FunClip v2.2.1

FunClip v2.2.1

FunClip v2.2.1 makes embedded subtitle colors reliable and packages the latest MOSS deployment boundaries as a stable, checksum-protected snapshot.

Highlights

• Preserve selected subtitle colors with a Pillow RGBA renderer and the bundled font. The standard subtitle path no longer depends on ImageMagick text rasterization.
• Keep subtitle timing, bottom-center placement, SubtitlesClip composition, and FFmpeg video encoding unchanged.
• Clarify that MOSS-Transcribe-Diarize produces anonymous speaker labels for turns within one recording. It does not identify a known person or provide voiceprint verification.
• Retain the third-party MOSS path for long-form ASR, segment timestamps, speaker SRT, and speaker-based clipping without an external VAD or speaker model.

Install or upgrade

Download either source archive below, verify it with SHA256SUMS, extract it, and install the declared dependencies:

sha256sum -c SHA256SUMS --ignore-missing
pip install -U -r requirements.txt
python funclip/launch.py

The current MOSS and subtitle integration requires funasr>=1.4.9. Model weights are downloaded separately when FunClip starts.

Assets

• FunClip-2.2.1.tar.gz: versioned source archive for Linux and macOS workflows.
• FunClip-2.2.1.zip: versioned source archive for Windows and general-purpose extraction.
• SHA256SUMS: SHA-256 digests for both archives.

The archives contain tracked FunClip application source, documentation, fonts, and dependency manifests. Runtime dependencies and model weights are not bundled; model weights are not bundled because every model retains its own license and distribution terms.

Validation

• Subtitle foreground regression covers black, white, green, and red pixels.
• SubtitlesClip plus CompositeVideoClip verifies final-frame color composition.
• A one-second encoded MP4 was decoded with 1,297 red subtitle pixels and zero white subtitle pixels.
• Release archives are built twice and compared byte-for-byte before the signed tag is published.

Changes since v2.2.0

See the full changelog.

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FunClip v2.2.1 通过 Pillow RGBA 渲染器和项目自带字体可靠保留用户选择的字幕颜色，标准字幕路径不再依赖 ImageMagick 文字栅格化。该版本同时明确 MOSS-Transcribe-Diarize 只返回同一录音内的匿名说话人标签，不提供已知人物身份识别或声纹验证。下载 tar.gz 或 zip 后请先使用 SHA256SUMS 校验，再执行 pip install -U -r requirements.txt。归档包含 Git 已跟踪的应用源码、文档、字体和依赖清单，不包含运行依赖或模型权重。

FunClip v2.2.0

MOSS speaker-aware clipping

FunClip v2.2.0 adds an opt-in --model moss path for the third-party OpenMOSS MOSS-Transcribe-Diarize model through a local or remote vLLM transcription service. OpenMOSS owns and maintains the model; this release integrates its public serving contract and does not bundle model weights.

What is included

• long-form ASR, anonymous speaker diarization labels, and segment timestamps without an external VAD or speaker model
• SRT generation with spkS01, spkS02, and other model-provided speaker IDs
• speaker-based audio/video clipping, including turns shorter than one second
• explicit failure on a truncated final MOSS segment instead of silently dropping partial output
• environment-only bearer credential handling through MOSS_API_KEY
• pinned model revision and bilingual deployment guidance

Start

pip install -U -r requirements.txt
python funclip/launch.py --model moss --moss-backend vllm

The default service URL is http://127.0.0.1:8898/v1; override it with --moss-base-url. See the production guide.

Boundaries

MOSS timestamps are segment-level. SRT, speaker clipping, and LLM timestamp clipping are supported; precise arbitrary text clipping remains on Paraformer. Do not attach an external vad_model or spk_model, because pre-chunking can break global speaker-label consistency.

Validation

The exact release content is commit c205bf32a8b11226ff5e8acb9a3c7a1f00cd3b06. The suite completed with 84 passed and 1 skipped. A live H100/vLLM test produced two speaker segments, valid SRT, and the expected S02 clip. Verify downloaded assets with SHA256SUMS.

Source PR: #207
OpenMOSS integration context: OpenMOSS/MOSS-Transcribe-Diarize#48

Deployment guide

• Chinese · English
• The guide keeps OpenMOSS ownership explicit and documents the validated vLLM boundary, segment-level timestamps, speaker SRT/clips, pinned model revision, and archive checksums.

FunClip v2.1.1

FunClip v2.1.1

FunClip v2.1.1 is a patch release for reliable fresh installs, safer container startup, and more predictable transcript-driven clipping.

