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Merge pull request #3897 from naxingyu/sync-pybind11-with-master
Sync pybind11 with master
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egs/aidatatang_200zh/s5/local/chain/compare_wer.sh

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#!/usr/bin/env bash
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# Copyright 2018 Emotech LTD (Author: Xuechen Liu)
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# compare wer between diff. models in aidatatang_200zh chain directory

egs/aidatatang_200zh/s5/local/chain/tuning/run_tdnn_1a.sh

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# This script is based on run_tdnn_7h.sh in swbd chain recipe.
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egs/aidatatang_200zh/s5/local/chain/tuning/run_tdnn_2a.sh

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# This script is based on run_tdnn_1a.sh.
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# This setup used online pitch to train the neural network.

egs/aidatatang_200zh/s5/local/data_prep.sh

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# Copyright 2017 Xingyu Na
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# Apache 2.0

egs/aidatatang_200zh/s5/local/download_and_untar.sh

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# Copyright 2014 Johns Hopkins University (author: Daniel Povey)
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# 2017 Xingyu Na

egs/aidatatang_200zh/s5/local/format_data.sh

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#
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. ./path.sh

egs/aidatatang_200zh/s5/local/nnet3/compare_wer.sh

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# Copyright 2018 Emotech LTD (Author: Xuechen Liu)
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# compare wer between diff. models in aidatatang_200zh nnet3 directory

egs/aidatatang_200zh/s5/local/nnet3/run_ivector_common.sh

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set -euo pipefail
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egs/aidatatang_200zh/s5/local/nnet3/tuning/run_tdnn.sh

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# This script is based on aishell/s5/local/nnet3/tuning/run_tdnn_1a.sh
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egs/aidatatang_200zh/s5/local/prepare_dict.sh

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#Copyright 2016 LeSpeech (Author: Xingyu Na)
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# prepare dictionary for aidatatang

egs/aidatatang_200zh/s5/local/score.sh

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set -e -o pipefail
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set -x

egs/aidatatang_200zh/s5/local/train_lms.sh

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# To be run from one directory above this script.

egs/aidatatang_200zh/s5/run.sh

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# Copyright 2019 Beijing DataTang Tech. Co. Ltd. (Author: Liyuan Wang)
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# 2017 Hui Bu

egs/aishell/s5/local/aishell_data_prep.sh

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# Copyright 2017 Xingyu Na
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# Apache 2.0

egs/aishell/s5/local/aishell_prepare_dict.sh

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# Copyright 2017 Xingyu Na
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# Apache 2.0

egs/aishell/s5/local/aishell_train_lms.sh

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# To be run from one directory above this script.

egs/aishell/s5/local/chain/tuning/run_tdnn_1a.sh

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# This script is based on run_tdnn_7h.sh in swbd chain recipe.
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egs/aishell/s5/local/chain/tuning/run_tdnn_2a.sh

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# This script is based on run_tdnn_1a.sh.
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# This setup used online pitch to train the neural network.

egs/aishell/s5/local/download_and_untar.sh

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# Copyright 2014 Johns Hopkins University (author: Daniel Povey)
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# 2017 Xingyu Na

egs/aishell/s5/local/nnet3/run_ivector_common.sh

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set -euo pipefail
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egs/aishell/s5/local/nnet3/tuning/run_tdnn_1a.sh

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# This script is based on swbd/s5c/local/nnet3/run_tdnn.sh
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egs/aishell/s5/local/nnet3/tuning/run_tdnn_2a.sh

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# This script is based on aishell/s5/local/nnet3/tuning/run_tdnn_1a.sh
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egs/aishell/s5/local/score.sh

