icefall/egs/ljspeech/TTS/prepare.sh
2024-10-29 15:04:04 +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 -1 ] && [ $stop_stage -ge -1 ]; then
log "Stage -1: 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 for vits already built"
fi
if [ ! -f ./matcha/monotonic_align/core.cpython-38-x86_64-linux-gnu.so ]; then
pushd matcha/monotonic_align
python3 setup.py build
mv -v build/lib.*/matcha/monotonic_align/core.*.so .
rm -rf build
rm core.c
ls -lh
popd
else
log "monotonic_align lib for matcha-tts already built"
fi
fi
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
log "Stage 0: Download data"
# The directory $dl_dir/LJSpeech-1.1 will contain:
# - wavs, which contains the audio files
# - metadata.csv, which provides the transcript text for each audio clip
# If you have pre-downloaded it to /path/to/LJSpeech-1.1, you can create a symlink
#
# ln -sfv /path/to/LJSpeech-1.1 $dl_dir/LJSpeech-1.1
#
if [ ! -d $dl_dir/LJSpeech-1.1 ]; then
lhotse download ljspeech $dl_dir
fi
fi
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
log "Stage 1: Prepare LJSpeech manifest"
# We assume that you have downloaded the LJSpeech corpus
# to $dl_dir/LJSpeech-1.1
mkdir -p data/manifests
if [ ! -e data/manifests/.ljspeech.done ]; then
lhotse prepare ljspeech $dl_dir/LJSpeech-1.1 data/manifests
touch data/manifests/.ljspeech.done
fi
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
log "Stage 2: Compute spectrogram for LJSpeech (used by ./vits)"
mkdir -p data/spectrogram
if [ ! -e data/spectrogram/.ljspeech.done ]; then
./local/compute_spectrogram_ljspeech.py
touch data/spectrogram/.ljspeech.done
fi
if [ ! -e data/spectrogram/.ljspeech-validated.done ]; then
log "Validating data/spectrogram for LJSpeech (used by ./vits)"
python3 ./local/validate_manifest.py \
data/spectrogram/ljspeech_cuts_all.jsonl.gz
touch data/spectrogram/.ljspeech-validated.done
fi
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Prepare phoneme tokens for LJSpeech (used by ./vits)"
# We assume you have installed piper_phonemize and espnet_tts_frontend.
# If not, please install them with:
# - piper_phonemize: pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html,
# - espnet_tts_frontend, `pip install espnet_tts_frontend`, refer to https://github.com/espnet/espnet_tts_frontend/
if [ ! -e data/spectrogram/.ljspeech_with_token.done ]; then
./local/prepare_tokens_ljspeech.py --in-out-dir ./data/spectrogram
mv data/spectrogram/ljspeech_cuts_with_tokens_all.jsonl.gz \
data/spectrogram/ljspeech_cuts_all.jsonl.gz
touch data/spectrogram/.ljspeech_with_token.done
fi
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Split the LJSpeech cuts into train, valid and test sets (used by vits)"
if [ ! -e data/spectrogram/.ljspeech_split.done ]; then
lhotse subset --last 600 \
data/spectrogram/ljspeech_cuts_all.jsonl.gz \
data/spectrogram/ljspeech_cuts_validtest.jsonl.gz
lhotse subset --first 100 \
data/spectrogram/ljspeech_cuts_validtest.jsonl.gz \
data/spectrogram/ljspeech_cuts_valid.jsonl.gz
lhotse subset --last 500 \
data/spectrogram/ljspeech_cuts_validtest.jsonl.gz \
data/spectrogram/ljspeech_cuts_test.jsonl.gz
rm data/spectrogram/ljspeech_cuts_validtest.jsonl.gz
n=$(( $(gunzip -c data/spectrogram/ljspeech_cuts_all.jsonl.gz | wc -l) - 600 ))
lhotse subset --first $n \
data/spectrogram/ljspeech_cuts_all.jsonl.gz \
data/spectrogram/ljspeech_cuts_train.jsonl.gz
touch data/spectrogram/.ljspeech_split.done
fi
fi
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Generate token file"
# We assume you have installed piper_phonemize and espnet_tts_frontend.
# If not, please install them with:
# - piper_phonemize: refer to https://github.com/rhasspy/piper-phonemize,
# could install the pre-built wheels from https://github.com/csukuangfj/piper-phonemize/releases/tag/2023.12.5
# - espnet_tts_frontend, `pip install espnet_tts_frontend`, refer to https://github.com/espnet/espnet_tts_frontend/
if [ ! -e data/tokens.txt ]; then
./local/prepare_token_file.py --tokens data/tokens.txt
fi
fi
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
log "Stage 6: Generate fbank (used by ./matcha)"
mkdir -p data/fbank
if [ ! -e data/fbank/.ljspeech.done ]; then
./local/compute_fbank_ljspeech.py
touch data/fbank/.ljspeech.done
fi
if [ ! -e data/fbank/.ljspeech-validated.done ]; then
log "Validating data/fbank for LJSpeech (used by ./matcha)"
python3 ./local/validate_manifest.py \
data/fbank/ljspeech_cuts_all.jsonl.gz
touch data/fbank/.ljspeech-validated.done
fi
fi
if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
log "Stage 7: Prepare phoneme tokens for LJSpeech (used by ./matcha)"
# We assume you have installed piper_phonemize and espnet_tts_frontend.
# If not, please install them with:
# - piper_phonemize: pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html,
# - espnet_tts_frontend, `pip install espnet_tts_frontend`, refer to https://github.com/espnet/espnet_tts_frontend/
if [ ! -e data/fbank/.ljspeech_with_token.done ]; then
./local/prepare_tokens_ljspeech.py --in-out-dir ./data/fbank
mv data/fbank/ljspeech_cuts_with_tokens_all.jsonl.gz \
data/fbank/ljspeech_cuts_all.jsonl.gz
touch data/fbank/.ljspeech_with_token.done
fi
fi
if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
log "Stage 8: Split the LJSpeech cuts into train, valid and test sets (used by ./matcha)"
if [ ! -e data/fbank/.ljspeech_split.done ]; then
lhotse subset --last 600 \
data/fbank/ljspeech_cuts_all.jsonl.gz \
data/fbank/ljspeech_cuts_validtest.jsonl.gz
lhotse subset --first 100 \
data/fbank/ljspeech_cuts_validtest.jsonl.gz \
data/fbank/ljspeech_cuts_valid.jsonl.gz
lhotse subset --last 500 \
data/fbank/ljspeech_cuts_validtest.jsonl.gz \
data/fbank/ljspeech_cuts_test.jsonl.gz
rm data/fbank/ljspeech_cuts_validtest.jsonl.gz
n=$(( $(gunzip -c data/fbank/ljspeech_cuts_all.jsonl.gz | wc -l) - 600 ))
lhotse subset --first $n \
data/fbank/ljspeech_cuts_all.jsonl.gz \
data/fbank/ljspeech_cuts_train.jsonl.gz
touch data/fbank/.ljspeech_split.done
fi
fi
if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
log "Stage 9: Compute fbank mean and std (used by ./matcha)"
if [ ! -f ./data/fbank/cmvn.json ]; then
./local/compute_fbank_statistics.py ./data/fbank/ljspeech_cuts_train.jsonl.gz ./data/fbank/cmvn.json
fi
fi