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PaddleSpeech/examples/csmsc/tts3/local/inference_streaming.sh

48 lines
1.5 KiB

#!/bin/bash
train_output_path=$1
stage=0
stop_stage=0
# pwgan
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
python3 ${BIN_DIR}/../inference_streaming.py \
--inference_dir=${train_output_path}/inference_streaming \
--am=fastspeech2_csmsc \
--am_stat=dump/train/speech_stats.npy \
--voc=pwgan_csmsc \
--text=${BIN_DIR}/../sentences.txt \
--output_dir=${train_output_path}/pd_infer_out_streaming \
--phones_dict=dump/phone_id_map.txt \
--am_streaming=True
fi
# for more GAN Vocoders
# multi band melgan
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
python3 ${BIN_DIR}/../inference_streaming.py \
--inference_dir=${train_output_path}/inference_streaming \
--am=fastspeech2_csmsc \
--am_stat=dump/train/speech_stats.npy \
--voc=mb_melgan_csmsc \
--text=${BIN_DIR}/../sentences.txt \
--output_dir=${train_output_path}/pd_infer_out_streaming \
--phones_dict=dump/phone_id_map.txt \
--am_streaming=True
fi
# hifigan
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
python3 ${BIN_DIR}/../inference_streaming.py \
--inference_dir=${train_output_path}/inference_streaming \
--am=fastspeech2_csmsc \
--am_stat=dump/train/speech_stats.npy \
--voc=hifigan_csmsc \
--text=${BIN_DIR}/../sentences.txt \
--output_dir=${train_output_path}/pd_infer_out_streaming \
--phones_dict=dump/phone_id_map.txt \
--am_streaming=True
fi