We provide a
DiffWave pretrained checkpoint, which is trained on 125 hours of speech data and 80 hours of singing voice data.
1git lfs install
2git clone https://huggingface.co/amphion/diffwave
1cd Amphion
2mkdir -p ckpts/vocoder
3ln -s "$(realpath ../diffwave/diffwave)" pretrained/diffwave
For analysis synthesis on the processed dataset, raw waveform, or predicted mel spectrograms, you can follow the inference part of
this recipe.
1sh egs/vocoder/diffusion/diffwave/run.sh --stage 3 \
2 --infer_mode [Your chosen inference mode] \
3 --infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \
4 --infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \
5 --infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \
6 --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
7 --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \