fix(audio): raise trim threshold, cap pad at net-neutral

Review feedback on the preprocessor:

1. ``trim_top_db=30`` was labelled "conservative" in the docstring but is
   actually *more* aggressive than librosa's default of 60. Normal
   speech dynamic range sits around 30 dB, so 30 dB would eat quiet
   trailing syllables and soft consonants. Raise the default to 40 dB —
   below normal speech dynamic range but still catching obvious edge
   silence — and fix the docstring.

2. Unconditional 100 ms edge padding ran even when ``librosa.effects.trim``
   removed nothing. For a well-recorded 29.9 s upload that path would
   push the waveform past the 30 s ceiling and trigger a spurious "too
   long" rejection. Only pad when trimming actually shortened the
   audio, and cap the pad so the output never exceeds the input length.

Adds a regression test for the net-neutral length behaviour.

Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>

.agents/skills/add-tts-engine/SKILL.md

@@ -0,0 +1,120 @@
+---
+name: add-tts-engine
+description: Use this skill to add a new TTS engine to Voicebox. It walks through dependency research, backend implementation, frontend wiring, PyInstaller bundling, and frozen-build testing. Always start with Phase 0 (dependency audit) before writing any code.
+---
+
+# Add TTS Engine
+
+## Goal
+
+Integrate a new text-to-speech engine into Voicebox end-to-end: dependency research, backend protocol implementation, frontend UI wiring, PyInstaller bundling, and frozen-build verification. The user should only need to test the final build locally.
+
+## Reference Doc
+
+The full phased guide lives at `docs/content/docs/developer/tts-engines.mdx`. **Read this file in its entirety before starting.** It contains:
+
+- Phase 0: Dependency research (mandatory before writing code)
+- Phase 1: Backend implementation (`TTSBackend` protocol)
+- Phase 2: Route and service integration (usually zero changes)
+- Phase 3: Frontend integration (5 files)
+- Phase 4: Dependencies (`requirements.txt`, justfile, CI, Docker)
+- Phase 5: PyInstaller bundling (`build_binary.py` + `server.py`)
+- Phase 6: Common upstream workarounds
+- Implementation checklist (gate between phases)
+
+## Workflow
+
+### 1. Read the guide
+
+```bash
+# Read the full TTS engines doc
+cat docs/content/docs/developer/tts-engines.mdx
+```
+
+Internalize all phases, especially Phase 0 and Phase 5. The v0.2.3 release was three patch releases because Phase 0 was skipped.
+
+### 2. Dependency research (Phase 0)
+
+Clone the model library into a temporary directory and audit it. Do NOT skip this.
+
+```bash
+mkdir /tmp/engine-research && cd /tmp/engine-research
+git clone <model-library-url>
+```
+
+Run the grep searches from Phase 0.2 in the guide against the cloned source and its transitive dependencies. Produce a written dependency audit covering:
+
+1. PyPI vs non-PyPI packages
+2. PyInstaller directives needed (`--collect-all`, `--copy-metadata`, `--hidden-import`)
+3. Runtime data files that must be bundled
+4. Native library paths that need env var overrides in frozen builds
+5. Monkey-patches needed (`torch.load`, float64, MPS, HF token)
+6. Sample rate
+7. Model download method (`from_pretrained` vs `snapshot_download` + `from_local`)
+
+Test model loading and generation on CPU in the throwaway venv before proceeding.
+
+### 3. Implement (Phases 1–4)
+
+Follow the guide's phases in order. Key files to modify:
+
+**Backend (Phase 1):**
+- Create `backend/backends/<engine>_backend.py`
+- Register in `backend/backends/__init__.py` (ModelConfig + TTS_ENGINES + factory)
+- Update regex in `backend/models.py`
+
+**Frontend (Phase 3):**
+- `app/src/lib/api/types.ts` — engine union type
+- `app/src/lib/constants/languages.ts` — ENGINE_LANGUAGES
+- `app/src/components/Generation/EngineModelSelector.tsx` — ENGINE_OPTIONS, ENGINE_DESCRIPTIONS
+- `app/src/lib/hooks/useGenerationForm.ts` — Zod schema, model-name mapping
+- `app/src/components/ServerSettings/ModelManagement.tsx` — MODEL_DESCRIPTIONS
+
+**Dependencies (Phase 4):**
+- `backend/requirements.txt`
+- `justfile` (setup-python, setup-python-release targets)
+- `.github/workflows/release.yml`
+- `Dockerfile` (if applicable)
+
+### 4. PyInstaller bundling (Phase 5)
+
+Register the engine in `backend/build_binary.py`:
+- `--hidden-import` for the backend module and model package
+- `--collect-all` for packages using `inspect.getsource`, shipping data files, or native libraries
+- `--copy-metadata` for packages using `importlib.metadata`
+
+If the engine has native data paths, add `os.environ.setdefault()` in `backend/server.py` inside the `if getattr(sys, 'frozen', False):` block.
+
+### 5. Verify in dev mode
+
+```bash
+just dev
+```
+
+Test the full chain: model download → load → generate → voice cloning.
+
+### 6. Use the checklist
+
+Walk through the Implementation Checklist at the bottom of `tts-engines.mdx`. Every item must be checked before handing the build to the user.
+
+## Key Lessons (from v0.2.3)
+
+These are the most common failure modes. Phase 0 research catches all of them:
+
+| Pattern | Symptom in Frozen Build | Fix |
+|---------|------------------------|-----|
+| `@typechecked` / `inspect.getsource()` | "could not get source code" | `--collect-all <package>` |
+| Package ships pretrained model files | `FileNotFoundError` for `.pth.tar`, `.yaml` | `--collect-all <package>` |
+| C library with hardcoded system paths | `FileNotFoundError` for `/usr/share/...` | `--collect-all` + env var in `server.py` |
+| `importlib.metadata.version()` | "No package metadata found" | `--copy-metadata <package>` |
+| `torch.load` without `map_location` | CUDA device not available on CPU build | Monkey-patch `torch.load` |
+| `torch.from_numpy` on float64 data | dtype mismatch RuntimeError | Cast to `.float()` |
+| `token=True` in HF download calls | Auth failure without stored HF token | Use `snapshot_download(token=None)` + `from_local()` |
+
+## Notes
+
+- The route and service layers have zero per-engine dispatch points. `main.py` requires zero changes.
+- The model config registry in `backends/__init__.py` handles all dispatch automatically.
+- Use `get_torch_device()` and `model_load_progress()` from `backends/base.py` — don't reimplement device detection or progress tracking.
+- Always test with a **clean HuggingFace cache** (no pre-downloaded models from dev).
+- Do NOT push or create a release. Hand the build to the user for local testing.

.agents/skills/triage-prs/SKILL.md

@@ -0,0 +1,299 @@
+---
+name: triage-prs
+description: Use this skill to triage the open PR queue before a release. Classifies every open PR into must-merge, candidate, superseded, or deferred; writes a working triage doc; and runs the merge loop end-to-end. Designed for the pre-release "PR speedrun" pass where a solo maintainer wants to clear the inbound backlog in a single session.
+---
+
+# Triage PRs
+
+## Goal
+
+Turn a backlog of open PRs into a shipped set of merges in a single focused session. Produce a tracked, resumable plan (`<VERSION>_PR_TRIAGE.md`), then work it — rebasing where needed, merging in isolation-safe batches, applying post-merge follow-ups, and closing superseded or partially-applicable PRs with credit to their authors.
+
+This skill pairs with `draft-release-notes` and `release-bump`: triage first, then draft notes against the new main, then cut the release.
+
+## When to use
+
+- Before a minor or major release when 10+ open PRs have accumulated
+- When you want to unblock merging without losing the narrative of what's landing
+- When you know you can't personally review every PR deeply, but need to land the critical subset fast
+
+## Prerequisites
+
+- `gh` CLI authenticated against the repo
+- A dedicated worktree for PR review (avoid contaminating `main` with checkouts of contributor branches)
+- Clarity on the target version — the triage doc is named after it (e.g. `0.4.0_PR_TRIAGE.md`)
+
+## Workflow
+
+### 1. Set up an isolated PR-review worktree
+
+```bash
+git worktree list  # check for stale ones first
+git worktree prune
+git worktree add ../voicebox-pr-review -b pr-review-<VERSION> main
+```
+
+Keep the main worktree for release-prep work (changelog drafts, direct-to-main follow-ups). Keep the review worktree for `gh pr checkout` — each checkout moves HEAD to a contributor branch, which you don't want to do in the main worktree.
+
+### 2. Gather metadata for every open PR
+
+```bash
+gh pr list --state open --limit 50 --json \
+  number,title,author,isDraft,mergeable,mergeStateStatus,files,additions,deletions,reviewDecision,statusCheckRollup,maintainerCanModify \
+  --jq '.[] | {num: .number, title, author: .author.login, mergeable, state: .mergeStateStatus, canModify: .maintainerCanModify, changes: "+\(.additions)/-\(.deletions)", files: [.files[].path]}'
+```
+
+You want, for each PR:
+- Size (`+additions/-deletions`)
+- Mergeable state (`CLEAN`, `UNSTABLE`, `DIRTY` = conflicts, `UNKNOWN` = GitHub still computing)
+- Whether maintainer edits are allowed on the branch (needed later if you rebase for the author)
+- File paths touched (helps spot overlaps between PRs)
+
+`UNKNOWN` is common right after a push to main — just try the merge and see.
+
+### 3. Classify into tiers
+
+Sort each PR into exactly one bucket:
+
+**Tier 1 — Merge:** small, mergeable, fixes a real bug, clean CI, low review cost. One-liners, dependency relaxations, targeted safety hardening. These are the easy wins.
+
+**Tier 2 — Candidate, review:** medium size (50-200 lines), touches more surface area, looks sound but needs a closer read. New user-facing features that fit the product direction.
+
+**Supersede:** the fix or feature is already covered by something merged. Close with a comment pointing to the superseding PR. Check carefully — "similar title" isn't proof; compare the actual diffs.
+
+**Defer to next release:** big features, dirty conflicts, draft PRs, anything touching the release pipeline in ways that would introduce risk. Don't merge these in a speedrun — they need dedicated focus.
+
+### 4. Write the triage doc
+
+Create `<VERSION>_PR_TRIAGE.md` in the PR-review worktree root. Structure:
+
+```markdown
+# <Repo> <VERSION> — PR Triage
+
+Working doc for tracking which open PRs land in <VERSION>. Delete after release cut.
+
+Last updated: <DATE>
+
+## Progress
+
+**Tier 1: 0 / N merged**
+**Tier 2: 0 / M handled**
+**Supersede triage: pending**
+
+---
+
+## Merge for <VERSION> — critical bug fixes
+
+| PR | Status | Size | What it fixes | Why must-have |
+|---|---|---|---|---|
+| [#123](url) | [ ] | +5/-0 | ... | ... |
+
+## Strong candidate — needs a quick review
+
+| PR | Status | Size | Summary |
+|---|---|---|---|
+
+## Close as superseded
+
+| PR | Status | Reason |
+|---|---|---|
+
+## Defer to <NEXT_VERSION>
+
+- [#xxx](url) ... — reason
+
+---
+
+## Order of attack
+
+1. Close superseded PRs (one-liner comments)
+2. Merge tier-1 in dependency-free batches — check file paths don't overlap
+3. Review tier-2 individually
+4. Rerun `draft-release-notes` to pick up everything
+5. Run `release-bump`
+```
+
+The **Progress** header is the most important part — it's your scoreboard and lets you resume cleanly if the session gets interrupted.
+
+### 5. Work the loop — per PR
+
+For each PR in the tier-1 / tier-2 list:
+
+**a. Checkout in the review worktree:**
+```bash
+cd ../voicebox-pr-review
+git checkout pr-review-<VERSION>  # reset to neutral base
+gh pr checkout <N>
+```
+
+**b. Read the *actual* commit, not `main..HEAD`:**
+
+```bash
+git show HEAD                    # the PR's actual changes
+git show --stat HEAD             # files touched + line counts
+```
+
+**Do NOT review via `git diff main..HEAD`** if the PR branch is older than main. That diff includes *every commit that landed on main after the PR was forked* as `-` (deletion) lines. A 3-line PR can look like a 700-line revert. This is the single easiest way to misjudge a PR.
+
+**c. Evaluate concerns:** correctness, scope, interaction with already-merged work, version compatibility (e.g. can't use an API that requires a dependency version we don't yet pin).
+
+**d. Rebase if the branch is behind main:**
+```bash
+git fetch origin main
+git rebase origin/main
+```
+
+This is **essential** before squash-merging. GitHub's squash computes `diff(PR-head, merge-base)` — on a stale branch, that diff includes reverting every in-between commit. Rebasing moves the merge-base forward so the squash is clean.
+
+**e. If maintainer edits are allowed, push the rebase back to the contributor's fork:**
+```bash
+git remote add <author> https://github.com/<author>/<repo>.git
+git fetch <author> <branch>              # get their ref first
+git push <author> HEAD:<branch> --force-with-lease
+```
+
+This keeps GitHub's PR UI in sync with the rebased state and makes the merge clean from the GitHub side.
+
+**f. Merge:**
+```bash
+gh pr merge <N> --squash
+```
+
+**g. Update the triage doc** — flip the checkbox to `✅ merged <sha>` (use the short SHA from `gh pr view <N> --json mergeCommit --jq '.mergeCommit.oid[0:7]'`). Update the Progress header.
+
+### 6. Batch tiny fixes
+
+PRs with ≤5 line changes, clean CI, non-overlapping file paths, and obviously-correct intent (e.g. one-line dependency relax, env var add, import path fix) can be merged in a single loop without the review-per-PR ceremony:
+
+```bash
+for pr in 425 384 416 429; do
+  echo "=== Merging PR $pr ==="
+  gh pr merge $pr --squash
+done
+```
+
+Verify afterward that each landed cleanly:
+```bash
+for pr in 425 384 416 429; do
+  gh pr view $pr --json state,mergeCommit --jq "{pr: $pr, state, sha: .mergeCommit.oid[0:7]}"
+done
+```
+
+### 7. Post-merge follow-ups
+
+Sometimes a PR is worth merging despite a known minor issue (e.g. incomplete dtype map, stale sentinel cleanup). Don't block the merge; apply the follow-up as a normal branch + PR right after:
+
+```bash
+cd <main-worktree>
+git pull --ff-only origin main
+git checkout -b fix/<short-name>
+# edit...
+git commit -m "fix(<area>): <one-liner>"
+git push -u origin fix/<short-name>
+gh pr create --title "..." --body "Follow-up to #<N>. ..."
+```
+
+Record both SHAs in the triage doc (`✅ merged <pr-sha> + follow-up <pr>`).
+
+**Direct-to-main exception:** only under an explicit, scoped policy (e.g. "release speedrun"). Don't default to it.
+
+### 8. Supersede: close with a credit-pointing comment
+
+```bash
+gh pr close <N> --comment "Closing — superseded by merged #<M> which landed <brief description>. Thanks!"
+```
+
+Check the diffs first — "similar title" is not enough. If the PR is *partially* superseded (the diagnosis is right but only half the changes are still needed), do a partial-apply instead.
+
+### 9. Partial-apply pattern
+
+When a PR has both valuable and questionable changes bundled:
+
+```bash
+cd <main-worktree>
+git pull --ff-only origin main
+
+# Cherry-pick specific files from the PR branch
+git checkout <pr-commit-sha> -- <file1> <file2>
+
+# Review the staged changes, adjust as needed
+git diff --cached
+
+# Apply any surgical edits to files you don't want to bulk-replace
+# (e.g. the PR's file predates a recent main commit you need to preserve)
+
+# Commit with a trailer crediting the original author
+git commit -m "$(cat <<'EOF'
+<subject>
+
+<body explaining what was kept vs dropped>
+
+Co-Authored-By: <author> <[email protected]>
+EOF
+)"
+git push ...  # branch + PR, unless under the direct-to-main exception
+```
+
+Then close the PR with a comment explaining what was applied and what was dropped, referencing the commit SHA.
+
+### 10. Keep the doc current
+
+Every merge, every close, every follow-up → update `<VERSION>_PR_TRIAGE.md`. The doc is your session log. If you're interrupted and resume tomorrow, the doc is the only source of truth for "where am I."
+
+### 11. When triage is done
+
+- Every PR in the doc has a terminal status (✅ merged / ✅ closed / deferred)
+- Progress header shows N/N for each tier
+- Next skill to run is `draft-release-notes` (to regenerate `[Unreleased]` against the new main), then `release-bump`
+
+You can delete the triage doc after the release ships, or keep it in version history as a record.
+
+## Gotchas
+
+- **`main..HEAD` on a stale branch lies.** It shows everything main gained since the branch split as deletions. Always review via `git show HEAD` for the PR's actual commit.
+- **Squash-merging an unrebased branch reverts in-between work.** The squash computes `diff(PR-head, merge-base)`. Rebase moves the merge-base forward.
+- **`mergeable=UNKNOWN`** is transient — GitHub is recomputing after a push. Just try the merge.
+- **Route ordering matters (FastAPI and similar):** `DELETE /history/failed` must be registered *before* `DELETE /history/{id}`, or the parameterized path will consume `"failed"` as an ID.
+- **Apple's `-weak_framework` overrides `-framework`** for the same framework, regardless of order — use it via `cargo:rustc-link-arg=-Wl,-weak_framework,Name` when a dependency hard-links something optional.
+- **Dependency version floors constrain what you can apply.** Before accepting a kwarg rename like `torch_dtype=` → `dtype=`, check the min-version pin supports it. Sometimes the right move is to cherry-pick half the PR.
+- **`cpal::Stream` and similar `!Send` audio types** can't cross `await` points or `spawn_blocking`. Sometimes a "not-ideal but correct" sync wait is the best available fix; flag but don't block.
+- **PyTorch nightly builds are not shippable for releases** — non-deterministic, can regress between runs. If a PR suggests switching to nightly to fix a GPU issue, prefer `TORCH_CUDA_ARCH_LIST=...+PTX` or wait for stable support instead.
+
+## Canonical commands reference
+
+```bash
+# Bulk PR metadata
+gh pr list --state open --limit 50 --json number,title,author,mergeable,mergeStateStatus,additions,deletions,maintainerCanModify,files
+
+# Detailed single-PR view
+gh pr view <N> --json body,author,headRefName,baseRefName,mergeable,maintainerCanModify,files,statusCheckRollup
+
+# The actual commit, not the branch-vs-main diff
+git show HEAD
+git show --stat HEAD
+gh pr diff <N>
+
+# Rebase contributor branch onto current main
+git fetch origin main && git rebase origin/main
+
+# Push rebase back to contributor fork (maintainerCanModify=true required)
+git remote add <author> https://github.com/<author>/<repo>.git
+git fetch <author> <branch>
+git push <author> HEAD:<branch> --force-with-lease
+
+# Merge
+gh pr merge <N> --squash
+
+# Confirm merge SHA for triage doc
+gh pr view <N> --json state,mergeCommit --jq '{state, sha: .mergeCommit.oid[0:7]}'
+
+# Close superseded
+gh pr close <N> --comment "Closing — superseded by merged #<M>. Thanks!"
+```
+
+## Notes
+
+- **Never review a stale branch via `main..HEAD`.** This is the single most important line in this skill.
+- **The triage doc is the session state.** Lose the doc, lose the session. Update it after every action.
+- **Credit contributors even on partial-applies.** Use `Co-Authored-By:` trailers and close comments that link to the applied commit.
+- **Don't let perfect be the enemy of shipped.** A fix that goes from "broken" to "works with a minor known issue" is a strict improvement. Flag the issue, file a follow-up, merge the fix.

