#!/bin/bash # Copyright 2019 Tomoki Hayashi # MIT License (https://opensource.org/licenses/MIT) . ./cmd.sh || exit 1; . ./path.sh || exit 1; # basic settings stage=-1 # stage to start stop_stage=100 # stage to stop verbose=1 # verbosity level (lower is less info) n_gpus=1 # number of gpus in training n_jobs=16 # number of parallel jobs in feature extraction # NOTE(kan-bayashi): renamed to conf to avoid conflict in parse_options.sh conf=conf/parallel_wavegan.v1.yaml # directory path setting download_dir=downloads # direcotry to save downloaded files dumpdir=dump # directory to dump features # training related setting tag="" # tag for directory to save model resume="" # checkpoint path to resume training # (e.g. //checkpoint-10000steps.pkl) # decoding related setting checkpoint="" # checkpoint path to be used for decoding # if not provided, the latest one will be used # (e.g. //checkpoint-400000steps.pkl) # shellcheck disable=SC1091 . utils/parse_options.sh || exit 1; train_set="train_nodev" # name of training data directory dev_set="dev" # name of development data direcotry eval_set="eval" # name of evaluation data direcotry set -euo pipefail if [ "${stage}" -le -1 ] && [ "${stop_stage}" -ge -1 ]; then echo "Stage -1: Data download" local/data_download.sh "${download_dir}" fi if [ "${stage}" -le 0 ] && [ "${stop_stage}" -ge 0 ]; then echo "Stage 0: Data preparation" local/data_prep.sh \ --train_set "${train_set}" \ --dev_set "${dev_set}" \ --eval_set "${eval_set}" \ --shuffle true \ "${download_dir}/sc_all" data fi stats_ext=$(grep -q "hdf5" <(yq ".format" "${conf}") && echo "h5" || echo "npy") if [ "${stage}" -le 1 ] && [ "${stop_stage}" -ge 1 ]; then echo "Stage 1: Feature extraction" # extract raw features pids=() for name in "${train_set}" "${dev_set}" "${eval_set}"; do ( [ ! -e "${dumpdir}/${name}/raw" ] && mkdir -p "${dumpdir}/${name}/raw" echo "Feature extraction start. See the progress via ${dumpdir}/${name}/raw/preprocessing.*.log." utils/make_subset_data.sh "data/${name}" "${n_jobs}" "${dumpdir}/${name}/raw" ${train_cmd} JOB=1:${n_jobs} "${dumpdir}/${name}/raw/preprocessing.JOB.log" \ parallel-wavegan-preprocess \ --config "${conf}" \ --scp "${dumpdir}/${name}/raw/wav.JOB.scp" \ --dumpdir "${dumpdir}/${name}/raw/dump.JOB" \ --verbose "${verbose}" echo "Successfully finished feature extraction of ${name} set." ) & pids+=($!) done i=0; for pid in "${pids[@]}"; do wait "${pid}" || ((++i)); done [ "${i}" -gt 0 ] && echo "$0: ${i} background jobs are failed." && exit 1; echo "Successfully finished feature extraction." # calculate statistics for normalization echo "Statistics computation start. See the progress via ${dumpdir}/${train_set}/compute_statistics.log." ${train_cmd} "${dumpdir}/${train_set}/compute_statistics.log" \ parallel-wavegan-compute-statistics \ --config "${conf}" \ --rootdir "${dumpdir}/${train_set}/raw" \ --dumpdir "${dumpdir}/${train_set}" \ --verbose "${verbose}" echo "Successfully finished calculation of statistics." # normalize and dump them pids=() for name in "${train_set}" "${dev_set}" "${eval_set}"; do ( [ ! -e "${dumpdir}/${name}/norm" ] && mkdir -p "${dumpdir}/${name}/norm" echo "Nomalization start. See the progress via ${dumpdir}/${name}/norm/normalize.*.log." ${train_cmd} JOB=1:${n_jobs} "${dumpdir}/${name}/norm/normalize.JOB.log" \ parallel-wavegan-normalize \ --config "${conf}" \ --stats "${dumpdir}/${train_set}/stats.${stats_ext}" \ --rootdir "${dumpdir}/${name}/raw/dump.JOB" \ --dumpdir "${dumpdir}/${name}/norm/dump.JOB" \ --verbose "${verbose}" echo "Successfully finished normalization of ${name} set." ) & pids+=($!) done i=0; for pid in "${pids[@]}"; do wait "${pid}" || ((++i)); done [ "${i}" -gt 0 ] && echo "$0: ${i} background jobs are failed." && exit 1; echo "Successfully finished normalization." fi if [ -z "${tag}" ]; then expdir="exp/${train_set}_speech_commands_$(basename "${conf}" .yaml)" else expdir="exp/${train_set}_speech_commands_${tag}" fi if [ "${stage}" -le 2 ] && [ "${stop_stage}" -ge 2 ]; then echo "Stage 2: Network training" [ ! -e "${expdir}" ] && mkdir -p "${expdir}" cp "${dumpdir}/${train_set}/stats.${stats_ext}" "${expdir}" if [ "${n_gpus}" -gt 1 ]; then train="python -m parallel_wavegan.distributed.launch --nproc_per_node ${n_gpus} -c parallel-wavegan-train" else train="parallel-wavegan-train" fi echo "Training start. See the progress via ${expdir}/train.log." ${cuda_cmd} --gpu "${n_gpus}" "${expdir}/train.log" \ ${train} \ --config "${conf}" \ --train-dumpdir "${dumpdir}/${train_set}/norm" \ --dev-dumpdir "${dumpdir}/${dev_set}/norm" \ --outdir "${expdir}" \ --resume "${resume}" \ --verbose "${verbose}" echo "Successfully finished training." fi if [ "${stage}" -le 3 ] && [ "${stop_stage}" -ge 3 ]; then echo "Stage 3: Network decoding" # shellcheck disable=SC2012 [ -z "${checkpoint}" ] && checkpoint="$(ls -dt "${expdir}"/*.pkl | head -1 || true)" outdir="${expdir}/wav/$(basename "${checkpoint}" .pkl)" pids=() for name in "${dev_set}" "${eval_set}"; do ( [ ! -e "${outdir}/${name}" ] && mkdir -p "${outdir}/${name}" [ "${n_gpus}" -gt 1 ] && n_gpus=1 echo "Decoding start. See the progress via ${outdir}/${name}/decode.log." ${cuda_cmd} --gpu "${n_gpus}" "${outdir}/${name}/decode.log" \ parallel-wavegan-decode \ --dumpdir "${dumpdir}/${name}/norm" \ --checkpoint "${checkpoint}" \ --outdir "${outdir}/${name}" \ --verbose "${verbose}" echo "Successfully finished decoding of ${name} set." ) & pids+=($!) done i=0; for pid in "${pids[@]}"; do wait "${pid}" || ((++i)); done [ "${i}" -gt 0 ] && echo "$0: ${i} background jobs are failed." && exit 1; echo "Successfully finished decoding." fi echo "Finished."