FunClip is a fully open-source, locally deployed automated video clipping tool. It leverages Alibaba TONGYI speech lab's open-source FunASR Paraformer series models to perform speech recognition on videos. Then, users can freely choose text segments or speakers from the recognition results and click the clip button to obtain the video clip corresponding to the selected segments (Quick Experience Modelscope⭐ ).
FunClip integrates Alibaba's open-source industrial-grade model Paraformer-Large, which is one of the best-performing open-source Chinese ASR models available, with over 13 million downloads on Modelscope. It can also accurately predict timestamps in an integrated manner.
FunClip incorporates the hotword customization feature of SeACo-Paraformer, allowing users to specify certain entity words, names, etc., as hotwords during the ASR process to enhance recognition results.
FunClip integrates the CAM++ speaker recognition model, enabling users to use the auto-recognized speaker ID as the target for trimming, to clip segments from a specific speaker.
The functionalities are realized through Gradio interaction, offering simple installation and ease of use. It can also be deployed on a server and accessed via a browser.
FunClip supports multi-segment free clipping and automatically returns full video SRT subtitles and target segment SRT subtitles, offering a simple and convenient user experience.
What's New🚀
2026/09/01 FunClip v2.2.1 preserves selected subtitle colors with a Pillow renderer and publishes the latest MOSS speaker-label boundaries in checksum-protected source archives.
2026/08/30 FunClip v2.2.0 adds the third-party MOSS-Transcribe-Diarize path for long-form ASR, timestamps, and anonymous speaker labels without external VAD or speaker models.
See GitHub Releases for the complete history and downloadable assets.
On Going🌵
FunClip will support Whisper model for English users, coming soon (ASR using Whisper with timestamp requires massive GPU memory, we support timestamp prediction for vanilla Paraformer in FunASR to achieving this).
FunClip will further explore large language model based AI clipping. Feedback and prompt-setting tips are welcome.
Reverse periods choosing while clipping.
Removing silence periods.
Install🔨
Python env install
Use a separate Python environment for FunClip. The commands below use Python 3.12; install a matching PyTorch/torchaudio pair for your platform before the remaining requirements. See the installation and troubleshooting guide for the Linux CPU recipe, Windows activation and certificate errors.
Keep this environment on the repository's Transformers 4.x requirements. Nano's separate native Transformers 5.x quickstart is not an upgrade command for an existing FunClip installation.
For a versioned snapshot, download FunClip-2.2.1.tar.gz or FunClip-2.2.1.zip, then verify it with the published SHA256SUMS. Model weights are downloaded separately when FunClip starts and are not included in these source archives.
FunClip v2.2.1 keeps the supported Gradio 4 runtime and renders built-in subtitles with Pillow so the selected foreground color survives video encoding. Existing installations should run python -m pip install -U -r requirements.txt inside their FunClip environment before restarting.
FunClip's current model and subtitle compatibility paths require funasr>=1.4.9. This includes the MOSS vLLM adapter, long-audio generation controls, normalized sentence_info speaker segments, and the earlier SenseVoice and realtime fixes. If you installed FunClip before this requirement was updated, run python -m pip install -U "funasr>=1.4.9" before starting the Gradio service. Release notes · PyPI
imagemagick install (Optional)
The built-in subtitle renderer in v2.2.1 uses Pillow and the bundled font, so standard subtitle clipping no longer requires ImageMagick. Install ImageMagick only for the legacy funclip/test/imagemagick_test.py example or your own MoviePy TextClip workflow.
