Amphion: An Open-Source Audio, Music, and Speech Generation Toolkit

What is the project about?

Amphion is a toolkit designed for research and development in audio, music, and speech generation. It aims to provide a platform for reproducible research and help newcomers get started in the field.

What problem does it solve?

• Provides a unified platform for various audio generation tasks, reducing the need to use multiple disparate tools.
• Offers reproducible research environments and pre-built models, making it easier to replicate and build upon existing work.
• Lowers the barrier to entry for junior researchers and engineers by providing visualizations and clear documentation.
• Addresses the need for high-quality vocoders and consistent evaluation metrics in audio generation.
• Facilitates the development of real-world applications by supporting large-scale dataset creation.

What are the features of the project?

• Multiple Generation Tasks: Supports Text-to-Speech (TTS), Singing Voice Synthesis (SVS), Voice Conversion (VC), Accent Conversion(AC), Singing Voice Conversion (SVC), Text-to-Audio (TTA), Text-to-Music (TTM), and more.
• State-of-the-Art Models: Includes implementations of popular and high-performing models like FastSpeech2, VITS, VALL-E, NaturalSpeech2, Jets, MaskGCT, Vevo, FACodec, Noro, and diffusion-based models.
• Vocoders: Integrates a variety of neural vocoders, including GAN-based (MelGAN, HiFi-GAN, NSF-HiFiGAN, BigVGAN, APNet), flow-based (WaveGlow), diffusion-based (Diffwave), and auto-regressive (WaveNet, WaveRNN) vocoders.
• Evaluation Metrics: Provides a comprehensive suite of objective evaluation metrics for assessing generated audio quality, including F0 modeling, energy modeling, intelligibility, spectrogram distortion, and speaker similarity.
• Dataset Support: Offers unified data preprocessing for numerous open-source datasets (AudioCaps, LibriTTS, LJSpeech, M4Singer, Opencpop, OpenSinger, SVCC, VCTK, etc.) and exclusive support for the Emilia dataset and Emilia-Pipe preprocessing pipeline.
• Visualization: Includes tools like SingVisio to visualize the internal workings of models, aiding in understanding and education.

What are the technologies used in the project?

• Python: The primary programming language.
• PyTorch: Deep learning framework.
• Transformer Networks: Used in many of the supported models.
• Generative Adversarial Networks (GANs): Used in vocoders and some generation models.
• Variational Autoencoders (VAEs): Used in some generation models.
• Flow-based Models: Used in vocoders and some generation models.
• Diffusion Models: Used in some generation models and vocoders.
• Docker: Provides containerization for easy setup and deployment.
• CUDA: For GPU acceleration.

What are the benefits of the project?

• Reproducibility: Facilitates reproducible research in audio generation.
• Accessibility: Makes it easier for newcomers to enter the field.
• Comprehensive: Offers a wide range of tools and models in one place.
• State-of-the-Art: Includes implementations of cutting-edge models.
• Extensible: Designed to support new models and tasks.
• Open-Source: Free to use for both research and commercial purposes (MIT License).
• Visualization: Provides tools to understand the models.

What are the use cases of the project?

• Research: Studying and developing new audio, music, and speech generation models and techniques.
• Education: Learning about audio generation models and their inner workings.
• Applications: Building real-world applications such as:
  • Text-to-speech systems for accessibility or entertainment.
  • Voice cloning and modification.
  • Singing voice synthesis.
  • Audio and music generation.
  • Accent conversion.
  • Developing datasets for training.