Phoneme_Hallucinator_v2/README.md

Phoneme_Hallucinator

This is the repository of the paper "Phoneme Hallucinator: One-shot Voice Conversion via Set Expansion" accepted by AAAI-2024. Some audio samples are provided here.

Inference Tutorial

1. If you only want to run our VC pipeline, please download Phoneme Hallucinator DEMO.ipynb in this repo and run it in google colab.

Training Tutorial

1. Prepare environment. Require Python 3.6.3 and the following packages

   pillow == 8.0.1
   torch == 1.10.2
   tensorflow == 1.15.5
   tensorflow-probability == 0.7.0
   tensorpack == 0.9.8
   h5py == 2.10.0
   numpy == 1.19.5
   pathlib == 1.0.1
   tqdm == 4.64.1
   easydict == 1.10
   matplotlib == 3.3.4
   scikit-learn == 0.24.2
   scipy == 1.5.4
   seaborn == 0.11.2
   

2. To prepare the training set, we need to use WavLM to extract speech representations. Go to kNN-VC repo and follow its instructions to extract speech representations. Namely, after placing LibriSpeech dataset in a correct location, run the command:

   python prematch_dataset.py --librispeech_path /path/to/librispeech/root --out_path /path/where/you/want/outputs/to/go --topk 4 --matching_layer 6 --synthesis_layer 6

   Note that we don't use the "--prematch" option, becuase we only need to extract representations, not to extract and then perform kNN regression.

3. After the above step, you can get a --out_path folder with three subfolders train-clean-100, test-clean and dev-clean where each folder contains the speech representation files (".pt").

4. Go to our repo ./dataset/speech.py and change the variables path_to_wavlm_feat and tfrecord_path accordingly. You need to change path_to_wavlm_feat to where the speech representations are stored in the previous step.

5. Start Training by the following command: python scripts/run.py --cfg_file=./exp/speech_XXL_cond/params.json --mode=train

   If tfrecord_path doesn't exist, our codes will create tfrecords and save them to tfrecord_path before training starts. Note that if you encounter numerical issues ("NaN, INF") when the training starts, just try re-run the command multiple times. Training los will be saved to ./exp/speech_XXL_cond/.