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Add XPU support for FastSpeech2 (#3514)

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* Add XPU support for FastSpeech2

* optimize
pull/3526/head
Kai Song 1 year ago committed by GitHub
parent 08599b7300
commit 9a50647592
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6 changed files with 349 additions and 3 deletions
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55
examples/csmsc/tts3/local/inference_xpu.sh
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#!/bin/bash
train_output_path=$1
stage=0
stop_stage=0
# pwgan
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
python3 ${BIN_DIR}/../inference.py \
--inference_dir=${train_output_path}/inference \
--am=fastspeech2_csmsc \
--voc=pwgan_csmsc \
--text=${BIN_DIR}/../../assets/sentences.txt \
--output_dir=${train_output_path}/pd_infer_out \
--phones_dict=dump/phone_id_map.txt \
--device xpu
fi
# for more GAN Vocoders
# multi band melgan
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
python3 ${BIN_DIR}/../inference.py \
--inference_dir=${train_output_path}/inference \
--am=fastspeech2_csmsc \
--voc=mb_melgan_csmsc \
--text=${BIN_DIR}/../../assets/sentences.txt \
--output_dir=${train_output_path}/pd_infer_out \
--phones_dict=dump/phone_id_map.txt \
--device xpu
fi
# hifigan
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
python3 ${BIN_DIR}/../inference.py \
--inference_dir=${train_output_path}/inference \
--am=fastspeech2_csmsc \
--voc=hifigan_csmsc \
--text=${BIN_DIR}/../../assets/sentences.txt \
--output_dir=${train_output_path}/pd_infer_out \
--phones_dict=dump/phone_id_map.txt \
--device xpu
fi
# wavernn
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
python3 ${BIN_DIR}/../inference.py \
--inference_dir=${train_output_path}/inference \
--am=fastspeech2_csmsc \
--voc=wavernn_csmsc \
--text=${BIN_DIR}/../../assets/sentences.txt \
--output_dir=${train_output_path}/pd_infer_out \
--phones_dict=dump/phone_id_map.txt \
--device xpu
fi

119
examples/csmsc/tts3/local/synthesize_e2e_xpu.sh
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#!/bin/bash
config_path=$1
train_output_path=$2
ckpt_name=$3
stage=0
stop_stage=0
# pwgan
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
FLAGS_allocator_strategy=naive_best_fit \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=pwgan_csmsc \
--voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
--voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
--voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../../assets/sentences.txt \
--output_dir=${train_output_path}/test_e2e \
--phones_dict=dump/phone_id_map.txt \
--inference_dir=${train_output_path}/inference \
--ngpu=0 \
--nxpu=1
fi
# for more GAN Vocoders
# multi band melgan
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
FLAGS_allocator_strategy=naive_best_fit \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=mb_melgan_csmsc \
--voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \
--voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\
--voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../../assets/sentences.txt \
--output_dir=${train_output_path}/test_e2e \
--phones_dict=dump/phone_id_map.txt \
--inference_dir=${train_output_path}/inference \
--ngpu=0 \
--nxpu=1
fi
# the pretrained models haven't release now
# style melgan
# style melgan's Dygraph to Static Graph is not ready now
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
FLAGS_allocator_strategy=naive_best_fit \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=style_melgan_csmsc \
--voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \
--voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \
--voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../../assets/sentences.txt \
--output_dir=${train_output_path}/test_e2e \
--phones_dict=dump/phone_id_map.txt \
--ngpu=0 \
--nxpu=1
# --inference_dir=${train_output_path}/inference
fi
# hifigan
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
echo "in hifigan syn_e2e"
FLAGS_allocator_strategy=naive_best_fit \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=hifigan_csmsc \
--voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \
--voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \
--voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../../assets/sentences.txt \
--output_dir=${train_output_path}/test_e2e \
--phones_dict=dump/phone_id_map.txt \
--inference_dir=${train_output_path}/inference \
--ngpu=0 \
--nxpu=1
fi
# wavernn
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
echo "in wavernn syn_e2e"
FLAGS_allocator_strategy=naive_best_fit \
python3 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=wavernn_csmsc \
--voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
--voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \
--voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \
--lang=zh \
--text=${BIN_DIR}/../../assets/sentences.txt \
--output_dir=${train_output_path}/test_e2e \
--phones_dict=dump/phone_id_map.txt \
--inference_dir=${train_output_path}/inference \
--ngpu=0 \
--nxpu=1
fi

105
examples/csmsc/tts3/local/synthesize_xpu.sh
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#!/bin/bash
config_path=$1
train_output_path=$2
ckpt_name=$3
stage=0
stop_stage=0
# pwgan
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
FLAGS_allocator_strategy=naive_best_fit \
python3 ${BIN_DIR}/../synthesize.py \
--am=fastspeech2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=pwgan_csmsc \
--voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
--voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
--voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
--test_metadata=dump/test/norm/metadata.jsonl \
--output_dir=${train_output_path}/test \
--phones_dict=dump/phone_id_map.txt \
--ngpu=0 \
--nxpu=1
fi
# for more GAN Vocoders
# multi band melgan
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
FLAGS_allocator_strategy=naive_best_fit \
python3 ${BIN_DIR}/../synthesize.py \
--am=fastspeech2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=mb_melgan_csmsc \
--voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \
--voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\
--voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
--test_metadata=dump/test/norm/metadata.jsonl \
--output_dir=${train_output_path}/test \
--phones_dict=dump/phone_id_map.txt \
--ngpu=0 \
--nxpu=1
fi
# style melgan
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
FLAGS_allocator_strategy=naive_best_fit \
python3 ${BIN_DIR}/../synthesize.py \
--am=fastspeech2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=style_melgan_csmsc \
--voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \
--voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \
--voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
--test_metadata=dump/test/norm/metadata.jsonl \
--output_dir=${train_output_path}/test \
--phones_dict=dump/phone_id_map.txt \
--ngpu=0 \
--nxpu=1
fi
# hifigan
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
echo "in hifigan syn"
FLAGS_allocator_strategy=naive_best_fit \
python3 ${BIN_DIR}/../synthesize.py \
--am=fastspeech2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=hifigan_csmsc \
--voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \
--voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \
--voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \
--test_metadata=dump/test/norm/metadata.jsonl \
--output_dir=${train_output_path}/test \
--phones_dict=dump/phone_id_map.txt \
--ngpu=0 \
--nxpu=1
fi
# wavernn
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
echo "in wavernn syn"
FLAGS_allocator_strategy=naive_best_fit \
python3 ${BIN_DIR}/../synthesize.py \
--am=fastspeech2_csmsc \
--am_config=${config_path} \
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--am_stat=dump/train/speech_stats.npy \
--voc=wavernn_csmsc \
--voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
--voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \
--voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \
--test_metadata=dump/test/norm/metadata.jsonl \
--output_dir=${train_output_path}/test \
--phones_dict=dump/phone_id_map.txt \
--ngpu=0 \
--nxpu=1
fi

13
examples/csmsc/tts3/local/train_xpu.sh
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#!/bin/bash
config_path=$1
train_output_path=$2
python3 ${BIN_DIR}/train.py \
--train-metadata=dump/train/norm/metadata.jsonl \
--dev-metadata=dump/dev/norm/metadata.jsonl \
--config=${config_path} \
--output-dir=${train_output_path} \
--ngpu=0 \
--nxpu=1 \
--phones-dict=dump/phone_id_map.txt

42
examples/csmsc/tts3/run_xpu.sh
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#!/bin/bash
set -e
source path.sh
xpus=0,1
stage=0
stop_stage=100
conf_path=conf/default.yaml
train_output_path=exp/default
ckpt_name=snapshot_iter_153.pdz
# with the following command, you can choose the stage range you want to run
# such as `./run.sh --stage 0 --stop-stage 0`
# this can not be mixed use with `$1`, `$2` ...
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data
./local/preprocess.sh ${conf_path} || exit -1
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
FLAGS_selected_xpus=${xpus} ./local/train_xpu.sh ${conf_path} ${train_output_path} || exit -1
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# synthesize, vocoder is pwgan by default
FLAGS_selected_xpus=${xpus} ./local/synthesize_xpu.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# synthesize_e2e, vocoder is pwgan by default
FLAGS_selected_xpus=${xpus} ./local/synthesize_e2e_xpu.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# inference with static model, vocoder is pwgan by default
FLAGS_selected_xpus=${xpus} ./local/inference_xpu.sh ${train_output_path} || exit -1
fi

18
paddlespeech/t2s/exps/fastspeech2/train.py
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@ -44,10 +44,17 @@ from paddlespeech.t2s.utils import str2bool
def train_sp(args, config):
# decides device type and whether to run in parallel
# setup running environment correctly
if (not paddle.is_compiled_with_cuda()) or args.ngpu == 0:
if args.ngpu > 0 and paddle.is_compiled_with_cuda():
paddle.set_device("gpu")
elif args.nxpu > 0 and paddle.is_compiled_with_xpu():
paddle.set_device("xpu")
elif args.ngpu == 0 and args.nxpu == 0:
paddle.set_device("cpu")
else:
paddle.set_device("gpu")
raise ValueError(
"Please make sure that the paddle you installed matches the device type you set, "
"and that ngpu and nxpu cannot be negative at the same time.")
world_size = paddle.distributed.get_world_size()
if world_size > 1:
paddle.distributed.init_parallel_env()
@ -183,7 +190,12 @@ def main():
parser.add_argument("--dev-metadata", type=str, help="dev data.")
parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
"--ngpu", type=int, default=1, help="if ngpu=0, use cpu or xpu.")
parser.add_argument(
"--nxpu",
type=int,
default=0,
help="if ngpu=0 and nxpu > 0, use xpu. if ngpu=0 and nxpu=0, use cpu.")
parser.add_argument(
"--phones-dict", type=str, default=None, help="phone vocabulary file.")
parser.add_argument(

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