add prepare.sh

egs/audioset/AT/prepare.sh

@@ -0,0 +1,104 @@
+#!/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
+
+# run step 0 to step 5 by default
+stage=-1
+stop_stage=4
+
+dl_dir=$PWD/download
+
+# we assume that you have your downloaded the AudioSet and placed
+# it under $dl_dir/audioset, the folder structure should look like
+# this:
+# - $dl_dir/audioset
+#       - balanced
+#       - eval
+#       - unbalanced
+# If you haven't downloaded the AudioSet, please refer to
+# https://github.com/RicherMans/SAT/blob/main/datasets/audioset/1_download_audioset.sh.
+
+. shared/parse_options.sh || exit 1
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+mkdir -p data
+
+log() {
+  # This function is from espnet
+  local fname=${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 -1 ] && [ $stop_stage -ge -1 ]; then
+  log "Stage 0: Download the necessary csv files"
+  if [ ! -e $dl_dir/audioset/.csv.done]; then
+    wget --continue "http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/class_labels_indices.csv" -O "${dl_dir}/audioset/class_labels_indices.csv"
+    wget --continue http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/balanced_train_segments.csv -O "${dl_dir}/audioset/balanced_train_segments.csv"
+    wget --continue http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/eval_segments.csv -O "${dl_dir}/audioset/eval_segments.csv"
+    touch $dl_dir/audioset/.csv.done
+  fi
+fi
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Construct the audioset manifest and compute the fbank features for balanced set"
+  fbank_dir=data/fbank
+  if [! -e $fbank_dir/.balanced.done]; then
+    python local/generate_audioset_manifest.py \
+      --dataset-dir $dl_dir/audioset \
+      --split balanced \
+      --feat-output-dir $fbank_dir
+    touch $fbank_dir/.balanced.done
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Construct the audioset manifest and compute the fbank features for unbalanced set"
+  fbank_dir=data/fbank
+  if [! -e $fbank_dir/.unbalanced.done]; then
+    python local/generate_audioset_manifest.py \
+      --dataset-dir $dl_dir/audioset \
+      --split unbalanced \
+      --feat-output-dir $fbank_dir
+    touch $fbank_dir/.unbalanced.done
+  fi
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Construct the audioset manifest and compute the fbank features for eval set"
+  fbank_dir=data/fbank
+  if [! -e $fbank_dir/.eval.done]; then
+    python local/generate_audioset_manifest.py \
+      --dataset-dir $dl_dir/audioset \
+      --split eval \
+      --feat-output-dir $fbank_dir
+    touch $fbank_dir/.eval.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 $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 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
+    touch data/fbank/.musan.done
+  fi
+fi