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A library for the rapid prototyping of hybrid quantum-classical neural networks in speech applications.

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QSpeech

QSpeech: Low-Qubit Quantum Speech Application Toolkit

Accepted by the International Joint Conference on Neural Networks (IJCNN) 2022

Introduction

This repository is the official implementation of QSpeech: Low-Qubit Quantum Speech Application Toolkit.

It proposes:

  • The low-qubit variational quantum circuit (VQC).
  • A library for the rapid prototyping of hybrid quantum-classical neural networks in speech applications.
Low-qubit VQC QSpeech Framework

For the hybrid quantum-classical neural networks in speech applications, we implement the Quantum M5(QM5), Quantum Tacotron(QTacotron) and Quantum Transformer-TTS(QTransformer-TTS).

Requirements

  • Linux (Test on Ubuntu18.04)
  • Python3.6+ (Test on Python3.6.8)
  • PyTorch
  • PennyLane
  • Librosa (version 0.7.2)
  • Numba (version 0.48.0)

Basic framework

  • QCircuit: the variational quantum circuit(VQC) and low-qubit VQC.
  • QLayer: the qlstm, qgru, qattention, qconv.
  • QModels: the qm5, qtransformer, qtacotron.

Notes

The code of qlstm and qtransformer are based on these two projects as follow:

How to use

Download the datasets

  • LJSpeech1.1
  • SpeechCommandV0.02

QM5

  • cd ./Examples/QM5
  • Modify the config.py, like the path of dataset
  • python3 speech-command-recognition.py

QTacotron

  • cd ./Examples/QTacotron
  • Modify the hyperparams.py, like the path of dataset
  • python3 train.py --batch_size 2

QTransformer-TTS

  • cd ./Examples/QTransformerTTS
  • Modify the config.py, like the path of dataset
  • python3 train.py

Citation

If you find QSpeech useful in your research, please consider citing:

@inproceedings{hong2022qspeech,
  title={QSpeech: Low-Qubit Quantum Speech Application Toolkit},
  author={Hong, Zhenhou and Wang, Jianzong and Qu, Xiaoyang and Zhao, Chendong and Tao, Wei and Xiao, Jing},
  booktitle={2022 International Joint Conference on Neural Networks (IJCNN)},
  pages={1--8},
  year={2022},
  organization={IEEE}
}

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A library for the rapid prototyping of hybrid quantum-classical neural networks in speech applications.

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