HuBERT

Training and inference scripts for the HuBERT content encoders 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, torchaudio

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

# Load audio
wav, sr = torchaudio.load("path/to/wav")
assert sr == 16000
wav = wav.unsqueeze(0).cuda()

# Extract speech units
units = hubert.units(x)

Script-Based Usage

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).

Training

Step 1: Dataset Preparation

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

│   lengths.json
│
└───wavs
    ├───dev-*
    │   ├───84
    │   ├───...
    │   └───8842
    └───train-*
        ├───19
        ├───...
        └───8975

The train-* and dev-* directories should contain the training and validation splits respectively. Note that there can be multiple train and dev folders e.g., train-clean-100, train-other-500, etc. Finally, the lengths.json file should contain key-value pairs with the file path and number of samples:

{
    "dev-clean/1272/128104/1272-128104-0000": 93680,
    "dev-clean/1272/128104/1272-128104-0001": 77040,
}

Step 2: Extract Discrete Speech Units

Encode LibriSpeech using the HuBERT-Discrete model and encode.py script:

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 discrete path/to/LibriSpeech/wavs path/to/LibriSpeech/discrete

At this point the directory tree should look like:

│   lengths.json
│
├───discrete
│   ├───...
└───wavs
    ├───...

Step 3: Train the HuBERT-Soft Content Encoder

usage: train.py [-h] [--resume RESUME] [--warmstart] [--mask] [--alpha ALPHA] dataset-dir checkpoint-dir

Train HuBERT soft content encoder.

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.
  --warmstart      whether to initialize from the fairseq HuBERT checkpoint.
  --mask           whether to use input masking.
  --alpha ALPHA    weight for the masked loss.

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}
}

bshall/hubert

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