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.2.3
+current_version = 0.4.1
 commit = True
 tag = True
 tag_name = v{new_version}

.dockerignore

@@ -38,7 +38,6 @@ biome.json
 .bumpversion.cfg
 .npmrc
 Makefile
-CHANGELOG.md
 CONTRIBUTING.md
 SECURITY.md
 LICENSE

.github/workflows/ci.yml

@@ -0,0 +1,26 @@
+name: CI
+
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+
+jobs:
+  frontend-quality:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Bun
+        uses: oven-sh/setup-bun@v2
+
+      - name: Install dependencies
+        run: bun install --frozen-lockfile
+
+      - name: Typecheck app + web
+        run: bun run typecheck
+
+      - name: Build web smoke test
+        run: bun run build:web

.github/workflows/release.yml

@@ -62,11 +62,21 @@ jobs:
           pip install pyinstaller
           pip install -r backend/requirements.txt
           pip install --no-deps chatterbox-tts
+          pip install --no-deps hume-tada
 
       - name: Install MLX dependencies (Apple Silicon only)
         if: matrix.backend == 'mlx'
         run: |
           pip install -r backend/requirements-mlx.txt
+          # mlx-audio>=0.3.1 and mlx-lm>=0.31.1 both declare transformers>=5.x,
+          # which conflicts with our 4.57.x cap. The runtime APIs we use work
+          # fine on transformers 4.57.x in practice (verified in dev), so install
+          # them --no-deps. mlx-audio's other runtime deps (huggingface_hub,
+          # librosa, numpy, numba, pyloudnorm) are already in requirements.txt;
+          # the rest (sounddevice, miniaudio, protobuf, sentencepiece, pyyaml,
+          # jinja2) are pulled in by other engines.
+          pip install --no-deps mlx-lm==0.31.1
+          pip install --no-deps mlx-audio==0.4.1
 
       - name: Build Python server (Linux/macOS)
         if: matrix.platform != 'windows-latest'
@@ -188,43 +198,54 @@ jobs:
           pip install pyinstaller
           pip install -r backend/requirements.txt
           pip install --no-deps chatterbox-tts
+          pip install --no-deps hume-tada
 
-      - name: Install PyTorch with CUDA 12.6
+      - name: Install PyTorch with CUDA 12.8
         run: |
-          pip install torch --index-url https://download.pytorch.org/whl/cu126 --force-reinstall --no-deps
-          pip install torchaudio --index-url https://download.pytorch.org/whl/cu126 --force-reinstall --no-deps
+          pip install torch --index-url https://download.pytorch.org/whl/cu128 --force-reinstall --no-deps
+          pip install torchaudio --index-url https://download.pytorch.org/whl/cu128 --force-reinstall --no-deps
 
       - name: Verify CUDA support in torch
         run: |
           python -c "import torch; print(f'CUDA available in build: {torch.cuda.is_available()}'); print(f'CUDA version: {torch.version.cuda}')"
 
-      - name: Build CUDA server binary
+      - name: Build CUDA server binary (onedir)
         shell: bash
         working-directory: backend
+        env:
+          # Include Blackwell (sm_120) via PTX forward compatibility.
+          # Pre-built PyTorch cu128 wheels ship native kernels for sm_80/86/89/90
+          # but not sm_120. Setting this env var causes torch.utils.cpp_extension
+          # (and any JIT-compiled kernels) to target Blackwell GPUs as well.
+          TORCH_CUDA_ARCH_LIST: "8.0;8.6;8.9;9.0;12.0+PTX"
         run: python build_binary.py --cuda
 
-      - name: Split binary for GitHub Releases
+      - name: Package into server core + CUDA libs archives
         shell: bash
         run: |
-          python scripts/split_binary.py \
-            backend/dist/voicebox-server-cuda.exe \
-            --output release-assets/
+          python scripts/package_cuda.py \
+            backend/dist/voicebox-server-cuda/ \
+            --output release-assets/ \
+            --cuda-libs-version cu128-v1 \
+            --torch-compat ">=2.7.0,<2.11.0"
 
-      - name: Upload split parts to GitHub Release
+      - name: Upload archives to GitHub Release
         if: startsWith(github.ref, 'refs/tags/')
-        uses: softprops/action-gh-release@v1
+        uses: softprops/action-gh-release@v2
         with:
           files: |
-            release-assets/voicebox-server-cuda.part*.exe
-            release-assets/voicebox-server-cuda.sha256
-            release-assets/voicebox-server-cuda.manifest
+            release-assets/voicebox-server-cuda.tar.gz
+            release-assets/voicebox-server-cuda.tar.gz.sha256
+            release-assets/cuda-libs-cu128-v1.tar.gz
+            release-assets/cuda-libs-cu128-v1.tar.gz.sha256
+            release-assets/cuda-libs.json
           draft: true
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Upload binary as workflow artifact
+      - name: Upload onedir as workflow artifact
         uses: actions/upload-artifact@v4
         with:
           name: voicebox-server-cuda-windows
-          path: backend/dist/voicebox-server-cuda.exe
+          path: backend/dist/voicebox-server-cuda/
           retention-days: 7

.gitignore

@@ -51,8 +51,20 @@ app/openapi.json
 tauri/src-tauri/binaries/*
 tauri/src-tauri/gen/Assets.car
 tauri/src-tauri/gen/voicebox.icns
+tauri/src-tauri/gen/partial.plist
+
+# PyInstaller
+*.spec
+
+# Windows artifacts
+nul
 
 # Temporary
 tmp/
 temp/
 *.tmp
+
+# E2E test artifacts
+backend/tests/results/
+backend/tests/fixtures/reference_voice.wav
+backend/tests/fixtures/reference_voice.txt

CHANGELOG.md

@@ -7,9 +7,176 @@
 
 ## [Unreleased]
 
-This release rewrites the backend into a modular architecture, migrates the documentation site to Fumadocs, and ships a batch of bug fixes and UI polish across the stack.
+## [0.4.1] - 2026-04-18
 
-The backend's 3,000-line monolith `main.py` has been decomposed into domain routers, a services layer, and a proper database package. A style guide and ruff configuration now enforce consistency. On the frontend, model loading status is now visible in the UI, effects presets get a dropdown, and several race conditions and accessibility gaps are closed.
+A fast follow-up to 0.4.0 focused on making the new engines actually load in the production binary — plus generation cancellation, Linux system-audio capture, and the repo's first PR-time type check. Five first-time contributors shipped in this release.
+
+0.4.0 introduced three new TTS engines, but the frozen PyInstaller binary tripped over several Python-ecosystem quirks that don't show up in the dev venv: `transformers` opening `.py` sources at runtime, `scipy.stats._distn_infrastructure` hitting a frozen-importer `NameError`, and `chatterbox-multilingual` failing to find its Chinese segmenter dictionary. This release patches all of those in one sweep.
+
+### Frozen-Binary Reliability ([#438](https://github.com/jamiepine/voicebox/pull/438))
+- **Kokoro** now bundles `.py` sources alongside `.pyc` via `--collect-all kokoro` so `transformers`' `_can_set_attn_implementation` regex scan can read them — previously `FileNotFoundError: kokoro/modules.py` killed Kokoro loading in production builds
+- **Chatterbox Multilingual** now bundles `spacy_pkuseg/dicts/default.pkl` and the package's native `.so` extensions via `--collect-all spacy_pkuseg` — previously the Chinese word segmenter crashed with `FileNotFoundError` on first load
+- **scipy.stats._distn_infrastructure** — new runtime hook source-patches the trailing `del obj` (which raises `NameError` under PyInstaller's frozen importer because the preceding list comprehension evaluates empty) to `globals().pop('obj', None)`, unblocking `librosa` → `scipy.signal` → `scipy.stats` for every TTS engine that depends on librosa
+- **transformers.masking_utils** — same runtime hook forces `_is_torch_greater_or_equal_than_2_6 = False` so the older `sdpa_mask_older_torch` path is selected; the 2.6+ path uses `TransformGetItemToIndex()`, a real `torch._dynamo` graph transform our permissive stub can't reproduce
+- **torch._dynamo** — no-op stub replaces the real module before `transformers` imports it, preventing the `torch._numpy._ufuncs` import crash (`NameError: name 'name' is not defined`) that blocked Kokoro and every engine pulling in `flex_attention`
+- `.spec` paths are now repo-relative instead of absolute, so the generated spec is portable across machines and CI
+
+### Generation
+- **Cancel queued or running generations** ([#444](https://github.com/jamiepine/voicebox/pull/444)) — new `/generate/{id}/cancel` endpoint and a Stop button on the history row while generating. The serial queue now tracks per-ID state (queued / running / cancelled) so queued jobs are skipped before the worker picks them up and running jobs are `.cancel()`-ed mid-flight; `run_generation` catches `CancelledError` and marks the row `failed` with a "cancelled" error.
+- **Legacy `data/` path prefix resolution** ([#440](https://github.com/jamiepine/voicebox/pull/440)) — generations stored with the old `data/` prefix under pre-0.4 installs now resolve correctly after the storage root moved, fixing 404s for historical audio.
+
+### Model Migration
+- Migration dialog no longer hangs when the cache is empty ([#439](https://github.com/jamiepine/voicebox/pull/439)) — the backend now emits a completion SSE event even when zero models are moved.
+- Storage-change flow surfaces a toast when there's nothing to migrate ([#433](https://github.com/jamiepine/voicebox/pull/433)) instead of proceeding with a no-op move and restarting the server.
+- Deleting all generations from a voice profile now deletes the associated version files and DB rows too ([#447](https://github.com/jamiepine/voicebox/pull/447)) — previously orphaned versions accumulated in storage.
+
+### Platform
+- **Linux system audio capture** ([#457](https://github.com/jamiepine/voicebox/pull/457)) — `cpal`'s ALSA backend doesn't expose PulseAudio/PipeWire monitor sources by name, so the previous device-name search never matched and silently fell back to the microphone. Detection now uses `pactl get-default-sink` + `pactl list short sources` and routes via `PULSE_SOURCE`, with the name-based search retained as a fallback when `pactl` is absent.
+
+### Frontend CI
+- First PR-time quality gate ([#418](https://github.com/jamiepine/voicebox/pull/418)) — new `.github/workflows/ci.yml` runs `bun run typecheck` + `bun run build:web` on every PR. Fixed pre-existing type issues that were being suppressed with `@ts-expect-error`, cleaned up a dep-array typo (`[platform.metadata.isTauricheckOnMountcheckForUpdates]`) in `useAutoUpdater`, and removed 100+ lines of dead `ModelItem` code from `ModelManagement.tsx`.
+- Follow-up: widened `apiClient.migrateModels()` return type to include `moved` and `errors` so the storage-change handler typechecks against the real backend response ([#470](https://github.com/jamiepine/voicebox/pull/470)).
+
+### Docs
+- Clarified in the Quick Start + README that paralinguistic tags (`[laugh]`, `[sigh]`) only work with Chatterbox Turbo; other engines read them as literal text ([#450](https://github.com/jamiepine/voicebox/pull/450)).
+
+### New Contributors
+- [@Bortlesboat](https://github.com/Bortlesboat) — generation cancellation (#444)
+- [@gaojulong](https://github.com/gaojulong) — migration dialog hang fix (#439)
+- [@fuleinist](https://github.com/fuleinist) — migration no-op toast (#433)
+- [@erionjuniordeandrade-a11y](https://github.com/erionjuniordeandrade-a11y) — frontend CI + type hardening (#418)
+- [@estefrac](https://github.com/estefrac) — Linux pactl system-audio capture (#457)
+
+## [0.4.0] - 2026-04-16
+
+The biggest Voicebox release yet. Three new TTS engines bring the lineup to **seven** — HumeAI TADA, Kokoro 82M, and Qwen CustomVoice join Qwen3-TTS, LuxTTS, Chatterbox Multilingual, and Chatterbox Turbo. GPU support broadens to Intel Arc (XPU) and NVIDIA Blackwell (RTX 50-series), with runtime diagnostics that warn when your PyTorch build doesn't match your GPU. The CUDA backend is now split into independently versioned server and library archives, so upgrading no longer redownloads 4 GB of PyTorch/CUDA DLLs.
+
+This release also marks a big community moment: **13 new contributors** shipped fixes and features in 0.4.0. Thirty-plus bug fixes target the most-reported issues in the tracker — numpy 2.x TTS crashes, Windows background-server reliability, macOS 11 launch failures, audio playback silence, Stories clip-splitting races, history status staleness, and more.
+
+### New TTS Engines
+
+#### HumeAI TADA — Expressive English & Multilingual ([#296](https://github.com/jamiepine/voicebox/pull/296))
+- Added `tada-1b` (English) and `tada-3b-ml` (multilingual) backends
+- Replaced `descript-audio-codec` with a lightweight DAC shim to cut dependencies
+- Switched audio decoding to `soundfile` to sidestep `torchcodec` bundling issues
+- Redirected gated Llama tokenizer lookups to an ungated mirror so model loading works out of the box
+- Fixed tokenizer patch that was corrupting `AutoTokenizer` for other engines
+- Fixed TorchScript error in frozen builds
+
+#### Kokoro 82M — Fast Lightweight TTS ([#325](https://github.com/jamiepine/voicebox/pull/325))
+- Added Kokoro 82M engine with a new voice profile type system that distinguishes preset voices from cloned profiles
+- Profile grid now handles engine compatibility directly — removed redundant dropdown filtering
+- Tightened Kokoro profile handling so preset voices can't be edited like cloned profiles
+
+#### Qwen CustomVoice ([#328](https://github.com/jamiepine/voicebox/pull/328))
+- Added `qwen-custom-voice` preset engine backed by Qwen3-TTS
+- Enforced preset/profile engine compatibility across the generation flow
+- Floating generator now shows all engines instead of silently filtering
+
+### Voice Profile UX
+
+Until 0.4, every engine in Voicebox was a cloning model, so every voice profile was usable with every engine and the profile grid just showed them all. Introducing Kokoro and Qwen CustomVoice — which work from preset voices rather than cloned samples — broke that assumption for the first time. An early cut on `main` filtered the grid by the selected engine, which left users running pre-release builds thinking their cloned voices had vanished whenever they switched to a preset-only engine.
+
+This release ships the resolution before it ever reaches a tagged version:
+
+- **Grey-out instead of filter** — all profiles are always visible; unsupported ones render dimmed with a compatibility hint at the bottom of the grid
+- **Auto-switch on selection** — clicking a greyed-out profile selects it AND switches the engine to a compatible one, instead of silently doing nothing
+- **Instruct toggle restored for Qwen CustomVoice** — the floating generate box now reveals a delivery-instructions input (tone, emotion, pace) when CustomVoice is selected. Hidden across the board while the new multi-engine lineup was stabilizing because most engines don't honor the kwarg; now conditionally exposed only for the one engine that was actually trained for instruction-based style control
+- Supported profiles sort first; the grid scrolls the selected profile into view after engine/sort changes
+- Fixed engine desync on tab navigation — the form now initializes its engine from the store
+- Fixed the disabled-and-selected card click edge case by bouncing selection to re-trigger the auto-switch
+- Cleaned up scroll effect timers (requestAnimationFrame + setTimeout) to prevent stale DOM writes on unmount or rapid selection changes
+
+### GPU & Platform
+
+#### Intel Arc (XPU) Support ([#320](https://github.com/jamiepine/voicebox/pull/320))
+- First-class Intel Arc support across all PyTorch-based backends
+- Device-aware seeding, XPU detection in the GPU status panel, and setup flow detection
+- Reports correct device name and VRAM in settings
+
+#### Blackwell / RTX 50-series Support ([#316](https://github.com/jamiepine/voicebox/pull/316), [#401](https://github.com/jamiepine/voicebox/pull/401))
+- Upgraded the CUDA backend from cu126 → cu128 for RTX 50-series support
+- Added `sm_120+PTX` to the CUDA build via `TORCH_CUDA_ARCH_LIST` for forward-compatibility with Blackwell architectures (closes 5 open reports: #386, #395, #396, #399, #400)
+- GPU settings UI fixes around install/uninstall state
+
+#### GPU Compatibility Diagnostics ([#367](https://github.com/jamiepine/voicebox/pull/367), adapted)
+- New `check_cuda_compatibility()` compares the current device's compute capability against the bundled PyTorch's architecture list
+- Health endpoint exposes a `gpu_compatibility_warning` field so the UI can surface mismatches
+- Startup logs a `WARN` when the installed PyTorch build doesn't support the detected GPU
+- GPU status label shows `[UNSUPPORTED - see logs]` — no more silent "no kernel image" failures
+
+#### Split CUDA Backend ([#298](https://github.com/jamiepine/voicebox/pull/298))
+- CUDA backend now ships as two independently versioned archives: a small server binary and a large libs archive (the ~4 GB of PyTorch/CUDA DLLs)
+- Upgrading Voicebox no longer redownloads the libs archive when only the server binary changed
+- Added `asyncio.Lock` around `download_cuda_binary()` so auto-update and manual download can't race on the same temp file ([#428](https://github.com/jamiepine/voicebox/pull/428))
+- Updated `package_cuda.py` for PyInstaller 6.18 onedir layout
+- Temp archives are always cleaned up on failure, even when the install aborts mid-extract
+
+### Bug Fixes
+
+#### Critical: TTS Generation
+- **numpy 2.x `torch.from_numpy` crash** ([#361](https://github.com/jamiepine/voicebox/pull/361)) — torch compiled against numpy 1.x ABI fails silently when paired with numpy 2.x, causing `RuntimeError: Numpy is not available` / `Unable to create tensor` on every TTS request in bundled macOS Intel / Rosetta builds. Pinned `numpy<2.0` in requirements and added a PyInstaller runtime hook with a `ctypes.memmove` fallback as belt-and-suspenders. Hardened afterward to raise on unknown dtypes instead of silently reinterpreting bytes as float32.
+
+#### Platform Reliability
+- **Windows background server** ([#402](https://github.com/jamiepine/voicebox/pull/402)) — "keep server running after close" now actually keeps the server running. The HTTP `/watchdog/disable` request could lose the race against process exit on Windows; added a `.keep-running` sentinel file as a synchronous fallback, with stale-sentinel cleanup on startup to avoid orphan server processes
+- **macOS 11 launch crash** ([#424](https://github.com/jamiepine/voicebox/pull/424)) — weak-linked ScreenCaptureKit so the app can launch on macOS < 12.3 instead of crashing at dyld resolution. Gated system audio capture behind a real `sw_vers` version check so unsupported systems cleanly advertise "not available" rather than crashing at runtime
+- **macOS Intel (x86_64) setup** ([#416](https://github.com/jamiepine/voicebox/pull/416)) — relaxed `torch>=2.7.0` → `torch>=2.2.0`. PyTorch dropped pre-built x86_64 wheels after 2.2.2, so Intel Mac devs could no longer `pip install`. Now resolves to the latest compatible torch per platform
+- **Offline model loading** ([#318](https://github.com/jamiepine/voicebox/pull/318)) — Qwen TTS and Whisper force offline mode when loading cached models, so startup works without network access
+- **GUI startup with external server** ([#319](https://github.com/jamiepine/voicebox/pull/319)) — fixed GUI launch when pointed at a remote/external server, and added data refresh on server switch; hardened health validation and error handling
+- **Qwen3-TTS cache split on Windows** (adapted from [#218](https://github.com/jamiepine/voicebox/pull/218)) — route `Qwen3TTSModel.from_pretrained` through `hf_constants.HF_HUB_CACHE` so the speech tokenizer and `preprocessor_config.json` resolve from a single cache root
+- **Qwen3-TTS bundling** ([#305](https://github.com/jamiepine/voicebox/pull/305)) — bundle `qwen_tts` source files in the PyInstaller build to fix `inspect.getsource` errors in frozen builds
+- **Backend import paths** ([#345](https://github.com/jamiepine/voicebox/pull/345)) — moved lazy imports to top-level with absolute paths to resolve the "Failed to Save" preset error caused by `ModuleNotFoundError` in production builds
+- **Effects service import** ([#384](https://github.com/jamiepine/voicebox/pull/384)) — fixed `ModuleNotFoundError` on preset create/update by switching to relative imports (#349)
+
+#### Audio & Playback
+- **cpal stream silent playback** ([#405](https://github.com/jamiepine/voicebox/pull/405)) — `cpal::Stream` was dropped on function return immediately after `play()`, causing every playback to fall silent. Now holds the stream until either the buffer drains or the stop flag fires (#404)
+
+#### Stories & History
+- **Clip-splitting race** ([#403](https://github.com/jamiepine/voicebox/pull/403)) — rapid double-clicks on split could race through `split_story_item` with inconsistent state. Added `with_for_update()` row locking on the backend and an `isPending` guard on the frontend (#366)
+- **History `status` staleness** ([#394](https://github.com/jamiepine/voicebox/pull/394)) — `GET /history/{id}` was hardcoding `status="completed"` regardless of the DB row, breaking any client polling for job completion. Now returns `status`, `error`, `engine`, `model_size`, and `is_favorited` from the actual row
+- **"Clear failed" bulk button** ([#412](https://github.com/jamiepine/voicebox/pull/412)) — new `DELETE /history/failed` endpoint and a header strip showing `"N failed generations"` with a Clear button, complementing the per-row trash icon added in #321 (#410)
+- **Delete failed generations** ([#321](https://github.com/jamiepine/voicebox/pull/321)) — added a trash icon next to the retry button so failed entries can be cleaned up without having to retry first
+
+#### Security & Safety
+- **Voice prompt cache hardening** ([#429](https://github.com/jamiepine/voicebox/pull/429)) — `torch.load(weights_only=True)` on cached voice prompts per PyTorch 2.6 recommendation; replaced string-based SPA path guard with `Path.is_relative_to()` for more robust path-traversal protection
+
+#### Infrastructure & Docker
+- **Docker web build** ([#344](https://github.com/jamiepine/voicebox/pull/344)) — include `CHANGELOG.md` in the Docker web build so the in-app changelog page works in Docker deployments
+- **Docker numba cache** ([#425](https://github.com/jamiepine/voicebox/pull/425)) — set `NUMBA_CACHE_DIR` in docker-compose so numba can write its JIT cache in container runtime (#308)
+- **Relative media paths** ([#332](https://github.com/jamiepine/voicebox/pull/332)) — media paths now stored relative to the configured data dir rather than resolved against CWD, so the data directory is portable between installs
+
+### Developer Tooling
+
+- New `triage-prs` agent skill — encodes the end-to-end PR-speedrun workflow (classification → triage doc → rebase → squash-merge → follow-ups) so future release cycles can reproduce it
+- Rewrote the TTS engine guide with the patterns learned from adding TADA and Kokoro
+- Added the API refactor plan and CUDA libs addon design doc
+- Fixed broken links in the Get Started section ([#332](https://github.com/jamiepine/voicebox/pull/332))
+
+### New Contributors
+
+Huge thank you to everyone who contributed their first PR to Voicebox in this release:
+
+[@liorshahverdi](https://github.com/liorshahverdi), [@nicoschtein](https://github.com/nicoschtein), [@ArfianID](https://github.com/ArfianID), [@aimaaaimaa](https://github.com/aimaaaimaa), [@maxmcoding](https://github.com/maxmcoding), [@Khalodddd](https://github.com/Khalodddd), [@LuisSambrano](https://github.com/LuisSambrano), [@shaun0927](https://github.com/shaun0927), [@malletfils](https://github.com/malletfils), [@mvanhorn](https://github.com/mvanhorn), [@kuishou68](https://github.com/kuishou68), [@txhno](https://github.com/txhno), [@MukundaKatta](https://github.com/MukundaKatta)
+
+## [0.3.0] - 2026-03-17
+
+This release rewrites the backend into a modular architecture, overhauls the settings UI into routed sub-pages, fixes audio player freezing, migrates documentation to Fumadocs, and ships a batch of bug fixes targeting the most-reported issues from the tracker.
+
+The backend's 3,000-line monolith `main.py` has been decomposed into domain routers, a services layer, and a proper database package. A style guide and ruff configuration now enforce consistency. On the frontend, settings have been split into dedicated routed pages with server logs, a changelog viewer, and an about page. The audio player no longer freezes mid-playback, and model loading status is now visible in the UI. Seven user-reported bugs have been fixed, including server crashes during sample uploads, generation list staleness, cryptic error messages, and CUDA support for RTX 50-series GPUs.
+
+### Settings Overhaul ([#294](https://github.com/jamiepine/voicebox/pull/294))
+- Split settings into routed sub-tabs: General, Generation, GPU, Logs, Changelog, About
+- Added live server log viewer with auto-scroll
+- Added in-app changelog page that parses `CHANGELOG.md` at build time
+- Added About page with version info, license, and generation folder quick-open
+- Extracted reusable `SettingRow` component for consistent setting layouts
+
+### Audio Player Fix ([#293](https://github.com/jamiepine/voicebox/pull/293))
+- Fixed audio player freezing during playback
+- Improved playback UX with better state management and listener cleanup
+- Fixed restart race condition during regeneration
+- Added stable keys for audio element re-rendering
+- Improved accessibility across player controls
 
