Add download method to prepare.sh

egs/reazonspeech/ASR/local/compute_fbank_reazonspeech.py

@@ -68,17 +68,17 @@ def make_cutset_blueprints(
         )
     )
 
-    # Create valid dataset
-    logging.info("Creating valid cuts.")
+    # Create dev dataset
+    logging.info("Creating dev cuts.")
     cut_sets.append(
         (
-            "valid",
+            "dev",
             CutSet.from_manifests(
                 recordings=RecordingSet.from_file(
-                    manifest_dir / "reazonspeech_recordings_valid.jsonl.gz"
+                    manifest_dir / "reazonspeech_recordings_dev.jsonl.gz"
                 ),
                 supervisions=SupervisionSet.from_file(
-                    manifest_dir / "reazonspeech_supervisions_valid.jsonl.gz"
+                    manifest_dir / "reazonspeech_supervisions_dev.jsonl.gz"
                 ),
             ),
         )

egs/reazonspeech/ASR/local/display_manifest_statistics.py

@@ -45,7 +45,7 @@ def get_parser():
 def main():
     args = get_parser()
 
-    for part in ["train", "valid"]:
+    for part in ["train", "dev"]:
         path = args.manifest_dir / f"reazonspeech_cuts_{part}.jsonl.gz"
         cuts: CutSet = load_manifest(path)
 

egs/reazonspeech/ASR/prepare.sh

@@ -5,48 +5,82 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 
 set -eou pipefail
 
-nj=8
+nj=15
 stage=-1
 stop_stage=100
 
-reazonspeech_dir=corpus
-reazonspeech_manifest_dir=data
+# We assume dl_dir (download dir) contains the following
+# directories and files. If not, they will be downloaded
+# by this script automatically.
+#
+#  - $dl_dir/ReazonSpeech
+#      You can find FLAC files in this directory.
+#      You can download them from https://huggingface.co/datasets/reazon-research/reazonspeech
+#
+#  - $dl_dir/dataset.json
+#      The metadata of the ReazonSpeech dataset.
+
+dl_dir=$PWD/download
 
 . shared/parse_options.sh || exit 1
 
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
 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 data"
+
+  # If you have pre-downloaded it to /path/to/ReazonSpeech,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/ReazonSpeech $dl_dir/ReazonSpeech
+  #
+  if [ ! -d $dl_dir/ReazonSpeech/downloads ]; then
+    # Download small-v1 by default.
+    lhotse download reazonspeech --subset small-v1 $dl_dir
+  fi
+fi
+
 if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
     log "Stage 1: Prepare ReazonSpeech manifest"
-    if [ ! -e $reazonspeech_manifest_dir/.reazonspeech.done ]; then
-        lhotse prepare reazonspeech $reazonspeech_dir $reazonspeech_manifest_dir
-        touch $reazonspeech_manifest_dir/.reazonspeech.done
+    # We assume that you have downloaded the ReazonSpeech corpus
+    # to $dl_dir/ReazonSpeech
+    mkdir -p data/manifests
+    if [ ! -e data/manifests/.reazonspeech.done ]; then
+        lhotse prepare reazonspeech -j $nj $dl_dir/ReazonSpeech data/manifests
+        touch data/manifests/.reazonspeech.done
     fi
 fi
 
 if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
     log "Stage 2: Compute ReazonSpeech fbank"
-    if [ ! -e $reazonspeech_manifest_dir/.reazonspeech-validated.done ]; then
-        python local/compute_fbank_reazonspeech.py --manifest-dir $reazonspeech_manifest_dir
-        python local/validate_manifest.py --manifest $reazonspeech_manifest_dir/reazonspeech_cuts_train.jsonl.gz
-        python local/validate_manifest.py --manifest $reazonspeech_manifest_dir/reazonspeech_cuts_valid.jsonl.gz
-        python local/validate_manifest.py --manifest $reazonspeech_manifest_dir/reazonspeech_cuts_test.jsonl.gz
-        touch $reazonspeech_manifest_dir/.reazonspeech-validated.done
+    if [ ! -e data/manifests/.reazonspeech-validated.done ]; then
+        python local/compute_fbank_reazonspeech.py --manifest-dir data/manifests
+        python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_train.jsonl.gz
+        python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_dev.jsonl.gz
+        python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_test.jsonl.gz
+        touch data/manifests/.reazonspeech-validated.done
     fi
 fi
 
 if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
     log "Stage 3: Prepare ReazonSpeech lang_char"
-    python local/prepare_lang_char.py $reazonspeech_manifest_dir/reazonspeech_cuts_train.jsonl.gz
+    python local/prepare_lang_char.py data/manifests/reazonspeech_cuts_train.jsonl.gz
 fi
 
 if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
     log "Stage 4: Show manifest statistics"
-    python local/display_manifest_statistics.py --manifest-dir $reazonspeech_manifest_dir > $reazonspeech_manifest_dir/manifest_statistics.txt
-    cat $reazonspeech_manifest_dir/manifest_statistics.txt
+    python local/display_manifest_statistics.py --manifest-dir data/manifests > data/manifests/manifest_statistics.txt
+    cat data/manifests/manifest_statistics.txt
 fi

egs/reazonspeech/ASR/zipformer/train.py

@@ -1069,9 +1069,6 @@ def train_one_epoch(
                     tb_writer, "train/valid_", params.batch_idx_train
                 )
 
-    # print('--------------------debug------------------')
-    # print(tot_loss)
-    # print(tot_loss["frames"])
     loss_value = tot_loss["loss"] / tot_loss["frames"]
     params.train_loss = loss_value
     if params.train_loss < params.best_train_loss: