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https://github.com/modelscope/FunASR
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egs/aishell/paraformer/local/download_and_untar.sh
Executable file
105
egs/aishell/paraformer/local/download_and_untar.sh
Executable file
@ -0,0 +1,105 @@
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#!/usr/bin/env bash
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# Copyright 2014 Johns Hopkins University (author: Daniel Povey)
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# 2017 Xingyu Na
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# Apache 2.0
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remove_archive=false
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if [ "$1" == --remove-archive ]; then
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remove_archive=true
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shift
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fi
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if [ $# -ne 3 ]; then
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echo "Usage: $0 [--remove-archive] <data-base> <url-base> <corpus-part>"
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echo "e.g.: $0 /export/a05/xna/data www.openslr.org/resources/33 data_aishell"
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echo "With --remove-archive it will remove the archive after successfully un-tarring it."
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echo "<corpus-part> can be one of: data_aishell, resource_aishell."
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fi
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data=$1
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url=$2
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part=$3
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if [ ! -d "$data" ]; then
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echo "$0: no such directory $data"
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exit 1;
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fi
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part_ok=false
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list="data_aishell resource_aishell"
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for x in $list; do
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if [ "$part" == $x ]; then part_ok=true; fi
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done
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if ! $part_ok; then
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echo "$0: expected <corpus-part> to be one of $list, but got '$part'"
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exit 1;
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fi
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if [ -z "$url" ]; then
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echo "$0: empty URL base."
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exit 1;
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fi
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if [ -f $data/$part/.complete ]; then
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echo "$0: data part $part was already successfully extracted, nothing to do."
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exit 0;
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fi
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# sizes of the archive files in bytes.
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sizes="15582913665 1246920"
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if [ -f $data/$part.tgz ]; then
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size=$(/bin/ls -l $data/$part.tgz | awk '{print $5}')
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size_ok=false
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for s in $sizes; do if [ $s == $size ]; then size_ok=true; fi; done
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if ! $size_ok; then
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echo "$0: removing existing file $data/$part.tgz because its size in bytes $size"
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echo "does not equal the size of one of the archives."
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rm $data/$part.tgz
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else
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echo "$data/$part.tgz exists and appears to be complete."
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fi
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fi
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if [ ! -f $data/$part.tgz ]; then
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if ! command -v wget >/dev/null; then
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echo "$0: wget is not installed."
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exit 1;
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fi
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full_url=$url/$part.tgz
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echo "$0: downloading data from $full_url. This may take some time, please be patient."
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cd $data || exit 1
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if ! wget --no-check-certificate $full_url; then
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echo "$0: error executing wget $full_url"
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exit 1;
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fi
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fi
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cd $data || exit 1
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if ! tar -xvzf $part.tgz; then
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echo "$0: error un-tarring archive $data/$part.tgz"
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exit 1;
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fi
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touch $data/$part/.complete
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if [ $part == "data_aishell" ]; then
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cd $data/$part/wav || exit 1
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for wav in ./*.tar.gz; do
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echo "Extracting wav from $wav"
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tar -zxf $wav && rm $wav
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done
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fi
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echo "$0: Successfully downloaded and un-tarred $data/$part.tgz"
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if $remove_archive; then
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echo "$0: removing $data/$part.tgz file since --remove-archive option was supplied."
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rm $data/$part.tgz
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fi
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exit 0;
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@ -1,53 +0,0 @@
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#!/usr/bin/env bash
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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
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# transform raw AISHELL-2 data to kaldi format
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. ./path.sh || exit 1;
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tmp=
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dir=
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if [ $# != 3 ]; then
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echo "Usage: $0 <corpus-data-dir> <tmp-dir> <output-dir>"
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echo " $0 /export/AISHELL-2/iOS/train data/local/train data/train"
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exit 1;
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fi
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corpus=$1
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tmp=$2
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dir=$3
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echo "prepare_data.sh: Preparing data in $corpus"
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mkdir -p $tmp
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mkdir -p $dir
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# corpus check
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if [ ! -d $corpus ] || [ ! -f $corpus/wav.scp ] || [ ! -f $corpus/trans.txt ]; then
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echo "Error: $0 requires wav.scp and trans.txt under $corpus directory."
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exit 1;
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fi
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# validate utt-key list, IC0803W0380 is a bad utterance
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awk '{print $1}' $corpus/wav.scp | grep -v 'IC0803W0380' > $tmp/wav_utt.list
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awk '{print $1}' $corpus/trans.txt > $tmp/trans_utt.list
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utils/filter_scp.pl -f 1 $tmp/wav_utt.list $tmp/trans_utt.list > $tmp/utt.list
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# wav.scp
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awk -F'\t' -v path_prefix=$corpus '{printf("%s\t%s/%s\n",$1,path_prefix,$2)}' $corpus/wav.scp > $tmp/tmp_wav.scp
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utils/filter_scp.pl -f 1 $tmp/utt.list $tmp/tmp_wav.scp | sort -k 1 | uniq > $tmp/wav.scp
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# text
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utils/filter_scp.pl -f 1 $tmp/utt.list $corpus/trans.txt | sort -k 1 | uniq > $tmp/text
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# copy prepared resources from tmp_dir to target dir
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mkdir -p $dir
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for f in wav.scp text; do
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cp $tmp/$f $dir/$f || exit 1;
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done
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echo "local/prepare_data.sh succeeded"
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exit 0;
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@ -13,7 +13,7 @@ train_cmd=utils/run.pl
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infer_cmd=utils/run.pl
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# general configuration
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feats_dir="/nfs/wangjiaming.wjm/Funasr_data_test/aishell" #feature output dictionary
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feats_dir="../DATA" #feature output dictionary
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exp_dir="."