 ### Backend Refactor ([#285](https://github.com/jamiepine/voicebox/pull/285))
 - Extracted all routes from `main.py` into 13 domain routers under `backend/routes/` — `main.py` dropped from ~3,100 lines to ~10
@@ -40,6 +207,17 @@ The backend's 3,000-line monolith `main.py` has been decomposed into domain rout
 - Softened select focus indicator opacity
 - Addressed 4 critical and 12 major issues from CodeRabbit review
 
+### Bug Fixes ([#295](https://github.com/jamiepine/voicebox/pull/295))
+- Fixed sample uploads crashing the server — audio decoding now runs in a thread pool instead of blocking the async event loop ([#278](https://github.com/jamiepine/voicebox/issues/278))
+- Fixed generation list not updating when a generation completes — switched to `refetchQueries` for reliable cache busting, added SSE error fallback, and page reset on completion ([#231](https://github.com/jamiepine/voicebox/issues/231))
+- Fixed error toasts showing `[object Object]` instead of the actual error message ([#290](https://github.com/jamiepine/voicebox/issues/290))
+- Added Whisper model selection (`base`, `small`, `medium`, `large`, `turbo`) and expanded language support to the `/transcribe` endpoint ([#233](https://github.com/jamiepine/voicebox/issues/233))
+- Upgraded CUDA backend build from cu121 to cu126 for RTX 50-series (Blackwell) GPU support ([#289](https://github.com/jamiepine/voicebox/issues/289))
+- Handled client disconnects in SSE and streaming endpoints to suppress `[Errno 32] Broken Pipe` errors ([#248](https://github.com/jamiepine/voicebox/issues/248))
+- Fixed Docker build failure from pip hash mismatch on Qwen3-TTS dependencies ([#286](https://github.com/jamiepine/voicebox/issues/286))
+- Added 50 MB upload size limit with chunked reads to prevent unbounded memory allocation on sample uploads
+- Eliminated redundant double audio decode in sample processing pipeline
+
 ### Platform Fixes
 - Replaced `netstat` with `TcpStream` + PowerShell for Windows port detection ([#277](https://github.com/jamiepine/voicebox/pull/277))
 - Fixed Docker frontend build and cleaned up Docker docs
@@ -417,7 +595,10 @@ The first public release of Voicebox — an open-source voice synthesis studio p
 
 Tauri v2, React, TypeScript, Tailwind CSS, FastAPI, Qwen3-TTS, Whisper, SQLite
 
-[Unreleased]: https://github.com/jamiepine/voicebox/compare/v0.2.3...HEAD
+[Unreleased]: https://github.com/jamiepine/voicebox/compare/v0.4.1...HEAD
+[0.4.1]: https://github.com/jamiepine/voicebox/compare/v0.4.0...v0.4.1
+[0.4.0]: https://github.com/jamiepine/voicebox/compare/v0.3.0...v0.4.0
+[0.3.0]: https://github.com/jamiepine/voicebox/compare/v0.2.3...v0.3.0
 [0.2.3]: https://github.com/jamiepine/voicebox/compare/v0.2.2...v0.2.3
 [0.2.2]: https://github.com/jamiepine/voicebox/compare/v0.2.1...v0.2.2
 [0.2.1]: https://github.com/jamiepine/voicebox/compare/v0.1.13...v0.2.1

CONTRIBUTING.md

@@ -260,7 +260,7 @@ voicebox/
 
 ### ✨ New Features
 
-- Check the roadmap in README.md
+- Check the roadmap in README.md and the engineering status in [`docs/PROJECT_STATUS.md`](docs/PROJECT_STATUS.md) before proposing work — it lists prioritized tasks (Tier 1 → 3), known architectural bottlenecks, and candidate TTS engines already under evaluation (including why some have been backlogged)
 - Discuss major features in an issue first
 - Keep features focused and well-scoped
 
@@ -359,7 +359,7 @@ Releases are managed by maintainers:
 
 ## Troubleshooting
 
-See [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md) for common issues and solutions.
+See [docs/content/docs/overview/troubleshooting.mdx](docs/content/docs/overview/troubleshooting.mdx) for common issues and solutions.
 
 **Quick fixes:**
 
@@ -372,12 +372,13 @@ See [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md) for common issues and sol
 - Open an issue for bugs or feature requests
 - Check existing issues and discussions
 - Review the codebase to understand patterns
-- See [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md) for common issues
+- See [docs/content/docs/overview/troubleshooting.mdx](docs/content/docs/overview/troubleshooting.mdx) for common issues
 
 ## Additional Resources
 
 - [README.md](README.md) - Project overview
 - [backend/README.md](backend/README.md) - API documentation
+- [docs/PROJECT_STATUS.md](docs/PROJECT_STATUS.md) - Living engineering roadmap: architecture, shipped vs in-flight work, prioritized open issues, candidate TTS engines under evaluation, architectural bottlenecks. Keep this updated when you ship significant features, close or backlog a model integration, or identify new bottlenecks.
 - [docs/AUTOUPDATER_QUICKSTART.md](docs/AUTOUPDATER_QUICKSTART.md) - Auto-updater setup
 - [SECURITY.md](SECURITY.md) - Security policy
 - [CHANGELOG.md](CHANGELOG.md) - Version history

Dockerfile

@@ -9,7 +9,7 @@ FROM oven/bun:1 AS frontend
 WORKDIR /build
 
 # Copy workspace config and frontend source
-COPY package.json bun.lock ./
+COPY package.json bun.lock CHANGELOG.md ./
 COPY app/ ./app/
 COPY web/ ./web/
 
@@ -35,6 +35,8 @@ RUN pip install --no-cache-dir --upgrade pip
 
 COPY backend/requirements.txt .
 RUN pip install --no-cache-dir --prefix=/install -r requirements.txt
+RUN pip install --no-cache-dir --prefix=/install --no-deps chatterbox-tts
+RUN pip install --no-cache-dir --prefix=/install --no-deps hume-tada
 RUN pip install --no-cache-dir --prefix=/install \
     git+https://github.com/QwenLM/Qwen3-TTS.git
 

README.md

@@ -23,6 +23,9 @@
   <a href="https://github.com/jamiepine/voicebox/blob/main/LICENSE">
     <img src="https://img.shields.io/github/license/jamiepine/voicebox?style=flat" alt="License" />
   </a>
+  <a href="https://deepwiki.com/jamiepine/voicebox">
+    <img src="https://img.shields.io/static/v1?label=Ask&message=DeepWiki&color=5B6EF7" alt="Ask DeepWiki" />
+  </a>
 </p>
 
 <p align="center">
@@ -30,7 +33,8 @@
   <a href="https://docs.voicebox.sh">Docs</a> •
   <a href="#download">Download</a> •
   <a href="#features">Features</a> •
-  <a href="#api">API</a>
+  <a href="#api">API</a> •
+  <a href="docs/content/docs/overview/troubleshooting.mdx">Troubleshooting</a>
 </p>
 
 <br/>
@@ -59,13 +63,14 @@
 
 ## What is Voicebox?
 
-Voicebox is a **local-first voice cloning studio** — a free and open-source alternative to ElevenLabs. Clone voices from a few seconds of audio, generate speech in 23 languages across 4 TTS engines, apply post-processing effects, and compose multi-voice projects with a timeline editor.
+Voicebox is a **local-first voice cloning studio** — a free and open-source alternative to ElevenLabs. Clone voices from a few seconds of audio or pick from 50+ preset voices, generate speech in 23 languages across 7 TTS engines, apply post-processing effects, and compose multi-voice projects with a timeline editor.
 
 - **Complete privacy** — models and voice data stay on your machine
-- **4 TTS engines** — Qwen3-TTS, LuxTTS, Chatterbox Multilingual, and Chatterbox Turbo
+- **7 TTS engines** — Qwen3-TTS, Qwen CustomVoice, LuxTTS, Chatterbox Multilingual, Chatterbox Turbo, HumeAI TADA, and Kokoro
+- **Cloning and preset voices** — zero-shot cloning from a reference sample, or curated preset voices via Kokoro (50 voices) and Qwen CustomVoice (9 voices)
 - **23 languages** — from English to Arabic, Japanese, Hindi, Swahili, and more
 - **Post-processing effects** — pitch shift, reverb, delay, chorus, compression, and filters
-- **Expressive speech** — paralinguistic tags like `[laugh]`, `[sigh]`, `[gasp]` via Chatterbox Turbo
+- **Expressive speech** — paralinguistic tags like `[laugh]`, `[sigh]`, `[gasp]` via Chatterbox Turbo; natural-language delivery control via Qwen CustomVoice
 - **Unlimited length** — auto-chunking with crossfade for scripts, articles, and chapters
 - **Stories editor** — multi-track timeline for conversations, podcasts, and narratives
 - **API-first** — REST API for integrating voice synthesis into your own projects
@@ -87,24 +92,34 @@ Voicebox is a **local-first voice cloning studio** — a free and open-source al
 
 > **Linux** — Pre-built binaries are not yet available. See [voicebox.sh/linux-install](https://voicebox.sh/linux-install) for build-from-source instructions.
 
+> **Having trouble?** See the [Troubleshooting Guide](docs/content/docs/overview/troubleshooting.mdx) for common install, generation, model-download, and GPU issues.
+
 ---
 
 ## Features
 
 ### Multi-Engine Voice Cloning
 
-Four TTS engines with different strengths, switchable per-generation:
+Seven TTS engines with different strengths, switchable per-generation:
 
 | Engine                      | Languages | Strengths                                                                                                                                |
 | --------------------------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
 | **Qwen3-TTS** (0.6B / 1.7B) | 10        | High-quality multilingual cloning, delivery instructions ("speak slowly", "whisper")                                                     |
+| **Qwen CustomVoice**        | 10        | 9 curated preset voices with natural-language delivery control — no reference audio required                                             |
 | **LuxTTS**                  | English   | Lightweight (~1GB VRAM), 48kHz output, 150x realtime on CPU                                                                              |
 | **Chatterbox Multilingual** | 23        | Broadest language coverage — Arabic, Danish, Finnish, Greek, Hebrew, Hindi, Malay, Norwegian, Polish, Swahili, Swedish, Turkish and more |
 | **Chatterbox Turbo**        | English   | Fast 350M model with paralinguistic emotion/sound tags                                                                                   |
+| **TADA** (1B / 3B)          | 10        | HumeAI speech-language model — 700s+ coherent audio, text-acoustic dual alignment                                                        |
+| **Kokoro**                  | 8         | 50 curated preset voices, tiny 82M model, fast CPU inference                                                                             |
 
 ### Emotions & Paralinguistic Tags
 
-Type `/` in the text input to insert expressive tags that the model synthesizes inline with speech (Chatterbox Turbo):
+Only **Chatterbox Turbo** interprets paralinguistic tags like `[laugh]` and
+`[sigh]`. Qwen3-TTS, LuxTTS, Chatterbox Multilingual, and HumeAI TADA read them
+literally as text.
+
+With **Chatterbox Turbo** selected, type `/` in the text input to open the tag
+inserter and add expressive tags inline with speech:
 
 `[laugh]` `[chuckle]` `[gasp]` `[cough]` `[sigh]` `[groan]` `[sniff]` `[shush]` `[clear throat]`
 
@@ -230,7 +245,7 @@ Full API documentation available at `http://localhost:17493/docs`.
 | Frontend      | React, TypeScript, Tailwind CSS                   |
 | State         | Zustand, React Query                              |
 | Backend       | FastAPI (Python)                                  |
-| TTS Engines   | Qwen3-TTS, LuxTTS, Chatterbox, Chatterbox Turbo   |
+| TTS Engines   | Qwen3-TTS, Qwen CustomVoice, LuxTTS, Chatterbox, Chatterbox Turbo, TADA, Kokoro |
 | Effects       | Pedalboard (Spotify)                              |
 | Transcription | Whisper / Whisper Turbo (PyTorch or MLX)          |
 | Inference     | MLX (Apple Silicon) / PyTorch (CUDA/ROCm/XPU/CPU) |
@@ -245,10 +260,12 @@ Full API documentation available at `http://localhost:17493/docs`.
 | ----------------------- | ---------------------------------------------- |
 | **Real-time Streaming** | Stream audio as it generates, word by word     |
 | **Voice Design**        | Create new voices from text descriptions       |
-| **More Models**         | XTTS, Bark, and other open-source voice models |
+| **More Models**         | XTTS, Bark, and other open-source voice models  |
 | **Plugin Architecture** | Extend with custom models and effects          |
 | **Mobile Companion**    | Control Voicebox from your phone               |
 
+For the **full engineering status, open-issue triage, and prioritized work queue**, see [`docs/PROJECT_STATUS.md`](docs/PROJECT_STATUS.md) — a living document that tracks what's shipped, what's in-flight, candidate TTS engines under evaluation, and why we've accepted or backlogged specific integrations.
+
 ---
 
