/env bash
# Copyright (c) 2015, Johns Hopkins University ( Yenda Trmal <jtrmal@gmail.com> )
# License: Apache 2.0
# Begin configuration section.
language="201-haitian"
corpus=/export/babel/data
indus=/export/babel/data/scoring/IndusDB
# End configuration section
echo >&2 "$0 $@"
. ./utils/parse_options.sh
... |
c371fc2b97cddaba89103794a5490c0e030faff4518ed24e205f2bf3904a8da0 | Shell | 10,959 | 261 | #!/usr/bin/env bash
# local/chain/compare_wer.sh exp/chain/e2e_cnn_1a/ exp/chain/cnn_e2eali_1a
# System e2e_cnn_1a cnn_e2eali_1a
# WER 13.72 8.14
# WER (rescored) 13.40 8.00
# CER 6.56 2.82
# CER (rescored) ... |
ebcc63fd1f8bbd2ab93f4b6e3b962d70fffcdb7a09e3127d1781bf571aef886e | Shell | 10,988 | 320 | #!/usr/bin/env bash
#Copyright 2016 LeSpeech (Author: Xingyu Na)
# prepare dictionary for aidatatang
# it is done for English and Chinese separately,
# For English, we use CMU dictionary, and Sequitur G2P
# for OOVs, while all englist phone set will concert to Chinese
# phone set at the end. For Chinese, we use an onl... |
94f70dd9514c050c9149f4987d11ec806c56d73e065c1e6507e4c96592435791 | Shell | 10,999 | 270 | #!/usr/bin/env bash
# same as 1e but uses batchnorm components instead of renorm also adding
# proportional-shrink 10, trained with 4 epochs
# local/chain/tuning/run_tdnn_1f.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned
# local/chain/compare_wer_general.sh sdm1 tdnn1e_sp_bi_ihmali t... |
e0cd9ae20f0f1911346262762a1b94a1c77a05bf30f75b285ee12403be7af6f0 | Shell | 11,000 | 271 | #!/usr/bin/env bash
# same as 1e but uses batchnorm components instead of renorm also adding
# proportional-shrink 10, trained with 6 epochs
# local/chain/tuning/run_tdnn_1g.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned
# local/chain/compare_wer_general.sh sdm1 tdnn1e_sp_bi_ihmali t... |
b764538886c143dcf0585a5937a8ea8fecc8cdd3aa8d1eadaa4eac9087147bd7 | Shell | 11,098 | 274 | #!/usr/bin/env bash
# same as 1g but adding two non-splicing layers towards the beginning
# of the network, trained with 9 epochs.
# local/chain/tuning/run_tdnn_1h.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned
# local/chain/compare_wer_general.sh sdm1 tdnn1g_sp_bi_ihmali tdnn1h_sp_bi... |
79f88388c0a6e37c965969425b3a351474e2a1ccddfee96429fa3630fb60d563 | Shell | 11,103 | 356 | #!/usr/bin/env bash
team=RADICAL
corpusid=
partition=
scase=BaEval #BaDev|BaEval
master=
version=1
sysid=
prim=c
cer=0
dryrun=true
dir="exp/sgmm5_mmi_b0.1/"
data=data/dev10h.seg
master=dev10h
extrasys=
final=false
#end of configuration
echo $0 " " "$@"
[ -f ./cmd.sh ] && . ./cmd.sh
[ -f ./path.sh ] && . ./path.sh... |
c191673fca7c5c38a3789b3d8a5b50c619e7b77ad7d2386c5a65d62a9d6b8598 | Shell | 11,128 | 356 | #!/usr/bin/env bash
team=RADICAL
corpusid=
partition=
scase=BaEval #BaDev|BaEval
master=
version=1
sysid=
prim=c
cer=0
dryrun=true
dir="exp/sgmm5_mmi_b0.1/"
data=data/dev10h.seg
master=dev10h
extrasys=
final=false
#end of configuration
echo $0 " " "$@"
