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118 lines
4.5 KiB
118 lines
4.5 KiB
#!/bin/bash
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config_path=$1
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train_output_path=$2
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ckpt_name=$3
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stage=0
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stop_stage=0
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=speedyspeech_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/feats_stats.npy \
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--voc=pwgan_csmsc \
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--voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
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--voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
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--voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--inference_dir=${train_output_path}/inference \
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--phones_dict=dump/phone_id_map.txt \
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--tones_dict=dump/tone_id_map.txt
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fi
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# for more GAN Vocoders
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# multi band melgan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=speedyspeech_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/feats_stats.npy \
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--voc=mb_melgan_csmsc \
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--voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \
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--voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\
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--voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--inference_dir=${train_output_path}/inference \
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--phones_dict=dump/phone_id_map.txt \
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--tones_dict=dump/tone_id_map.txt
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fi
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# the pretrained models haven't release now
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# style melgan
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# style melgan's Dygraph to Static Graph is not ready now
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=speedyspeech_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/feats_stats.npy \
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--voc=style_melgan_csmsc \
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--voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \
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--voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \
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--voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--tones_dict=dump/tone_id_map.txt
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# --inference_dir=${train_output_path}/inference
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fi
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# hifigan
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=speedyspeech_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/feats_stats.npy \
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--voc=hifigan_csmsc \
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--voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \
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--voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \
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--voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--inference_dir=${train_output_path}/inference \
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--phones_dict=dump/phone_id_map.txt \
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--tones_dict=dump/tone_id_map.txt
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fi
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# wavernn
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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echo "in wavernn syn_e2e"
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=speedyspeech_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/feats_stats.npy \
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--voc=wavernn_csmsc \
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--voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
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--voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \
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--voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--tones_dict=dump/tone_id_map.txt \
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--inference_dir=${train_output_path}/inference
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fi
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