 ## Development
@@ -276,6 +293,12 @@ just build          # Build CPU server binary + Tauri app
 just build-local    # (Windows) Build CPU + CUDA server binaries + Tauri app
 ```
 
+### Adding New Voice Models
+
+The multi-engine architecture makes adding new TTS engines straightforward. A [step-by-step guide](docs/content/docs/developer/tts-engines.mdx) covers the full process: dependency research, backend protocol implementation, frontend wiring, and PyInstaller bundling.
+
+The guide is optimized for AI coding agents. An [agent skill](.agents/skills/add-tts-engine/SKILL.md) can pick up a model name and handle the entire integration autonomously — you just test the build locally.
+
 ### Project Structure
 
 ```

SECURITY.md

@@ -6,8 +6,8 @@ We release patches for security vulnerabilities. Which versions are eligible for
 
 | Version | Supported          |
 | ------- | ------------------ |
-| 0.1.x   | :white_check_mark: |
-| < 0.1   | :x:                |
+| 0.3.x   | :white_check_mark: |
+| < 0.3   | :x:                |
 
 ## Reporting a Vulnerability
 
@@ -82,7 +82,7 @@ Timeline may vary based on severity and complexity.
 ## Security Updates
 
 Security updates will be:
-- Released as patch versions (e.g., 0.1.1)
+- Released as patch versions (e.g., 0.3.2)
 - Documented in CHANGELOG.md
 - Announced via GitHub releases
 - Automatically delivered via auto-updater

app/package.json

@@ -1,11 +1,12 @@
 {
   "name": "@voicebox/app",
-  "version": "0.2.3",
+  "version": "0.4.1",
   "private": true,
   "type": "module",
   "scripts": {
     "dev": "vite",
     "build": "vite build",
+    "typecheck": "tsc -p tsconfig.json --noEmit",
     "preview": "vite preview",
     "lint": "biome lint src",
     "lint:fix": "biome lint --write src",

app/src/App.tsx

@@ -4,6 +4,8 @@ import voiceboxLogo from '@/assets/voicebox-logo.png';
 import ShinyText from '@/components/ShinyText';
 import { TitleBarDragRegion } from '@/components/TitleBarDragRegion';
 import { useAutoUpdater } from '@/hooks/useAutoUpdater';
+import { apiClient } from '@/lib/api/client';
+import type { HealthResponse } from '@/lib/api/types';
 import { TOP_SAFE_AREA_PADDING } from '@/lib/constants/ui';
 import { cn } from '@/lib/utils/cn';
 import { usePlatform } from '@/platform/PlatformContext';
@@ -11,6 +13,33 @@ import { router } from '@/router';
 import { useLogStore } from '@/stores/logStore';
 import { useServerStore } from '@/stores/serverStore';
 
+/**
+ * Validate that a health response has the expected Voicebox-specific shape.
+ * Prevents misidentifying an unrelated service on the same port.
+ */
+function isVoiceboxHealthResponse(health: HealthResponse): boolean {
+  return (
+    health?.status === 'healthy' &&
+    typeof health.model_loaded === 'boolean' &&
+    typeof health.gpu_available === 'boolean'
+  );
+}
+
+/**
+ * Check whether a startup error indicates the port is occupied by an external
+ * server (which we should try to reuse via health-check polling) vs. a real
+ * failure (missing sidecar, signing issue, etc.) that should surface immediately.
+ */
+function isPortInUseError(error: unknown): boolean {
+  const msg = error instanceof Error ? error.message : String(error);
+  return (
+    msg.includes('already in use') ||
+    msg.includes('port') ||
+    msg.includes('EADDRINUSE') ||
+    msg.includes('address already in use')
+  );
+}
+
 const LOADING_MESSAGES = [
   'Warming up tensors...',
   'Calibrating synthesizer engine...',
@@ -37,6 +66,7 @@ const LOADING_MESSAGES = [
 function App() {
   const platform = usePlatform();
   const [serverReady, setServerReady] = useState(false);
+  const [startupError, setStartupError] = useState<string | null>(null);
   const [loadingMessageIndex, setLoadingMessageIndex] = useState(0);
   const serverStartingRef = useRef(false);
 
@@ -91,7 +121,6 @@ function App() {
       console.log('Dev mode: Skipping auto-start of server (run it separately)');
       setServerReady(true); // Mark as ready so UI doesn't show loading screen
       // Mark that server was not started by app (so we don't try to stop it on close)
-      // @ts-expect-error - adding property to window
       window.__voiceboxServerStartedByApp = false;
       return;
     }
@@ -114,14 +143,52 @@ function App() {
         useServerStore.getState().setServerUrl(serverUrl);
         setServerReady(true);
         // Mark that we started the server (so we know to stop it on close)
-        // @ts-expect-error - adding property to window
         window.__voiceboxServerStartedByApp = true;
       })
       .catch((error) => {
         console.error('Failed to auto-start server:', error);
         serverStartingRef.current = false;
-        // @ts-expect-error - adding property to window
         window.__voiceboxServerStartedByApp = false;
+
+        // Only fall back to health-check polling when the error indicates the
+        // port is occupied (likely an external server). For real failures
+        // (missing sidecar, signing issues, etc.) surface the error immediately.
+        if (!isPortInUseError(error)) {
+          const msg = error instanceof Error ? error.message : String(error);
+          console.error('Real startup failure — not polling:', msg);
+          setStartupError(msg);
+          return;
+        }
+
+        // Fall back to polling: the server may already be running externally
+        // (e.g. started via python/uvicorn/Docker). Poll the health endpoint
+        // until it responds with a valid Voicebox payload, then transition to
+        // the main UI.
+        console.log('Falling back to health-check polling...');
+        const pollInterval = setInterval(async () => {
+          try {
+            const health = await apiClient.getHealth();
+            if (!isVoiceboxHealthResponse(health)) {
+              console.log('Health response is not from a Voicebox server, keep polling...');
+              return;
+            }
+            console.log('External Voicebox server detected via health check');
+            clearInterval(pollInterval);
+            setServerReady(true);
+          } catch {
+            // Server not ready yet, keep polling
+          }
+        }, 2000);
+
+        // Stop polling after 2 minutes and surface the failure
+        setTimeout(() => {
+          clearInterval(pollInterval);
+          serverStartingRef.current = false;
+          setStartupError(
+            'Could not connect to a Voicebox server within 2 minutes. ' +
+              'Please check that the server is running and try again.',
+          );
+        }, 120_000);
       });
 
     // Cleanup: stop server on actual unmount (not StrictMode remount)
@@ -168,15 +235,34 @@ function App() {
               className="w-48 h-48 object-contain animate-fade-in-scale relative z-10"
             />
           </div>
-          <div className="animate-fade-in-delayed">
-            <ShinyText
-              text={LOADING_MESSAGES[loadingMessageIndex]}
-              className="text-lg font-medium text-muted-foreground"
-              speed={2}
-              color="hsl(var(--muted-foreground))"
-              shineColor="hsl(var(--foreground))"
-            />
-          </div>
+          {startupError ? (
+            <div className="animate-fade-in-delayed max-w-md mx-auto space-y-3">
+              <p className="text-lg font-medium text-destructive">Server startup failed</p>
+              <p className="text-sm text-muted-foreground">{startupError}</p>
+              <button
+                type="button"
+                className="mt-2 px-4 py-2 text-sm rounded-md bg-primary text-primary-foreground hover:bg-primary/90 transition-colors"
+                onClick={() => {
+                  setStartupError(null);
+                  serverStartingRef.current = false;
+                  // Trigger a re-mount of the effect by toggling state
+                  window.location.reload();
+                }}
+              >
+                Retry
+              </button>
+            </div>
+          ) : (
+            <div className="animate-fade-in-delayed">
+              <ShinyText
+                text={LOADING_MESSAGES[loadingMessageIndex]}
+                className="text-lg font-medium text-muted-foreground"
+                speed={2}
+                color="hsl(var(--muted-foreground))"
+                shineColor="hsl(var(--foreground))"
+              />
+            </div>
+          )}
         </div>
       </div>
     );

app/src/components/AppFrame/AppFrame.tsx

@@ -1,5 +1,6 @@
 import { useRouterState } from '@tanstack/react-router';
 import { TitleBarDragRegion } from '@/components/TitleBarDragRegion';
+import { AudioKeepAlive } from '@/components/AudioPlayer/AudioKeepAlive';
 import { AudioPlayer } from '@/components/AudioPlayer/AudioPlayer';
 import { StoryTrackEditor } from '@/components/StoriesTab/StoryTrackEditor';
 import { TOP_SAFE_AREA_PADDING } from '@/lib/constants/ui';
@@ -14,16 +15,19 @@ interface AppFrameProps {
 export function AppFrame({ children }: AppFrameProps) {
   const routerState = useRouterState();
   const isStoriesRoute = routerState.location.pathname === '/stories';
-  
+
   const selectedStoryId = useStoryStore((state) => state.selectedStoryId);
   const { data: story } = useStory(selectedStoryId);
-  
+
   // Show track editor when on stories route with a selected story that has items
   const showTrackEditor = isStoriesRoute && selectedStoryId && story && story.items.length > 0;
 
   return (
-    <div className={cn('h-screen bg-background flex flex-col overflow-hidden', TOP_SAFE_AREA_PADDING)}>
+    <div
+      className={cn('h-screen bg-background flex flex-col overflow-hidden', TOP_SAFE_AREA_PADDING)}
+    >
       <TitleBarDragRegion />
+      <AudioKeepAlive />
       {children}
       {showTrackEditor ? (
         <StoryTrackEditor storyId={story.id} items={story.items} />

app/src/components/AudioPlayer/AudioKeepAlive.tsx

@@ -0,0 +1,85 @@
+import { useEffect, useRef } from 'react';
+import { debug } from '@/lib/utils/debug';
+
+// WKWebView tears down the app's CoreAudio output when idle for long enough,
+// and a JS-level reload (cmd+R) does NOT restore it — only relaunching the
+// Tauri app does. Keeping a silent <audio> element looping forever prevents
+// the OS audio session from ever going dormant.
+//
+// Real silence (zero PCM samples) at full volume is preferred over a muted
+// element: browsers/WebKit can optimize muted media away, which defeats the
+// purpose of holding the session open.
+
+function buildSilentWavUrl(seconds = 1, sampleRate = 8000): string {
+  const numSamples = seconds * sampleRate;
+  const bytes = 44 + numSamples * 2;
+  const buffer = new ArrayBuffer(bytes);
+  const view = new DataView(buffer);
+  const write = (offset: number, str: string) => {
+    for (let i = 0; i < str.length; i++) view.setUint8(offset + i, str.charCodeAt(i));
+  };
+  write(0, 'RIFF');
+  view.setUint32(4, bytes - 8, true);
+  write(8, 'WAVE');
+  write(12, 'fmt ');
+  view.setUint32(16, 16, true);
+  view.setUint16(20, 1, true);
+  view.setUint16(22, 1, true);
+  view.setUint32(24, sampleRate, true);
+  view.setUint32(28, sampleRate * 2, true);
+  view.setUint16(32, 2, true);
+  view.setUint16(34, 16, true);
+  write(36, 'data');
+  view.setUint32(40, numSamples * 2, true);
+  return URL.createObjectURL(new Blob([buffer], { type: 'audio/wav' }));
+}
+
+export function AudioKeepAlive() {
+  const audioRef = useRef<HTMLAudioElement | null>(null);
+
+  useEffect(() => {
+    const url = buildSilentWavUrl(1, 8000);
+    const el = new Audio(url);
+    el.loop = true;
+    el.volume = 1;
+    el.preload = 'auto';
+    audioRef.current = el;
+
+    const tryPlay = () => {
+      if (!audioRef.current) return;
+      if (!audioRef.current.paused) return;
+      audioRef.current.play().catch((err) => {
+        debug.log('[AudioKeepAlive] play blocked (will retry on next gesture):', err);
+      });
+    };
+
+    tryPlay();
+
+    // Autoplay may be blocked until first user interaction — re-attempt then.
+    const onGesture = () => tryPlay();
+    window.addEventListener('pointerdown', onGesture, { once: false });
+    window.addEventListener('keydown', onGesture, { once: false });
+
+    // If the webview ever pauses the element on background, resume on return.
+    const onWake = () => {
+      if (!document.hidden) tryPlay();
+    };
+    document.addEventListener('visibilitychange', onWake);
+    window.addEventListener('focus', onWake);
+    window.addEventListener('pageshow', onWake);
+
+    return () => {
+      window.removeEventListener('pointerdown', onGesture);
+      window.removeEventListener('keydown', onGesture);
+      document.removeEventListener('visibilitychange', onWake);
+      window.removeEventListener('focus', onWake);
+      window.removeEventListener('pageshow', onWake);
+      el.pause();
+      el.src = '';
+      URL.revokeObjectURL(url);
+      audioRef.current = null;
+    };
+  }, []);
+
+  return null;
+}

app/src/components/AudioTab/AudioTab.tsx

@@ -124,7 +124,7 @@ export function AudioTab() {
     );
   }
 
-  const handleChannelDelete = async (e, channelId) => {
+  const handleChannelDelete = async (e: React.MouseEvent, channelId: string) => {
     e.stopPropagation();
     if (await confirm('Delete this channel?')) {
       deleteChannel.mutate(channelId);

app/src/components/EffectsTab/EffectsTab.tsx

@@ -1,20 +1,20 @@
-import {EffectsDetail} from "./EffectsDetail";
-import {EffectsList} from "./EffectsList";
+import { EffectsDetail } from './EffectsDetail';
+import { EffectsList } from './EffectsList';
 
 export function EffectsTab() {
-	return (
-		<div className="flex flex-col h-full min-h-0 overflow-hidden">
-			<div className="flex-1 min-h-0 flex gap-6 overflow-hidden">
-				{/* Left - Presets list */}
-				<div className="w-full max-w-[360px] shrink-0 flex flex-col min-h-0">
-					<EffectsList />
-				</div>
+  return (
+    <div className="flex flex-col h-full min-h-0 overflow-hidden">
+      <div className="flex-1 min-h-0 flex gap-6 overflow-hidden">
+        {/* Left - Presets list */}
+        <div className="w-full max-w-[360px] shrink-0 flex flex-col min-h-0">
+          <EffectsList />
+        </div>
 
-				{/* Right - Detail / editor */}
-				<div className="flex-1 min-h-0 flex flex-col">
-					<EffectsDetail />
-				</div>
-			</div>
-		</div>
-	);
+        {/* Right - Detail / editor */}
+        <div className="flex-1 min-h-0 flex flex-col">
+          <EffectsDetail />
+        </div>
+      </div>
+    </div>
+  );
 }

app/src/components/Generation/EngineModelSelector.tsx

@@ -1,3 +1,4 @@
+import { useEffect } from 'react';
 import type { UseFormReturn } from 'react-hook-form';
 import { FormControl } from '@/components/ui/form';
 import {
@@ -7,6 +8,7 @@ import {
   SelectTrigger,
   SelectValue,
 } from '@/components/ui/select';
+import type { VoiceProfileResponse } from '@/lib/api/types';
 import { getLanguageOptionsForEngine } from '@/lib/constants/languages';
 import type { GenerationFormValues } from '@/lib/hooks/useGenerationForm';
 
@@ -15,30 +17,57 @@ import type { GenerationFormValues } from '@/lib/hooks/useGenerationForm';
  * Adding a new engine means adding one entry here.
  */
 const ENGINE_OPTIONS = [
-  { value: 'qwen:1.7B', label: 'Qwen3-TTS 1.7B' },
-  { value: 'qwen:0.6B', label: 'Qwen3-TTS 0.6B' },
-  { value: 'luxtts', label: 'LuxTTS' },
-  { value: 'chatterbox', label: 'Chatterbox' },
-  { value: 'chatterbox_turbo', label: 'Chatterbox Turbo' },
+  { value: 'qwen:1.7B', label: 'Qwen3-TTS 1.7B', engine: 'qwen' },
+  { value: 'qwen:0.6B', label: 'Qwen3-TTS 0.6B', engine: 'qwen' },
+  { value: 'qwen_custom_voice:1.7B', label: 'Qwen CustomVoice 1.7B', engine: 'qwen_custom_voice' },
+  { value: 'qwen_custom_voice:0.6B', label: 'Qwen CustomVoice 0.6B', engine: 'qwen_custom_voice' },
+  { value: 'luxtts', label: 'LuxTTS', engine: 'luxtts' },
+  { value: 'chatterbox', label: 'Chatterbox', engine: 'chatterbox' },
+  { value: 'chatterbox_turbo', label: 'Chatterbox Turbo', engine: 'chatterbox_turbo' },
+  { value: 'tada:1B', label: 'TADA 1B', engine: 'tada' },
+  { value: 'tada:3B', label: 'TADA 3B Multilingual', engine: 'tada' },
+  { value: 'kokoro', label: 'Kokoro 82M', engine: 'kokoro' },
 ] as const;
 
 const ENGINE_DESCRIPTIONS: Record<string, string> = {
   qwen: 'Multi-language, two sizes',
+  qwen_custom_voice: '9 preset voices, instruct control',
   luxtts: 'Fast, English-focused',
   chatterbox: '23 languages, incl. Hebrew',
   chatterbox_turbo: 'English, [laugh] [cough] tags',
+  tada: 'HumeAI, 700s+ coherent audio',
+  kokoro: '82M params, CPU realtime, 8 langs',
 };
 
 /** Engines that only support English and should force language to 'en' on select. */
 const ENGLISH_ONLY_ENGINES = new Set(['luxtts', 'chatterbox_turbo']);
 
+/** Engines that support cloned (reference audio) profiles. */
+const CLONING_ENGINES = new Set(['qwen', 'luxtts', 'chatterbox', 'chatterbox_turbo', 'tada']);
+
+function getAvailableOptions(selectedProfile?: VoiceProfileResponse | null) {
+  if (!selectedProfile) return ENGINE_OPTIONS;
+  return ENGINE_OPTIONS.filter((opt) => isProfileCompatibleWithEngine(selectedProfile, opt.engine));
+}
+
 function getSelectValue(engine: string, modelSize?: string): string {
   if (engine === 'qwen') return `qwen:${modelSize || '1.7B'}`;
+  if (engine === 'qwen_custom_voice') return `qwen_custom_voice:${modelSize || '1.7B'}`;
+  if (engine === 'tada') return `tada:${modelSize || '1B'}`;
   return engine;
 }
 
-function handleEngineChange(form: UseFormReturn<GenerationFormValues>, value: string) {
-  if (value.startsWith('qwen:')) {
+export function applyEngineSelection(form: UseFormReturn<GenerationFormValues>, value: string) {
+  if (value.startsWith('qwen_custom_voice:')) {
+    const [, modelSize] = value.split(':');
+    form.setValue('engine', 'qwen_custom_voice');
+    form.setValue('modelSize', modelSize as '1.7B' | '0.6B');
+    const currentLang = form.getValues('language');
+    const available = getLanguageOptionsForEngine('qwen_custom_voice');
+    if (!available.some((l) => l.value === currentLang)) {
+      form.setValue('language', available[0]?.value ?? 'en');
+    }
+  } else if (value.startsWith('qwen:')) {
     const [, modelSize] = value.split(':');
     form.setValue('engine', 'qwen');
     form.setValue('modelSize', modelSize as '1.7B' | '0.6B');
@@ -48,6 +77,20 @@ function handleEngineChange(form: UseFormReturn<GenerationFormValues>, value: st
     if (!available.some((l) => l.value === currentLang)) {
       form.setValue('language', available[0]?.value ?? 'en');
     }
+  } else if (value.startsWith('tada:')) {
+    const [, modelSize] = value.split(':');
+    form.setValue('engine', 'tada');
+    form.setValue('modelSize', modelSize as '1B' | '3B');
+    // TADA 1B is English-only; 3B is multilingual
+    if (modelSize === '1B') {
+      form.setValue('language', 'en');
+    } else {
+      const currentLang = form.getValues('language');
+      const available = getLanguageOptionsForEngine('tada');
+      if (!available.some((l) => l.value === currentLang)) {
+        form.setValue('language', available[0]?.value ?? 'en');
+      }
+    }
   } else {
     form.setValue('engine', value as GenerationFormValues['engine']);
     form.setValue('modelSize', undefined as unknown as '1.7B' | '0.6B');
@@ -67,12 +110,22 @@ function handleEngineChange(form: UseFormReturn<GenerationFormValues>, value: st
 interface EngineModelSelectorProps {
   form: UseFormReturn<GenerationFormValues>;
   compact?: boolean;
+  selectedProfile?: VoiceProfileResponse | null;
 }
 