[ -f ./cmd.sh ] && . ./cmd.sh
[ -f ./path.sh ] && . ./path.sh... |
198420279d3eba9cb76d148c7fba8ab0477cac01f3d6dbab8af22c03f4fabd56 | Shell | 11,164 | 286 | #!/usr/bin/env bash
# 1a is same as 1h setup in WSJ
# local/chain/compare_wer.sh exp/chain/tdnn1a_sp
# System tdnn1a_sp
# WER eval92 (tgpr_5k) 7.67
# WER 0166 (tgpr_5k) 7.88
# Final train prob -0.0338
# Final valid prob -0.0602
# Final train prob (xent) -0.7632
# Final v... |
4092b1c974b09b337d1a09de81a08f7690e2377253a61180be8718f1998c9033 | Shell | 11,184 | 251 | #!/usr/bin/env bash
# Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
# Apache 2.0
# The model training procedure is similar to run_blstm_6j.sh under egs/swbd/s5c
# ./local/chain/compare_wer_general.sh blstm_6j_sp
# System blstm_6j_sp
# WER on eval2000(tg) 12.3
# WE... |
f4c0b3c4485c177fe47ccaf2bf895c52baf5c8cfaf74544ddad7bcd197a7ec1f | Shell | 11,196 | 271 | #!/usr/bin/env bash
#
# Copyright 2018 Nagendra Goel, Saikiran Valluri Apache 2.0
# Copyright 2014 Gaurav Kumar. Apache 2.0
# Recipe for Fisher/Callhome-Spanish
stage=0
train_stage=-20
train_sgmm2=false
# call the next line with the directory where the Spanish Fisher data is
# (the values below are just an examp... |
264e9ff47c6ab5b17faf1a78dd7d277007a4b21639d7ffcc1d8b52cf774750a7 | Shell | 11,216 | 276 | #!/usr/bin/env bash
# same as 1h but replacing proportional-shrink with l2-regularize.
# The results match those from 1h.
# local/chain/tuning/run_tdnn_1i.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned
# local/chain/compare_wer_general.sh sdm1 tdnn1h_sp_bi_ihmali tdnn1i_sp_bi_ihmali
#... |
4e38f92be96e835d5c53072e745ac7f0b829d3622412dfb9ffdca91993f8186e | Shell | 11,264 | 288 | #!/usr/bin/env bash
# 1g is like 1f but upgrading to a "resnet-style TDNN-F model", i.e.
# with bypass resnet connections, and re-tuned.
# compute-wer --text --mode=present ark:exp/chain/multipsplice_tdnn/decode_fsp_train_test/scoring_kaldi/test_filt.txt ark,p:-
# %WER 22.21 [ 8847 / 39831, 1965 ins, 2127 del, 4755... |
2f76bf6cd736ecc5dcd2ba102aaa5169a6944be061a6a190fe69cb84b737a3ca | Shell | 11,282 | 257 | #!/bin/bash
set -e
#############
# Functions #
#############
usage () {
cat 1>&2 << EOF # ${0##*/} parameter expansion substitution with variable '0' to get shell script filename without path
Usage: ${0##*/} [OPTION] -q query.faa -f (embl|gbk) > blast_hits.tsv
or: ${0##*/} [OPTION] -q query.faa -s subject.faa ... |
a18695d5a6b28349a1814a13e1d6d6dad49742dad25312aa34923d74d760801f | Shell | 11,282 | 434 | #!/bin/bash
# annotate VCF using ANNOVAR
# script filename
script_path="${BASH_SOURCE[0]}"
script_name=$(basename "$script_path")
segment_name=${script_name/%.sh/}
echo -e "\n ========== SEGMENT: $segment_name ========== \n" >&2
# check for correct number of arguments
if [ ! $# == 3 ] ; then
echo -e "\n $script_n... |
edc087118615ad50093f418462edf6ee8642a0d22b4cfbde8fe0048153c701c5 | Shell | 11,291 | 301 | #!/usr/bin/env bash
