Clips courts
ai-highlight-clip
Desktop highlight editor with Whisper transcription and LLM clip scoring.
★ 105 étoilesMIT
Voir le guide →WhisperX est un projet de clips courts open source, créé par m-bain (m-bain/whisperX sur GitHub). Il compte 24 669 étoiles sur GitHub et 2 490 forks, il est publié sous la licence BSD-2-Clause et a été mis à jour pour la dernière fois sur GitHub le 18 octobre 2026.
WhisperX — Whisper transcription with word-level timestamps and speaker diarization: 70x realtime batched inference, forced phoneme alignment, BSD-2-Clause
The simple route from the README: pip install whisperx (install the CUDA toolkit first if you want GPU acceleration; CPU works without it).
Run whisperx on an audio or video file — it transcribes, aligns word-level timestamps and (with diarization enabled and a Hugging Face token) labels speakers.
Take the JSON/SRT/VTT output into your subtitle, clipping or editing pipeline — word timings and speaker turns are already in the file.
Forced phoneme alignment turns Whisper segments into accurate per-word timings — the feature plain Whisper lacks.
Who said what, labelled per segment, alongside the transcript.
VAD-based batched inference on faster-whisper makes long recordings cheap to process on a GPU.
JSON, SRT, VTT and other subtitle formats that clipping and editing tools consume directly.
The README recommended route.
pip install whisperxThe README fast extra: Numba JIT for the alignment loops on long files.
pip install "whisperx[fast]"Basic CLI use; add --diarize and alignment options per the README for the full pipeline.
whisperx path/to/audio.mp3 --model large-v2Transcription feeds two smaller projects on this shelf: AI Clipping Generator packages an AI clipping business as a production-ready Next.js SaaS boilerplate, and Second Brain keeps the results in a fully local, private, self-hostable knowledge base. Readers working through the Clipping shelf also stop at maxhub-api-skills, Agent skills that pull data from 21 social platforms into your AI workflow.
Max Bain (m-bain/whisperX on GitHub); the underlying ASR is OpenAI Whisper via faster-whisper, with wav2vec2 alignment models.
Yes — BSD-2-Clause licensed. Diarization models from Hugging Face may ask you to accept their terms and use a token, per the README.
Whisper gives you segment-level text. WhisperX adds accurate word-level timestamps via forced alignment, speaker diarization, and much faster batched inference — the three things production pipelines actually need.
Against faster-whisper alone, WhisperX is the full pipeline (alignment + diarization) rather than just the fast ASR engine — it is built on faster-whisper. Against hosted transcription APIs, it runs locally and free, at the cost of your own GPU time.
pip install whisperx, then whisperx yourfile.mp3 --model large-v2. Add --diarize once you have a Hugging Face token configured.
Desktop highlight editor with Whisper transcription and LLM clip scoring.
FunASR-powered video transcription, subtitles and LLM-assisted clipping with a local Gradio UI.
Free YouTube-to-viral-clip cutter with auto transcription and 9:16 edits.
Auto-download livestreams, AI-clip the highlights, upload in realtime.
Local-first clipper: Whisper plus Ollama turn long video into captioned 9:16 clips.
Traceable AI pipeline for vertical videos across TikTok, Shorts, and Reels.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.