-export function EngineModelSelector({ form, compact }: EngineModelSelectorProps) {
+export function EngineModelSelector({ form, compact, selectedProfile }: EngineModelSelectorProps) {
   const engine = form.watch('engine') || 'qwen';
   const modelSize = form.watch('modelSize');
   const selectValue = getSelectValue(engine, modelSize);
+  const availableOptions = getAvailableOptions(selectedProfile);
+
+  const currentEngineAvailable = availableOptions.some((opt) => opt.value === selectValue);
+
+  useEffect(() => {
+    if (!currentEngineAvailable && availableOptions.length > 0) {
+      applyEngineSelection(form, availableOptions[0].value);
+    }
+  }, [availableOptions, currentEngineAvailable, form]);
 
   const itemClass = compact ? 'text-xs text-muted-foreground' : undefined;
   const triggerClass = compact
@@ -80,14 +133,14 @@ export function EngineModelSelector({ form, compact }: EngineModelSelectorProps)
     : undefined;
 
   return (
-    <Select value={selectValue} onValueChange={(v) => handleEngineChange(form, v)}>
+    <Select value={selectValue} onValueChange={(v) => applyEngineSelection(form, v)}>
       <FormControl>
         <SelectTrigger className={triggerClass}>
           <SelectValue />
         </SelectTrigger>
       </FormControl>
       <SelectContent>
-        {ENGINE_OPTIONS.map((opt) => (
+        {availableOptions.map((opt) => (
           <SelectItem key={opt.value} value={opt.value} className={itemClass}>
             {opt.label}
           </SelectItem>
@@ -101,3 +154,17 @@ export function EngineModelSelector({ form, compact }: EngineModelSelectorProps)
 export function getEngineDescription(engine: string): string {
   return ENGINE_DESCRIPTIONS[engine] ?? '';
 }
+
+/**
+ * Check if a profile is compatible with the currently selected engine.
+ * Useful for UI hints.
+ */
+export function isProfileCompatibleWithEngine(
+  profile: VoiceProfileResponse,
+  engine: string,
+): boolean {
+  const voiceType = profile.voice_type || 'cloned';
+  if (voiceType === 'preset') return profile.preset_engine === engine;
+  if (voiceType === 'cloned') return CLONING_ENGINES.has(engine);
+  return true; // designed — future
+}

app/src/components/Generation/FloatingGenerateBox.tsx

@@ -1,7 +1,7 @@
 import { useQuery } from '@tanstack/react-query';
 import { useMatchRoute } from '@tanstack/react-router';
 import { AnimatePresence, motion } from 'framer-motion';
-import { Loader2, Sparkles } from 'lucide-react';
+import { Loader2, SlidersHorizontal, Sparkles } from 'lucide-react';
 import { useEffect, useRef, useState } from 'react';
 import { Button } from '@/components/ui/button';
 import { Form, FormControl, FormField, FormItem, FormMessage } from '@/components/ui/form';
@@ -36,9 +36,11 @@ export function FloatingGenerateBox({
 }: FloatingGenerateBoxProps) {
   const selectedProfileId = useUIStore((state) => state.selectedProfileId);
   const setSelectedProfileId = useUIStore((state) => state.setSelectedProfileId);
+  const setSelectedEngine = useUIStore((state) => state.setSelectedEngine);
   const { data: selectedProfile } = useProfile(selectedProfileId || '');
   const { data: profiles } = useProfiles();
   const [isExpanded, setIsExpanded] = useState(false);
+  const [isInstructExpanded, setIsInstructExpanded] = useState(false);
   const [selectedPresetId, setSelectedPresetId] = useState<string | null>(null);
   const containerRef = useRef<HTMLDivElement>(null);
   const textareaRef = useRef<HTMLTextAreaElement | null>(null);
@@ -67,7 +69,12 @@ export function FloatingGenerateBox({
       }
     },
     getEffectsChain: () => {
-      if (!selectedPresetId || !effectPresets) return undefined;
+      if (!selectedPresetId) return undefined;
+      // Profile's own effects chain (no matching preset)
+      if (selectedPresetId === '_profile') {
+        return selectedProfile?.effects_chain ?? undefined;
+      }
+      if (!effectPresets) return undefined;
       const preset = effectPresets.find((p) => p.id === selectedPresetId);
       return preset?.effects_chain;
     },
@@ -110,12 +117,63 @@ export function FloatingGenerateBox({
     }
   }, [selectedProfileId, profiles, setSelectedProfileId]);
 
-  // Sync generation form language with selected profile's language
+  // Sync engine selection to global store so ProfileList can filter
+  const watchedEngine = form.watch('engine');
+  useEffect(() => {
+    if (watchedEngine) {
+      setSelectedEngine(watchedEngine);
+    }
+  }, [watchedEngine, setSelectedEngine]);
+
+  // Sync generation form language, engine, and effects with selected profile
+  type EngineValue =
+    | 'qwen'
+    | 'luxtts'
+    | 'chatterbox'
+    | 'chatterbox_turbo'
+    | 'tada'
+    | 'kokoro'
+    | 'qwen_custom_voice';
   useEffect(() => {
     if (selectedProfile?.language) {
       form.setValue('language', selectedProfile.language as LanguageCode);
     }
-  }, [selectedProfile, form]);
+    // Auto-switch engine to match the profile
+    const engine = selectedProfile?.default_engine ?? selectedProfile?.preset_engine;
+    if (engine) {
+      form.setValue('engine', engine as EngineValue);
+    } else if (selectedProfile && selectedProfile.voice_type !== 'preset') {
+      // Cloned/designed profile with no default — ensure a compatible (non-preset) engine
+      const currentEngine = form.getValues('engine');
+      const presetEngines = new Set(['kokoro', 'qwen_custom_voice']);
+      if (currentEngine && presetEngines.has(currentEngine)) {
+        form.setValue('engine', 'qwen');
+      }
+    }
+    // Pre-fill effects from profile defaults
+    if (
+      selectedProfile?.effects_chain &&
+      selectedProfile.effects_chain.length > 0 &&
+      effectPresets
+    ) {
+      // Try to match against a known preset
+      const profileChainJson = JSON.stringify(selectedProfile.effects_chain);
+      const matchingPreset = effectPresets.find(
+        (p) => JSON.stringify(p.effects_chain) === profileChainJson,
+      );
+      if (matchingPreset) {
+        setSelectedPresetId(matchingPreset.id);
+      } else {
+        // No matching preset — use special value to pass profile chain directly
+        setSelectedPresetId('_profile');
+      }
+    } else if (
+      selectedProfile &&
+      (!selectedProfile.effects_chain || selectedProfile.effects_chain.length === 0)
+    ) {
+      setSelectedPresetId(null);
+    }
+  }, [selectedProfile, effectPresets, form]);
 
   // Auto-resize textarea based on content (only when expanded)
   useEffect(() => {
@@ -296,9 +354,80 @@ export function FloatingGenerateBox({
                         : 'Generate speech'}
                   </span>
                 </div>
+
+                {/* Instruct toggle — only for Qwen CustomVoice, which actually honors the kwarg */}
+                <AnimatePresence>
+                  {isExpanded && form.watch('engine') === 'qwen_custom_voice' && (
+                    <motion.div
+                      initial={{ opacity: 0, scale: 0.8 }}
+                      animate={{ opacity: 1, scale: 1 }}
+                      exit={{ opacity: 0, scale: 0.8 }}
+                      transition={{ duration: 0.2 }}
+                      className="absolute top-0 right-[calc(100%+0.5rem)]"
+                    >
+                      <div className="group relative">
+                        <Button
+                          type="button"
+                          variant="ghost"
+                          size="icon"
+                          onClick={() => setIsInstructExpanded((prev) => !prev)}
+                          className={cn(
+                            'h-10 w-10 rounded-full transition-all duration-200',
+                            isInstructExpanded
+                              ? 'bg-accent text-accent-foreground border border-accent hover:bg-accent/90'
+                              : 'bg-card border border-border hover:bg-background/50',
+                          )}
+                          aria-label={
+                            isInstructExpanded
+                              ? 'Hide delivery instructions'
+                              : 'Show delivery instructions'
+                          }
+                          aria-pressed={isInstructExpanded}
+                        >
+                          <SlidersHorizontal className="h-4 w-4" />
+                        </Button>
+                        <span className="pointer-events-none absolute bottom-full left-1/2 -translate-x-1/2 mb-2 whitespace-nowrap rounded-md bg-popover px-3 py-1.5 text-xs text-popover-foreground border border-border opacity-0 transition-opacity group-hover:opacity-100 z-[9999]">
+                          Delivery instructions (tone, emotion, pace)
+                        </span>
+                      </div>
+                    </motion.div>
+                  )}
+                </AnimatePresence>
               </div>
             </div>
 
+            {/* Additive instruct textarea — shown below main text when toggle is on and engine supports it */}
+            <AnimatePresence>
+              {isInstructExpanded && form.watch('engine') === 'qwen_custom_voice' && (
+                <motion.div
+                  initial={{ opacity: 0, height: 0 }}
+                  animate={{ opacity: 1, height: 'auto' }}
+                  exit={{ opacity: 0, height: 0 }}
+                  transition={{ duration: 0.2, ease: 'easeOut' }}
+                  className="overflow-hidden"
+                >
+                  <FormField
+                    control={form.control}
+                    name="instruct"
+                    render={({ field }) => (
+                      <FormItem className="mt-2">
+                        <FormControl>
+                          <Textarea
+                            {...field}
+                            placeholder="Delivery instructions — e.g. Speak slowly with warmth, Authoritative and clear..."
+                            className="resize-none bg-transparent border border-accent/20 focus-visible:ring-1 focus-visible:ring-accent/40 rounded-2xl text-sm placeholder:text-muted-foreground/60 w-full px-3 py-2"
+                            style={{ minHeight: '60px', maxHeight: '160px' }}
+                            maxLength={500}
+                          />
+                        </FormControl>
+                        <FormMessage className="text-xs" />
+                      </FormItem>
+                    )}
+                  />
+                </motion.div>
+              )}
+            </AnimatePresence>
+
             <AnimatePresence>
               <motion.div
                 initial={{ height: 0, opacity: 0 }}
@@ -375,6 +504,12 @@ export function FloatingGenerateBox({
                         <SelectItem value="none" className="text-xs">
                           No effects
                         </SelectItem>
+                        {selectedProfile?.effects_chain &&
+                          selectedProfile.effects_chain.length > 0 && (
+                            <SelectItem value="_profile" className="text-xs">
+                              Profile default
+                            </SelectItem>
+                          )}
                         {effectPresets?.map((preset) => (
                           <SelectItem key={preset.id} value={preset.id} className="text-xs">
                             {preset.name}

app/src/components/Generation/GenerationForm.tsx

@@ -1,4 +1,5 @@
 import { Loader2, Mic } from 'lucide-react';
+import { useEffect } from 'react';
 import { Button } from '@/components/ui/button';
 import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
 import {
@@ -19,19 +20,45 @@ import {
   SelectValue,
 } from '@/components/ui/select';
 import { Textarea } from '@/components/ui/textarea';
-import { getLanguageOptionsForEngine } from '@/lib/constants/languages';
+import { getLanguageOptionsForEngine, type LanguageCode } from '@/lib/constants/languages';
 import { useGenerationForm } from '@/lib/hooks/useGenerationForm';
 import { useProfile } from '@/lib/hooks/useProfiles';
 import { useUIStore } from '@/stores/uiStore';
-import { EngineModelSelector, getEngineDescription } from './EngineModelSelector';
+import {
+  applyEngineSelection,
+  EngineModelSelector,
+  getEngineDescription,
+} from './EngineModelSelector';
 import { ParalinguisticInput } from './ParalinguisticInput';
 
+function getEngineSelectValue(engine: string): string {
+  if (engine === 'qwen') return 'qwen:1.7B';
+  if (engine === 'qwen_custom_voice') return 'qwen_custom_voice:1.7B';
+  if (engine === 'tada') return 'tada:1B';
+  return engine;
+}
+
 export function GenerationForm() {
   const selectedProfileId = useUIStore((state) => state.selectedProfileId);
   const { data: selectedProfile } = useProfile(selectedProfileId || '');
 
   const { form, handleSubmit, isPending } = useGenerationForm();
 
+  useEffect(() => {
+    if (!selectedProfile) {
+      return;
+    }
+
+    if (selectedProfile.language) {
+      form.setValue('language', selectedProfile.language as LanguageCode);
+    }
+
+    const preferredEngine = selectedProfile.default_engine || selectedProfile.preset_engine;
+    if (preferredEngine) {
+      applyEngineSelection(form, getEngineSelectValue(preferredEngine));
+    }
+  }, [form, selectedProfile]);
+
   async function onSubmit(data: Parameters<typeof handleSubmit>[0]) {
     await handleSubmit(data, selectedProfileId);
   }
@@ -91,7 +118,7 @@ export function GenerationForm() {
               )}
             />
 
-            {form.watch('engine') === 'qwen' && (
+            {form.watch('engine') === 'qwen_custom_voice' && (
               <FormField
                 control={form.control}
                 name="instruct"
@@ -118,7 +145,7 @@ export function GenerationForm() {
             <div className="grid gap-4 md:grid-cols-3">
               <FormItem>
                 <FormLabel>Model</FormLabel>
-                <EngineModelSelector form={form} />
+                <EngineModelSelector form={form} selectedProfile={selectedProfile} />
                 <FormDescription>
                   {getEngineDescription(form.watch('engine') || 'qwen')}
                 </FormDescription>

app/src/components/History/HistoryTable.tsx

@@ -1,15 +1,14 @@
-import { useQueryClient } from '@tanstack/react-query';
+import { useMutation, useQueryClient } from '@tanstack/react-query';
 import { AnimatePresence, motion } from 'framer-motion';
 import {
-  AlignCenter,
   AudioLines,
-  AudioWaveform,
   Download,
   FileArchive,
   Loader2,
   MoreHorizontal,
   Play,
   RotateCcw,
+  Square,
   Star,
   Trash2,
   Wand2,
@@ -45,6 +44,7 @@ import { apiClient } from '@/lib/api/client';
 import type { EffectConfig, GenerationVersionResponse, HistoryResponse } from '@/lib/api/types';
 import { BOTTOM_SAFE_AREA_PADDING } from '@/lib/constants/ui';
 import {
+  useClearFailedGenerations,
   useDeleteGeneration,
   useExportGeneration,
   useExportGenerationAudio,
@@ -124,9 +124,28 @@ export function HistoryTable() {
   });
 
   const deleteGeneration = useDeleteGeneration();
+  const clearFailed = useClearFailedGenerations();
+  const [clearFailedDialogOpen, setClearFailedDialogOpen] = useState(false);
   const exportGeneration = useExportGeneration();
   const exportGenerationAudio = useExportGenerationAudio();
   const importGeneration = useImportGeneration();
+  const cancelGeneration = useMutation({
+    mutationFn: (generationId: string) => apiClient.cancelGeneration(generationId),
+    onSuccess: async (data) => {
+      await queryClient.invalidateQueries({ queryKey: ['history'] });
+      toast({
+        title: 'Cancelling generation',
+        description: data.message,
+      });
+    },
+    onError: (error) => {
+      toast({
+        title: 'Cancel failed',
+        description: error instanceof Error ? error.message : 'Could not cancel generation',
+        variant: 'destructive',
+      });
+    },
+  });
   const addPendingGeneration = useGenerationStore((state) => state.addPendingGeneration);
   const setAudioWithAutoPlay = usePlayerStore((state) => state.setAudioWithAutoPlay);
   const restartCurrentAudio = usePlayerStore((state) => state.restartCurrentAudio);
@@ -157,11 +176,11 @@ export function HistoryTable() {
   const pendingCount = useGenerationStore((state) => state.pendingGenerationIds.size);
   const prevPendingCountRef = useRef(pendingCount);
   useEffect(() => {
-    if (deleteGeneration.isSuccess || importGeneration.isSuccess) {
+    if (deleteGeneration.isSuccess || importGeneration.isSuccess || clearFailed.isSuccess) {
       setPage(0);
       setAllHistory([]);
     }
-  }, [deleteGeneration.isSuccess, importGeneration.isSuccess]);
+  }, [deleteGeneration.isSuccess, importGeneration.isSuccess, clearFailed.isSuccess]);
 
   useEffect(() => {
     // A generation finished (pending count decreased) — scroll back to show it
@@ -415,6 +434,27 @@ export function HistoryTable() {
 
   const history = allHistory;
   const hasMore = allHistory.length < total;
+  const failedCount = history.filter((g) => g.status === 'failed').length;
+
+  const handleClearFailedConfirm = () => {
+    clearFailed.mutate(undefined, {
+      onSuccess: (data) => {
+        setClearFailedDialogOpen(false);
+        toast({
+          title: 'Cleared failed generations',
+          description: `${data.deleted} failed ${data.deleted === 1 ? 'generation' : 'generations'} removed.`,
+        });
+      },
+      onError: (error) => {
+        setClearFailedDialogOpen(false);
+        toast({
+          title: 'Failed to clear',
+          description: error instanceof Error ? error.message : 'Unknown error',
+          variant: 'destructive',
+        });
+      },
+    });
+  };
 
   return (
     <div className="flex flex-col h-full min-h-0 relative">
@@ -424,6 +464,23 @@ export function HistoryTable() {
         </div>
       ) : (
         <>
+          {failedCount > 0 && (
+            <div className="flex items-center justify-between px-1 pb-2">
+              <span className="text-xs text-muted-foreground">
+                {failedCount} failed {failedCount === 1 ? 'generation' : 'generations'}
+              </span>
+              <Button
+                variant="ghost"
+                size="sm"
+                className="h-7 text-xs text-muted-foreground hover:text-destructive"
+                onClick={() => setClearFailedDialogOpen(true)}
+                disabled={clearFailed.isPending}
+              >
+                <Trash2 className="h-3 w-3 mr-1.5" />
+                {clearFailed.isPending ? 'Clearing...' : 'Clear failed'}
+              </Button>
+            </div>
+          )}
           {isScrolled && (
             <div className="absolute top-0 left-0 right-0 h-16 bg-gradient-to-b from-background to-transparent z-10 pointer-events-none" />
           )}
@@ -442,6 +499,8 @@ export function HistoryTable() {
               const isPlayable = !isGenerating && !isFailed;
               const hasVersions = gen.versions && gen.versions.length > 1;
               const isVersionsExpanded = expandedVersionsId === gen.id;
+              const isCancelling =
+                cancelGeneration.isPending && cancelGeneration.variables === gen.id;
               return (
                 <div
                   key={gen.id}
@@ -569,69 +628,92 @@ export function HistoryTable() {
                       )}
 
