Add prepare.sh for KsponSpeech recipe. (#1656)

egs/ksponspeech/ASR/prepare.sh

@@ -0,0 +1,162 @@
+#!/usr/bin/env bash
+
+# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+set -eou pipefail
+
+nj=15
+stage=0
+stop_stage=100
+
+# Note: This script just prepare the minimal requirements that needed by a
+# transducer training with bpe units.
+#
+# We assume dl_dir (download dir) contains the following
+# directories and files. 
+# This script downloads only musan dataset automatically.
+#
+#  - $dl_dir/KsponSpeech
+#      This script doesn't download KsponSpeech dataset automatically.
+#      For more details, please visit:
+#          Dataset: https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123
+#          Paper: https://www.mdpi.com/2076-3417/10/19/6936
+#
+#  - $dl_dir/musan
+#      This directory contains the following directories downloaded from
+#       http://www.openslr.org/17/
+#
+#     - music
+#     - noise
+#     - speech
+
+dl_dir=$PWD/download
+
+# vocab size for sentence piece models.
+# It will generate data/lang_bpe_xxx,
+# data/lang_bpe_yyy if the array contains xxx, yyy
+vocab_sizes=(
+    5000
+)
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+data=$PWD/data
+
+. shared/parse_options.sh || exit 1
+
+mkdir -p $data
+
+log() {
+    # This function is from espnet
+    local fname=${BASH_SOURCE[1]##*/}
+    echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+log "Running prepare.sh"
+
+log "dl_dir: $dl_dir"
+
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+    log "Stage 0: Download MUSAN data"
+    # Befor you run this script, you must get the KsponSpeech dataset
+    # from https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123
+    # If you have pre-downloaded it to /path/to/KsponSpeech,
+    # you can create a symlink
+    #
+    #   ln -svf /path/to/KsponSpeech $dl_dir/KsponSpeech
+    #
+    # If you have pre-downloaded it to /path/to/musan,
+    # you can create a symlink
+    #
+    #   ln -sfv /path/to/musan $dl_dir/musan
+    #
+    if [ ! -d $dl_dir/musan ]; then
+        lhotse download musan $dl_dir
+    fi
+fi
+
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+    log "Stage 1: Prepare KsponSpeech manifest"
+    # We assume that you have downloaded the KsponSpeech corpus
+    # to $dl_dir/KsponSpeech
+    mkdir -p $data/manifests
+    if [ ! -e $data/manifests/.ksponspeech.done ]; then
+        lhotse prepare ksponspeech -j $nj $dl_dir/KsponSpeech $data/manifests
+        touch $data/manifests/.ksponspeech.done
+    fi
+fi
+
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+    log "Stage 2: Prepare musan manifest"
+    # We assume that you have downloaded the musan corpus
+    # to $dl_dir/musan
+    mkdir -p $data/manifests
+    if [ ! -e $data/manifests/.musan.done ]; then
+        lhotse prepare musan $dl_dir/musan $data/manifests
+        touch $data/manifests/.musan.done
+    fi
+fi
+
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+    log "Stage 3: Compute fbank for KsponSpeech"
+    mkdir -p $data/fbank
+    if [ ! -e $data/fbank/.ksponspeech.done ]; then
+        ./local/compute_fbank_ksponspeech.py --data-dir $data
+        touch $data/fbank/.ksponspeech.done
+    fi
+    
+    if [ ! -e $data/fbank/.ksponspeech-validated.done ]; then
+        log "Validating data/fbank for KsponSpeech"
+        parts=(
+            train
+            dev
+            eval_clean
+            eval_other
+        )
+        for part in ${parts[@]}; do
+            ./local/validate_manifest.py \
+                $data/fbank/ksponspeech_cuts_${part}.jsonl.gz
+        done
+        touch $data/fbank/.ksponspeech-validated.done
+    fi
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+    log "Stage 4: Compute fbank for musan"
+    mkdir -p $data/fbank
+    if [ ! -e $data/fbank/.musan.done ]; then
+        ./local/compute_fbank_musan.py \
+            --src-dir $data/manifests \
+            --output-dir $data/fbank
+        touch $data/fbank/.musan.done
+    fi
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+    log "Stage 5: Prepare BPE based lang"
+    
+    for vocab_size in ${vocab_sizes[@]}; do
+        lang_dir=$data/lang_bpe_${vocab_size}
+        mkdir -p $lang_dir
+        
+        if [ ! -f $lang_dir/transcript_words.txt ]; then
+            log "Generate data for BPE training"
+            files=$(
+                find "$data/fbank" -name "ksponspeech_cuts_*.jsonl.gz"
+            )
+            gunzip -c ${files} | awk -F '"' '{print $30}' > $lang_dir/transcript_words.txt
+        fi
+        
+        if [ ! -f $lang_dir/bpe.model ]; then
+            ./local/train_bpe_model.py \
+                --lang-dir $lang_dir \
+                --vocab-size $vocab_size \
+                --transcript $lang_dir/transcript_words.txt
+        fi
+    done
+fi