huggingface/diffusers

F5-TTS Integration

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#10,043 opened on Nov 28, 2024

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Description

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

Contributor guide

Tech stack
pythonpytorch
Domain
machine learningai
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
over 1 week
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
PyTorch basicsUnderstanding of diffusion modelsFamiliarity with TTS systems
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
25
Research direction
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.