                       {isFailed ? (
+                        <>
+                          <Button
+                            variant="ghost"
+                            size="icon"
+                            className="h-6 w-6 text-muted-foreground/50 hover:bg-muted-foreground/20 hover:text-muted-foreground"
+                            aria-label="Retry generation"
+                            onClick={() => handleRetry(gen.id)}
+                          >
+                            <RotateCcw className="h-2 w-2" />
+                          </Button>
+                          <Button
+                            variant="ghost"
+                            size="icon"
+                            className="h-6 w-6 text-muted-foreground/50 hover:bg-muted-foreground/20 hover:text-muted-foreground"
+                            aria-label="Delete generation"
+                            disabled={deleteGeneration.isPending}
+                            onClick={() => handleDeleteClick(gen.id, gen.profile_name)}
+                          >
+                            <Trash2 className="h-2 w-2" />
+                          </Button>
+                        </>
+                      ) : isGenerating ? (
                         <Button
                           variant="ghost"
                           size="icon"
                           className="h-6 w-6 text-muted-foreground/50 hover:bg-muted-foreground/20 hover:text-muted-foreground"
-                          aria-label="Retry generation"
-                          onClick={() => handleRetry(gen.id)}
+                          aria-label="Cancel generation"
+                          disabled={isCancelling}
+                          onClick={() => cancelGeneration.mutate(gen.id)}
                         >
-                          <RotateCcw className="h-2 w-2" />
+                          {isCancelling ? (
+                            <Loader2 className="h-2 w-2 animate-spin" />
+                          ) : (
+                            <Square className="h-2 w-2" />
+                          )}
                         </Button>
                       ) : (
-                        <>
-                          <DropdownMenu>
-                            <DropdownMenuTrigger asChild>
-                              <Button
-                                variant="ghost"
-                                size="icon"
-                                className="h-6 w-6 text-muted-foreground/50 hover:bg-muted-foreground/20 hover:text-muted-foreground"
-                                aria-label="Actions"
-                                disabled={isGenerating}
-                              >
-                                <MoreHorizontal className="h-2 w-2" />
-                              </Button>
-                            </DropdownMenuTrigger>
-                            <DropdownMenuContent align="end">
-                              <DropdownMenuItem
-                                onClick={() => handlePlay(gen.id, gen.text, gen.profile_id)}
-                              >
-                                <Play className="mr-2 h-4 w-4" />
-                                Play
-                              </DropdownMenuItem>
-                              <DropdownMenuItem
-                                onClick={() => handleDownloadAudio(gen.id, gen.text)}
-                                disabled={exportGenerationAudio.isPending}
-                              >
-                                <Download className="mr-2 h-4 w-4" />
-                                Export Audio
-                              </DropdownMenuItem>
-                              <DropdownMenuItem
-                                onClick={() => handleExportPackage(gen.id, gen.text)}
-                                disabled={exportGeneration.isPending}
-                              >
-                                <FileArchive className="mr-2 h-4 w-4" />
-                                Export Package
-                              </DropdownMenuItem>
-                              <DropdownMenuItem onClick={() => handleApplyEffects(gen.id)}>
-                                <Wand2 className="mr-2 h-4 w-4" />
-                                Apply Effects
-                              </DropdownMenuItem>
-                              <DropdownMenuItem onClick={() => handleRegenerate(gen.id)}>
-                                <RotateCcw className="mr-2 h-4 w-4" />
-                                Regenerate
-                              </DropdownMenuItem>
-                              <DropdownMenuItem
-                                onClick={() => handleDeleteClick(gen.id, gen.profile_name)}
-                                disabled={deleteGeneration.isPending}
-                                // className="text-destructive focus:text-destructive"
-                              >
-                                <Trash2 className="mr-2 h-4 w-4" />
-                                Delete
-                              </DropdownMenuItem>
-                            </DropdownMenuContent>
-                          </DropdownMenu>
-                        </>
+                        <DropdownMenu>
+                          <DropdownMenuTrigger asChild>
+                            <Button
+                              variant="ghost"
+                              size="icon"
+                              className="h-6 w-6 text-muted-foreground/50 hover:bg-muted-foreground/20 hover:text-muted-foreground"
+                              aria-label="Actions"
+                              disabled={isGenerating}
+                            >
+                              <MoreHorizontal className="h-2 w-2" />
+                            </Button>
+                          </DropdownMenuTrigger>
+                          <DropdownMenuContent align="end">
+                            <DropdownMenuItem onClick={() => handlePlay(gen.id, gen.text, gen.profile_id)}>
+                              <Play className="mr-2 h-4 w-4" />
+                              Play
+                            </DropdownMenuItem>
+                            <DropdownMenuItem
+                              onClick={() => handleDownloadAudio(gen.id, gen.text)}
+                              disabled={exportGenerationAudio.isPending}
+                            >
+                              <Download className="mr-2 h-4 w-4" />
+                              Export Audio
+                            </DropdownMenuItem>
+                            <DropdownMenuItem
+                              onClick={() => handleExportPackage(gen.id, gen.text)}
+                              disabled={exportGeneration.isPending}
+                            >
+                              <FileArchive className="mr-2 h-4 w-4" />
+                              Export Package
+                            </DropdownMenuItem>
+                            <DropdownMenuItem onClick={() => handleApplyEffects(gen.id)}>
+                              <Wand2 className="mr-2 h-4 w-4" />
+                              Apply Effects
+                            </DropdownMenuItem>
+                            <DropdownMenuItem onClick={() => handleRegenerate(gen.id)}>
+                              <RotateCcw className="mr-2 h-4 w-4" />
+                              Regenerate
+                            </DropdownMenuItem>
+                            <DropdownMenuItem
+                              onClick={() => handleDeleteClick(gen.id, gen.profile_name)}
+                              disabled={deleteGeneration.isPending}
+                              // className="text-destructive focus:text-destructive"
+                            >
+                              <Trash2 className="mr-2 h-4 w-4" />
+                              Delete
+                            </DropdownMenuItem>
+                          </DropdownMenuContent>
+                        </DropdownMenu>
                       )}
                     </div>
                   </div>
@@ -747,6 +829,31 @@ export function HistoryTable() {
         </DialogContent>
       </Dialog>
 
+      <Dialog open={clearFailedDialogOpen} onOpenChange={setClearFailedDialogOpen}>
+        <DialogContent>
+          <DialogHeader>
+            <DialogTitle>Clear failed generations</DialogTitle>
+            <DialogDescription>
+              This will permanently delete {failedCount} failed{' '}
+              {failedCount === 1 ? 'generation' : 'generations'} from your history. This cannot be
+              undone.
+            </DialogDescription>
+          </DialogHeader>
+          <DialogFooter>
+            <Button variant="outline" onClick={() => setClearFailedDialogOpen(false)}>
+              Cancel
+            </Button>
+            <Button
+              variant="destructive"
+              onClick={handleClearFailedConfirm}
+              disabled={clearFailed.isPending}
+            >
+              {clearFailed.isPending ? 'Clearing...' : 'Clear all'}
+            </Button>
+          </DialogFooter>
+        </DialogContent>
+      </Dialog>
+
       <Dialog open={importDialogOpen} onOpenChange={setImportDialogOpen}>
         <DialogContent>
           <DialogHeader>

app/src/components/ServerSettings/GpuAcceleration.tsx

@@ -243,7 +243,40 @@ export function GpuAcceleration() {
 
         {/* Native GPU detected - no CUDA download needed */}
 
-        {/* CUDA download section - only show when no GPU is active (native or CUDA) */}
+        {/* Currently running CUDA - show switch back to CPU */}
+        {isCurrentlyCuda && platform.metadata.isTauri && (
+          <>
+            {restartPhase !== 'idle' ? (
+              <div className="flex items-center gap-2 p-3 rounded-lg bg-primary/5 border">
+                <Loader2 className="h-4 w-4 animate-spin" />
+                <span className="text-sm">
+                  {restartPhase === 'stopping' && 'Stopping server...'}
+                  {restartPhase === 'waiting' && 'Restarting server...'}
+                  {restartPhase === 'ready' && 'Server restarted successfully!'}
+                </span>
+              </div>
+            ) : (
+              <div className="space-y-3">
+                <p className="text-sm text-muted-foreground">
+                  Running with CUDA GPU acceleration. Switch back to CPU if needed (you can
+                  re-download later).
+                </p>
+                <Button onClick={handleSwitchToCpu} variant="outline" className="w-full" size="sm">
+                  <RotateCw className="h-4 w-4 mr-2" />
+                  Switch to CPU Backend
+                </Button>
+              </div>
+            )}
+            {error && (
+              <div className="flex items-center gap-2 text-sm text-destructive">
+                <AlertCircle className="h-4 w-4 shrink-0" />
+                <span>{error}</span>
+              </div>
+            )}
+          </>
+        )}
+
+        {/* CUDA download/manage section - show when no native GPU and not currently running CUDA */}
         {!hasNativeGpu && !isCurrentlyCuda && (
           <>
             {/* Download progress (manual download or auto-update) */}
@@ -315,7 +348,7 @@ export function GpuAcceleration() {
                 )}
 
                 {/* Downloaded but not active - show switch button */}
-                {cudaAvailable && !isCurrentlyCuda && platform.metadata.isTauri && (
+                {cudaAvailable && platform.metadata.isTauri && (
                   <div className="space-y-3">
                     <p className="text-sm text-muted-foreground">
                       CUDA backend is downloaded and ready. Restart the server to enable GPU
@@ -328,27 +361,8 @@ export function GpuAcceleration() {
                   </div>
                 )}
 
-                {/* Currently active - show switch back to CPU */}
-                {isCurrentlyCuda && platform.metadata.isTauri && (
-                  <div className="space-y-3">
-                    <p className="text-sm text-muted-foreground">
-                      Running with CUDA GPU acceleration. Switch back to CPU if needed (you can
-                      re-download later).
-                    </p>
-                    <Button
-                      onClick={handleSwitchToCpu}
-                      variant="outline"
-                      className="w-full"
-                      size="sm"
-                    >
-                      <RotateCw className="h-4 w-4 mr-2" />
-                      Switch to CPU Backend
-                    </Button>
-                  </div>
-                )}
-
                 {/* Delete option when downloaded (and not active) */}
-                {cudaAvailable && !isCurrentlyCuda && (
+                {cudaAvailable && (
                   <Button
                     onClick={handleDelete}
                     variant="ghost"

app/src/components/ServerSettings/ModelManagement.tsx

@@ -62,6 +62,16 @@ const MODEL_DESCRIPTIONS: Record<string, string> = {
     'Production-grade open source TTS by Resemble AI. Supports 23 languages with voice cloning and emotion exaggeration control.',
   'chatterbox-turbo':
     'Streamlined 350M parameter TTS by Resemble AI. High-quality English speech with less compute and VRAM than larger models.',
+  'tada-1b':
+    'HumeAI TADA 1B — English speech-language model built on Llama 3.2 1B. Generates 700s+ of coherent audio with synchronized text-acoustic alignment.',
+  'tada-3b-ml':
+    'HumeAI TADA 3B Multilingual — built on Llama 3.2 3B. Supports 10 languages with high-fidelity voice cloning via text-acoustic dual alignment.',
+  kokoro:
+    'Kokoro 82M by hexgrad. Tiny 82M-parameter TTS that runs at CPU realtime. Supports 8 languages with pre-built voice styles. Apache 2.0 licensed.',
+  'qwen-custom-voice-1.7B':
+    'Qwen3-TTS CustomVoice 1.7B by Alibaba. 9 premium preset voices with instruct-based style control for tone, emotion, and prosody. Supports 10 languages.',
+  'qwen-custom-voice-0.6B':
+    'Qwen3-TTS CustomVoice 0.6B by Alibaba. Lightweight version with the same 9 preset voices and instruct control. Faster inference for lower-end hardware.',
   'whisper-base':
     'Smallest Whisper model (74M parameters). Fast transcription with moderate accuracy.',
   'whisper-small':
@@ -390,8 +400,11 @@ export function ModelManagement() {
     modelStatus?.models.filter(
       (m) =>
         m.model_name.startsWith('qwen-tts') ||
+        m.model_name.startsWith('qwen-custom-voice') ||
         m.model_name.startsWith('luxtts') ||
-        m.model_name.startsWith('chatterbox'),
+        m.model_name.startsWith('chatterbox') ||
+        m.model_name.startsWith('tada') ||
+        m.model_name.startsWith('kokoro'),
     ) ?? [];
   const whisperModels = modelStatus?.models.filter((m) => m.model_name.startsWith('whisper')) ?? [];
 
@@ -964,7 +977,19 @@ export function ModelManagement() {
                 });
                 try {
                   // Start the migration (background task)
-                  await apiClient.migrateModels(newDir);
+                  const migrationResult = await apiClient.migrateModels(newDir);
+
+                  // If no models to migrate, warn user and skip the change
+                  if (migrationResult.moved === 0) {
+                    setMigrating(false);
+                    setMigrationProgress(null);
+                    toast({
+                      title: 'No models to migrate',
+                      description: 'Download at least one model before changing the storage location.',
+                    });
+                    setPendingMigrateDir(null);
+                    return;
+                  }
 
                   // Connect to SSE for progress
                   await new Promise<void>((resolve, reject) => {
@@ -1051,105 +1076,3 @@ export function ModelManagement() {
   );
 }
 
-interface ModelItemProps {
-  model: {
-    model_name: string;
-    display_name: string;
-    downloaded: boolean;
-    downloading?: boolean; // From server - true if download in progress
-    size_mb?: number;
-    loaded: boolean;
-  };
-  onDownload: () => void;
-  onDelete: () => void;
-  isDownloading: boolean; // Local state - true if user just clicked download
-  formatSize: (sizeMb?: number) => string;
-}
-
-function ModelItem({ model, onDownload, onDelete, isDownloading, formatSize }: ModelItemProps) {
-  // Use server's downloading state OR local state (for immediate feedback before server updates)
-  const showDownloading = model.downloading || isDownloading;
-
-  const statusText = model.loaded
-    ? 'Loaded'
-    : showDownloading
-      ? 'Downloading'
-      : model.downloaded
-        ? 'Downloaded'
-        : 'Not downloaded';
-  const sizeText =
-    model.downloaded && model.size_mb && !showDownloading ? `, ${formatSize(model.size_mb)}` : '';
-  const rowLabel = `${model.display_name}, ${statusText}${sizeText}. Use Tab to reach Download or Delete.`;
-
-  return (
-    <div
-      className="flex items-center justify-between p-3 border rounded-lg"
-      role="group"
-      tabIndex={0}
-      aria-label={rowLabel}
-    >
-      <div className="flex-1">
-        <div className="flex items-center gap-2">
-          <span className="font-medium text-sm">{model.display_name}</span>
-          {model.loaded && (
-            <Badge variant="default" className="text-xs">
-              Loaded
-            </Badge>
-          )}
-          {/* Only show Downloaded if actually downloaded AND not downloading */}
-          {model.downloaded && !model.loaded && !showDownloading && (
-            <Badge variant="secondary" className="text-xs">
-              Downloaded
-            </Badge>
-          )}
-        </div>
-        {model.downloaded && model.size_mb && !showDownloading && (
-          <div className="text-xs text-muted-foreground mt-1">
-            Size: {formatSize(model.size_mb)}
-          </div>
-        )}
-      </div>
-      <div className="flex items-center gap-2">
-        {model.downloaded && !showDownloading ? (
-          <div className="flex items-center gap-2">
-            <div className="flex items-center gap-1 text-sm text-muted-foreground">
-              <span>Ready</span>
-            </div>
-            <Button
-              size="sm"
-              onClick={onDelete}
-              variant="outline"
-              disabled={model.loaded}
-              title={model.loaded ? 'Unload model before deleting' : 'Delete model'}
-              aria-label={
-                model.loaded ? 'Unload model before deleting' : `Delete ${model.display_name}`
-              }
-            >
-              <Trash2 className="h-4 w-4" />
-            </Button>
-          </div>
-        ) : showDownloading ? (
-          <Button
-            size="sm"
-            variant="outline"
-            disabled
-            aria-label={`${model.display_name} downloading`}
-          >
-            <Loader2 className="h-4 w-4 mr-2 animate-spin" />
-            Downloading...
-          </Button>
-        ) : (
-          <Button
-            size="sm"
-            onClick={onDownload}
-            variant="outline"
-            aria-label={`Download ${model.display_name}`}
-          >
-            <Download className="h-4 w-4 mr-2" />
-            Download
-          </Button>
-        )}
-      </div>
-    </div>
-  );
-}

app/src/components/ServerSettings/ModelProgress.tsx

@@ -12,7 +12,11 @@ interface ModelProgressProps {
   isDownloading?: boolean;
 }
 
-export function ModelProgress({ modelName, displayName, isDownloading = false }: ModelProgressProps) {
+export function ModelProgress({
+  modelName,
+  displayName,
+  isDownloading = false,
+}: ModelProgressProps) {
   const [progress, setProgress] = useState<ModelProgressType | null>(null);
   const serverUrl = useServerStore((state) => state.serverUrl);
 

app/src/components/ServerTab/ChangelogPage.tsx

@@ -182,8 +182,8 @@ function ChangelogEntryCard({ entry }: { entry: ChangelogEntry }) {
 
   return (
     <div className="border-b border-border/50 pb-6">
-      <div className="flex items-baseline gap-3 mb-1">
-        <h3 className="text-sm font-medium">{entry.version}</h3>
+      <div className="flex items-baseline gap-3 mb-3">
+        <h3 className="text-xl font-semibold tracking-tight">{entry.version}</h3>
         {entry.date && <span className="text-xs text-muted-foreground">{entry.date}</span>}
         {entry.version === 'Unreleased' && <Badge variant="outline">dev</Badge>}
       </div>

app/src/components/StoriesTab/StoryChatItem.tsx

@@ -87,7 +87,7 @@ export function StoryChatItem({
               alt={`${item.profile_name} avatar`}
               className={cn(
                 'h-full w-full object-cover transition-all duration-200',
-                !isCurrentlyPlaying && 'grayscale'
+                !isCurrentlyPlaying && 'grayscale',
               )}
               onError={() => setAvatarError(true)}
             />
@@ -127,7 +127,10 @@ export function StoryChatItem({
               <Play className="mr-2 h-4 w-4" />
               Play from here
             </DropdownMenuItem>
-            <DropdownMenuItem onClick={onRemove} className="text-destructive focus:text-destructive">
+            <DropdownMenuItem
+              onClick={onRemove}
+              className="text-destructive focus:text-destructive"
+            >
               <Trash2 className="mr-2 h-4 w-4" />
               Remove from Story
             </DropdownMenuItem>
@@ -139,15 +142,12 @@ export function StoryChatItem({
 }
 
