SWivid / SWivid/F5-TTS

Deployed on edge devices

Open
#694 2 comments 1 reaction 0 assignees View on GitHub

Nobody has claimed this yet.

documentation question
Dominant language
Python
Stars
15.3k
Forks
2.2k
PR merge metrics
No merged PRs in 30d

Description

Checks
  • This template is only for question, not feature requests or bug reports.
  • I have thoroughly reviewed the project documentation and read the related paper(s).
  • I have searched for existing issues, including closed ones, no similar questions.
  • I confirm that I am using English to submit this report in order to facilitate communication.
Question details

Hello, everyone. I want to ask, has anyone successfully deployed the F5-TTS model on edge devices? Such as deploy on qualcomm chips using QNN?I have used QNN to deployed F5_Transformer part on SM8550 with HTP fp16 precision, but the latency is too heavy to accept, it takes me about 5s to run F5_Transformer once(my test audio length is about 6s).

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reviewing the F5_Transformer deployment on Qualcomm SM8550 with QNN and HTP fp16 described in the issue. Compare the reported 5-second latency for 6-second audio with the project's documented deployment guidance; done would require an agreed edge-device deployment approach and acceptable latency target.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
ai, embedded-iot
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.