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Commits
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3e4230ffc2 |
fix(settings): make model downloads explicit and cancel the whole intent (#1006)
* fix(settings): make model downloads explicit and cancel the whole intent "Use it" on the recommendation card applied a different model than the one shown, a cancelled or failed apply left the staged model behind so the next Test Dictation downloaded it, and cancelling a download tore down and then reloaded the previous model while the dialog waited. - Recommendation is now passive text, compared by size tier, with a link to docs/MODEL_SELECTION.md. The "Use it" button is gone. - Every Settings download asks first and shows size and source host. Declining, cancelling or failing drops the staged model spec and puts the pickers back on the saved config. - Test Dictation refuses a model that is not on disk instead of applying it. - reconfigure() fetches a missing model before releasing the loaded one, so a cancel restores state without reloading or rehashing the old model. Cancellation state is reset only when a download claim is taken or ended. - Follow-layout mode shows the resolved language in the simple Main language control, read-only, and simple edits keep the "layout" setting. Fixes #979 Fixes #980 Fixes #981 Fixes #982 Fixes #985 Fixes #988 * fix(settings): restore follow-layout mode when a model change is declined * fix(settings): keep follow-layout and dictation consistent during model changes * style(tests): format follow-layout regression tests * fix(recognition): skip resume reinit while a model download is running * fix(recognition): keep a lazy reload pending when resume is skipped during a download --------- Co-authored-by: Jatin K Malik <jatinkrmalik@gmail.com> |
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Jatin K MalikandGitHub
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26f90b6998 |
chore(release): prepare v0.17.0
Minor on the stable line. Faster Whisper and Parakeet extras, Speech Model simple setup, Snap uinput, Flatpak bundles on the tag, and QA fixes from the release branch. Fixes tracked in the PR. Post-release follow-ups: #834. |
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8f4aeca2f1 |
feat(asr): add Parakeet TDT 0.6B engine via sherpa-onnx (#802)
* feat(asr): add Parakeet engine via sherpa-onnx Adds a "parakeet" engine backed by sherpa-onnx, running NVIDIA Parakeet TDT 0.6B int8 models on CPU: v3 (25 European languages, auto-detected) and v2 (English). Output is punctuated and capitalized. On an Intel Core Ultra 7 155H it decodes at about 12x realtime on CPU (RTF 0.08), roughly 8x faster than whisper.cpp large-v3-turbo over the same clips with its Vulkan GPU backend (RTF 0.59). Being a transducer it also has no 30s window to pad, so a short push-to-talk utterance returns in ~0.3s instead of ~2.4s. Follows the existing engine layout: utils/parakeet_model_info.py holds the catalog and the get_model_path / is_model_downloaded / list_downloaded_models / delete_model helpers, while _init_parakeet / _transcribe_with_parakeet / _download_parakeet_model follow the same shape as their whisper.cpp counterparts and reuse _stream_model_download for progress and cancellation. Registered in the engine dispatch, the settings engine/model/language pickers, the model info card, the download prompts, the unused-downloads list and install.sh. Models download to <models_dir>/parakeet/<model>/ via temp files renamed on success; is_model_downloaded requires all four files, so an interrupted download is retried rather than loaded. Requires the new "parakeet" extra (sherpa-onnx). * fix(install): write only the engine key for parakeet The parakeet branch duplicated a full default config.json. ConfigManager deep-merges DEFAULT_CONFIG on load, so every other key was redundant: a config holding only "engine": "parakeet" still resolves parakeet_model_size to v3-european along with the VAD, shortcut and text-injection defaults. * fix(install): keep push-to-talk defaults and switch existing configs Writing only the engine key left config.json without a shortcuts section, which _migrate_shortcuts_config reads as a pre-push-to-talk upgrade and pins back to ctrl+ctrl/toggle - contradicting the installer's own "Hold Right Alt while you speak" message. Seed that section too. Point the config at parakeet with set_configured_engine rather than only on first create, so --engine=parakeet also switches a machine that already has a config.json, and give print_welcome_message a parakeet arm so the summary is not blank. The interactive picker overwrites SELECTED_ENGINE, so document the flag combination that reaches this branch. * fix(asr): pin Parakeet bundles and report one progress bar Pin each bundle to a Hugging Face commit instead of resolve/main, so a file replaced upstream cannot change what an install downloads. This is the same approach whispercpp_model_info takes with its pinned revision. Remap each file's progress into its slice of the bundle. The four files were reported through the single-file downloader independently, so the fraction ran to 1.0 and restarted four times; it now advances once and names the file being fetched. * fix(asr): reject a