 // Sortable wrapper component
-export function SortableStoryChatItem(props: Omit<StoryChatItemProps, 'dragHandleProps' | 'isDragging'>) {
-  const {
-    attributes,
-    listeners,
-    setNodeRef,
-    transform,
-    transition,
-    isDragging,
-  } = useSortable({ id: props.item.generation_id });
+export function SortableStoryChatItem(
+  props: Omit<StoryChatItemProps, 'dragHandleProps' | 'isDragging'>,
+) {
+  const { attributes, listeners, setNodeRef, transform, transition, isDragging } = useSortable({
+    id: props.item.generation_id,
+  });
 
   const style = {
     transform: CSS.Transform.toString(transform),
@@ -156,11 +156,7 @@ export function SortableStoryChatItem(props: Omit<StoryChatItemProps, 'dragHandl
 
   return (
     <div ref={setNodeRef} style={style} {...attributes}>
-      <StoryChatItem
-        {...props}
-        dragHandleProps={listeners}
-        isDragging={isDragging}
-      />
+      <StoryChatItem {...props} dragHandleProps={listeners} isDragging={isDragging} />
     </div>
   );
 }

app/src/components/StoriesTab/StoryTrackEditor.tsx

@@ -371,7 +371,7 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
     }
   }, [isResizing, handleResizeMove, handleResizeEnd]);
 
-  const handleTimelineClick = (e: React.MouseEvent<HTMLDivElement>) => {
+  const handleTimelineClick = (e: React.MouseEvent<HTMLElement>) => {
     if (!tracksRef.current || draggingItem || trimmingItem) return;
     const rect = tracksRef.current.getBoundingClientRect();
     const x = e.clientX - rect.left + tracksRef.current.scrollLeft;
@@ -500,7 +500,7 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
   }, [trimmingItem, trimSide, tempTrimValues, storyId, trimItem, toast]);
 
   const handleSplit = useCallback(() => {
-    if (!selectedClipId) return;
+    if (!selectedClipId || splitItem.isPending) return;
 
     const item = items.find((i) => i.id === selectedClipId);
     if (!item) return;

app/src/components/VoiceProfiles/AudioSampleRecording.tsx

@@ -14,12 +14,7 @@ const MemoizedWaveform = memo(function MemoizedWaveform({
     <div className="absolute inset-0 pointer-events-none flex items-center justify-center opacity-30">
       <Visualizer audio={audioStream} autoStart strokeColor="#b39a3d">
         {({ canvasRef }) => (
-          <canvas
-            ref={canvasRef}
-            width={500}
-            height={150}
-            className="w-full h-full"
-          />
+          <canvas ref={canvasRef} width={500} height={150} className="w-full h-full" />
         )}
       </Visualizer>
     </div>
@@ -87,9 +82,7 @@ export function AudioSampleRecording({
         <div className="space-y-4">
           {!isRecording && !file && (
             <div className="relative flex flex-col items-center justify-center gap-4 p-4 border-2 border-dashed rounded-lg min-h-[180px] overflow-hidden">
-              {showWaveform && audioStream && (
-                <MemoizedWaveform audioStream={audioStream} />
-              )}
+              {showWaveform && audioStream && <MemoizedWaveform audioStream={audioStream} />}
               <Button
                 type="button"
                 onClick={onStart}
@@ -107,9 +100,7 @@ export function AudioSampleRecording({
 
           {isRecording && (
             <div className="relative flex flex-col items-center justify-center gap-4 p-4 border-2 border-accent rounded-lg bg-accent/5 min-h-[180px] overflow-hidden">
-              {showWaveform && audioStream && (
-                <MemoizedWaveform audioStream={audioStream} />
-              )}
+              {showWaveform && audioStream && <MemoizedWaveform audioStream={audioStream} />}
               <div className="relative z-10 flex items-center gap-4">
                 <div className="flex items-center gap-2">
                   <div className="h-3 w-3 rounded-full bg-accent animate-pulse" />

app/src/components/VoiceProfiles/ProfileCard.tsx

@@ -17,11 +17,18 @@ import { useDeleteProfile, useExportProfile } from '@/lib/hooks/useProfiles';
 import { cn } from '@/lib/utils/cn';
 import { useUIStore } from '@/stores/uiStore';
 
+/** Human-readable display names for preset engine badges. */
+const ENGINE_DISPLAY_NAMES: Record<string, string> = {
+  kokoro: 'Kokoro',
+  qwen_custom_voice: 'CustomVoice',
+};
+
 interface ProfileCardProps {
   profile: VoiceProfileResponse;
+  disabled?: boolean;
 }
 
-export function ProfileCard({ profile }: ProfileCardProps) {
+export function ProfileCard({ profile, disabled }: ProfileCardProps) {
   const [deleteDialogOpen, setDeleteDialogOpen] = useState(false);
 
   const deleteProfile = useDeleteProfile();
@@ -34,6 +41,12 @@ export function ProfileCard({ profile }: ProfileCardProps) {
   const isSelected = selectedProfileId === profile.id;
 
   const handleSelect = () => {
+    // If disabled but already selected, bounce the selection to re-trigger engine auto-switch
+    if (disabled && isSelected) {
+      setSelectedProfileId(null);
+      setTimeout(() => setSelectedProfileId(profile.id), 0);
+      return;
+    }
     setSelectedProfileId(isSelected ? null : profile.id);
   };
 
@@ -74,8 +87,9 @@ export function ProfileCard({ profile }: ProfileCardProps) {
     <>
       <Card
         className={cn(
-          'cursor-pointer hover:shadow-md transition-all flex flex-col h-[162px]',
-          isSelected && 'ring-2 ring-accent shadow-md',
+          'cursor-pointer transition-all flex flex-col h-[162px]',
+          disabled ? 'opacity-40 hover:opacity-60' : 'hover:shadow-md',
+          isSelected && !disabled && 'ring-2 ring-accent shadow-md',
         )}
         onClick={handleSelect}
         tabIndex={0}
@@ -97,6 +111,16 @@ export function ProfileCard({ profile }: ProfileCardProps) {
             <Badge variant="outline" className="text-xs h-5 px-1.5 text-muted-foreground">
               {profile.language}
             </Badge>
+            {profile.voice_type === 'preset' && (
+              <Badge variant="secondary" className="text-xs h-5 px-1.5">
+                {ENGINE_DISPLAY_NAMES[profile.preset_engine ?? ''] ?? profile.preset_engine}
+              </Badge>
+            )}
+            {profile.voice_type === 'designed' && (
+              <Badge variant="secondary" className="text-xs h-5 px-1.5">
+                designed
+              </Badge>
+            )}
             {profile.effects_chain && profile.effects_chain.length > 0 && (
               <Sparkles className="h-3.5 w-3.5 text-accent fill-accent" />
             )}

app/src/components/VoiceProfiles/ProfileForm.tsx

@@ -1,9 +1,11 @@
 import { zodResolver } from '@hookform/resolvers/zod';
-import { Edit2, Mic, Monitor, Upload, X } from 'lucide-react';
+import { useQuery } from '@tanstack/react-query';
+import { Edit2, Mic, Monitor, Music, Upload, X } from 'lucide-react';
 import { useEffect, useRef, useState } from 'react';
 import { useForm } from 'react-hook-form';
 import * as z from 'zod';
 import { EffectsChainEditor } from '@/components/Effects/EffectsChainEditor';
+import { Badge } from '@/components/ui/badge';
 import { Button } from '@/components/ui/button';
 import {
   Dialog,
@@ -15,6 +17,7 @@ import {
 import {
   Form,
   FormControl,
+  FormDescription,
   FormField,
   FormItem,
   FormLabel,
@@ -32,7 +35,7 @@ import { Tabs, TabsContent, TabsList, TabsTrigger } from '@/components/ui/tabs';
 import { Textarea } from '@/components/ui/textarea';
 import { useToast } from '@/components/ui/use-toast';
 import { apiClient } from '@/lib/api/client';
-import type { EffectConfig } from '@/lib/api/types';
+import type { EffectConfig, PresetVoice, VoiceType } from '@/lib/api/types';
 import { LANGUAGE_CODES, LANGUAGE_OPTIONS, type LanguageCode } from '@/lib/constants/languages';
 import { useAudioPlayer } from '@/lib/hooks/useAudioPlayer';
 import { useAudioRecording } from '@/lib/hooks/useAudioRecording';
@@ -40,6 +43,7 @@ import {
   useAddSample,
   useCreateProfile,
   useDeleteAvatar,
+  useDeleteProfile,
   useProfile,
   useUpdateProfile,
   useUploadAvatar,
@@ -56,6 +60,16 @@ import { AudioSampleUpload } from './AudioSampleUpload';
 import { SampleList } from './SampleList';
 
 const MAX_AUDIO_DURATION_SECONDS = 30;
+const PRESET_ONLY_ENGINES = new Set(['kokoro', 'qwen_custom_voice']);
+const DEFAULT_ENGINE_OPTIONS = [
+  { value: 'qwen', label: 'Qwen3-TTS' },
+  { value: 'qwen_custom_voice', label: 'Qwen CustomVoice' },
+  { value: 'luxtts', label: 'LuxTTS' },
+  { value: 'chatterbox', label: 'Chatterbox' },
+  { value: 'chatterbox_turbo', label: 'Chatterbox Turbo' },
+  { value: 'tada', label: 'TADA' },
+  { value: 'kokoro', label: 'Kokoro 82M' },
+] as const;
 
 const baseProfileSchema = z.object({
   name: z.string().min(1, 'Name is required').max(100),
@@ -116,20 +130,25 @@ export function ProfileForm() {
   const createProfile = useCreateProfile();
   const updateProfile = useUpdateProfile();
   const addSample = useAddSample();
+  const deleteProfile = useDeleteProfile();
   const uploadAvatar = useUploadAvatar();
   const deleteAvatar = useDeleteAvatar();
   const transcribe = useTranscription();
   const { toast } = useToast();
+  const [voiceSource, setVoiceSource] = useState<'clone' | 'builtin'>('clone');
   const [sampleMode, setSampleMode] = useState<'upload' | 'record' | 'system'>('record');
   const [audioDuration, setAudioDuration] = useState<number | null>(null);
   const [isValidatingAudio, setIsValidatingAudio] = useState(false);
   const [avatarPreview, setAvatarPreview] = useState<string | null>(null);
+  const [selectedPresetEngine, setSelectedPresetEngine] = useState<string>('kokoro');
+  const [selectedPresetVoiceId, setSelectedPresetVoiceId] = useState<string>('');
   const avatarInputRef = useRef<HTMLInputElement>(null);
   const { isPlaying, playPause, cleanup: cleanupAudio } = useAudioPlayer();
   const isCreating = !editingProfileId;
   const serverUrl = useServerStore((state) => state.serverUrl);
   const [profileEffectsChain, setProfileEffectsChain] = useState<EffectConfig[]>([]);
   const [effectsDirty, setEffectsDirty] = useState(false);
+  const [defaultEngine, setDefaultEngine] = useState<string>('');
 
   const form = useForm<ProfileFormValues>({
     resolver: zodResolver(profileSchema),
@@ -239,6 +258,26 @@ export function ProfileForm() {
     },
   });
 
+  // Fetch available preset voices for the selected engine
+  const presetEngineToQuery = isCreating
+    ? selectedPresetEngine
+    : (editingProfile?.preset_engine ?? '');
+  const { data: presetVoicesData } = useQuery({
+    queryKey: ['presetVoices', presetEngineToQuery],
+    queryFn: () => apiClient.listPresetVoices(presetEngineToQuery),
+    enabled:
+      !!presetEngineToQuery &&
+      ((voiceSource === 'builtin' && isCreating) ||
+        (!isCreating && editingProfile?.voice_type === 'preset')),
+  });
+  const presetVoices = presetVoicesData?.voices ?? [];
+  const isSampleBasedProfile = isCreating
+    ? voiceSource === 'clone'
+    : editingProfile?.voice_type !== 'preset';
+  const availableDefaultEngines = DEFAULT_ENGINE_OPTIONS.filter(
+    (option) => !isSampleBasedProfile || !PRESET_ONLY_ENGINES.has(option.value),
+  );
+
   // Show recording errors
   useEffect(() => {
     if (recordingError) {
@@ -287,6 +326,7 @@ export function ProfileForm() {
       });
       setProfileEffectsChain(editingProfile.effects_chain ?? []);
       setEffectsDirty(false);
+      setDefaultEngine(editingProfile.default_engine ?? '');
     } else if (profileFormDraft && open) {
       // Restore from draft when opening in create mode
       form.reset({
@@ -326,6 +366,24 @@ export function ProfileForm() {
     }
   }, [editingProfile, profileFormDraft, open, form]);
 
+  useEffect(() => {
+    if (
+      defaultEngine &&
+      !availableDefaultEngines.some((option) => option.value === defaultEngine)
+    ) {
+      setDefaultEngine('');
+    }
+  }, [availableDefaultEngines, defaultEngine]);
+
+  useEffect(() => {
+    if (!selectedPresetVoiceId) {
+      return;
+    }
+
+    if (!presetVoices.some((voice: PresetVoice) => voice.voice_id === selectedPresetVoiceId)) {
+      setSelectedPresetVoiceId('');
+    }
+  }, [presetVoices, selectedPresetVoiceId]);
   async function handleTranscribe() {
     const file = form.getValues('sampleFile');
     if (!file) {
@@ -415,13 +473,14 @@ export function ProfileForm() {
   async function onSubmit(data: ProfileFormValues) {
     try {
       if (editingProfileId) {
-        // Editing: just update profile
+        // Editing: update profile
         await updateProfile.mutateAsync({
           profileId: editingProfileId,
           data: {
             name: data.name,
             description: data.description,
             language: data.language,
+            default_engine: defaultEngine || undefined,
           },
         });
 
@@ -464,8 +523,50 @@ export function ProfileForm() {
           title: 'Voice updated',
           description: `"${data.name}" has been updated successfully.`,
         });
+      } else if (voiceSource === 'builtin') {
+        // Creating preset profile from built-in voice
+        if (!selectedPresetVoiceId) {
+          toast({
+            title: 'No voice selected',
+            description: 'Please select a built-in voice.',
+            variant: 'destructive',
+          });
+          return;
+        }
+
+        const profile = await createProfile.mutateAsync({
+          name: data.name,
+          description: data.description,
+          language: data.language,
+          voice_type: 'preset' as VoiceType,
+          preset_engine: selectedPresetEngine,
+          preset_voice_id: selectedPresetVoiceId,
+          default_engine: selectedPresetEngine,
+        });
+
+        // Handle avatar upload if provided
+        if (data.avatarFile) {
+          try {
+            await uploadAvatar.mutateAsync({
+              profileId: profile.id,
+              file: data.avatarFile,
+            });
+          } catch (avatarError) {
+            toast({
+              title: 'Avatar upload failed',
+              description:
+                avatarError instanceof Error ? avatarError.message : 'Failed to upload avatar',
+              variant: 'destructive',
+            });
+          }
+        }
+
+        toast({
+          title: 'Profile created',
+          description: `"${data.name}" has been created with a built-in voice.`,
+        });
       } else {
-        // Creating: require sample file and reference text
+        // Creating cloned profile: require sample file and reference text
         const sampleFile = form.getValues('sampleFile');
         const referenceText = form.getValues('referenceText');
 
@@ -528,6 +629,7 @@ export function ProfileForm() {
           name: data.name,
           description: data.description,
           language: data.language,
+          default_engine: defaultEngine || undefined,
         });
 
         // Convert non-WAV uploads to WAV so the backend can always use soundfile.
@@ -572,12 +674,32 @@ export function ProfileForm() {
             description: `"${data.name}" has been created with a sample.`,
           });
         } catch (sampleError) {
-          // Profile was created but sample failed - still show error
+          let rollbackSucceeded = false;
+          try {
+            await deleteProfile.mutateAsync(profile.id);
+            rollbackSucceeded = true;
+          } catch (rollbackError) {
+            toast({
+              title: 'Rollback failed',
+              description:
+                rollbackError instanceof Error
+                  ? rollbackError.message
+                  : 'Created profile could not be removed after sample upload failure.',
+              variant: 'destructive',
+            });
+          }
+
           toast({
             title: 'Failed to add sample',
-            description: `Profile "${data.name}" was created, but failed to add sample: ${sampleError instanceof Error ? sampleError.message : 'Unknown error'}`,
+            description:
+              sampleError instanceof Error
+                ? `${sampleError.message}${rollbackSucceeded ? ' The profile was rolled back.' : ''}`
+                : rollbackSucceeded
+                  ? 'Failed to add sample. The profile was rolled back.'
+                  : 'Failed to add sample.',
             variant: 'destructive',
           });
+          return;
         }
       }
 
@@ -642,16 +764,16 @@ export function ProfileForm() {
 
   return (
     <Dialog open={open} onOpenChange={handleOpenChange}>
-      <DialogContent className="max-w-none w-screen h-screen left-0 top-0 translate-x-0 translate-y-0 rounded-none p-6 overflow-y-auto">
-        <div className="max-w-5xl max-h-[85vh] mx-auto my-auto w-full flex flex-col">
+      <DialogContent className="max-w-none w-screen h-screen left-0 top-0 translate-x-0 translate-y-0 rounded-none p-6 overflow-hidden">
+        <div className="max-w-5xl h-[85vh] mx-auto my-auto w-full flex flex-col overflow-hidden">
           <DialogHeader>
             <DialogTitle className="text-2xl">
-              {editingProfileId ? 'Edit Voice' : 'Clone voice'}
+              {editingProfileId ? 'Edit Voice' : 'Create Voice'}
             </DialogTitle>
             <DialogDescription>
               {editingProfileId
                 ? 'Update your voice profile details and manage samples.'
-                : 'Create a new voice profile with an audio sample to clone the voice.'}
+                : 'Create a new voice profile from an audio sample or a built-in voice.'}
             </DialogDescription>
             {isCreating && profileFormDraft && (
               <div className="flex items-center gap-2 pt-2">
@@ -682,143 +804,276 @@ export function ProfileForm() {
 
