icefall/egs/aishell3/TTS/prepare.sh
2024-04-06 21:49:32 +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=-1
stop_stage=100
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: build monotonic_align lib"
if [ ! -d vits/monotonic_align/build ]; then
cd vits/monotonic_align
python3 setup.py build_ext --inplace
cd ../../
else
log "monotonic_align lib already built"
fi
fi
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
log "Stage 1: Download data"
# The directory $dl_dir/aishell3 will contain the following files
# and sub directories
# ChangeLog ReadMe.txt phone_set.txt spk-info.txt test train
# If you have pre-downloaded it to /path/to/aishell3, you can create a symlink
#
# ln -sfv /path/to/aishell3 $dl_dir/
# touch $dl_dir/aishell3/.completed
#
if [ ! -d $dl_dir/aishell3 ]; then
lhotse download aishell3 $dl_dir
fi
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
log "Stage 2: Prepare aishell3 manifest (may take 13 minutes)"
# We assume that you have downloaded the baker corpus
# to $dl_dir/aishell3.
# You can find files like spk-info.txt inside $dl_dir/aishell3
mkdir -p data/manifests
if [ ! -e data/manifests/.aishell3.done ]; then
lhotse prepare aishell3 $dl_dir/aishell3 data/manifests
touch data/manifests/.aishell3.done
fi
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Compute spectrogram for aishell3 (may take 5 minutes)"
mkdir -p data/spectrogram
if [ ! -e data/spectrogram/.aishell3.done ]; then
./local/compute_spectrogram_aishell3.py
touch data/spectrogram/.aishell3.done
fi
if [ ! -e data/spectrogram/.aishell3-validated.done ]; then
log "Validating data/spectrogram for aishell3"
python3 ./local/validate_manifest.py \
data/spectrogram/aishell3_cuts_train.jsonl.gz
python3 ./local/validate_manifest.py \
data/spectrogram/aishell3_cuts_test.jsonl.gz
touch data/spectrogram/.aishell3-validated.done
fi
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Prepare tokens for aishell3 (may take 20 seconds)"
if [ ! -e data/spectrogram/.aishell3_with_token.done ]; then
./local/prepare_tokens_aishell3.py
mv -v data/spectrogram/aishell3_cuts_with_tokens_train.jsonl.gz \
data/spectrogram/aishell3_cuts_train.jsonl.gz
mv -v data/spectrogram/aishell3_cuts_with_tokens_test.jsonl.gz \
data/spectrogram/aishell3_cuts_test.jsonl.gz
touch data/spectrogram/.aishell3_with_token.done
fi
fi
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Split the aishell3 cuts into train, valid and test sets (may take 25 seconds)"
if [ ! -e data/spectrogram/.aishell3_split.done ]; then
lhotse subset --last 1000 \
data/spectrogram/aishell3_cuts_test.jsonl.gz \
data/spectrogram/aishell3_cuts_valid.jsonl.gz
n=$(( $(gunzip -c data/spectrogram/aishell3_cuts_test.jsonl.gz | wc -l) - 1000 ))
lhotse subset --first $n \
data/spectrogram/aishell3_cuts_test.jsonl.gz \
data/spectrogram/aishell3_cuts_test2.jsonl.gz
mv data/spectrogram/aishell3_cuts_test2.jsonl.gz data/spectrogram/aishell3_cuts_test.jsonl.gz
touch data/spectrogram/.aishell3_split.done
fi
fi
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
log "Stage 6: Generate token file"
if [ ! -e data/tokens.txt ]; then
./local/prepare_token_file.py --tokens data/tokens.txt
fi
fi
if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
log "Stage 7: Generate speakers file"
if [ ! -e data/speakers.txt ]; then
gunzip -c data/manifests/aishell3_supervisions_train.jsonl.gz \
| jq '.speaker' | sed 's/"//g' \
| sort | uniq > data/speakers.txt
fi
fi