Feature: Add FunASR as a self-hosted ASR backend
Nobody has claimed this yet.
- Dominant language
- Go
- Stars
- 49.2k
- Forks
- 4.5k
- Avg merge
- 1d 3m
- Merged PRs (30d)
- 239
Description
Implementation status
PR #10090 adds a local FunASR backend to LocalAI's existing audio-transcription API.
The current backend:
- loads
iic/SenseVoiceSmallthrough FunASR; - provides explicit CPU, CUDA 11, CUDA 12, and Intel profiles;
- includes Torch and Torchaudio in profiles that install Torch;
- accepts LocalAI's
languageand transcription request options; - strips SenseVoice language, emotion, and audio-event markup before returning OpenAI-compatible transcript text;
- runs through LocalAI's existing backend install, test, gRPC
LoadModel, andAudioTranscriptionpaths.
SenseVoiceSmall supports Mandarin, Cantonese, English, Japanese, and Korean. Long-audio segmentation can be composed with the separate FSMN-VAD model. Speaker diarization is not a native SenseVoice output and is not claimed by this backend.
Validation
The PR has been exercised on ind-gpu8 with:
- six focused tests;
- real CPU and CUDA SenseVoiceSmall inference;
- the actual LocalAI installer and shared unit-test helper;
- gRPC model loading and transcription;
- shell syntax, Python compilation, and diff checks.
The PR is clean and mergeable; its remaining gate is maintainer review.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with PR #10090 and review how the FunASR backend connects to LocalAI’s existing backend installer, gRPC LoadModel path, and AudioTranscription API. Check the six focused tests and the documented CPU, CUDA, Intel, and real-inference validation; done means maintainer review and a clean, mergeable implementation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- go, grpc, python
- Domain
- api, backend, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Clearly specified
- Newbie friendliness
- 20/100