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55 lines
1.7 KiB
55 lines
1.7 KiB
#!/bin/bash
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train_output_path=$1
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stage=0
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stop_stage=0
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# voc: pwgan_aishell3
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# the spk_id=174 means baker speaker, default
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# the spk_id=175 means ljspeech speaker
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/../inference.py \
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--inference_dir=${train_output_path}/inference \
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--am=fastspeech2_mix \
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--voc=pwgan_aishell3 \
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--text=${BIN_DIR}/../sentences_mix.txt \
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--output_dir=${train_output_path}/pd_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--speaker_dict=dump/speaker_id_map.txt \
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--lang=mix \
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--spk_id=174
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fi
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# voc: hifigan_aishell3
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# the spk_id=174 means baker speaker, default
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# the spk_id=175 means ljspeech speaker
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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python3 ${BIN_DIR}/../inference.py \
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--inference_dir=${train_output_path}/inference \
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--am=fastspeech2_mix \
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--voc=hifigan_aishell3 \
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--text=${BIN_DIR}/../sentences_mix.txt \
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--output_dir=${train_output_path}/pd_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--speaker_dict=dump/speaker_id_map.txt \
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--lang=mix \
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--spk_id=174
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fi
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# voc: hifigan_csmsc
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# when speaker is 174 (csmsc), use csmsc's vocoder is better than aishell3's
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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python3 ${BIN_DIR}/../inference.py \
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--inference_dir=${train_output_path}/inference \
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--am=fastspeech2_mix \
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--voc=hifigan_csmsc \
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--text=${BIN_DIR}/../sentences_mix.txt \
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--output_dir=${train_output_path}/pd_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--speaker_dict=dump/speaker_id_map.txt \
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--lang=mix \
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--spk_id=174
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fi
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