FunASR vs pyannote
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
Python toolkit for figuring out who spoke when in an audio recording, using pretrained PyTorch models.
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 pyannote if you need…
- Developers who want to add speaker separation to a Python audio workflow
- Researchers comparing or fine-tuning speaker diarization models
- Teams processing meeting or interview recordings who can run PyTorch code
- Users who want a pretrained pipeline without training their own model
Side-by-side
| FunASR | pyannote | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 20.9k | 10.8k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Jupyter Notebook |
| Last code update | 4 days ago | 12 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.
pyannote
pyannote.audio is an open-source Python toolkit for speaker diarization, which means splitting a recording into segments and labeling which speaker is talking at each point. It is built on PyTorch and ships with pretrained models and pipelines that can be fine-tuned on your own data. The README also describes a paid hosted service, pyannoteAI, as an optional premium option.
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
pyannote
- Developers who want to add speaker separation to a Python audio workflow
- Researchers comparing or fine-tuning speaker diarization models
- Teams processing meeting or interview recordings who can run PyTorch code
- Users who want a pretrained pipeline without training their own model
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
pyannote
- Built on PyTorch with pretrained pipelines available on the Hugging Face model hub
- Python-first API with a short example that produces readable speaker turns
- MIT licensed and actively maintained, with a latest release of 4.0.7 and a recent push
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
pyannote
- People who need a no-code app: the README only shows Python code samples
- Users who cannot accept the model conditions and Hugging Face token step, since the pretrained pipeline requires them
- Anyone with no Python experience, because setup involves ffmpeg, a package install, and an access token
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
pyannote
- Pretrained pipelines require accepting user conditions and creating a Hugging Face access token before use
- Benchmark numbers in the README come from the project itself and are not independently verified here
- Some features and the fastest version point to the paid pyannoteAI service
Getting started
FunASR
Install PyTorch and torchaudio (CPU-only)
pip install torch torchaudioInstall FunASR
pip install funasrpyannote
Install with uv (recommended)
uv add pyannote.audioInstall with pip
pip install pyannote.audioFrequently asked questions
What is the difference between FunASR and pyannote?
FunASR: Open-source speech-to-text toolkit with streaming, voice detection, punctuation and speaker pipelines. pyannote: Python toolkit for figuring out who spoke when in an audio recording, using pretrained PyTorch models. FunASR suits developers who want to transcribe audio in python with a pretrained model, while pyannote suits developers who want to add speaker separation to a python audio workflow.
Are FunASR and pyannote free?
FunASR: Free and open source. pyannote: Free and open source.
Is FunASR or pyannote more popular?
FunASR has more GitHub stars (20.9k) than pyannote (10.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to FunASR and pyannote?
Other options people compare them with include Whisper, faster-whisper, whisper.cpp, Silero VAD, Spleeter.
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.