ControlVC: Zero-Shot Voice Conversion with Time-Varying Controls on Pitch and Speed

Demo page with audio samples: https://bit.ly/3PsrKLJ

Paper link: https://arxiv.org/abs/2209.11866

This is the implementation of our paper: "ControlVC: Zero-Shot Voice Conversion with Time-Varying Controls on Pitch and Speed" by Meiying Chen and Zhiyao Duan.

Usage

A detailed example can be found in inference.sh

Setup

Install Python >= 3.6
Run pip install -r requirements.txt
Download all pre-trained checkpoints and put under checkpoints directory.

Prepare data for voice conversion

Create a folder for each speaker and put all the samples uttered by this speaker under one folder.
Trim, pad and using TD-PSOLA to modify prosody.

python3 scripts/preprocess.py \
    --srcdir $WAV_DIR_IN \
    --outdir $WAV_DIR_PROCESSED \
    --postfix $EXT \
    --pad --keepfolder \
    --rhythm_cruve

Extract and parse HuBERT code

python3 infer_hubert.py \
    --feature_type hubert \
    --kmeans_model_path ${CKPT_DIR}/km.bin \
    --acoustic_model_path ${CKPT_DIR}/hubert_base_ls960.pt \
    --layer 6 \
    --wav_path $WAV_DIR_PROCESSED \
    --out_quantized_file_path $OUT_QUANTIZED_FILE \
    --extension $EXT

python3 scripts/parse_hubert_codes.py \
    --codes $OUT_QUANTIZED_FILE \
    --manifest ${MANI_DIR}/wavlist.txt \
    --outdir $MANI_DIR \
    --all-test

Extract and parse speaker embedding

python3 scripts/extract_mel4spkembd.py \
    --wavdir $WAV_DIR_PROCESSED \
    --meldir $MEL_DIR \
    --ext $EXT

python3 infer_spk_embd.py \
    --srcdir $MEL_DIR \
    --outdir $MANI_DIR \
    --checkpoint_path ${CKPT_DIR}/3000000-BL.ckpt \
    --num_utts -1 \
    --len_crop -1

python scripts/parse_spk_embed.py \
    --embed_file ${MANI_DIR}/spk_embed.pkl \
    --manifest ${MANI_DIR}/test.txt \
    --outdir $MANI_DIR

Get speaker statistics (optional)

python scripts/get_f0_stats.py \
    --srcdir $WAV_DIR_PROCESSED \
    --outdir $MANI_DIR

Pitch control and audio generation

python infer_main.py \
     --input_code_file ${MANI_DIR}/test.txt \
     --checkpoint_file ${CKPT_DIR}/embed_f0stat2 \
     --output_dir $OUT_DIR \
     --f0_stats ${MANI_DIR}/f0_stats.pkl \
     --spk_embed ${MANI_DIR}/spk_embed.pkl

Pretrained Models

Please download checkoints from this link:

https://drive.google.com/drive/folders/1APVHQFIb1871UhvymdK_oewWKJWrInYK?usp=sharing

In the folder:

Model	Checkpoint
speaker embedding model	3000000-BL.ckpt
huert model	hubert_base_ls960.pt
hubert k-means quantizer	km.bin
f0 quantizer	vctk_f0_vq
main voice conversion model	embed_f0stat2

Train from Scratch

Training VQ-VAE F0 model

Preprocess data (trim and pad)

python3 scripts/preprocess.py \
    --srcdir $WAV_DIR_IN \
    --outdir $WAV_DIR_PROCESSED \
    --postfix $EXT \
    --pad

Traning

python3 train_f0_vq.py \
--checkpoint_path checkpoints/debug \
--config configs/f0_vqvae.json

Training main voice conversion model

Preprocess your own datasets using all steps in inference except the infer_main.py, which includes:
- preprocess (trim and pad)
- extract and parse HuBERT code
- extract and parse speaker embedding
- get f0 stats (optional)
Training

python3 train_main.py \
--checkpoint_path checkpoints/debug \
--config configs/hifigan.json

Citation

To cite this paper or repo, please use the following BibTeX entry:

@inproceedings{chen23r_interspeech, author={Meiying Chen and Zhiyao Duan}, title={{ControlVC: Zero-Shot Voice Conversion with Time-Varying Controls on Pitch and Speed}}, year=2023, booktitle={Proc. INTERSPEECH 2023}, pages={2098--2102}, doi={10.21437/Interspeech.2023-1788} }

Acknowledgements

This project in based on the following repos (in alphabetic order):

We appreciate those authors for their generous contribution!

License

Please refer to LICENSE.txt for details.

MelissaChen15/control-vc

MelissaChen15

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