mirror of
https://github.com/k2-fsa/icefall.git
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150 lines
5.5 KiB
Bash
150 lines
5.5 KiB
Bash
#!/usr/bin/env bash
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set -eou pipefail
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stage=-1
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stop_stage=100
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# We assume dl_dir (download dir) contains the following
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# directories and files. If not, they will be downloaded
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# by this script automatically.
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#
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# - $dl_dir/GRID
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# You can find lip, audio, align_text inside it.
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# Here, for using easily and running our recipe quickly,
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# we provide the processed lip data.
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# If you want to know more entails about the original GRID
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# dataset, you can have a look about this paper:
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# An audio-visual corpus for speech perception and automatic
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# speech recognition.
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# You can also download the GRID dataset form this url:
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# https://zenodo.org/record/3625687#.Ybn7HagzY2w.
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#
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# - audio_25k
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# - lip
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# - GRID_align_txt
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# - unseen_train.txt
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# - unseen_val.txt
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#
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# - $dl_dir/lm
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# This directory contains the language model(LM) downloaded from
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# https://huggingface.co/luomingshuang/grid_lm.
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# About how to get these LM files, you can know it
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# from https://github.com/luomingshuang/Train_LM_with_kaldilm.
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#
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# - lm_3_gram.arpa
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# - lm_4_gram.arpa
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#
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dl_dir=$PWD/download
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. shared/parse_options.sh || exit 1
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# All files generated by this script are saved in "data".
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# You can safely remove "data" and rerun this script to regenerate it.
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mkdir -p data
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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log "dl_dir: $dl_dir"
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if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
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log "Stage -1: Download LM"
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# We assume that you have installed the git-lfs, if not, you could install it
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# using: `sudo apt-get install git-lfs && git-lfs install`
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#[ ! -e $dl_dir/lm ] && mkdir -p $dl_dir/lm
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#git clone https://huggingface.co/luomingshuang/grid_lm $dl_dir/lm
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#cd $dl_dir/lm && git lfs pull
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# You can also use the following commands to download the lm files
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wget -P $dl_dir/lm https://huggingface.co/luomingshuang/grid_lm/resolve/main/lm_3_gram.arpa
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wget -P $dl_dir/lm https://huggingface.co/luomingshuang/grid_lm/resolve/main/lm_4_gram.arpa
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# Because the texts among the samples in GRID are very similar,
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# the lm_4_gram.arpa is nearly no use for decoding when use LM.
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fi
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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# The process of extracting lip region takes much time.
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# Here, we provide the processed data (lip region) for using.
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# So you can run this recipe quickly and easily.
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#
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# If you want to know more details about getting lip region,
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# You can have a look at https://github.com/Fengdalu/LipNet-PyTorch/tree/master/scripts
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[ ! -e $dl_dir/GRID ] && mkdir -p $dl_dir/GRID
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# Download the GRID lip region data and text
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# You can use the following commands to download the processed lip region data and text
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wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/grid_lip_160_80/resolve/main/GRID_LIP_160x80_TXT.zip.00
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wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/grid_lip_160_80/resolve/main/GRID_LIP_160x80_TXT.zip.01
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wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/grid_lip_160_80/resolve/main/GRID_LIP_160x80_TXT.zip.02
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wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/grid_lip_160_80/resolve/main/GRID_LIP_160x80_TXT.zip.03
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wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/grid_lip_160_80/resolve/main/GRID_LIP_160x80_TXT.zip.04
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cat $dl_dir/GRID/GRID_LIP_160x80_TXT.zip.* > $dl_dir/GRID/GRID_LIP_160x80_TXT.zip
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unzip $dl_dir/GRID/GRID_LIP_160x80_TXT.zip -d $dl_dir/GRID/
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rm -rf $dl_dir/GRID/GRID_LIP_160x80_TXT.zip
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# Download the GRID audio data
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wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/GRID_audio/resolve/main/audio_25k.zip
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unzip $dl_dir/GRID/audio_25k.zip -d $dl_dir/GRID/
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rm -rf $dl_dir/GRID/audio_25k.zip
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# Download the spliting files for train and val
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# Here, we just consider the unseen case, which means
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# that there is no common speakers among train and val.
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wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/GRID_text/resolve/main/unseen_train.txt
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wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/GRID_text/resolve/main/unseen_val.txt
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fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare character-based lang"
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lang_dir=data/lang_character
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mkdir -p $lang_dir
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./local/prepare_lexicon.py \
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--samples-txt $dl_dir/GRID/unseen_train.txt \
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--align-dir $dl_dir/GRID/GRID_align_txt \
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--lang-dir $lang_dir
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if [ ! -f $lang_dir/L_disambig.pt ]; then
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./local/prepare_lang.py --lang-dir $lang_dir
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fi
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Prepare G"
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# We assume you have installed kaldilm, if not, please install
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# it using: pip install kaldilm
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mkdir -p data/lm
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if [ ! -f data/lm/G_3_gram.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="data/lang_character/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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$dl_dir/lm/lm_3_gram.arpa > data/lm/G_3_gram.fst.txt
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fi
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if [ ! -f data/lm/G_4_gram.fst.txt ]; then
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# It is used for LM rescoring
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python3 -m kaldilm \
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--read-symbol-table="data/lang_character/words.txt" \
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--disambig-symbol='#0' \
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--max-order=4 \
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$dl_dir/lm/lm_4_gram.arpa > data/lm/G_4_gram.fst.txt
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fi
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Compile HLG"
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./local/compile_hlg.py --lang-dir data/lang_character
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fi
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