Media Processing

Immich109.9k★

Immich is the self-hosted photo and video management platform that actually competes with the big players. Upload from your phone, browse on the web, search by faces, places, or objects using on-device ML. AGPL v3, TypeScript backend with a Flutter mobile app. Automatic backup from iOS/Android, facial recognition, reverse geocoding, timeline view, shared albums, and a map view. Machine learning runs locally on your server for object detection and face clustering. Self-hosting is free. Docker Compose is the recommended path; you need decent storage and ideally a GPU for faster ML, but CPU works too. Immich offers a paid license for businesses, but the software is fully functional without paying. Solo: perfect. Replace Google Photos for your personal library. Families: shared albums work great. Growing orgs: this isn't designed for enterprise photo management. Large: look at purpose-built DAM (digital asset management) solutions. The catch: Immich is pre-1.0 and the developers explicitly warn against using it as your only backup. The API and schema can change between updates. Keep your originals backed up elsewhere. ML features need real compute: 4GB RAM minimum, more for large libraries.

whisper106.8k★

Whisper turns speech into text, and it set the bar the moment OpenAI released it. Feed it an audio file in almost any of 99 languages and you get back a transcript, optionally translated to English. The model weights and the code are MIT licensed, so you can run the whole thing on your own machine for nothing. Running it yourself is a pip install and an ffmpeg dependency away, but the catch is hardware. The tiny model fits in about 1GB of VRAM and is fast and rough; the large model wants roughly 10GB and a real GPU to run at a sane speed. On a CPU it works, but you will wait. A newer turbo model is much faster for plain transcription, though it drops the translation trick. For a one-off transcript, OpenAI's hosted Whisper API runs about half a cent per minute and saves you the setup. Run it locally when the audio is sensitive, when you are processing a lot of it, or when you just do not want a per-minute bill. Solo and small teams: local on a decent GPU is plenty. Higher volume: budget a GPU box and self-host. The catch is that Whisper is a model, not an app. It does straight transcription, not speaker labels or live captioning out of the box. If you want a GUI with those niceties, look at buzz or vibe, which both wrap this exact model.

FFmpeg63.0k★

It's the command-line tool that powers nearly every media application you've used. YouTube, VLC, OBS, Discord, they all depend on FFmpeg. The entire project is free under LGPL/GPL (depending on which codecs you enable). No paid tier. No cloud service. No company selling premium features. This is one of the most important pieces of free software ever written, maintained by a relatively small team. There's nothing to host. It's a command-line tool you install on any machine. Every Linux distro packages it. macOS gets it via Homebrew. Windows has pre-built binaries. It runs, processes your media, and exits. Solo developers: you'll use it the moment you need to do anything with audio or video. Small teams: wrap it in a script for your media pipeline. Enterprise: you're already using it whether you know it or not. The catch: the command-line interface is notoriously complex. The man page is a novel. Simple things are simple (`ffmpeg -i input.mp4 output.avi`) but anything beyond basic conversion requires reading docs or StackOverflow. The learning curve is the only real barrier, and it's steep.

Remotion55.7k★

Remotion lets you write video content as React components and render them to MP4, perfect for data-driven animations and personalized video at scale. Instead of After Effects templates, you write JSX. The idea is wild and it works: your video is a React component, each frame is a render, and Remotion stitches them into video using FFmpeg. You can use any React library: charts, maps, 3D with Three.js, and it becomes video. The core Remotion player and composition tools are free. Rendering to video requires a license for companies with revenue above $100K/yr. Remotion Lambda (serverless rendering on AWS) is a separate paid product. Individual and small-revenue companies can use it free. The catch: rendering is slow and compute-heavy. A 60-second video can take minutes to render locally. Remotion Lambda speeds this up but adds AWS costs. The license model is unusual: it's free below a revenue threshold, then you pay. Check their pricing page carefully. And if you just need simple video editing (trim, concatenate, add text), FFmpeg with a wrapper is way simpler.

PhotoPrism40.0k★

PhotoPrism gives you automatic face recognition, location mapping, and AI-powered search across your photo library without sending your photos to anyone's cloud. Point it at a folder of images, and it indexes everything with object detection, color analysis, and GPS data. This is the most popular self-hosted photo management tool. It handles RAW files, videos, live photos, and generates thumbnails automatically. The search is good: type "beach" and it finds your beach photos without you tagging anything. The Community Edition is free. PhotoPrism Essentials ($3.99/mo) and PhotoPrism Plus ($7.99/mo) add features like enhanced video support, premium themes, and priority access to new features. The AI features (face recognition, search) are in the free tier. The catch: self-hosting PhotoPrism is not trivial. It needs Docker, a database (MariaDB recommended), and decent hardware for the AI indexing; a Raspberry Pi 4 works but initial indexing of a large library will take days. RAM usage is real: 4GB minimum, 8GB+ recommended for large libraries. And the license is AGPL-adjacent (custom); check terms carefully if you're building a product on top of it.

faster-whisper24.8k★

faster-whisper runs OpenAI's Whisper speech-to-text models up to four times faster than the original implementation while using less memory, with the same accuracy. It reimplements inference on CTranslate2, and it has quietly become the engine inside most self-hosted transcription stacks. MIT licensed, completely free. It's a Python library, not a service: pip install, pick a model size, feed it audio. A GPU gets you faster-than-realtime transcription; CPU works fine with the small and medium models. Wrapping it in an internal API for your team is an afternoon with FastAPI, and 8GB of VRAM comfortably runs the large model in int8. Anyone paying AssemblyAI or Deepgram per minute for plain transcription should run the math. A cheap GPU instance chews through hours of audio for pennies, and openai/whisper accuracy is the same thing you're renting. The paid APIs keep winning on streaming, speaker diarization, and zero-ops. The catch: the models are open but the pipeline is yours. Chunking long files, retries, scaling workers, word-level timestamps, all of it. The per-minute APIs charge for exactly that boredom.

buzz20.8k★

Buzz is the desktop app that makes Whisper usable for people who do not live in a terminal. It transcribes and translates audio and video files, YouTube links, even live microphone input, all offline on your own machine. It is MIT licensed and free, and it runs on macOS, Windows, and Linux. Under the hood it is OpenAI's Whisper with a real UI bolted on: speaker separation for messy audio, export to SRT, VTT, and plain text, a watch folder that auto-transcribes new files, and GPU acceleration whether you are on an Nvidia card, Apple Silicon, or Vulkan. You download it and go. No Python environment, no ffmpeg wrangling. There is a paid Buzz Captions build on the Mac App Store for a few dollars if you want a native, auto-updating package, but the open-source app does everything and costs nothing. Solo users and small teams: grab the free build. This is the easy on-ramp to local transcription, and paying for the convenience version is fair if you want it. The catch is that quality and speed are still Whisper's. A big model on weak hardware is slow, and a small model on hard audio makes mistakes. Buzz makes Whisper convenient; it does not make it faster than your GPU allows.

subtitleedit13.7k★

SubtitleEdit is the desktop tool video editors and translators have used to fix subtitles for over fifteen years. You load a video, see the audio waveform, and edit subtitle timing and text against it. Handles 300+ subtitle formats, batch conversions, OCR for image-based subs, and optional integrations with translation and speech-to-text APIs. MIT licensed, fully free. It is a desktop app. Windows users get a clean installer. macOS users have to install mpv and ffmpeg through Homebrew, then clear the unsigned-app quarantine flag the first time they launch it. Linux users can use Flatpak or install through their package manager. For one-off subtitle fixes, batch format conversions, or cleaning up raw transcription output, this is the standard. Paid cloud tools like Kapwing or Rev offer one-click translation and automated transcription, but you pay per minute and lose the format flexibility. The catch: the UI looks like Windows software from 2010 because that is the era it is from. Functional, dense, no design system. If you want something that feels modern, this is not it.

