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[TTS] add svs frontend #3062
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[TTS] add svs frontend #3062
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,53 @@ | ||
#!/bin/bash | ||
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config_path=$1 | ||
train_output_path=$2 | ||
ckpt_name=$3 | ||
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stage=0 | ||
stop_stage=0 | ||
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# pwgan | ||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then | ||
FLAGS_allocator_strategy=naive_best_fit \ | ||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \ | ||
python3 ${BIN_DIR}/../synthesize_e2e.py \ | ||
--am=diffsinger_opencpop \ | ||
--am_config=${config_path} \ | ||
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ | ||
--am_stat=dump/train/speech_stats.npy \ | ||
--voc=pwgan_opencpop \ | ||
--voc_config=pwgan_opencpop_ckpt_1.4.0/default.yaml \ | ||
--voc_ckpt=pwgan_opencpop_ckpt_1.4.0/snapshot_iter_100000.pdz \ | ||
--voc_stat=pwgan_opencpop_ckpt_1.4.0/feats_stats.npy \ | ||
--lang=sing \ | ||
--text=${BIN_DIR}/../sentences_sing.txt \ | ||
--output_dir=${train_output_path}/test_e2e \ | ||
--phones_dict=dump/phone_id_map.txt \ | ||
--speech_stretchs=dump/train/speech_stretchs.npy \ | ||
--pinyin_phone=./pinyin_to_phone.txt | ||
fi | ||
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# for more GAN Vocoders | ||
# hifigan | ||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then | ||
echo "in hifigan syn_e2e" | ||
FLAGS_allocator_strategy=naive_best_fit \ | ||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \ | ||
python3 ${BIN_DIR}/../synthesize_e2e.py \ | ||
--am=diffsinger_opencpop \ | ||
--am_config=${config_path} \ | ||
--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ | ||
--am_stat=dump/train/speech_stats.npy \ | ||
--voc=hifigan_opencpop \ | ||
--voc_config=hifigan_opencpop_ckpt_1.4.0/default.yaml \ | ||
--voc_ckpt=hifigan_opencpop_ckpt_1.4.0/snapshot_iter_625000.pdz \ | ||
--voc_stat=hifigan_opencpop_ckpt_1.4.0/feats_stats.npy \ | ||
--lang=sing \ | ||
--text=${BIN_DIR}/../sentences_sing.txt \ | ||
--output_dir=${train_output_path}/test_e2e \ | ||
--phones_dict=dump/phone_id_map.txt \ | ||
--speech_stretchs=dump/train/speech_stretchs.npy \ | ||
--pinyin_phone=./pinyin_to_phone.txt | ||
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fi |
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pinyin_to_phone.txt 这个文件在训练的时候有步骤获取吗,好像没有看到
是否一定要用 pinyin_to_phone.txt 这个文件,因为感觉文本前端除了转 id, 其他部分应该是模型无关的,但是 pinyin_to_phone.txt 又是模型相关的,我大概看了下,这个文件其实就是把拼音拆成声母和韵母(可能会有一些特殊的情况),好像也可以做到模型无关(不一定非要在模型的数据预处理阶段获取?)。是否可以直接用不带 tone 的 pypinyin 的输出加一些特殊的规则解决?
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要,因为opencpop 是是根据这个获取的,不对应的话就可能不对