Acoustic-Model

Training and inference scripts for the acoustic models in A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion. For more details see soft-vc. Audio samples can be found here. Colab demo can be found here.

^{Fig 1: Architecture of the voice conversion system. a) The discrete content encoder clusters audio features to produce a sequence of discrete speech units. b) The soft content encoder is trained to predict the discrete units. The acoustic model transforms the discrete/soft speech units into a target spectrogram. The vocoder converts the spectrogram into an audio waveform.}

Example Usage

Programmatic Usage

import torch
import numpy as np

# Load checkpoint (either hubert_soft or hubert_discrete)
acoustic = torch.hub.load("bshall/acoustic-model:main", "hubert_soft").cuda()

# Load speech units
units = torch.from_numpy(np.load("path/to/units"))

# Generate mel-spectrogram
mel = acoustic.generate(units)

Script-Based Usage

usage: generate.py [-h] {soft,discrete} in-dir out-dir

Generate spectrograms from input speech units (discrete or soft).

positional arguments:
  {soft,discrete}  available models (HuBERT-Soft or HuBERT-Discrete)
  in-dir           path to the dataset directory.
  out-dir          path to the output directory.

optional arguments:
  -h, --help       show this help message and exit

Training

Step 1: Dataset Preparation

Download and extract the LJSpeech dataset. The training script expects the following tree structure for the dataset directory:

└───wavs
    ├───dev
    │   ├───LJ001-0001.wav
    │   ├───...
    │   └───LJ050-0278.wav
    └───train
        ├───LJ002-0332.wav
        ├───...
        └───LJ047-0007.wav

The train and dev directories should contain the training and validation splits respectively. The splits used for the paper can be found here.

Step 2: Extract Spectrograms

Extract mel-spectrograms using the mel.py script:

usage: mels.py [-h] in-dir out-dir

Extract mel-spectrograms for an audio dataset.

positional arguments:
  in-dir      path to the dataset directory.
  out-dir     path to the output directory.

optional arguments:
  -h, --help  show this help message and exit

for example:

python mel.py path/to/LJSpeech-1.1/wavs path/to/LJSpeech-1.1/mels

At this point the directory tree should look like:

├───mels
│   ├───...
└───wavs
    ├───...

Step 3: Extract Discrete or Soft Speech Units

Use the HuBERT-Soft or HuBERT-Discrete content encoders to extract speech units. First clone the content encoder repo and then run encode.py (see the repo for details):

usage: encode.py [-h] [--extension EXTENSION] {soft,discrete} in-dir out-dir

Encode an audio dataset.

positional arguments:
  {soft,discrete}       available models (HuBERT-Soft or HuBERT-Discrete)
  in-dir                path to the dataset directory.
  out-dir               path to the output directory.

optional arguments:
  -h, --help            show this help message and exit
  --extension EXTENSION
                        extension of the audio files (defaults to .flac).

for example:

python encode.py soft path/to/LJSpeech-1.1/wavs path/to/LJSpeech-1.1/soft --extension .wav

At this point the directory tree should look like:

├───mels
│   ├───...
├───soft/discrete
│   ├───...
└───wavs
    ├───...

Step 4: Train the Acoustic-Model

usage: train.py [-h] [--resume RESUME] [--discrete] dataset-dir checkpoint-dir

Train the acoustic model.

positional arguments:
  dataset-dir      path to the data directory.
  checkpoint-dir   path to the checkpoint directory.

optional arguments:
  -h, --help       show this help message and exit
  --resume RESUME  path to the checkpoint to resume from.
  --discrete       Use discrete units.

Citation

If you found this work helpful please consider citing our paper:

@inproceedings{
    soft-vc-2022,
    author={van Niekerk, Benjamin and Carbonneau, Marc-André and Zaïdi, Julian and Baas, Matthew and Seuté, Hugo and Kamper, Herman},
    booktitle={ICASSP}, 
    title={A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion}, 
    year={2022}
}

seastar105 / acoustic-model