Fix preparing char based lang and add multiprocessing for wenetspeech text segmentation

egs/wenetspeech/ASR/local/text2segments.py

@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 # Copyright    2021  Xiaomi Corp.        (authors: Mingshuang Luo)
+#              2022  Xiaomi Corp.        (authors: Weiji Zhuang)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -29,10 +30,18 @@
 
 
 import argparse
+from multiprocessing import Pool
 
 import jieba
+import paddle
 from tqdm import tqdm
 
+# In PaddlePaddle 2.x, dynamic graph mode is turned on by default,
+# and 'data()' is only supported in static graph mode. So if you
+# want to use this api, should call 'paddle.enable_static()' before
+# this api to enter static graph mode.
+paddle.enable_static()
+paddle.disable_signal_handler()
 jieba.enable_paddle()
 
 
@@ -41,14 +50,23 @@ def get_parser():
         description="Chinese Word Segmentation for text",
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
+    parser.add_argument(
+        "--num-process",
+        "-n",
+        default=20,
+        type=int,
+        help="the number of processes",
+    )
     parser.add_argument(
         "--input-file",
+        "-i",
         default="data/lang_char/text",
         type=str,
         help="the input text file for WenetSpeech",
     )
     parser.add_argument(
         "--output-file",
+        "-o",
         default="data/lang_char/text_words_segmentation",
         type=str,
         help="the text implemented with words segmenting for WenetSpeech",
@@ -57,26 +75,33 @@ def get_parser():
     return parser
 
 
+def cut(lines):
+    if lines is not None:
+        cut_lines = jieba.cut(lines, use_paddle=True)
+        return [i for i in cut_lines]
+    else:
+        return None
+
+
 def main():
     parser = get_parser()
     args = parser.parse_args()
 
+    num_process = args.num_process
     input_file = args.input_file
     output_file = args.output_file
+    # parallel mode does not support use_paddle
+    # jieba.enable_parallel(num_process)
+
+    with open(input_file, "r", encoding="utf-8") as fr:
+        lines = fr.readlines()
+
+    with Pool(processes=num_process) as p:
+        new_lines = list(tqdm(p.imap(cut, lines), total=len(lines)))
 
-    f = open(input_file, "r", encoding="utf-8")
-    lines = f.readlines()
-    new_lines = []
-    for i in tqdm(range(len(lines))):
-        x = lines[i].rstrip()
-        seg_list = jieba.cut(x, use_paddle=True)
-        new_line = " ".join(seg_list)
-        new_lines.append(new_line)
-
-    f_new = open(output_file, "w", encoding="utf-8")
-    for line in new_lines:
-        f_new.write(line)
-        f_new.write("\n")
+    with open(output_file, "w", encoding="utf-8") as fw:
+        for line in new_lines:
+            fw.write(" ".join(line) + "\n")
 
 
 if __name__ == "__main__":

egs/wenetspeech/ASR/prepare.sh

@@ -28,6 +28,7 @@ num_splits=1000
 #     - speech
 
 dl_dir=$PWD/download
+lang_char_dir=data/lang_char
 
 . shared/parse_options.sh || exit 1
 
@@ -186,24 +187,27 @@ fi
 
 if [ $stage -le 15 ] && [ $stop_stage -ge 15 ]; then
   log "Stage 15: Prepare char based lang"
-  lang_char_dir=data/lang_char
   mkdir -p $lang_char_dir
 
-  # Prepare text.
-  # Note: in Linux, you can install jq with the following command:
-  # 1. wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
-  # 2. chmod +x ./jq
-  # 3. cp jq /usr/bin
-  if [ ! -f $lang_char_dir/text ]; then
-    gunzip -c data/manifests/supervisions_L.jsonl.gz \
-      | jq 'text' | sed 's/"//g' \
+  if ! which jq; then
+      echo "This script is intended to be used with jq but you have not installed jq 
+      Note: in Linux, you can install jq with the following command:
+      1. wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
+      2. chmod +x ./jq
+      3. cp jq /usr/bin" && exit 1
+  fi
+  if [ ! -f $lang_char_dir/text ] || [ ! -s $lang_char_dir/text ]; then
+    log "Prepare text."
+    gunzip -c data/manifests/wenetspeech_supervisions_L.jsonl.gz \
+      | jq '.text' | sed 's/"//g' \
       | ./local/text2token.py -t "char" > $lang_char_dir/text
   fi
 
   # The implementation of chinese word segmentation for text,
   # and it will take about 15 minutes.
   if [ ! -f $lang_char_dir/text_words_segmentation ]; then
-    python ./local/text2segments.py \
+    python3 ./local/text2segments.py \
+      --num-process $nj \
       --input-file $lang_char_dir/text \
       --output-file $lang_char_dir/text_words_segmentation
   fi
@@ -212,7 +216,7 @@ if [ $stage -le 15 ] && [ $stop_stage -ge 15 ]; then
     | sort -u | sed '/^$/d' | uniq > $lang_char_dir/words_no_ids.txt
 
   if [ ! -f $lang_char_dir/words.txt ]; then
-    python ./local/prepare_words.py \
+    python3 ./local/prepare_words.py \
       --input-file $lang_char_dir/words_no_ids.txt \
       --output-file $lang_char_dir/words.txt
   fi
@@ -221,7 +225,7 @@ fi
 if [ $stage -le 16 ] && [ $stop_stage -ge 16 ]; then
   log "Stage 16: Prepare char based L_disambig.pt"
   if [ ! -f data/lang_char/L_disambig.pt ]; then
-    python ./local/prepare_char.py \
+    python3 ./local/prepare_char.py \
       --lang-dir data/lang_char
   fi
 fi
@@ -232,9 +236,8 @@ if [ $stage -le 17 ] && [ $stop_stage -ge 17 ]; then
   # It will take about 20 minutes.
   # We assume you have install kaldilm, if not, please install
   # it using: pip install kaldilm
-  lang_char_dir=data/lang_char
   if [ ! -f $lang_char_dir/3-gram.unpruned.arpa ]; then
-    python ./shared/make_kn_lm.py \
+    python3 ./shared/make_kn_lm.py \
       -ngram-order 3 \
       -text $lang_char_dir/text_words_segmentation \
       -lm $lang_char_dir/3-gram.unpruned.arpa
@@ -253,6 +256,5 @@ fi
 
 if [ $stage -le 18 ] && [ $stop_stage -ge 18 ]; then
   log "Stage 18: Compile LG"
-  lang_char_dir=data/lang_char
   python ./local/compile_lg.py --lang-dir $lang_char_dir
 fi