icefall/egs/libritts/ASR/prepare.sh
2024-09-08 11:16:12 +08:00

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#!/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
stage=0
stop_stage=100
sampling_rate=24000
nj=32
perturb_speed=true
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 "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/LibriTTS,
# you can create a symlink
#
# ln -sfv /path/to/LibriTTS $dl_dir/LibriTTS
#
if [ ! -d $dl_dir/LibriTTS ]; then
lhotse download libritts $dl_dir
fi
# If you have pre-downloaded it to /path/to/musan,
# you can create a symlink
#
# ln -sfv /path/to/musan $dl_dir/musan
#
if [ ! -d $dl_dir/musan ]; then
lhotse download musan $dl_dir
fi
fi
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
log "Stage 1: Prepare LibriTTS manifest"
# We assume that you have downloaded the LibriTTS corpus
# to $dl_dir/LibriTTS
mkdir -p data/manifests
if [ ! -e data/manifests/.libritts.done ]; then
lhotse prepare libritts --num-jobs 32 $dl_dir/LibriTTS data/manifests
touch data/manifests/.libritts.done
fi
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
log "Stage 2: 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 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Compute Fbank for LibriTTS"
mkdir -p data/fbank
if [ ! -e data/fbank/.libritts.done ]; then
./local/compute_fbank_libritts.py \
--sampling-rate $sampling_rate \
--perturb-speed $perturb_speed
touch data/fbank/.libritts.done
fi
# Here we shuffle and combine the train-clean-100, train-clean-360 and
# train-other-500 together to form the training set.
if [ ! -f data/fbank/libritts_cuts_train-all-shuf.jsonl.gz ]; then
cat <(gunzip -c data/fbank/libritts_cuts_train-clean-100.jsonl.gz) \
<(gunzip -c data/fbank/libritts_cuts_train-clean-360.jsonl.gz) \
<(gunzip -c data/fbank/libritts_cuts_train-other-500.jsonl.gz) | \
shuf | gzip -c > data/fbank/libritts_cuts_train-all-shuf.jsonl.gz
fi
if [ ! -e data/fbank/.libritts-validated.done ]; then
log "Validating data/fbank for LibriTTS"
./local/validate_manifest.py \
data/fbank/libritts_cuts_train-all-shuf.jsonl.gz
touch data/fbank/.libritts-validated.done
fi
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Compute fbank for musan"
if [ ! -f data/fbank/.msuan.done ]; then
mkdir -p data/fbank
./local/compute_fbank_musan.py
touch data/fbank/.msuan.done
fi
fi