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PaddleSpeech/examples/zh_en_tts/tts3/local/preprocess.sh

150 lines
5.0 KiB

#!/bin/bash
stage=0
stop_stage=100
config_path=$1
datasets_root_dir=$2
mfa_root_dir=$3
# 1. get durations from MFA's result
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
echo "Generate durations_baker.txt from MFA results ..."
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
--inputdir=${mfa_root_dir}/baker_alignment_tone \
--output durations_baker.txt \
--config=${config_path}
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "Generate durations_ljspeech.txt from MFA results ..."
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
--inputdir=${mfa_root_dir}/ljspeech_alignment \
--output durations_ljspeech.txt \
--config=${config_path}
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "Generate durations_aishell3.txt from MFA results ..."
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
--inputdir=${mfa_root_dir}/aishell3_alignment_tone \
--output durations_aishell3.txt \
--config=${config_path}
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
echo "Generate durations_vctk.txt from MFA results ..."
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
--inputdir=${mfa_root_dir}/vctk_alignment \
--output durations_vctk.txt \
--config=${config_path}
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# concat duration file
echo "concat durations_baker.txt, durations_ljspeech.txt, durations_aishell3.txt and durations_vctk.txt to durations.txt"
cat durations_baker.txt durations_ljspeech.txt durations_aishell3.txt durations_vctk.txt > durations.txt
fi
# 2. extract features
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
echo "Extract baker features ..."
python3 ${BIN_DIR}/preprocess.py \
--dataset=baker \
--rootdir=${datasets_root_dir}/BZNSYP/ \
--dumpdir=dump \
--dur-file=durations.txt \
--config=${config_path} \
--num-cpu=20 \
--cut-sil=True \
--write_metadata_method=a
fi
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
echo "Extract ljspeech features ..."
python3 ${BIN_DIR}/preprocess.py \
--dataset=ljspeech \
--rootdir=${datasets_root_dir}/LJSpeech-1.1/ \
--dumpdir=dump \
--dur-file=durations.txt \
--config=${config_path} \
--num-cpu=20 \
--cut-sil=True \
--write_metadata_method=a
fi
if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
echo "Extract aishell3 features ..."
python3 ${BIN_DIR}/preprocess.py \
--dataset=aishell3 \
--rootdir=${datasets_root_dir}/data_aishell3/ \
--dumpdir=dump \
--dur-file=durations.txt \
--config=${config_path} \
--num-cpu=20 \
--cut-sil=True \
--write_metadata_method=a
fi
if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
echo "Extract vctk features ..."
python3 ${BIN_DIR}/preprocess.py \
--dataset=vctk \
--rootdir=${datasets_root_dir}/VCTK-Corpus-0.92/ \
--dumpdir=dump \
--dur-file=durations.txt \
--config=${config_path} \
--num-cpu=20 \
--cut-sil=True \
--write_metadata_method=a
fi
# 3. get features' stats(mean and std)
if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then
echo "Get features' stats ..."
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
--metadata=dump/train/raw/metadata.jsonl \
--field-name="speech"
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
--metadata=dump/train/raw/metadata.jsonl \
--field-name="pitch"
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
--metadata=dump/train/raw/metadata.jsonl \
--field-name="energy"
fi
# 4. normalize and covert phone/speaker to id, dev and test should use train's stats
if [ ${stage} -le 10 ] && [ ${stop_stage} -ge 10 ]; then
echo "Normalize ..."
python3 ${BIN_DIR}/normalize.py \
--metadata=dump/train/raw/metadata.jsonl \
--dumpdir=dump/train/norm \
--speech-stats=dump/train/speech_stats.npy \
--pitch-stats=dump/train/pitch_stats.npy \
--energy-stats=dump/train/energy_stats.npy \
--phones-dict=dump/phone_id_map.txt \
--speaker-dict=dump/speaker_id_map.txt
python3 ${BIN_DIR}/normalize.py \
--metadata=dump/dev/raw/metadata.jsonl \
--dumpdir=dump/dev/norm \
--speech-stats=dump/train/speech_stats.npy \
--pitch-stats=dump/train/pitch_stats.npy \
--energy-stats=dump/train/energy_stats.npy \
--phones-dict=dump/phone_id_map.txt \
--speaker-dict=dump/speaker_id_map.txt
python3 ${BIN_DIR}/normalize.py \
--metadata=dump/test/raw/metadata.jsonl \
--dumpdir=dump/test/norm \
--speech-stats=dump/train/speech_stats.npy \
--pitch-stats=dump/train/pitch_stats.npy \
--energy-stats=dump/train/energy_stats.npy \
--phones-dict=dump/phone_id_map.txt \
--speaker-dict=dump/speaker_id_map.txt
fi