Amphion is a comprehensive open-source toolkit from the OpenMMLab ecosystem that provides unified implementations of state-of-the-art models for audio, music, and speech generation research. The project covers the full spectrum of audio AI tasks including text-to-speech synthesis with models like MaskGCT, DualCodec, VITS, and VALL-E, voice conversion and accent conversion for transforming speaker characteristics, singing voice synthesis and conversion for music applications, and text-to-audio generation for producing sound effects and ambient audio from natural language descriptions.
Designed with reproducibility as a core principle, Amphion targets junior researchers and students entering the audio AI field by providing built-in architecture visualizations, standardized training pipelines, and consistent evaluation metrics across all supported models. The toolkit ships with the Emilia-Large dataset containing 200,000 hours of multilingual speech data, removing one of the biggest barriers to entry in speech research. Multiple vocoder implementations allow researchers to compare neural audio synthesis approaches under controlled conditions.
With 9,700 GitHub stars and an MIT license, Amphion has become a reference implementation for the audio generation research community. The latest release from March 2026 includes updated models and expanded support for emerging architectures. For developers building production audio applications, the toolkit provides a well-tested starting point with models that can be fine-tuned on domain-specific data, though the primary focus remains on enabling reproducible academic research rather than production deployment.
