EasyVC

Work in progress.

A voice conversion framework for different types of encoders, decoders and vocoders.

The encoder-decoder framework is demonstrated in the following figure.

More specifically, three encoders are used to extract representations from speech, including a linguistic encoder, a prosodic encoder and a speaker encoder. Then a decoder is used to reconstruct speech mel-spectrograms. Finally, a vocoder converts mel-spectrograms to waveforms. Note that this repo also supports decoders that directly reconstruct waveforms (e.g. VITS), in these case, vocoders are not needed.

This repo covers all the steps of a voice conversion pipeline from dataset downloading to evaluation.

I am currently working on my own to maintain this repo. I am planning to integrate more encoders and decoders.

Please be aware that this repo is currently very unstable and under very fast developement.

Conda env

create a conda env

conda create --name torch_1.9 --file requirements.txt

Working progress

Dataset
- VCTK
- LibriTTS
- M4Singer
- NUS-48E
- NHSS
Linguistic Encoder
- conformer_ppg from ppg-vc
- vq-wav2vec from fairseq
- hubert_soft from soft-vc
- contentvec_100 from contentvec
- contentvec_500 from contentvec
- whisper_ppg from whisper_ppg
Prosodic Encoder
- log-f0 from ppg-vc
- pitch + energy from fastspeech2
Speaker Encoder
- d-vector from ppg-vc
- ECAPA-TDNN from speechbrain
Decoder
- fastspeech2 from fastspeech2
- taco_ar from s3prl-vc
- taco_mol from ppg-vc
- vits from vits
- grad_tts from Grad_TTS
- diffwave from DiffWave
Vocoder
- hifigan (vctk) from ppg-vc
- BigVGAN from bigvgan
Evaluation
- UTMOS22 mos prediction from UTMOS22
- ASR WER
- ASV EER
- MCD, F0-RMSE, F0-CORR

How to run

Step1: Dataset download

This part of codes are mostly from parallel_wavegan

./bin/download_vctk_dataset.sh

./bin/download_libritts_dataset.sh

Step2: Generate metadata.csv

./bin/preprocess_vctk.sh

./bin/preprocess_libritts.sh

Step3: Extract features

A ESPNET style bash script has been provided for extracting features, including spectrograms, linguistic, speaker, and prosodic representations. Before start extracting features, you need to decide the setups of your encoders, decoder and vocoder.

e.g.

./extract_features.sh --stage 1 \
                      --stop_stage 4 \
                      --dataset vctk \
                      --linguistic_encoder vqwav2vec \
                      --speaker_encoder utt_dvec \
                      --prosodic_encoder ppgvc_f0 \
                      --decoder fastspeech2 \
                      --vocoder ppgvc_hifigan

Options:

dataset:
- vctk
- libritts
speaker_encoder:
- utt_dvec
- utt_ecapa_tdnn
linguistic_encoder:
- vqwav2vec
- conformer_ppg
- hubert_soft
- contentvec_100
- contentvec_500
- whisper_ppg
prosodic_encoder:
- ppgvc_f0
- fastspeech2_pitch_energy
decoder:
- fastspeech2
- taco_ar
- taco_mol
- vits
vocoder:
- ppgvc_hifigan
- vctk_hifigan
- libritts_hifigan

Step4: Training

To run training, you need to select a config file from configs/. The config files are named following the format ${dataset}_${linguistic_encoder}_${speaker_encoder}_${prosodic_encoder}_${decoder}_${vocoder} E.g.

./bin/train.sh configs/vctk_vqwav2vec_uttdvec_ppgvcf0_fs2_ppgvchifigan.yaml

Authors

Mingjie Chen, University of Sheffield
Prof. Thomas Hain, University of Sheffield

Xsx93/EasyVC