Speaker-invariant Clustering (Spin)

• Introduction
• Citation
• Getting Started
• Pre-trained Models
• References
• Contact

Introduction

This repository is the official PyTorch implementation of the Speaker-invariant Clustering (Spin) proposed in the Interspeech 2023 paper Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering (Heng-Jui Chang, Alexander H. Liu, James Glass; MIT CSAIL).

Spin is a novel self-supervised learning method that clusters speech representations and performs swapped prediction between the original and speaker-perturbed utterances. Spin disentangles speaker information and preserves content representations with just 45 minutes of fine-tuning on a single GPU (HuBERT Base models). Spin improves pre-trained networks and outperforms prior methods in speech recognition and acoustic unit discovery.

Citation

Please cite our paper if you find this repository and/or the paper useful.

@inproceedings{chang2023spin,
  author={Heng-Jui Chang and Alexander H. Liu and James Glass},
  title={{Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering}},
  year=2023,
  booktitle={Proc. Interspeech}
}

Getting Started

1. Environment

Make sure sox is installed and your Python version is at least 3.6.

# Create virtual environment
conda create --name spin python=3.8
conda activate spin

# Install s3prl
git clone https://github.com/s3prl/s3prl.git
cd s3prl
pip install -e ".[all]"
cd ..

# Clone this repository and intall dependencies
git clone https://github.com/vectominist/spin.git
cd spin/
pip install -r requirements.txt

# Modify some s3prl files
cp s3prl_py/wav2vec2_model.py ../s3prl/s3prl/upstream/wav2vec2/wav2vec2_model.py
cp s3prl_py/WavLM.py ../s3prl/s3prl/upstream/wavlm/WavLM.py

2. Prepare Data

Download required data.

# Create a directory to save data (or any other path you like)
mkdir data
cd data

# LibriSpeech (skip if you already have this)
wget https://www.openslr.org/resources/12/train-clean-100.tar.gz
wget https://www.openslr.org/resources/12/dev-clean.tar.gz
wget https://www.openslr.org/resources/12/dev-other.tar.gz
# Decompress
tar zxvf train-clean-100.tar.gz
tar zxvf dev-clean.tar.gz
tar zxvf dev-clean.tar.gz
rm train-clean-100.tar.gz dev-clean.tar.gz dev-clean.tar.gz

# LibriSpeech Phoneme Alignments (for monitoring progress only)
wget https://huggingface.co/datasets/vectominist/spin_data/resolve/main/dev-clean.tsv
wget https://huggingface.co/datasets/vectominist/spin_data/resolve/main/dev-other.tsv

# Speaker Information
# Source: https://github.com/auspicious3000/contentvec
wget https://huggingface.co/datasets/vectominist/spin_data/resolve/main/spk2info.dict

Prepare LibriSpeech dataset, see script/prepare.sh.

• libri_dir: the directory of the LibriSpeech corpus
• json_dir: the directory to save .json files generated from prepare_data.py

bash script/prepare.sh ${libri_dir} ${json_dir}

3. Customize Configurations

See config/spin.yaml.

• Modify json_dir, spk2info, and phn_dir according to the directories with the downloaded and preprocessed data.
• Modify logger to switch to other loggers or simply setting it to False to disable logging.

data:
  json_dir: /path/to/json_dir
  spk2info: /path/to/spk2info.dict

val_data:
  json_dir: /path/to/json_dir
  phn_dir: /path/to/phoneme/alignments/dir

trainer:
  logger: wandb  # specify a pytorch-lightning logger you prefer

4. Training

See script/train.sh.

• exp_dir: the directory to save checkpoints
• exp_name: experiment name
• See src/task/train_spin.py for details about available arguments like number of GPUs to be used.

bash script/train.sh ${exp_dir} ${exp_name}

The trained model checkpoints can be found in ${exp_dir}/${exp_name}. Note that we use last.ckpt for evaluation and downstream tasks.

5. Downstream Evaluation

We use the s3prl toolkit for SUPERB downstream tasks.

• Modify line 26 of s3prl_py/spin/expert.py to the absolute path to spin/.
• Copy the s3prl_py/spin directory to s3prl so that the toolkit can load the models.

  cp -R s3prl_py/spin ../s3prl/s3prl/upstream/spin

• Finally, add the following line to ../s3prl/s3prl/hub.py:

  from s3prl.upstream.spin.hubconf import *

Pre-trained Models

All models are trained on a single NVIDIA A5000 GPU with 24GB VRAM. To reproduce similar or better performance, we suggest using GPUs larger than 24GB or specifying strategy: ddp under trainer in config/spin.yaml to enable multiple GPU training. Note that the following checkpoints are reproduced with the same recipe, so the results are slightly different from our paper. The training logs can be found in this link.

Base Model	Clusters	PNMI	Checkpoint
HuBERT	128	0.625	link
HuBERT	256	0.658	link
HuBERT	512	0.707	link
HuBERT	1024	0.745	link
HuBERT	2048	0.774	link
WavLM	128	0.604	link
WavLM	256	0.658	link
WavLM	512	0.714	link
WavLM	1024	0.748	link
WavLM	2048	0.775	link

References

• s3prl
• contentvec
• fairseq
• MiniASR
• PyTorch
• PyTorch Lightning

Contact

If you have any questions, please open an issue or send me an email hengjui@mit.edu.