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egs/aishell/transformer/local/download_and_untar.sh
Executable file
105
egs/aishell/transformer/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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@ -3,12 +3,12 @@
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. ./path.sh || exit 1;
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# machines configuration
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CUDA_VISIBLE_DEVICES="2,3"
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CUDA_VISIBLE_DEVICES="0,1"
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gpu_num=2
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count=1
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gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
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# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
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njob=1
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njob=5
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train_cmd=utils/run.pl
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infer_cmd=utils/run.pl
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@ -16,13 +16,11 @@ infer_cmd=utils/run.pl
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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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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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scp=wav.scp
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stage=3
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stop_stage=3
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stop_stage=4
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# feature configuration
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feats_dim=80
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@ -48,7 +46,7 @@ valid_set=dev
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test_sets="dev test"
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asr_config=conf/train_asr_transformer.yaml
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
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inference_config=conf/decode_asr_transformer.yaml
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inference_asr_model=valid.acc.ave_10best.pb
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@ -143,4 +141,61 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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} &
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done
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wait
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fi
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# Testing Stage
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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echo "stage 4: Inference"
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for dset in ${test_sets}; do
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asr_exp=${exp_dir}/exp/${model_dir}
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inference_tag="$(basename "${inference_config}" .yaml)"
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_dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
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_logdir="${_dir}/logdir"
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if [ -d ${_dir} ]; then
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echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
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exit 0
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fi
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mkdir -p "${_logdir}"
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_data="${feats_dir}/data/${dset}"
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key_file=${_data}/${scp}
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num_scp_file="$(<${key_file} wc -l)"
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_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
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split_scps=
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for n in $(seq "${_nj}"); do
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split_scps+=" ${_logdir}/keys.${n}.scp"
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done
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# shellcheck disable=SC2086
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utils/split_scp.pl "${key_file}" ${split_scps}
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_opts=
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if [ -n "${inference_config}" ]; then
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_opts+="--config ${inference_config} "
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fi
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${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
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python -m funasr.bin.asr_inference_launch \
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--batch_size 1 \
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--ngpu "${_ngpu}" \
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--njob ${njob} \
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--gpuid_list ${gpuid_list} \
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--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
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--cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
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--key_file "${_logdir}"/keys.JOB.scp \
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--asr_train_config "${asr_exp}"/config.yaml \
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--asr_model_file "${asr_exp}"/"${inference_asr_model}" \
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--output_dir "${_logdir}"/output.JOB \
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--mode asr \
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${_opts}
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for f in token token_int score text; do
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if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
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for i in $(seq "${_nj}"); do
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cat "${_logdir}/output.${i}/1best_recog/${f}"
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done | sort -k1 >"${_dir}/${f}"
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fi
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done
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python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
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python utils/proce_text.py ${_data}/text ${_data}/text.proc
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python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
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tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
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cat ${_dir}/text.cer.txt
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done
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fi
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