some small changes for aidatatang_200zh (#542)

* Update prepare.sh

* Update compute_fbank_aidatatang_200zh.py

egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py

@@ -43,7 +43,7 @@ torch.set_num_interop_threads(1)
 
 
 def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
-    src_dir = Path("data/manifests")
+    src_dir = Path("data/manifests/aidatatang_200zh")
     output_dir = Path("data/fbank")
     num_jobs = min(15, os.cpu_count())
 

egs/aidatatang_200zh/ASR/prepare.sh

@@ -50,28 +50,19 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
 fi
 
 if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
-  log "Stage 2: Process aidatatang_200zh"
-  if [ ! -f data/fbank/aidatatang_200zh/.fbank.done ]; then
-    mkdir -p data/fbank/aidatatang_200zh
-    lhotse prepare aidatatang-200zh $dl_dir data/manifests/aidatatang_200zh
-    touch data/fbank/aidatatang_200zh/.fbank.done
+  log "Stage 2: Prepare musan manifest"
+  # We assume that you have downloaded the musan corpus
+  # to data/musan
+  if [ ! -f data/manifests/.manifests.done ]; then
+    log "It may take 6 minutes"
+    mkdir -p data/manifests/
+    lhotse prepare musan $dl_dir/musan data/manifests/
+    touch data/manifests/.manifests.done
   fi
 fi
 
 if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
-  log "Stage 3: Prepare musan manifest"
-  # We assume that you have downloaded the musan corpus
-  # to data/musan
-  if [ ! -f data/manifests/.musan_manifests.done ]; then
-    log "It may take 6 minutes"
-    mkdir -p data/manifests
-    lhotse prepare musan $dl_dir/musan data/manifests
-    touch data/manifests/.musan_manifests.done
-  fi
-fi
-
-if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
-  log "Stage 4: Compute fbank for musan"
+  log "Stage 3: Compute fbank for musan"
   if [ ! -f data/fbank/.msuan.done ]; then
     mkdir -p data/fbank
     ./local/compute_fbank_musan.py
@@ -79,8 +70,8 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
   fi
 fi
 
-if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
-  log "Stage 5: Compute fbank for aidatatang_200zh"
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Compute fbank for aidatatang_200zh"
   if [ ! -f data/fbank/.aidatatang_200zh.done ]; then
     mkdir -p data/fbank
     ./local/compute_fbank_aidatatang_200zh.py
@@ -88,31 +79,38 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
   fi
 fi
 
-if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
-  log "Stage 6: Prepare char based lang"
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Prepare char based lang"
   lang_char_dir=data/lang_char
   mkdir -p $lang_char_dir
-
   # Prepare text.
-  grep "\"text\":" data/manifests/aidatatang_200zh/supervisions_train.json \
-    | sed -e 's/["text:\t ]*//g' | sed 's/,//g' \
-    | ./local/text2token.py -t "char" > $lang_char_dir/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/aidatatang_200zh/aidatatang_supervisions_train.jsonl.gz \
+      |jq '.text' |sed -e 's/["text:\t ]*//g' | sed 's/"//g' \
+      | ./local/text2token.py -t "char" > $lang_char_dir/text
+  fi
   # Prepare words.txt
-  grep "\"text\":" data/manifests/aidatatang_200zh/supervisions_train.json \
-    | sed -e 's/["text:\t]*//g' | sed 's/,//g' \
-    | ./local/text2token.py -t "char" > $lang_char_dir/text_words
+  if [ ! -f $lang_char_dir/text_words ]; then
+    gunzip -c data/manifests/aidatatang_200zh/aidatatang_supervisions_train.jsonl.gz \
+      | jq '.text' | sed -e 's/["text:\t]*//g' | sed 's/"//g' \
+      | ./local/text2token.py -t "char" > $lang_char_dir/text_words
+  fi
 
   cat $lang_char_dir/text_words | sed 's/ /\n/g' | sort -u | sed '/^$/d' \
     | uniq > $lang_char_dir/words_no_ids.txt
 
   if [ ! -f $lang_char_dir/words.txt ]; then
     ./local/prepare_words.py \
-      --input-file $lang_char_dir/words_no_ids.txt
-      --output-file $lang_char_dir/words.txt
+      --input-file $lang_char_dir/words_no_ids.txt \
+      --output-file $lang_char_dir/words.txt 
   fi
 
   if [ ! -f $lang_char_dir/L_disambig.pt ]; then
     ./local/prepare_char.py
   fi
 fi
+

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py

@@ -522,63 +522,14 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    # Note: Please use "pip install webdataset==0.1.103"
-    # for installing the webdataset.
-    import glob
-    import os
-
-    from lhotse import CutSet
-    from lhotse.dataset.webdataset import export_to_webdataset
-
     # we need cut ids to display recognition results.
     args.return_cuts = True
     aidatatang_200zh = Aidatatang_200zhAsrDataModule(args)
 
-    dev = "dev"
-    test = "test"
-
-    if not os.path.exists(f"{dev}/shared-0.tar"):
-        os.makedirs(dev)
-        dev_cuts = aidatatang_200zh.valid_cuts()
-        export_to_webdataset(
-            dev_cuts,
-            output_path=f"{dev}/shared-%d.tar",
-            shard_size=300,
-        )
-
-    if not os.path.exists(f"{test}/shared-0.tar"):
-        os.makedirs(test)
-        test_cuts = aidatatang_200zh.test_cuts()
-        export_to_webdataset(
-            test_cuts,
-            output_path=f"{test}/shared-%d.tar",
-            shard_size=300,
-        )
-
-    dev_shards = [
-        str(path)
-        for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar")))
-    ]
-    cuts_dev_webdataset = CutSet.from_webdataset(
-        dev_shards,
-        split_by_worker=True,
-        split_by_node=True,
-        shuffle_shards=True,
-    )
-
-    test_shards = [
-        str(path)
-        for path in sorted(glob.glob(os.path.join(test, "shared-*.tar")))
-    ]
-    cuts_test_webdataset = CutSet.from_webdataset(
-        test_shards,
-        split_by_worker=True,
-        split_by_node=True,
-        shuffle_shards=True,
-    )
-
-    dev_dl = aidatatang_200zh.valid_dataloaders(cuts_dev_webdataset)
-    test_dl = aidatatang_200zh.test_dataloaders(cuts_test_webdataset)
+    dev_cuts = aidatatang_200zh.valid_cuts()
+    test_cuts = aidatatang_200zh.test_cuts()
+    dev_dl = aidatatang_200zh.valid_dataloaders(dev_cuts)
+    test_dl = aidatatang_200zh.test_dataloaders(test_cuts)
 
     test_sets = ["dev", "test"]
     test_dl = [dev_dl, test_dl]