Unofficial PyTorch Implementation of Multi-Singer

Overview

Multi-Singer

Unofficial PyTorch Implementation of Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus.

Requirements

See requirements in requirement.txt:

  • linux
  • python 3.6
  • pytorch 1.0+
  • librosa
  • json, tqdm, logging

TODO

  • 1026: upload code
  • 1024: implement multi-singer & perceptual loss
  • 1023: implement singer encoder

Getting started

Apply recipe to your own dataset

  • Put any wav files in data directory
  • Edit configuration in config/config.yaml

1. Pretrain

Pretrain the Singer Embedding Extractor using repository here, and set the 'enc_model_fpath' in config/config.yaml

Note: Please set params as those in 'encoder/params_data' and 'encoder/params_model'.

2. Preprocess

Extract mel-spectrogram

python preprocess.py -i data/wavs -o data/feature -c config/config.yaml

-i your audio folder

-o output acoustic feature folder

-c config file

3. Train

Training conditioned on mel-spectrogram

python train.py -i data/feature -o checkpoints/ --config config/config.yaml

-i acoustic feature folder

-o directory to save checkpoints

-c config file

4. Inference

python inference.py -i data/feature -o outputs/  -c checkpoints/*.pkl -g config/config.yaml

-i acoustic feature folder

-o directory to save generated speech

-c checkpoints file

-c config file

5. Singing Voice Synthesis

For Singing Voice Synthesis:

  • Take modified FastSpeech for mel-spectrogram synthesis
  • Use synthesized mel-spectrogram in Multi-Singer for waveform synthesis.

Acknowledgements

Citation

Please cite this repository by the "Cite this repository" of About section (top right of the main page).

Question

Feel free to contact me at [email protected]

Owner
SunMail-hub
Interested in tts, vocoder, vc.
SunMail-hub
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