FunASR vs Silero VAD
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Open-source speech-to-text toolkit with streaming, voice detection, punctuation and speaker pipelines.
Open source · MIT
Pre-trained voice activity detector that finds where speech occurs in audio, for Python, ONNX, and other runti
The short answer
Choose FunASR if you need…
- Developers who want to transcribe audio in Python with a pretrained model
- Teams planning a move away from Whisper or a cloud ASR provider, who want to benchmark on their own audio
- Builders who need live (streaming) transcription
- Projects that need speaker-aware segments from recordings
Choose Silero VAD if you need…
- Developers adding speech detection to Python apps
- Teams building telephony, call-center, or voice bot pipelines
- Engineers preparing audio datasets by removing non-speech sections
- Edge or mobile projects that need a small model (the README says the JIT model is about two megabytes)
Side-by-side
| FunASR | Silero VAD | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 20.9k | 10.6k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 15 days ago |
What each one is
FunASR
FunASR is a toolkit for turning spoken audio into text. It bundles speech recognition models with helpers for detecting when someone is speaking, adding punctuation, and grouping segments by speaker, and it can be run from Python or served as an API. People use it to transcribe recordings or build live transcription, and the README says it supports offline, streaming, and edge deployment.
Silero VAD
Silero VAD is a pre-trained model that listens to audio and marks the parts that contain human speech. Developers use it to cut silence out of recordings, decide when someone has started or stopped talking, or clean datasets before further processing. The README says it runs on PyTorch and ONNX and can be used from Python or other languages through ONNX Runtime.
Best for
FunASR
- Developers who want to transcribe audio in Python with a pretrained model
- Teams planning a move away from Whisper or a cloud ASR provider, who want to benchmark on their own audio
- Builders who need live (streaming) transcription
- Projects that need speaker-aware segments from recordings
Silero VAD
- Developers adding speech detection to Python apps
- Teams building telephony, call-center, or voice bot pipelines
- Engineers preparing audio datasets by removing non-speech sections
- Edge or mobile projects that need a small model (the README says the JIT model is about two megabytes)
Strengths
FunASR
- Covers several tasks in one toolkit: transcription, voice activity detection, punctuation, and speaker grouping
- Provides a documented CPU path and a GPU path, plus a Colab quickstart for trying it without local setup
- MIT licensed, with an active project and a README that spells out model-specific limits
Silero VAD
- The README describes the model as pre-trained, so you do not need to train anything first
- The model is small and the README reports a single chunk takes under 1 ms on one CPU thread
- MIT license and no telemetry, keys, or registration, according to the README
Not ideal for
FunASR
- People who only need a point-and-click transcription app, since the README centers on Python and command-line-style setup
- Users needing identification of named speakers, because speaker labels are anonymous indices within one recording
- Anyone needing a language the chosen checkpoint does not cover, since language coverage varies by model
Silero VAD
- People who need speech-to-text; the README points to separate STT models in another repository
- Non-programmers looking for a ready-made app, since this is a library with no GUI described
- Projects needing sample rates other than 8000 Hz or 16000 Hz, which the README lists as the supported rates
Trade-offs
FunASR
- Capabilities differ by checkpoint and runtime, so choosing the right model takes reading the docs carefully
- GPU use requires matching PyTorch and CUDA setup that the user must manage
- Speaker outputs are local to a recording and are not identity recognition
Silero VAD
- The README badge reads CC BY-NC 4.0 while the linked license is MIT, so check the LICENSE file before commercial use
- Requires a CPU with AVX-family instructions and at least 1G RAM for the Python examples
- Audio loading needs an extra backend such as torchaudio, torchcodec, or FFmpeg, which adds setup work
Getting started
FunASR
Install PyTorch and torchaudio (CPU-only)
pip install torch torchaudioInstall FunASR
pip install funasrSilero VAD
Install with audio loading helpers
pip install silero-vad[audio]Install minimal package (if you load audio yourself)
pip install silero-vadFrequently asked questions
What is the difference between FunASR and Silero VAD?
FunASR: Open-source speech-to-text toolkit with streaming, voice detection, punctuation and speaker pipelines. Silero VAD: Pre-trained voice activity detector that finds where speech occurs in audio, for Python, ONNX, and other runti. FunASR suits developers who want to transcribe audio in python with a pretrained model, while Silero VAD suits developers adding speech detection to python apps.
Are FunASR and Silero VAD free?
FunASR: Free and open source. Silero VAD: Free and open source.
Is FunASR or Silero VAD more popular?
FunASR has more GitHub stars (20.9k) than Silero VAD (10.6k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to FunASR and Silero VAD?
Other options people compare them with include Whisper, faster-whisper, pyannote, whisper.cpp, ONNX Runtime.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.