Use FunClip
A. Use FunClip as local Gradio Service
You can establish your own FunClip service which is same as Modelscope Space as follow:
python funclip/launch.py
# '-m fun-asr-nano' for the flagship Fun-ASR-Nano model (Mandarin, English,
# Japanese, 7 Chinese dialect groups, and 26 regional accents)
# '-m sensevoice' for SenseVoice model (multilingual ASR + emotion + audio event detection)
# '--model moss' for OpenMOSS long-form ASR + anonymous speaker labels + timestamps
# '-l en' for English audio recognize
# '-p xxx' for setting port number
# '-s True' for establishing service for public accessing
Model selection quick start
Scenario
Command
Default Chinese video clipping with Paraformer
python funclip/launch.py
High-accuracy transcription with the flagship Fun-ASR-Nano checkpoint (use Paraformer for precise text-based clipping)
python funclip/launch.py -m fun-asr-nano
Multilingual ASR with emotion and audio event tags
English video clipping with the Paraformer English model
python funclip/launch.py -l en
MOSS-Transcribe-Diarize backend
MOSS-Transcribe-Diarize is a third-party OpenMOSS model, not a FunASR or FunClip model. FunClip pins the published Hugging Face object OpenMOSS-Team/MOSS-Transcribe-Diarize at revision e8681d68e7042738ffca8ac8212bc8fcb1131ab8. Start and verify the vLLM service using the bilingual production guide, then run:
# vLLM is the default MOSS backend and defaults to http://127.0.0.1:8898/v1
python funclip/launch.py --model moss --moss-backend vllm
# Optional bearer credentials are read from the environment, not the command line
MOSS_API_KEY=replace-me python funclip/launch.py --model moss
MOSS performs segmentation and speaker diarization end to end. Its spkS01, spkS02, ... values are anonymous speaker labels within the current recording; the model does not identify a known person, verify an enrolled voiceprint, or guarantee label continuity across separate recordings. Do not attach an external vad_model or spk_model, because chunking would break consistent speaker assignment within the recording. Its timestamps are segment-level: SRT, speaker clipping, and LLM timestamp clipping are supported, while precise arbitrary text clipping still requires Paraformer's token timestamps. FunClip currently exposes the vLLM path because it is compatible with the standard Transformers 4.x environment and has an end-to-end tested OpenAI transcription contract.
then visit localhost:7860 you will get a Gradio service like below and you can use FunClip following the steps:
Step1: Upload your video file (or try the example videos below)
Step2: Copy the text segments you need to 'Text to Clip'
Step3: Adjust subtitle settings (if needed)
Step4: Click 'Clip' or 'Clip and Generate Subtitles'
Follow the guide below to explore LLM based clipping:
Using OrcaRouter as your LLM gateway (optional)
Besides the transcript-based LLMs above, FunClip can route LLM-assisted clipping through OrcaRouter, an OpenAI-compatible smart-routing gateway. Select any orcarouter/ model in the LLM Model Name dropdown (orcarouter/auto routes each request to the best model for the task), paste an OrcaRouter API key in the APIKEY box, and click 'LLM Inference' — FunClip sends the transcript and prompts to https://api.orcarouter.ai/v1/chat/completions, and the returned segments work with the existing 'AI Clip' button unchanged.
OrcaRouter exposes one endpoint for all frontier and open-weight models, so you can switch routing targets without changing FunClip. It also offers gateway-level, zero-trust controls for AI agents on the same endpoint. Enforcement follows the Guardrail or Firewall policy attached to the relevant scope for prompts, responses, and tool calls that traverse the gateway; select the documented tight posture to enable default-deny behavior. Enforcement is opt-in — this integration supplies only the base URL, API key, and model, and does not attach a policy by itself. Apply a policy via the security quickstart; see Guardrails and Firewall for how coverage is scoped.
Set ORCAROUTER_API_KEY (and optionally ORCAROUTER_API_BASE, which defaults to https://api.orcarouter.ai/v1) instead of pasting the key into the UI if you prefer environment-based configuration. A key is available at https://www.orcarouter.ai.
Using Cheaper Inference as your LLM gateway (optional)
FunClip can also send LLM-assisted clipping through Cheaper Inference, an OpenAI-compatible LLM gateway. One API key gives access to models from several labs. Select any cheaperinference/ model in the LLM Model Name dropdown, paste a Cheaper Inference API key in the APIKEY box, and click 'LLM Inference'. FunClip removes the cheaperinference/ prefix and sends the transcript and prompts to https://api.cheaperinference.com/v1/chat/completions. The returned segments work with the existing 'AI Clip' button.
Set CHEAPER_INFERENCE_API_KEY (and optionally CHEAPER_INFERENCE_API_BASE, which defaults to https://api.cheaperinference.com/v1) instead of pasting the key into the UI if you prefer environment-based configuration. A key is available at https://cheaperinference.com/signup.
Content-aware clipping with TwelveLabs Pegasus (optional)
Besides the transcript-based LLMs above, FunClip can optionally use TwelveLabs Pegasus, a video understanding model that reasons over the actual video (visuals + audio) rather than only the ASR transcript. This helps pick highlight segments even when the transcript alone is ambiguous (e.g. action, scene changes, on-screen events). To use it, select the pegasus1.5 model name, paste your TwelveLabs API key, upload a video, and click 'LLM Inference' — Pegasus returns segments in the same N. [start-end] text format, so the existing 'AI Clip' button works unchanged. It needs pip install twelvelabs, and a free API key is available at https://twelvelabs.io.
FunClip source code is licensed under the MIT License.
Model weights are downloaded separately and are governed by the terms on their model pages. The default Paraformer-Large, SeACo-Paraformer, and CAM++ pages currently list Apache License 2.0; check the applicable model page before redistribution.