palmier-pro13.2k★

Palmier Pro is a native macOS video editor with AI wired directly into the timeline. The editor itself is open source under GPLv3 and free to use with no login. Where it gets interesting: you can generate video and images from models like Kling and Seedance right inside your edit, and it hooks into AI agents (Claude, Codex, Cursor) over MCP so an assistant can edit the project alongside you. There's not much to "host" here, it's a desktop app you install and run. The setup catch is hardware: it's Apple Silicon only and requires macOS 26 (Tahoe), so it won't run on older Macs or anything non-Apple. The core editing, the MCP server, and the agent chat are all in the open source build. The free editor is a capable tool on its own. The generative AI features are the paid part: those run closed source and need a login and a subscription. So the split is clean, free editing forever, pay only when you want in-timeline generation. Solo creators and developers curious about agent-driven editing: the free build is worth a look. If you'd lean on the AI generation daily, price the subscription against how much you'll actually use it. The catch is that the headline feature, the AI generation, is exactly the piece that's closed and paid. The open source part is a solid editor; the "built for AI" magic is a subscription. That's a fair way to monetize, just know going in that "open source AI video editor" is half the story.

OmniVoice-Studio9.6k★

OmniVoice Studio is a local, open source answer to ElevenLabs. It clones a voice from a three-second clip, does text-to-speech across 14 engines, dubs video (transcribe, translate, re-voice, export MP4), builds audiobooks from EPUB or PDF, and runs a system-wide dictation widget. All of it runs on your own machine, AGPL-3.0 licensed, no subscription. Running it well needs a real GPU. The minimum is 8GB RAM and CPU-only mode, but the recommended setup is 16GB-plus RAM and 8GB-plus of VRAM, with support for NVIDIA CUDA, Apple Silicon, and AMD ROCm. Prebuilt installers exist for macOS, Windows, and Linux, plus Docker, so getting it running is not the hard part; having hardware that makes it fast is. For a solo creator or a small team doing voiceover, dubbing, or audiobook work, the fact that this is free and local is the whole pitch. No per-character billing, no clips leaving your machine, which matters when you are cloning voices. Larger teams can run it too, but the AGPL license means if you build it into a hosted product, you owe source; a commercial license is coming for that case. The catch is voice cloning ethics and quality. Cloning a voice from three seconds is powerful and easy to misuse, and that is on you to handle responsibly. On quality, local open models are close to the commercial services but not always ahead of them, so test against your actual use case before you cancel the paid tool.

vibe7.0k★

Vibe is another desktop transcription app built on Whisper, and what sets it apart is how much it does after the transcript. Everything runs locally, it handles almost every language with translation to English, and it exports to more formats than most people will ever need: SRT, VTT, TXT, HTML, PDF, JSON, and DOCX. MIT licensed, completely free. It is built on whisper.cpp, the C++ port of Whisper, which tends to be lighter on resources than the Python original. You get batch transcription, speaker identification, microphone and system-audio recording, and a command-line interface plus an HTTP API for wiring it into other tools. The standout feature is AI summaries: point it at Claude or a local Ollama model and it condenses a transcript for you. Solo and small teams: it is a free download on macOS, Windows, and Linux, with mobile on the way. If buzz is the no-frills on-ramp, vibe is for people who want the transcript to feed straight into a workflow. The export options and the HTTP API are the reason to pick it. The catch is that the AI summary feature leans on an outside model, so the local-only promise holds for transcription but not for summaries unless you run Ollama yourself. And like every Whisper wrapper, accuracy is only as good as the model and the hardware you give it.

openwhispr5.2k★

OpenWhispr is voice-to-text for your desktop. Hit a hotkey, talk, and it types into whatever app you're in, and it'll transcribe meetings with speaker labels too. The transcription runs on local models (Whisper or NVIDIA's Parakeet) with no API keys required, so your audio never has to leave your machine. MIT, free, cross-platform. Install is trivial, there are prebuilt installers for Mac, Windows, and Linux. Local models download on first use, and if you've got a GPU it'll use Metal, CUDA, or Vulkan to speed things up, but CPU works fine. You can optionally bring your own cloud LLM keys for cleanup, but you don't need to. Everything you need for personal dictation is free and local. There's a separate hosted OpenWhispr Cloud for teams: free up to 2,000 words a week, $6.67/user/mo for Pro, $16.67/user/mo for Business with speaker labels and agent mode. Solo users should just run the free desktop app and skip the cloud entirely. Teams that want shared, unlimited transcription and sync are who the paid tiers are for. This is a real alternative to Otter and Rev if you value keeping audio local. The catch is the usual local-versus-cloud tradeoff. The free local app is good, but the polished team features, unlimited cloud transcription, sync, speaker labels at scale, sit behind the subscription, and the free cloud tier's 2,000-words-a-week cap is tight. For one person it's all upside. For a team, price the Business tier against what you're paying Otter now.