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set -e -o pipefail
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set -x

egs/aishell/s5/run.sh

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# Copyright 2017 Beijing Shell Shell Tech. Co. Ltd. (Authors: Hui Bu)
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# 2017 Jiayu Du

egs/aishell/v1/local/aishell_data_prep.sh

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# Copyright 2017 Xingyu Na
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# Apache 2.0

egs/aishell/v1/local/download_and_untar.sh

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# Copyright 2014 Johns Hopkins University (author: Daniel Povey)
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# 2017 Xingyu Na

egs/aishell/v1/run.sh

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# Copyright 2017 Beijing Shell Shell Tech. Co. Ltd. (Authors: Hui Bu)
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# 2017 Jiayu Du
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# 2017 Chao Li

egs/aishell2/s5/local/chain/compare_wer.sh

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# Copyright 2018 Emotech LTD (Author: Xuechen LIU)
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# Apache 2.0

egs/aishell2/s5/local/chain/tuning/run_tdnn_1a.sh

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# this is the original baseline scripts, which is supposed to be deprecated.
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egs/aishell2/s5/local/chain/tuning/run_tdnn_1b.sh

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# _1b is as _1a, but with pitch feats, i-vector and dropout schedule added, referenced from wsj
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egs/aishell2/s5/local/nnet3/compare_wer.sh

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# Copyright 2018 Emotech LTD (Author: Xuechen LIU)
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# Apache 2.0

egs/aishell2/s5/local/nnet3/tuning/run_tdnn_1a.sh

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# This script is based on swbd/s5c/local/nnet3/run_tdnn.sh
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egs/aishell2/s5/local/nnet3/tuning/run_tdnn_1b.sh

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# This script is based on run_tdnn_1a.sh, but with pitch features applied
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egs/aishell2/s5/local/prepare_all.sh

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# Copyright 2018 AIShell-Foundation(Authors:Jiayu DU, Xingyu NA, Bengu WU, Hao ZHENG)
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# 2018 Beijing Shell Shell Tech. Co. Ltd. (Author: Hui BU)

egs/aishell2/s5/local/prepare_data.sh

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# Copyright 2018 AIShell-Foundation(Authors:Jiayu DU, Xingyu NA, Bengu WU, Hao ZHENG)
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# 2018 Beijing Shell Shell Tech. Co. Ltd. (Author: Hui BU)
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# Apache 2.0

egs/aishell2/s5/local/prepare_dict.sh

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# Copyright 2018 AIShell-Foundation(Authors:Jiayu DU, Xingyu NA, Bengu WU, Hao ZHENG)
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# 2018 Beijing Shell Shell Tech. Co. Ltd. (Author: Hui BU)
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# Apache 2.0

egs/aishell2/s5/local/run_gmm.sh

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# Copyright 2018 AIShell-Foundation(Authors:Jiayu DU, Xingyu NA, Bengu WU, Hao ZHENG)
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# 2018 Beijing Shell Shell Tech. Co. Ltd. (Author: Hui BU)
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# 2018 Emotech LTD (Author: Xuechen LIU)

egs/aishell2/s5/local/score.sh

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set -e -o pipefail
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set -x

egs/aishell2/s5/local/train_lms.sh

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# Copyright 2018 AIShell-Foundation(Authors:Jiayu DU, Xingyu NA, Bengu WU, Hao ZHENG)
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# 2018 Beijing Shell Shell Tech. Co. Ltd. (Author: Hui BU)
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# Apache 2.0

egs/aishell2/s5/run.sh

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# Copyright 2018 AIShell-Foundation(Authors:Jiayu DU, Xingyu NA, Bengu WU, Hao ZHENG)
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# 2018 Beijing Shell Shell Tech. Co. Ltd. (Author: Hui BU)

egs/ami/s5/local/ami_beamform.sh

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# Copyright 2014, University of Edinburgh (Author: Pawel Swietojanski)
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# Apache 2.0

egs/ami/s5/local/ami_download.sh

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# Copyright 2014, University of Edinburgh (Author: Pawel Swietojanski, Jonathan Kilgour)
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# Copyright 2015, Brno University of Technology (Author: Karel Vesely)
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manifest="wget --continue -O $adir/MANIFEST.TXT http://groups.inf.ed.ac.uk/ami/download/temp/amiBuild-0153-Tue-Oct-2-2018.manifest.txt"
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echo "#!/usr/bin/env bash" > $wgetfile
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echo $manifest >> $wgetfile
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while read line; do