# 1b is as 1a but a re-tuned model with quite a few changes, including moving to
# a resnet-style factored TDNN-F model.
#
# ./local/chain/compare_wer.sh exp/chain/tdnn1a_sp exp/chain/tdnn1b_sp
# System tdnn1a_sp tdnn1b_sp
# %WER devtest 13.10 12.99
# %WER ... |
5e5d5366d01d407b9ca295818cf337b0f682b0e82f606c0b10fe290b06060daa | Shell | 11,335 | 309 | #!/usr/bin/env bash
# local/chain/compare_wer.sh exp/chain/tdnn1a_sp
# ./local/chain/compare_wer.sh exp/chain/tdnn1a_sp
# System tdnn1a_sp
# %WER devtest 13.10
# %WER test 15.53
# %WER native 10.14
# %WER nonnative 19.78
# Final train prob ... |
13d1e4c552c9c918dbcc37d564e10a949b88a196e46a773365900306b37553d8 | Shell | 11,388 | 117 | wget https://www.physionet.org/physiobank/database/sleep-edfx/sleep-cassette/SC4001E0-PSG.edf
wget https://www.physionet.org/physiobank/database/sleep-edfx/sleep-cassette/SC4001E0-PSG.edf.hyp
wget https://www.physionet.org/physiobank/database/sleep-edfx/sleep-cassette/SC4001EC-Hypnogram.edf
wget https://www.physionet.o... |
ef6e143005bd6060d5b080980cb7c86d6289c33fa94b95213168f185f5de3b1c | Shell | 11,427 | 277 | #!/usr/bin/env bash
# This factorized TDNN (TDNN-F) script is ported from s5b recipe
# It uses resnet-style skip connections.
# For details, refer to the paper:
# "Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks", Daniel Povey, Gaofeng Cheng, Yiming Wang, Ke Li, Hainan Xu, Mahsa Yarmohamadi, San... |
00f8358b19924244318d223371969107fbd004d7c086aedcbd2d8cf284d486ff | Shell | 11,465 | 229 | #!/usr/bin/env bash
# Copyright 2017 Johns Hopkins University (Author: Daniel Povey)
# 2017 Johns Hopkins University (Author: Daniel Garcia-Romero)
# 2018 Ewald Enzinger
# 2020 Tsinghua University (Author: Ruiqi Liu and Lantian Li)
# Apache 2.0.
#
# This is an x-vector-... |
0fac4572b10b781641306dfbb22256885cfe1826b065dad8822997bca51bb0f5 | Shell | 11,520 | 414 | #!/bin/bash
NSP_HOME="/usr/local/nsp-server"
export PATH=$PATH:/usr/local/genesis-2.4/genesis:/usr/local/genesis-2.4/pgenesis/bin:${NSP_HOME}/src/sh;
writeDebug.sh `basename "$0 started."`
writeOutput.sh "The arguments passed in are : $@"
function usage {
writeOutput.sh "./$(basename $0) -h --> ... |
766a93db8a51c5bd070c596d07b46643df131dfe75d1dd67bdc58a7823677abb | Shell | 11,532 | 288 | #!/usr/bin/env bash
# TDNN+LSTM architecture similar to swbd/tdnn_lstm_1b
# results on sdm1 with ihm ali
#System tdnn1b tdnn_lstm1a
#WER on dev 39.9 38.9
#WER on eval 43.9 42.2
#Final train prob -0.186387 -0.142585
#Final valid prob -0.259997 -0.251197
#Final train prob ... |
147c9ac58389ce5d56e2668e141c8225f2bb65f5d8915fef42aa5a6a913f5340 | Shell | 11,564 | 340 | #!/usr/bin/env bash
no_feats=false
no_image=false
no_text=false
no_spk_sort=false
for x in `seq 4`; do
if [ "$1" == "--no-feats" ]; then
no_feats=true
shift;
fi
if [ "$1" == "--no-text" ]; then
no_text=true
shift;
fi
if [ "$1" == "--no-image" ]; then
no_image=true
shift;
fi
if [... |
bd85b8f5c2c5dc76a1989ce9386a67bee914a5ec6aa05d68970f71d7f5ffea46 | Shell | 11,574 | 300 | #!/usr/bin/env bash
#
# Chime-6 Track 2 baseline. Based mostly on the Chime-5 recipe, with the exception
# that we are required to perform speech activity detection and speaker
# diarization before ASR, since we do not have access to the oracle SAD and
# diarization labels.
#
# Copyright 2017 Johns Hopkins Universit... |
7b16122d96a68855c067fa901c27404b2015ffb72ae743cd0b4b79b81cc4f0d4 | Shell | 11,648 | 291 | #!/usr/bin/env bash
# same as 1a, but with more TDNN layers between each LSTM
# results on sdm1 with ihm ali
#System 1a 1c
#WER on dev 38.9 39.0
#WER on eval 42.2 41.9
#Final train prob -0.142585 -0.142951
#Final valid prob -0.251197 -0.249901
#Final train prob (xent... |
8bff77d4c628ec922a7b3453055ab5d72ee0c282c2c148cfb83434a359241091 | Shell | 11,664 | 275 | #!/usr/bin/env bash
# Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
# Apache 2.0
# Based on tdnn_7m (from egs/swbd/s5c).
# But tdnn_7c enjoys two more TDNN layers to make [-21,21] input temporal context,
# this is intended as an extra baseline for 7d.
# System tdnn_7b_sp... |
0eff648063ac2a8fd51b158af746fa598c82334355dc8a69f9a1c206c51cb8b4 | Shell | 11,669 | 279 | #!/usr/bin/env bash
# Copyright 2017-2018 Johns Hopkins University (author: Daniel Povey)
# 2017-2018 Yiming Wang
# 2019 Fei Wu
# Based on material recipe for low-resource languages
# Factored TDNN with skip connectiong and splicing (two bottle neck layers)
# WER results on dev
# Mode... |
4ff9a453886f8fe99d26761a4d59d913e8e2bbf5d4c22931483cb6a651ab5531 | Shell | 11,672 | 292 | #!/usr/bin/env bash
# same as 1a but the neural network has two more TDNN layers (0,3 0,3)
# above the lstm
# results on sdm1 with ihm ali
#System 1a 1b
#WER on dev 38.9 39.6
#WER on eval 42.2 42.9
#Final train prob -0.142585 -0.152283
#Final valid prob -0.251197 -0... |
a127e3c65772a117791644778e81d78d6c89b840ed8aa4d43761a14f8fa09c89 | Shell | 11,711 | 293 | #!/usr/bin/env bash
# same as 1c but with only right context for the TDNNs i.e., (0,3) in place
# of (-3,0,3)
# results on sdm1 with ihm ali
#System tdnn_lstm1c_sp_bi_ihmali_ld5tdnn_lstm1e_sp_bi_ihmali_ld5
#WER on dev 39.0 39.4
#WER on eval 41.9 42.4
#Final train prob -0.142... |
f1c9e0274242dabaa1dc680a11fa6a8cc75c6631e0d46ef8d21792939d93184e | Shell | 11,713 | 292 | #!/usr/bin/env bash
# same as 1a but the neural network has two more TDNN layers (0,3 0,3)
# above the lstm
# results on sdm1 with ihm ali
#
#System tdnn_lstm1a_sp_bi_ihmali_ld5tdnn_lstm1f_sp_bi_ihmali_ld5
#WER on dev 38.9 39.4
#WER on eval 42.2 42.7
#Final train prob -0.1425... |
600171a05632ed6cd9cf74879f2243c6cb483e1c70e2557eccf0a1389f4296e7 | Shell | 11,717 | 281 | #!/usr/bin/env bash
# Start, probe and stop a CARLA UE 5.8 server.
#
# bash run_server.sh detect what is runnable in this tree
# bash run_server.sh game [MAP] editor binary in -game mode <- the working headless server
# bash run_server.sh package [MAP] the packaged shipping server
# ... |
4c72788c07ce5d9519ab4d79701997f0561bd5401c65c1ae17eb3c059853e561 | Shell | 11,747 | 297 | #!/usr/bin/env bash
# Set -e here so that we catch if any executable fails immediately
set -euo pipefail
# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
stage=0
nj=96
train_set=train_worn_u400k_cleaned
test_sets="dev_worn dev_beamformit_ref... |
3a85d5189ece78ea6732a9b729c22a206a8d224d56f5d39e6ba26fcf14d41a94 | Shell | 11,772 | 293 | #!/usr/bin/env bash
# same as 1g but with TDNN output dim 1024 instead of 512
# (num-params 1g:21309812 1i: 43447156)
# results on sdm1 using ihm ali
#System tdnn_lstm1g_sp_bi_ihmali_ld5 tdnn_lstm1i_sp_bi_ihmali_ld5
#WER on dev 38.3 37.6
#WER on eval 41.6 40.9
#Final train prob ... |
307d52f20d44f1c29a37840b5024754fed5aae3ee700c468380aa30c3de7da48 | Shell | 11,782 | 293 | #!/usr/bin/env bash
