huggingface/diffusers

F5-TTS Integration

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#10,043 创建于 2024年11月28日

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描述

Model/Pipeline/Scheduler description

F5-TTS is a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT). It has excellent voice cloning capabilities, and audio generation is of quite high quality.

Open source status

  • The model implementation is available.
  • The model weights are available (Only relevant if addition is not a scheduler).

Provide useful links for the implementation

Paper - https://arxiv.org/abs/2410.06885 Code - https://github.com/SWivid/F5-TTS?tab=readme-ov-file Weights - https://huggingface.co/SWivid/F5-TTS

Author - @SWivid

贡献者指南

技术栈
pythonpytorch
领域
machine learningai
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
over 1 week
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
fresh
清晰度议题是否清楚说明期望改动、验收标准和下一步。
mostly clear
前置要求
PyTorch basicsUnderstanding of diffusion modelsFamiliarity with TTS systems
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
25
研究方向
First, study the F5 TTS paper (arXiv:2410.06885) and its reference implementation at github.com/SWivid/F5 TTS. Identify the core components: the Diffusion Transformer (DiT) backbone, flow matching loss, and voice cloning logic. Then, review the diffusers library structure, specifically the AudioDiffusion pipeline (e.g., diffusers/examples/audio diffusion) to understand how to add a new pipeline. Create a prototype that loads the pre trained weights from huggingface.co/SWivid/F5 TTS and implements the forward pass. Check existing issues or PRs for similar integrations to avoid duplication.