v3.1.0 #65
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v3.1.0
#65
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This release moves every long-running media operation into a background job system,
adds batch processing so one operation can run over many files at once, and makes the
GPU Docker images much smaller. As a
v3.xrelease (see thev0.1.0note above) itincludes a few breaking changes, listed first
Breaking Changes
REST API→ the old one-shot endpoints for transcription, subtitlegeneration, conversion, and subtitle cleanup were removed. These operations now run
through the background job system instead
REST API→ word breakdowns and sentence explanations are now sent as a livestream while they are generated, rather than as a single response at the end
CLI→ themirumoji servercommand moved into a new development-only groupand is now
mirumoji dev serverDatabase→ the database format changed and is not upgraded automaticallyfrom
3.0.0. Reset it when upgrading by runningmirumoji down -v(this clearslocal data, in keeping with the pre-1.0 note above)
Added
Server→ long operations (transcription, subtitle generation, conversion,and LLM subtitle cleanup) now run in the background. A file is uploaded once, and
any number of operations can then run on it without uploading it again
Server→ batch processing runs one operation across many files at once andtracks each file on its own. On the Modal backend the files are processed in
parallel
Frontend→ a task tray keeps your running and finished jobs visible as youmove around the app, and loads each result back in when it is done
Frontend→ newFilesandTaskssections on the dashboard let you uploadfiles or a whole folder, select several and run them as a batch, browse the full
job history, and open each result
Frontend→ when setting up an LLM, you can now pick the model from asearchable list of your provider's models instead of typing its exact name
Frontend→ video conversion now has a quality preset (Performance,Balanced, orQuality) alongside the resolution and bitrate options, so you cantrade encode speed for output quality
CLI→mirumoji dev upbuilds and runs the app from a local sourcecheckout, for testing the Docker setup during development
Changed
Frontend→ running an operation in the player no longer freezes the toolbar.It is handed to the task tray, which loads the result back in when it finishes. The
video player was also reworked into a shared component that clip previews reuse
Frontend→ a phone held sideways now uses the mobile layout, since thedesktop layout only starts at tablet width
Launcher→ the launcher's logs and the Docker download progress are tidierand easier to follow, in both the CLI and the desktop app
Docker Images→ the GPU images are smaller. PyTorch was removed becausetranscription does not need it, and the speech model now downloads on first use into a
persistent cache instead of being baked into the local GPU images
Server→ video conversion is faster and leaner. On the GPU the whole decode,scale, and encode pipeline now stays on the GPU instead of copying frames back and
forth, and the GPU and Modal images ship a newer FFmpeg build to support it.
Conversion also picks the encoder from what the GPU can actually do, so machines whose
GPU cannot encode (and CPU-only setups) go straight to a fast CPU encode. Converted
videos keep their original aspect ratio now rather than being padded with black bars
Fixed
Frontend→ the player now handles video formats your browser cannot play(such as
.mkvon iOS) by offering to convert them to MP4 instead of showing abroken player. Loading a different video also resets the saved position so it never
starts past the end of a shorter one
Frontend→ the navigation menu scrolls on short screens (such as a phoneheld sideways) so every item stays reachable, and long entries on the
Homepageno longer stretch the layout out of shape
Server→ saving a clip no longer fails for clips with long Japanese text,and saved clips now use a server-generated filename that closes a security issue
where a crafted upload name could write outside its folder
Server→ deleting a file also removes the jobs that used it (and is blockedwhile one of those jobs is still running), cancelling or deleting a running job no
longer leaves the job list in a broken state, and a failed job now shows a clear
reason
Server→ converting a video on a cloud GPU that has no video encoder (such asan
A100,H100, orB200) no longer wastes time on a failed hardware-encodeattempt before falling back, which had made GPU conversion slower than plain CPU
CLI→mirumoji logs -fno longer crashes when you press Ctrl+C, andre-running
mirumoji upno longer contacts Docker Hub when the images are alreadydownloaded
This discussion was created from the release v3.1.0.
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