# same as 1c but with smaller minibatch
# using smaller minibatches seems to be better in TDNN+LSTM archs.
# not much difference in other archs.
# results on sdm1 using ihm ali
#System tdnn_lstm1c_sp_bi_ihmali_ld5tdnn_lstm1g_sp_bi_ihmali_ld5
#WER on dev 39.0 38.3
#WER on e... |
1fef603237198d2b6aa40096cd4d075374a14b29d1b3d250e929d291fa76bb8a | Shell | 11,800 | 258 | #!/usr/bin/env bash
# Cook and package CarlaUE4 into Dist/.
#
# PACKAGES=Carla -> full release: Dist/CARLA_<tag>.tar.gz
# PACKAGES=MyMaps -> standalone assets: Dist/MyMaps_<tag>.tar.gz
#
# Content selection comes from Config/DefaultGame.ini (release) or from
# <Name>.Package.json (asset package). DefaultG... |
aabfe6fd2a8e467117ed08cafcb05d6ffe9af143cbf7d5b80832fe3b02df2044 | Shell | 11,816 | 293 | #!/usr/bin/env bash
# same as 1c, but with more TDNN layers between each LSTM
# results on sdm1 with ihm ali
#System tdnn_lstm1c_sp_bi_ihmali_ld5 tdnn_lstm1d_sp_bi_ihmali_ld5
#WER on dev 39.0 39.1
#WER on eval 41.9 42.0
#Final train prob -0.142951 -0.150625
#Final valid prob ... |
bb9e9fe43899e3809237225337ad25ffcac1bc2925f0aeba6247c3eae11453af | Shell | 11,849 | 268 | #!/usr/bin/env bash
# This CNN-TDNN-F architecture (6 CNN layers + 16 TDNN-F layers) is adapted from SWBD recipe tuning/run_cnn_tdnn_1a.sh.
# It uses resnet-style skip connections.
# For details, refer to the paper:
# "Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks", Daniel Povey, Gaofeng Cheng... |
bc7dfd30503f38d04d3a61d72125a7905198a73ea675d1558da06fe28dd60c2d | Shell | 11,919 | 262 | #!/usr/bin/env bash
set -e
# based on run_blstm_6h.sh in fisher_swbd recipe
# configs for 'chain'
stage=11 # assuming you already ran the xent systems
train_stage=-10
get_egs_stage=-10
dir=exp/chain/blstm_7b
decode_iter=
# training options
num_epochs=4
remove_egs=false
common_egs_dir=
num_data_reps=3
min_seg_len=... |
d1007447e475cac531b157c587409dd0f047359d805658157395288da282a669 | Shell | 11,989 | 294 | #!/usr/bin/env bash
# same as 1c but with one more stack of TDNN and LSTM layers
# results on sdm1 using ihm ali
#System tdnn_lstm1c_sp_bi_ihmali_ld5 tdnn_lstm1h_sp_bi_ihmali_ld5
#WER on dev 39.0 39.4
#WER on eval 41.9 42.6
#Final train prob -0.142951 -0.157634
#Final valid prob... |
eeb0df24a9436c1e809f9f962eedb772674c7c0ccb729e1d2595ab415f88bd49 | Shell | 12,017 | 304 | #!/usr/bin/env bash
# Set -e here so that we catch if any executable fails immediately
set -euo pipefail
# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
stage=0
nj=96
train_set=train_worn_u400k_cleaned
test_sets="dev_beamformit_ref"
gmm=tri... |
433fbcfecbc7c1aa5ff0eb6831ec6d0baf002d0524d7c54da387082219da66d1 | Shell | 12,032 | 310 | #!/usr/bin/env bash
set -e
# based on run_tdnn_7b.sh in the swbd recipe
# System exp/chain/tdnn_lstm_1a
# WER on dev_aspire (fg) 22.9
# Final train prob -0.118
# Final valid prob -0.123
# Final train prob (xent) -1.... |
e458b14486e4ad34a10961fc96be54e5ec16d6b4f8e4bd32d855e49f894f8f74 | Shell | 12,052 | 300 | #!/usr/bin/env bash