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lang=zh
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dumpdir=dump/fbank
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@ -21,8 +21,8 @@ feats_type=fbank
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token_type=char
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scp=wav.scp
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type=sound
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stage=3
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stop_stage=4
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stage=1
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stop_stage=1
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# feature configuration
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feats_dim=80
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@ -31,7 +31,8 @@ nj=32
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speed_perturb="0.9,1.0,1.1"
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# data
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data_aishell=
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raw_data=
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data_url=www.openslr.org/resources/33
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# exp tag
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tag=""
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@ -66,10 +67,16 @@ else
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_ngpu=0
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fi
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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echo "stage -1: Data Download"
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local/download_and_untar.sh ${raw_data} ${data_url} data_aishell
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local/download_and_untar.sh ${raw_data} ${data_url} resource_aishell
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "stage 0: Data preparation"
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# Data preparation
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local/aishell_data_prep.sh ${data_aishell}/data_aishell/wav ${data_aishell}/data_aishell/transcript ${feats_dir}
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local/aishell_data_prep.sh ${raw_data}/data_aishell/wav ${raw_data}/data_aishell/transcript ${feats_dir}
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for x in train dev test; do
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cp ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
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paste -d " " <(cut -f 1 -d" " ${feats_dir}/data/${x}/text.org) <(cut -f 2- -d" " ${feats_dir}/data/${x}/text.org | tr -d " ") \
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@ -80,45 +87,10 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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fi
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feat_train_dir=${feats_dir}/${dumpdir}/train; mkdir -p ${feat_train_dir}
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feat_dev_dir=${feats_dir}/${dumpdir}/dev; mkdir -p ${feat_dev_dir}
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feat_test_dir=${feats_dir}/${dumpdir}/test; mkdir -p ${feat_test_dir}
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Feature Generation"
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# compute fbank features
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fbankdir=${feats_dir}/fbank
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utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} --speed_perturb ${speed_perturb} \
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${feats_dir}/data/train ${exp_dir}/exp/make_fbank/train ${fbankdir}/train
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utils/fix_data_feat.sh ${fbankdir}/train
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utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \
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${feats_dir}/data/dev ${exp_dir}/exp/make_fbank/dev ${fbankdir}/dev
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utils/fix_data_feat.sh ${fbankdir}/dev
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utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \
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${feats_dir}/data/test ${exp_dir}/exp/make_fbank/test ${fbankdir}/test
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utils/fix_data_feat.sh ${fbankdir}/test
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# compute global cmvn
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echo "stage 1: Feature and CMVN Generation"
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utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} \
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${fbankdir}/train ${exp_dir}/exp/make_fbank/train
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# apply cmvn
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utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
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${fbankdir}/train ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/train ${feat_train_dir}
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utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
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${fbankdir}/dev ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/dev ${feat_dev_dir}
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utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
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${fbankdir}/test ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/test ${feat_test_dir}
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cp ${fbankdir}/train/text ${fbankdir}/train/speech_shape ${fbankdir}/train/text_shape ${feat_train_dir}
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cp ${fbankdir}/dev/text ${fbankdir}/dev/speech_shape ${fbankdir}/dev/text_shape ${feat_dev_dir}
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cp ${fbankdir}/test/text ${fbankdir}/test/speech_shape ${fbankdir}/test/text_shape ${feat_test_dir}
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utils/fix_data_feat.sh ${feat_train_dir}
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utils/fix_data_feat.sh ${feat_dev_dir}
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utils/fix_data_feat.sh ${feat_test_dir}
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#generate ark list
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utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_train_dir} ${fbankdir}/train ${feat_train_dir}
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utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_dev_dir} ${fbankdir}/dev ${feat_dev_dir}
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${feats_dir}/data/${train_set} ${exp_dir}/exp/make_fbank/${train_set}
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fi
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token_list=${feats_dir}/data/${lang}_token_list/char/tokens.txt
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@ -136,12 +108,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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num_token=$(cat ${token_list} | wc -l)
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echo "<unk>" >> ${token_list}
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vocab_size=$(cat ${token_list} | wc -l)
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awk -v v=,${vocab_size} '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.char
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awk -v v=,${vocab_size} '{print $0v}' ${feat_dev_dir}/text_shape > ${feat_dev_dir}/text_shape.char
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mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/train
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mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/dev
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cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/train
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cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/dev
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fi
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# Training Stage
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@ -13,13 +13,17 @@ echo "$0 $@"
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fbankdir=$1
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logdir=$2
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output_dir=${fbankdir}/cmvn; mkdir -p ${output_dir}
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mkdir -p ${logdir}
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output_dir=${fbankdir}/cmvn/split_${nj};
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split_scps=""
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for n in $(seq $nj); do
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split_scps="$split_scps $output_dir/wav.$n.scp"
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done
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utils/split_scp.pl ${fbankdir}/wav.scp $split_scps || exit 1;
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$cmd JOB=1:$nj $logdir/cmvn.JOB.log \
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python utils/compute_cmvn.py -d ${feats_dim} -a $fbankdir/ark -i JOB -o ${output_dir} \
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|| exit 1;
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python utils/combine_cmvn_file.py -d ${feats_dim} -c ${output_dir} -n $nj -o $fbankdir
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echo "$0: Succeeded compute global cmvn"
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#$cmd JOB=1:$nj $logdir/cmvn.JOB.log \
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# python utils/compute_cmvn.py -d ${feats_dim} -a $fbankdir/ark -i JOB -o ${output_dir} \
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# || exit 1;
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#
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#python utils/combine_cmvn_file.py -d ${feats_dim} -c ${output_dir} -n $nj -o $fbankdir
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#
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#echo "$0: Succeeded compute global cmvn"
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