Highlights

• Keep the supported Gradio 4 runtime on starlette<1.0. Starlette 1.x changed Jinja2Templates.TemplateResponse incompatibly and caused the FunClip index route to return HTTP 500 on fresh installations.
• Allow explicit --listen deployments to bypass Gradio's internal localhost probe without automatically enabling share=True. A public Gradio tunnel is created only when the user passes --share.
• Match requested clipping text case-insensitively while preserving the original transcript text and timestamps in generated clips and subtitles.
• Add MiniMax M2.7 and MiniMax M2.7-highspeed to the OpenAI-compatible LLM routing used for transcript-driven clipping.
• Retain Fun-ASR-Nano, SenseVoice, Paraformer, TwelveLabs Pegasus, and the existing community API fallback paths.

Install or upgrade

Download either source archive below, verify it with SHA256SUMS, extract it, and install the declared dependencies:

sha256sum -c SHA256SUMS --ignore-missing
pip install -U -r requirements.txt
python funclip/launch.py

For a container or remote host, bind all interfaces explicitly:

python funclip/launch.py --listen

Add --share only when you intentionally want a public Gradio sharing URL. Windows users can verify a downloaded archive with Get-FileHash -Algorithm SHA256 and compare it with SHA256SUMS.

FunClip's Fun-ASR-Nano, SenseVoice, and subtitle compatibility paths continue to require funasr>=1.3.29.

Assets

• FunClip-2.1.1.tar.gz: versioned source archive for Linux and macOS workflows.
• FunClip-2.1.1.zip: versioned source archive for Windows and general-purpose extraction.
• SHA256SUMS: SHA-256 digests for both archives.

The archives contain the tracked FunClip application source, documentation, and dependency manifest. Runtime dependencies and model weights are not bundled; model weights are not bundled because each model retains its own license and distribution terms.

Validation

• Gradio 4.31.3 and 4.44.1 index-route smoke tests return HTTP 200 with FastAPI 0.141.1 and Starlette 0.52.1.
• The maintained repository suite passes 67 tests; the only skipped case is the live TwelveLabs test requiring an external API key.
• Release assets are built twice and compared byte-for-byte before publication.

Changes since v2.1.0

See the full changelog.

---

FunClip v2.1.1 是面向新安装、容器部署与文本剪辑稳定性的补丁版本。该版本将 Gradio 4 的运行环境约束为 starlette<1.0，避免首页模板返回 HTTP 500；显式 --listen 只跳过容器内 localhost 探测，不会自动开启 share=True，只有用户主动传入 --share 才创建公网链接。同时，文本剪辑匹配改为大小写不敏感，并新增 MiniMax M2.7 与 MiniMax M2.7-highspeed 路由。下载 tar.gz 或 zip 后请先使用 SHA256SUMS 校验，再执行 pip install -U -r requirements.txt。归档包含 Git 已跟踪的应用源码、文档和依赖清单，不包含运行依赖或模型权重。

FunClip v2.1.0

FunClip v2.1.0

FunClip v2.1.0 is the project's first versioned GitHub release and a stable rollback point for the current local video-clipping application.

Highlights

• Select Paraformer for precise timestamp-based clipping, Fun-ASR-Nano for high-accuracy transcription, or SenseVoice for multilingual ASR with emotion and audio-event tags.
• Use transcript-driven LLM clipping through the supported providers, or optional TwelveLabs Pegasus for content-aware video selection.
• Require funasr>=1.3.29, including SenseVoice VAD-region sentence_info timestamps plus the real-time final-text and short-tail fixes from 1.3.28.
• Run the same public application in the FunClip Hugging Face Space.

Install

Download either source archive below, verify it with SHA256SUMS, extract it, and install the declared dependencies:

sha256sum -c SHA256SUMS --ignore-missing
pip install -r requirements.txt
python funclip/launch.py

Windows users can verify a downloaded archive with Get-FileHash -Algorithm SHA256 and compare the result with SHA256SUMS.

Assets

• FunClip-2.1.0.tar.gz: versioned source archive for Linux and macOS workflows.
• FunClip-2.1.0.zip: versioned source archive for Windows and general-purpose extraction.
• SHA256SUMS: SHA-256 digests for both archives.

The archives contain the tracked FunClip application source, documentation, and dependency manifest. Runtime dependencies and model weights are not bundled; model weights are not bundled because each model retains its own license and distribution terms.

---

FunClip v2.1.0 是项目首个带版本号的 GitHub Release，为当前本地视频剪辑应用提供稳定下载和回退节点。版本支持 Paraformer、Fun-ASR-Nano、SenseVoice、基于字幕的大模型智能剪辑及可选的 TwelveLabs Pegasus，并要求 funasr>=1.3.29。下载 tar.gz 或 zip 后，请先使用 SHA256SUMS 校验，再安装 requirements.txt。归档仅包含 Git 已跟踪的应用源码、文档和依赖清单，不包含运行依赖或模型权重。