AI-Youtube-Shorts-Generator4.5k★

This one turns long YouTube videos into vertical shorts. It pulls the transcript, has an LLM rank the segments most likely to pop, and auto-crops them to 9:16. It bills itself, accurately, as the open-source alternative to Opus Clip, Klap, Vidyo.ai, and SubMagic, and the code is MIT. Here's the honest part: "open source" does not mean "free to run." Every mode needs a paid API key. The default mode wants a paid MUAPI key for download, transcription, and cropping. The local mode drops MUAPI but still needs a paid OpenAI or Gemini key for the ranking step, plus ffmpeg installed. So you own the code, but you rent the intelligence. Setup is a standard Python clone, venv, pip, configure-keys affair, moderate, nothing scary. For a developer who wants to clip at volume and control the pipeline, paying per-use API costs can undercut a monthly SaaS subscription, and there are no watermarks. Solo creators and small teams are the audience. Compare your expected API spend against Opus Clip's monthly price before you decide. The catch is twofold. One, it's pay-to-run, so "free" is the wrong mental model, budget for the API calls. Two, the project's commit history is thin, which for a tool leaning on fast-moving APIs is a maintenance risk worth watching. It works today and the code is real, but treat it as a scaffold you may end up maintaining yourself, not a turnkey product.

SmartSub4.5k★

SmartSub takes audio or video and produces translated subtitles, start to finish, on your own machine. It transcribes speech to text, translates it, lets you proofread against a video preview, then burns the subtitles into the video or muxes them as a soft track. Nothing gets uploaded, which matters if you're working with footage you can't send to a cloud service. MIT-licensed and free on Windows, macOS, and Linux. It ships six transcription engines, including whisper.cpp and faster-whisper that run fully offline, and hooks into 17+ translation providers from Google and Baidu to Gemini, DeepSeek, and any OpenAI-style API. GPU acceleration covers NVIDIA CUDA, AMD and Intel via Vulkan, and Apple Metal, with automatic detection and CPU fallback. Setup is heavier than a pure web tool: you download models, pick an engine, and configure providers, but it's a desktop install, not a server. Solo creators and translators: this is a strong free alternative to paid subtitle services, and the local engines cost nothing to run. Small teams doing video work: same, with no per-seat fee. Large teams: workable, though there's no central account or shared workflow. The catch: "free" stops at the app boundary. The bundled offline engines are free, but the moment you wire up Gemini, DeepSeek, or a cloud translation API for better quality, you're paying those vendors per use on your own keys. Stick to whisper.cpp and faster-whisper and it's genuinely free end to end.

ffmpeg-normalize1.5k★

ffmpeg-normalize fixes audio that is too loud, too quiet, or all over the place from clip to clip. Point it at a file or a whole folder and it normalizes loudness to broadcast-style targets (EBU R128) or simple peak and RMS levels, using FFmpeg under the hood. If you have ever stitched together video where one clip blows out your speakers and the next is a whisper, this is the tool that makes them match. It is free, MIT-licensed, and it is both a command-line tool and a Python module. There is almost nothing to set up beyond having FFmpeg installed; it is a pip install or a Docker image, and it is maintained by Werner Robitza, who is steady and responsive about it. The one thing to understand is that loudness normalization is opinionated: EBU R128 targets a standard that is right for spoken-word and broadcast but can feel flat for music, so the defaults will not suit every job. The flags to override that are all there once you read the docs. For anyone doing podcasts, video editing, or batch audio cleanup, this belongs in your toolbox. There is no paid version and no reason for one. The alternatives are doing it by hand in an audio editor, which is slower, or wiring up raw FFmpeg loudnorm filters yourself, which this tool exists to save you from.