truncated Parakeet download _stream_model_download only rejects an empty body, so a truncated but nonempty response was renamed into the model directory and then reported as installed, because is_model_downloaded checks existence. Record the byte size of each file at the pinned revision and check it before the rename, the same order whisper.cpp verifies its digest in. A short file is a truncated transfer rather than an upstream change, since the revision is pinned, so it raises and the temp file is removed. * style(asr): annotate the Parakeet model catalog mypy inferred the catalog values as object, so reading file_sizes out of an entry raised attr-defined and no-any-return under `just typecheck`. * fix(asr): pin Parakeet digests in model_checksums.txt AGENTS.md requires every speech model to be verified against a pinned digest before install, failing closed on an unpinned one. Parakeet was checking only the byte size. Teach scripts/generate-model-checksums.py to emit Parakeet entries: the ONNX files are LFS so Hugging Face publishes their sha256, and tokens.txt is hashed from the downloaded bytes the way VOSK archives are. Several bundles ship the same file names, so a key carries the model name. _download_parakeet_model now calls verify_model_file before the rename and catches ChecksumError alongside the other download failures, matching the whisper.cpp downloader. * fix(asr): verify on-disk Parakeet models before load Existing bundle files were trusted by path alone, so a leftover or copied-in tree could reach sherpa-onnx unchecked. Hash each file against its pin on download skip and on init, and cover the Parakeet pins in CI. * fix(ui): hide Parakeet language picker that ignores the setting Parakeet language coverage is the model choice, not a catalog language. Hide the picker and persist auto so Settings does not write a lying value. * fix(asr): normalize Parakeet language to auto at startup CLI --language and stale saved languages still reached SpeechRecognitionManager even though Parakeet ignores catalog language. Force auto on the shared helper used by main and the manager so stored settings stay honest. * fix(ui): restore language after leaving Parakeet Remember the prior Whisper/cpp language across a Parakeet visit so switching back does not leave recognition stuck on auto. Keep Parakeet forced to auto and the picker hidden. Also satisfy black on the startup normalizer path. * fix(ui): keep language memory across Vosk fallback Vosk's unsupported-language en-us coercion was writing into _last_non_parakeet_language, so Whisper→Parakeet→Vosk→Whisper restored en-us instead of the user's Greek (or other) preference. Remember the pre-coercion catalog language and prefer it when leaving Vosk. * fix(ui): keep language memory when entering Parakeet from Vosk Vosk's coerced en-us fallback was still written into _last_non_parakeet_language on Parakeet entry, so Vosk→Parakeet→Whisper restored en-us instead of the pre-coercion preference (Greek or auto). Share one coerced-fallback predicate across entry, leave-Vosk, and language sync, and cover those paths with regression tests. * fix(tests): set initializing flags false on language-change stubs Plain Mock attributes are truthy and were short-circuiting the handler. --------- Co-authored-by: Alex Thunder <AlexThunder@gmail.com> |
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9a0a677271 |
feat(models): make the recommendation applicable and stop ignoring the disk (#797)
* feat(models): make the recommendation applicable and stop ignoring the disk The panel worked out which model fits the hardware and the language, printed it as a plain label, and left the pickers on the wrong one. Applying it meant hitting two dropdowns by hand after decoding that "Small EN" means size Small plus specialization English-only. Three changes: - the recommendation gets a button that moves both pickers at once - before offering a download, look at what is already on disk. A candidate has to be no smaller than the recommendation, and an English-only recommendation is not answered with multilingual weights, which weigh the same and recognise English worse (#776). So nothing is ever quietly downgraded. - the size list shows weight and the downloaded marker, the way the specialization list already did. A size is a download decision too. Closes #778 Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TKVa2qTLQYMCHffjp97yqT * fix(settings): keep recommendation fresh across language and apply Changing EN to PL left Use it pointed at the old English-only target, and clicking it briefly auto-applied the default size (full large) before the intended turbo specialization landed. Refresh the recommendation card after language changes, and move both pickers under _populating_models before the single apply. * fix(settings): type the recommendation language and apply callbacks * style(settings): wrap whispercpp model info import for isort Lint Python was failing the isort check on the single-line WHISPERCPP_MODEL_INFO import. Parenthesize it so black/isort agree. --------- Co-authored-by: Kacper Paczos <23406555+kacperpaczos@users.noreply.github.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Co-authored-by: Jatin K Malik <jatinkrmalik@gmail.com> |