egs/ami/s5/local/ami_format_data.sh

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#
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if [ -f path.sh ]; then . ./path.sh; fi

egs/ami/s5/local/ami_ihm_data_prep.sh

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# Copyright 2014, University of Edinburgh (Author: Pawel Swietojanski)
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# AMI Corpus training data preparation

egs/ami/s5/local/ami_ihm_scoring_data_prep.sh

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# Copyright 2014, University of Edinburgh (Author: Pawel Swietojanski)
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# AMI Corpus dev/eval data preparation

egs/ami/s5/local/ami_mdm_data_prep.sh

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# Copyright 2014, University of Edinburgh (Author: Pawel Swietojanski)
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# AMI Corpus dev/eval data preparation

egs/ami/s5/local/ami_mdm_scoring_data_prep.sh

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# Copyright 2014, University of Edinburgh (Author: Pawel Swietojanski)
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# AMI Corpus dev/eval data preparation

egs/ami/s5/local/ami_prepare_dict.sh

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#adapted from fisher dict preparation script, Author: Pawel Swietojanski
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egs/ami/s5/local/ami_sdm_data_prep.sh

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# Copyright 2014, University of Edinburgh (Author: Pawel Swietojanski)
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# AMI Corpus dev/eval data preparation

egs/ami/s5/local/ami_sdm_scoring_data_prep.sh

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# Copyright 2014, University of Edinburgh (Author: Pawel Swietojanski)
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# AMI Corpus dev/eval data preparation

egs/ami/s5/local/ami_text_prep.sh

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# Copyright 2015, Brno University of Technology (Author: Karel Vesely)
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# Copyright 2014, University of Edinburgh (Author: Pawel Swietojanski), 2014, Apache 2.0

egs/ami/s5/local/ami_train_lms.sh

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# Copyright 2013 Arnab Ghoshal, Pawel Swietojanski
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egs/ami/s5/local/ami_xml2text.sh

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# Copyright, University of Edinburgh (Pawel Swietojanski and Jonathan Kilgour)
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egs/ami/s5/local/beamformit.sh

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# Copyright 2014, University of Edinburgh (Author: Pawel Swietojanski)
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egs/ami/s5/local/chain/run_blstm_ami_5.sh

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###

egs/ami/s5/local/chain/run_chain_common.sh

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# this script has common stages shared across AMI chain recipes
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set -e

egs/ami/s5/local/chain/run_tdnn_ami_5.sh

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#adapted from swbd's local/chain/6z.sh script. We change the TDNN config
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# These are the other modifications:

egs/ami/s5/local/confidence_calibration.sh

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. ./cmd.sh
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. ./path.sh
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egs/ami/s5/local/nnet/prepare_ivectors.sh

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# Copyright 2016, Brno University of Technology (Author: Karel Vesely)
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# Apache 2.0

egs/ami/s5/local/nnet3/prepare_parallel_datadirs.sh

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# this script creates a new data directory data/$new_mic
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# where the train, dev and eval directories are copied from $original_mic

egs/ami/s5/local/nnet3/prepare_parallel_perturbed_alignments.sh

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# This script creates the parallel data dir based on ihm data,
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# creates speed perturbed versions of this parallel data

egs/ami/s5/local/nnet3/prepare_perturbed_alignments.sh

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# This script creates speed perturbed versions of the training data
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# and generates the corresponding alignments

egs/ami/s5/local/nnet3/run_ivector_common.sh

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# this script contains some common (shared) parts of the run_nnet*.sh scripts.
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# speed perturbation is done for the training data

egs/ami/s5/local/nnet3/run_lstm.sh

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# Copyright 2015 Johns Hopkins University (Author: Daniel Povey).
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# 2015 Vijayaditya Peddinti

egs/ami/s5/local/nnet3/run_tdnn.sh

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# this is the standard "tdnn" system, built in nnet3; it's what we use to
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# call multi-splice.

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