aTrain1.2k★

aTrain turns speech recordings into text on your own machine, with no cloud upload and no subscription. It runs OpenAI's Whisper model locally for transcription in 99 languages and adds speaker diarization (working out who said what) through pyannote. It is a real desktop app with installers on the Microsoft Store and Flathub, not a script you have to babysit. AGPL-3.0, fully free. Because everything runs on your device, nothing you record leaves your computer, which is the whole point for anyone handling interview, medical, or legal audio. On a plain CPU it is slow; with an NVIDIA GPU and the CUDA toolkit installed, the best model runs at roughly three times the audio length. It exports straight into MAXQDA, ATLAS.ti, and NVivo, so qualitative researchers are clearly the target audience. Weigh this against Otter.ai, Rev, and Trint, which are faster and need no setup but send your audio to their servers and bill you monthly. If privacy matters, or you transcribe enough hours that subscriptions add up, aTrain wins outright. Solo researchers and journalists can install it and stop paying per minute. Teams with sensitive recordings may find local processing is the only option compliance allows. The catch: local means your hardware is the bottleneck. Without a decent GPU, long recordings take real time, and accuracy still depends on audio quality the way every transcription tool does. It trades a monthly bill for your own patience and a CUDA install.

jzsub927★

jzsub takes a video link and hands back a finished MP4: highest quality available, cover art, and bilingual subtitles burned in, translated by GPT. It works across YouTube, Bilibili, TikTok, Vimeo, and anything else yt-dlp can reach. MIT licensed and free; you bring your own LLM key for the translation step. The setup is the tax. Python 3.10+, yt-dlp, Deno, and an FFmpeg build compiled with libass for subtitle rendering, plus a specific font. That FFmpeg requirement is what trips people up. The docs are Chinese-first, so English speakers are translating as they go. Use it if you archive or localize video with dual-language captions and want one command instead of a four-tool pipeline. For plain downloads, yt-dlp alone is simpler. For transcription without the download step, Whisper-based tools do the job. The catch: this is one maintainer's pipeline, tuned to their workflow. If your FFmpeg build or font doesn't match, you're debugging the plumbing yourself.

pireel910★

Pireel is a video editor for talking-head content, the kind of thing you'd make for YouTube or a product demo, and it runs entirely in your browser. Timeline, animated captions, themes, graphics, live preview, export, no account and no server for the core editing. The editor is open source under AGPL and free. Running the editor yourself is easy, it's a browser app. The AI features are where it gets real. Narration, transcription, image and video generation, cross-device sync: those aren't self-contained. You either wire in your own AI providers and backend, or you use Pireel's hosted service. So "self-hosted AI video editor" is half true. The editing self-hosts, the AI doesn't. Hosted pricing runs free for 20 credits a day, $17-20/mo for Pro with actual video generation, and up to $83-100/mo for the heavy tiers, plus credit top-ups. Solo creators who just need to cut and caption talking-head video can self-host the editor and pay nothing. If you want the generative features without standing up a backend, the paid cloud is the honest path. Descript, Veed, and Submagic are the paid tools this is aimed at. The catch is that split between the open editor and the paid AI, with AGPL on top. The editor is free and capable, but the "AI" in the pitch lives behind either your own infrastructure or Pireel's subscription, and AGPL makes commercial forks a legal question, not a copy-paste. Know which half you're getting.

story-to-handdrawn-video835★

Feed this a short story, or a set of ordered images, and it renders a vertical hand-drawn "diary comic" video with handwritten captions, then hands you a silent MP4 to drop a voiceover onto later. It's built on Remotion, and it ships both the renderer and an agent-skill wrapper so you can drive the whole thing in plain language. Under the hood it wants Node 20+, Python, FFmpeg, and Chrome, plus your own OpenAI API key for the copy and image steps. So the tool is free but the generation isn't: you pay OpenAI for whatever it calls. Setup is more involved than a one-liner, which is why this leans moderate rather than trivial. For solo creators and small teams it costs nothing beyond the API usage. Reach for it when you want a specific aesthetic, the hand-drawn storybook look, without hiring an animator. The catch is that this is a novelty with a narrow lane. It's oriented around Chinese story copy, the output style is fixed, and it solves exactly one creative problem. Impressive to watch, but don't expect a general video pipeline.