mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 18:12:19 +00:00
105 lines
3.4 KiB
Bash
Executable File
105 lines
3.4 KiB
Bash
Executable File
#!/usr/bin/env bash
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# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
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export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
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set -eou pipefail
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# run step 0 to step 5 by default
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stage=-1
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stop_stage=4
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dl_dir=$PWD/download
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# we assume that you have your downloaded the AudioSet and placed
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# it under $dl_dir/audioset, the folder structure should look like
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# this:
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# - $dl_dir/audioset
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# - balanced
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# - eval
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# - unbalanced
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# If you haven't downloaded the AudioSet, please refer to
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# https://github.com/RicherMans/SAT/blob/main/datasets/audioset/1_download_audioset.sh.
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. shared/parse_options.sh || exit 1
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# All files generated by this script are saved in "data".
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# You can safely remove "data" and rerun this script to regenerate it.
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mkdir -p data
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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log "Running prepare.sh"
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log "dl_dir: $dl_dir"
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if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
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log "Stage 0: Download the necessary csv files"
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if [ ! -e $dl_dir/audioset/.csv.done]; then
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wget --continue "http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/class_labels_indices.csv" -O "${dl_dir}/audioset/class_labels_indices.csv"
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wget --continue http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/balanced_train_segments.csv -O "${dl_dir}/audioset/balanced_train_segments.csv"
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wget --continue http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/eval_segments.csv -O "${dl_dir}/audioset/eval_segments.csv"
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touch $dl_dir/audioset/.csv.done
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fi
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fi
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Construct the audioset manifest and compute the fbank features for balanced set"
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fbank_dir=data/fbank
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if [! -e $fbank_dir/.balanced.done]; then
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python local/generate_audioset_manifest.py \
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--dataset-dir $dl_dir/audioset \
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--split balanced \
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--feat-output-dir $fbank_dir
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touch $fbank_dir/.balanced.done
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fi
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fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Construct the audioset manifest and compute the fbank features for unbalanced set"
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fbank_dir=data/fbank
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if [! -e $fbank_dir/.unbalanced.done]; then
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python local/generate_audioset_manifest.py \
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--dataset-dir $dl_dir/audioset \
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--split unbalanced \
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--feat-output-dir $fbank_dir
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touch $fbank_dir/.unbalanced.done
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fi
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Construct the audioset manifest and compute the fbank features for eval set"
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fbank_dir=data/fbank
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if [! -e $fbank_dir/.eval.done]; then
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python local/generate_audioset_manifest.py \
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--dataset-dir $dl_dir/audioset \
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--split eval \
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--feat-output-dir $fbank_dir
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touch $fbank_dir/.eval.done
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fi
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Prepare musan manifest"
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# We assume that you have downloaded the musan corpus
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# to $dl_dir/musan
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mkdir -p data/manifests
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if [ ! -e data/manifests/.musan.done ]; then
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lhotse prepare musan $dl_dir/musan data/manifests
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touch data/manifests/.musan.done
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fi
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fi
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if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: Compute fbank for musan"
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mkdir -p data/fbank
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if [ ! -e data/fbank/.musan.done ]; then
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./local/compute_fbank_musan.py
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touch data/fbank/.musan.done
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fi
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fi
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