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Amphion

API—OSS—FREE—DOCS4/5
AM5.3#172 of 215
—Web—Windows—MacoutLinux—Android—iOS

Ranked in Text-to-Speech Software ·No free plan on record

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About

Amphion is ranked #172 of 215 in text-to-speech software on Inferse. It runs on Linux, Self-hosted.

Compared on text-to-speech software

Free plan
Yesgithub.com
Voice cloning
Yesgithub.com
Audio upload formats
WAV, MP3, FLACgithub.com
Export formats
WAVgithub.com

Facts

Purpose
Amphion is an open-source toolkit for audio, music, and speech generation aimed at supporting reproducible research and helping junior researchers and engineers get started.github.com · 4 Oct 2026
Generation tasks
Supported tasks include text-to-speech, singing voice synthesis, voice conversion, accent conversion, singing voice conversion, and text-to-audio; text-to-music is listed as in development.github.com · 4 Oct 2026
Voice and speech models
The toolkit includes architectures such as FastSpeech2, VITS, VALL-E, NaturalSpeech2, MaskGCT, and Vevo-TTS for text-to-speech, plus Vevo, FACodec, and Noro for voice conversion.github.com · 4 Oct 2026
Singing generation
Vevo2 supports controllable speech and singing generation, including voice conversion, singing voice conversion, singing voice editing, singing style conversion, and melody control.github.com · 4 Oct 2026
Text to audio
Amphion supports text-to-audio generation using a latent diffusion model and describes it as the official implementation of the text-to-audio generation part of its NeurIPS 2023 paper.github.com · 4 Oct 2026
Audio evaluation
It provides objective evaluation metrics for F0 and energy modeling, intelligibility, spectrogram distortion, and speaker similarity.github.com · 4 Oct 2026
Visualization
Amphion includes interactive visualizations of classic model processing, including SingVisio for the diffusion model used in singing voice conversion.github.com · 4 Oct 2026
Dataset preprocessing
It unifies preprocessing for multiple open-source audio datasets and supports the Emilia dataset and Emilia-Pipe pipeline for in-the-wild speech data.github.com · 4 Oct 2026
Integrations and dependencies
The project lists WeNet, Whisper, and ContentVec among pretrained models used for content-based features, and its Docker instructions require NVIDIA drivers, NVIDIA Container Toolkit, and CUDA.github.com · 4 Oct 2026
Installation
The README documents setup using Python 3.9.15 with Conda or a Docker image, and the Docker instructions require mounting the dataset into the container.github.com · 4 Oct 2026
License and cost
Amphion is licensed under MIT and the README says it is free for research and commercial use.github.com · 4 Oct 2026
Support and community
The project invites contributions and links to a Discord channel for community engagement.github.com · 4 Oct 2026

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Where it ranks on Inferse

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