# Copyright 2015 David Snyder
# 2015 Johns Hopkins University (Author: Daniel Garcia-Romero)
# 2015 Johns Hopkins University (Author: Daniel Povey)
# 2017 Radboud University (Author Emre Yilmaz)
# Apache 2.0.
#
# See README.txt for more info on data required.
# ... |
7945f8a8e57e74858950b32a0721e5f82c937903a4384c6817922013c9eeeb1f | Shell | 12,089 | 229 | #!/usr/bin/env bash
export LC_ALL=C
words_file=
train_text=
dev_text=
oov_symbol="<UNK>"
echo "$0 $@"
[ -f path.sh ] && . ./path.sh
. ./utils/parse_options.sh || exit 1
echo "-------------------------------------"
echo "Building an SRILM language model "
echo "-------------------------------------"
if [ $# -n... |
df763a1eb677b311cc8ca84db90e070ecefbce136c29e0f7362976b655a284b6 | Shell | 12,104 | 298 | #!/usr/bin/env bash
# same as 1i but with batchnorm replacing all renorm in TDNN
# and using proportional-shrink with value 10, this model uses
# 5 epochs to train
# local/chain/tuning/run_tdnn_lstm_1n.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned
# local/chain/compare_wer_general.sh ... |
cbbd8c245cd91557b5c9c2daf63c7434c40ec55929fbf0233f74eb7fc2bd13a8 | Shell | 12,139 | 260 | #!/usr/bin/env bash
# Get a map into CARLA on UE 5.8. Four routes; see SKILL.md for which to pick.
#
# bash import_map.sh routes explain the four routes for this tree
# bash import_map.sh opendrive <file.xodr> Route C: runtime OpenDRIVE world, no build
# bash import_map.sh plan --... |
675acb73e913f0f04120bdafb8c6b86b5a8bc77780edfe745b9ff092190b694a | Shell | 12,141 | 253 | #!/usr/bin/env bash
# Prerequisite checks for run-autoware-ue58 (CARLA on UE 5.8). Read-only, no sudo, fast —
# the MCP check_prerequisites(name) tool must never hang.
# Hard blockers: no checkout, wrong engine major.minor, no engine binary, no cmake.
# On-camera banner: every skill run announces itself, so a terminal ... |
3804015d3356af1dd1e0e023a931d4978659c0c2611fdd452819ab07e7641764 | Shell | 12,152 | 350 | #!/usr/bin/env bash
# shellcheck source=../utils.sh
source $( cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )/../utils.sh
# Check if there is input
[[ ( $# -eq 0 ) ]] && displayhelp $0 1
# Preparing the default values for variables
overwrite=no
run_prep=yes
run_anat=yes
run_sbref=yes
anat1sfx=acq-uni_T1w
anat2sfx... |
3762cf8d62516f43f7b4aa54f7dd3ad7629af6f8c521aa89c0fa1c001592cf87 | Shell | 12,181 | 287 | #!/usr/bin/env bash
## This is taken from mini_librispeech.
# local/chain/compare_wer.sh --online exp/chain/tdnn1a_sp exp/chain/cnn_tdnn1a_sp
# System tdnn1a_sp cnn_tdnn1a_sp
#WER test 14.19 13.47
# [online:] 14.26 13.57
# Final train prob -0... |
897641f5896f3961436e6e0269ffce4b319f7a58d8407d7b4fd1bdcdd26a3504 | Shell | 12,202 | 335 | #!/usr/bin/env bash
die() {
local msg="${1}"
echo "extensive_test: ${msg}" >&2
exit 1
}
set_cpu_count() {
if command -v nproc > /dev/null; then
NCPU="$(nproc --all)"
else
NCPU=8
fi
}
handle_tests(){
numfailed=$(wc -l < "${testdir}"/floatdiff.txt)
numcases=$(wc -l < "${testdir}"/meth_... |
d6777508eb2af25f98661cd2403ec0495494525ad6484d316281a015d7f90a8a | Shell | 12,209 | 281 | #!/usr/bin/env bash
# 1j is same as swbd 7q. It uses modified topology with resnet-style skip connections, more layers,
# skinnier bottlenecks.
# local/chain/tuning/run_tdnn_1j.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned
# local/chain/compare_wer_general.sh sdm1 tdnn1i_sp_bi_ihma... |
3d9c98916ba8ae4d73f3f518d254ffb9914b58c114acaa67adc950d2ac2290c1 | Shell | 12,274 | 284 | #!/usr/bin/env bash
# Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
# Apache 2.0
# Based on tdnn_7n (from egs/swbd/s5c).
# With the semi-orthogonal matrix used and skip connections added (wait reference here).
# Difference between tdnn_7c and tdnn_7d:
# skip connections No Yes
#... |
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