           <Form {...form}>
             <form onSubmit={form.handleSubmit(onSubmit)} className="flex-1 min-h-0 flex flex-col">
-              <div className="grid gap-6 grid-cols-2 flex-1 overflow-y-auto min-h-0">
+              <div className="grid gap-6 grid-cols-2 flex-1 min-h-0 overflow-hidden">
                 {/* Left column: Sample management */}
-                <div className="space-y-4 border-r pr-6">
+                <div className="space-y-4 border-r pr-6 overflow-y-auto min-h-0">
                   {isCreating ? (
                     <>
-                      <Tabs
-                        className="pt-4"
-                        value={sampleMode}
-                        onValueChange={(v) => {
-                          const newMode = v as 'upload' | 'record' | 'system';
-                          // Cancel any active recordings when switching modes
-                          if (isRecording && newMode !== 'record') {
-                            cancelRecording();
-                          }
-                          if (isSystemRecording && newMode !== 'system') {
-                            cancelSystemRecording();
-                          }
-                          setSampleMode(newMode);
-                        }}
-                      >
-                        <TabsList
-                          className={`grid w-full ${platform.metadata.isTauri && isSystemAudioSupported ? 'grid-cols-3' : 'grid-cols-2'}`}
-                        >
-                          <TabsTrigger value="upload" className="flex items-center gap-2">
-                            <Upload className="h-4 w-4 shrink-0" />
-                            Upload
-                          </TabsTrigger>
-                          <TabsTrigger value="record" className="flex items-center gap-2">
-                            <Mic className="h-4 w-4 shrink-0" />
-                            Record
-                          </TabsTrigger>
-                          {platform.metadata.isTauri && isSystemAudioSupported && (
-                            <TabsTrigger value="system" className="flex items-center gap-2">
-                              <Monitor className="h-4 w-4 shrink-0" />
-                              System Audio
-                            </TabsTrigger>
-                          )}
-                        </TabsList>
+                      {/* Voice source selector */}
+                      <div className="flex pt-4 pb-2">
+                        <div className="inline-flex rounded-lg border border-border p-0.5 bg-muted/50">
+                          <button
+                            type="button"
+                            onClick={() => setVoiceSource('clone')}
+                            className={`inline-flex items-center gap-2 px-3 py-1.5 text-sm rounded-md transition-colors ${
+                              voiceSource === 'clone'
+                                ? 'bg-accent text-accent-foreground shadow-sm'
+                                : 'text-muted-foreground hover:text-foreground'
+                            }`}
+                          >
+                            <Mic className="h-3.5 w-3.5" />
+                            Clone from audio
+                          </button>
+                          <button
+                            type="button"
+                            onClick={() => setVoiceSource('builtin')}
+                            className={`inline-flex items-center gap-2 px-3 py-1.5 text-sm rounded-md transition-colors ${
+                              voiceSource === 'builtin'
+                                ? 'bg-accent text-accent-foreground shadow-sm'
+                                : 'text-muted-foreground hover:text-foreground'
+                            }`}
+                          >
+                            <Music className="h-3.5 w-3.5" />
+                            Built-in voice
+                          </button>
+                        </div>
+                      </div>
 
-                        <TabsContent value="upload" className="space-y-4">
-                          <FormField
-                            control={form.control}
-                            name="sampleFile"
-                            render={({ field: { onChange, name } }) => (
-                              <AudioSampleUpload
-                                file={selectedFile}
-                                onFileChange={onChange}
-                                onTranscribe={handleTranscribe}
-                                onPlayPause={handlePlayPause}
-                                isPlaying={isPlaying}
-                                isValidating={isValidatingAudio}
-                                isTranscribing={transcribe.isPending}
-                                isDisabled={
-                                  audioDuration !== null &&
-                                  audioDuration > MAX_AUDIO_DURATION_SECONDS
-                                }
-                                fieldName={name}
-                              />
-                            )}
-                          />
-                        </TabsContent>
+                      {voiceSource === 'builtin' ? (
+                        <div className="space-y-4">
+                          <FormDescription>
+                            Choose a pre-built voice. These don't require an audio sample.
+                          </FormDescription>
 
-                        <TabsContent value="record" className="space-y-4">
-                          <FormField
-                            control={form.control}
-                            name="sampleFile"
-                            render={() => (
-                              <AudioSampleRecording
-                                file={selectedFile}
-                                isRecording={isRecording}
-                                duration={duration}
-                                onStart={startRecording}
-                                onStop={stopRecording}
-                                onCancel={handleCancelRecording}
-                                onTranscribe={handleTranscribe}
-                                onPlayPause={handlePlayPause}
-                                isPlaying={isPlaying}
-                                isTranscribing={transcribe.isPending}
-                              />
-                            )}
-                          />
-                        </TabsContent>
-
-                        {platform.metadata.isTauri && isSystemAudioSupported && (
-                          <TabsContent value="system" className="space-y-4">
-                            <FormField
-                              control={form.control}
-                              name="sampleFile"
-                              render={() => (
-                                <AudioSampleSystem
-                                  file={selectedFile}
-                                  isRecording={isSystemRecording}
-                                  duration={systemDuration}
-                                  onStart={startSystemRecording}
-                                  onStop={stopSystemRecording}
-                                  onCancel={handleCancelRecording}
-                                  onTranscribe={handleTranscribe}
-                                  onPlayPause={handlePlayPause}
-                                  isPlaying={isPlaying}
-                                  isTranscribing={transcribe.isPending}
-                                />
-                              )}
-                            />
-                          </TabsContent>
-                        )}
-                      </Tabs>
-
-                      <FormField
-                        control={form.control}
-                        name="referenceText"
-                        render={({ field }) => (
+                          {/* Engine selector */}
                           <FormItem>
-                            <FormLabel>Reference Text</FormLabel>
-                            <FormControl>
-                              <Textarea
-                                placeholder="Enter the exact text spoken in the audio..."
-                                className="min-h-[100px]"
-                                {...field}
-                              />
-                            </FormControl>
-                            <FormMessage />
+                            <FormLabel>Engine</FormLabel>
+                            <Select
+                              value={selectedPresetEngine}
+                              onValueChange={setSelectedPresetEngine}
+                            >
+                              <FormControl>
+                                <SelectTrigger>
+                                  <SelectValue />
+                                </SelectTrigger>
+                              </FormControl>
+                              <SelectContent>
+                                <SelectItem value="kokoro">Kokoro 82M</SelectItem>
+                                <SelectItem value="qwen_custom_voice">Qwen CustomVoice</SelectItem>
+                              </SelectContent>
+                            </Select>
                           </FormItem>
-                        )}
-                      />
+
+                          {/* Voice picker */}
+                          <FormItem>
+                            <FormLabel>Voice</FormLabel>
+                            <div className="grid grid-cols-2 gap-1.5 max-h-[340px] overflow-y-auto pr-1">
+                              {presetVoices.map((voice: PresetVoice) => (
+                                <button
+                                  key={voice.voice_id}
+                                  type="button"
+                                  onClick={() => {
+                                    setSelectedPresetVoiceId(voice.voice_id);
+                                    // Auto-set language from voice
+                                    if (voice.language) {
+                                      form.setValue('language', voice.language as LanguageCode);
+                                    }
+                                  }}
+                                  className={`text-left px-3 py-2 rounded-md border text-sm transition-colors ${
+                                    selectedPresetVoiceId === voice.voice_id
+                                      ? 'border-accent bg-accent/10 text-accent-foreground'
+                                      : 'border-border hover:bg-muted'
+                                  }`}
+                                >
+                                  <div className="font-medium">{voice.name}</div>
+                                  <div className="flex gap-1.5 mt-0.5">
+                                    <Badge variant="outline" className="text-[10px] h-4 px-1">
+                                      {voice.gender}
+                                    </Badge>
+                                    <Badge variant="outline" className="text-[10px] h-4 px-1">
+                                      {voice.language}
+                                    </Badge>
+                                  </div>
+                                </button>
+                              ))}
+                            </div>
+                          </FormItem>
+                        </div>
+                      ) : (
+                        <>
+                          <Tabs
+                            className="pt-0"
+                            value={sampleMode}
+                            onValueChange={(v) => {
+                              const newMode = v as 'upload' | 'record' | 'system';
+                              // Cancel any active recordings when switching modes
+                              if (isRecording && newMode !== 'record') {
+                                cancelRecording();
+                              }
+                              if (isSystemRecording && newMode !== 'system') {
+                                cancelSystemRecording();
+                              }
+                              setSampleMode(newMode);
+                            }}
+                          >
+                            <TabsList
+                              className={`grid w-full ${platform.metadata.isTauri && isSystemAudioSupported ? 'grid-cols-3' : 'grid-cols-2'}`}
+                            >
+                              <TabsTrigger value="upload" className="flex items-center gap-2">
+                                <Upload className="h-4 w-4 shrink-0" />
+                                Upload
+                              </TabsTrigger>
+                              <TabsTrigger value="record" className="flex items-center gap-2">
+                                <Mic className="h-4 w-4 shrink-0" />
+                                Record
+                              </TabsTrigger>
+                              {platform.metadata.isTauri && isSystemAudioSupported && (
+                                <TabsTrigger value="system" className="flex items-center gap-2">
+                                  <Monitor className="h-4 w-4 shrink-0" />
+                                  System Audio
+                                </TabsTrigger>
+                              )}
+                            </TabsList>
+
+                            <TabsContent value="upload" className="space-y-4">
+                              <FormField
+                                control={form.control}
+                                name="sampleFile"
+                                render={({ field: { onChange, name } }) => (
+                                  <AudioSampleUpload
+                                    file={selectedFile}
+                                    onFileChange={onChange}
+                                    onTranscribe={handleTranscribe}
+                                    onPlayPause={handlePlayPause}
+                                    isPlaying={isPlaying}
+                                    isValidating={isValidatingAudio}
+                                    isTranscribing={transcribe.isPending}
+                                    isDisabled={
+                                      audioDuration !== null &&
+                                      audioDuration > MAX_AUDIO_DURATION_SECONDS
+                                    }
+                                    fieldName={name}
+                                  />
+                                )}
+                              />
+                            </TabsContent>
+
+                            <TabsContent value="record" className="space-y-4">
+                              <FormField
+                                control={form.control}
+                                name="sampleFile"
+                                render={() => (
+                                  <AudioSampleRecording
+                                    file={selectedFile}
+                                    isRecording={isRecording}
+                                    duration={duration}
+                                    onStart={startRecording}
+                                    onStop={stopRecording}
+                                    onCancel={handleCancelRecording}
+                                    onTranscribe={handleTranscribe}
+                                    onPlayPause={handlePlayPause}
+                                    isPlaying={isPlaying}
+                                    isTranscribing={transcribe.isPending}
+                                  />
+                                )}
+                              />
+                            </TabsContent>
+
+                            {platform.metadata.isTauri && isSystemAudioSupported && (
+                              <TabsContent value="system" className="space-y-4">
+                                <FormField
+                                  control={form.control}
+                                  name="sampleFile"
+                                  render={() => (
+                                    <AudioSampleSystem
+                                      file={selectedFile}
+                                      isRecording={isSystemRecording}
+                                      duration={systemDuration}
+                                      onStart={startSystemRecording}
+                                      onStop={stopSystemRecording}
+                                      onCancel={handleCancelRecording}
+                                      onTranscribe={handleTranscribe}
+                                      onPlayPause={handlePlayPause}
+                                      isPlaying={isPlaying}
+                                      isTranscribing={transcribe.isPending}
+                                    />
+                                  )}
+                                />
+                              </TabsContent>
+                            )}
+                          </Tabs>
+
+                          <FormField
+                            control={form.control}
+                            name="referenceText"
+                            render={({ field }) => (
+                              <FormItem>
+                                <FormLabel>Reference Text</FormLabel>
+                                <FormControl>
+                                  <Textarea
+                                    placeholder="Enter the exact text spoken in the audio..."
+                                    className="min-h-[100px]"
+                                    {...field}
+                                  />
+                                </FormControl>
+                                <FormMessage />
+                              </FormItem>
+                            )}
+                          />
+                        </>
+                      )}
                     </>
                   ) : (
-                    // Show sample list when editing
-                    editingProfileId && (
+                    // Editing mode
+                    editingProfileId &&
+                    editingProfile &&
+                    (editingProfile.voice_type === 'preset' ? (
+                      <div className="space-y-4 pt-4">
+                        <div className="rounded-lg border border-border p-4 space-y-3">
+                          <div className="text-sm font-medium text-muted-foreground">
+                            Built-in Voice
+                          </div>
+                          <div className="flex items-center gap-3">
+                            <div className="text-lg font-semibold">
+                              {presetVoices.find(
+                                (v: PresetVoice) => v.voice_id === editingProfile.preset_voice_id,
+                              )?.name ?? editingProfile.preset_voice_id}
+                            </div>
+                            <Badge variant="secondary" className="text-xs">
+                              {editingProfile.preset_engine}
+                            </Badge>
+                          </div>
+                          {(() => {
+                            const voice = presetVoices.find(
+                              (v: PresetVoice) => v.voice_id === editingProfile.preset_voice_id,
+                            );
+                            return voice ? (
+                              <div className="flex gap-1.5">
+                                <Badge variant="outline" className="text-xs">
+                                  {voice.gender}
+                                </Badge>
+                                <Badge variant="outline" className="text-xs">
+                                  {voice.language}
+                                </Badge>
+                              </div>
+                            ) : null;
+                          })()}
+                        </div>
+                        <p className="text-xs text-muted-foreground">
+                          This profile uses a built-in voice. The voice cannot be changed after
+                          creation.
+                        </p>
+                      </div>
+                    ) : (
                       <div>
                         <SampleList profileId={editingProfileId} />
                       </div>
-                    )
+                    ))
                   )}
                 </div>
 
                 {/* Right column: Profile info */}
-                <div className="space-y-4">
+                <div className="space-y-4 overflow-y-auto min-h-0">
                   {/* Avatar Upload */}
                   <FormField
                     control={form.control}
@@ -924,6 +1179,36 @@ export function ProfileForm() {
                     )}
                   />
 
+                  <FormItem>
+                    <FormLabel>Default Engine</FormLabel>
+                    <Select
+                      value={defaultEngine || '_none'}
+                      onValueChange={(v) => {
+                        setDefaultEngine(v === '_none' ? '' : v);
+                      }}
+                      disabled={
+                        voiceSource === 'builtin' || editingProfile?.voice_type === 'preset'
+                      }
+                    >
+                      <FormControl>
+                        <SelectTrigger>
+                          <SelectValue placeholder="No preference" />
+                        </SelectTrigger>
+                      </FormControl>
+                      <SelectContent>
+                        <SelectItem value="_none">No preference</SelectItem>
+                        {availableDefaultEngines.map((option) => (
+                          <SelectItem key={option.value} value={option.value}>
+                            {option.label}
+                          </SelectItem>
+                        ))}
+                      </SelectContent>
+                    </Select>
+                    <p className="text-xs text-muted-foreground">
+                      Auto-selects this engine when the profile is chosen.
+                    </p>
+                  </FormItem>
+
                   {editingProfileId && (
                     <div className="space-y-2">
                       <FormLabel>Default Effects</FormLabel>

app/src/components/VoiceProfiles/ProfileList.tsx

@@ -1,4 +1,5 @@
-import { Mic, Sparkles } from 'lucide-react';
+import { Info, Mic, Sparkles } from 'lucide-react';
+import { useEffect, useRef } from 'react';
 import { Button } from '@/components/ui/button';
 import { Card, CardContent } from '@/components/ui/card';
 import { useProfiles } from '@/lib/hooks/useProfiles';
@@ -6,9 +7,36 @@ import { useUIStore } from '@/stores/uiStore';
 import { ProfileCard } from './ProfileCard';
 import { ProfileForm } from './ProfileForm';
 
+/** Engines that use preset (built-in) voices instead of cloned profiles. */
+const PRESET_ENGINES = new Set(['kokoro', 'qwen_custom_voice']);
+
 export function ProfileList() {
   const { data: profiles, isLoading, error } = useProfiles();
   const setDialogOpen = useUIStore((state) => state.setProfileDialogOpen);
+  const selectedEngine = useUIStore((state) => state.selectedEngine);
+  const selectedProfileId = useUIStore((state) => state.selectedProfileId);
+  const cardRefs = useRef<Map<string, HTMLDivElement>>(new Map());
+
+  // Scroll to the selected profile after engine/sort changes
+  useEffect(() => {
+    if (!selectedProfileId) return;
+    let timeoutId: ReturnType<typeof setTimeout> | null = null;
+    const rafId = requestAnimationFrame(() => {
+      const el = cardRefs.current.get(selectedProfileId);
+      if (!el) return;
+
+      // Temporarily apply scroll-margin so it doesn't land flush at the top
+      el.style.scrollMarginTop = '180px';
+      el.scrollIntoView({ behavior: 'smooth', block: 'nearest', inline: 'nearest' });
+      timeoutId = setTimeout(() => {
+        el.style.scrollMarginTop = '';
+      }, 500);
+    });
+    return () => {
+      cancelAnimationFrame(rafId);
+      if (timeoutId) clearTimeout(timeoutId);
+    };
+  }, [selectedProfileId, selectedEngine]);
 
   if (isLoading) {
     return null;
@@ -23,6 +51,20 @@ export function ProfileList() {
   }
 
   const allProfiles = profiles || [];
+  const isPresetEngine = PRESET_ENGINES.has(selectedEngine);
+
+  /** Whether a profile is supported by the currently selected engine. */
+  const isSupported = (p: (typeof allProfiles)[number]) =>
+    isPresetEngine
+      ? p.voice_type === 'preset' && p.preset_engine === selectedEngine
+      : p.voice_type !== 'preset';
+
+  // Sort so supported profiles come first
+  const sortedProfiles = [...allProfiles].sort(
+    (a, b) => (isSupported(a) ? 0 : 1) - (isSupported(b) ? 0 : 1),
+  );
+
+  const hasUnsupported = sortedProfiles.some((p) => !isSupported(p));
 
   return (
     <div className="flex flex-col">
@@ -42,11 +84,24 @@ export function ProfileList() {
           </Card>
         ) : (
           <div className="flex gap-4 overflow-x-auto p-1 pb-1 lg:grid lg:grid-cols-3 lg:auto-rows-auto lg:overflow-x-visible lg:pb-[150px]">
-            {allProfiles.map((profile) => (
-              <div key={profile.id} className="shrink-0 w-[200px] lg:w-auto lg:shrink">
-                <ProfileCard profile={profile} />
+            {sortedProfiles.map((profile) => (
+              <div
+                key={profile.id}
+                className="shrink-0 w-[200px] lg:w-auto lg:shrink"
+                ref={(el) => {
+                  if (el) cardRefs.current.set(profile.id, el);
+                  else cardRefs.current.delete(profile.id);
+                }}
+              >
+                <ProfileCard profile={profile} disabled={!isSupported(profile)} />
               </div>
             ))}
+            {hasUnsupported && (
+              <div className="col-span-full flex items-center gap-2 text-xs text-muted-foreground py-2">
+                <Info className="h-3.5 w-3.5 shrink-0" />
+                <span>Only supported voice profiles can be selected for the current model.</span>
+              </div>
+            )}
           </div>
         )}
       </div>

app/src/components/ui/alert-dialog.tsx

@@ -111,4 +111,4 @@ export {
   AlertDialogDescription,
   AlertDialogAction,
   AlertDialogCancel,
-};
+};

app/src/hooks/useAutoUpdater.ts

@@ -5,7 +5,15 @@ import type { UpdateStatus } from '@/platform/types';
 // Re-export UpdateStatus for backwards compatibility
 export type { UpdateStatus };
 
-export function useAutoUpdater(checkOnMount = false) {
+interface UseAutoUpdaterOptions {
+  checkOnMount?: boolean;
+  showToast?: boolean;
+}
+
+export function useAutoUpdater(options: boolean | UseAutoUpdaterOptions = false) {
+  const { checkOnMount } =
+    typeof options === 'boolean' ? { checkOnMount: options } : { checkOnMount: options.checkOnMount ?? false };
+
   const platform = usePlatform();
   const [status, setStatus] = useState<UpdateStatus>(platform.updater.getStatus());
   const hasCheckedRef = useRef(false);
@@ -38,10 +46,11 @@ export function useAutoUpdater(checkOnMount = false) {
   useEffect(() => {
     if (checkOnMount && platform.metadata.isTauri && !hasCheckedRef.current) {
       hasCheckedRef.current = true;
-      checkForUpdates();
+      checkForUpdates().catch((error) => {
+        console.error('Auto update check failed:', error);
+      });
     }
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [platform.metadata.isTauricheckOnMountcheckForUpdates]);
+  }, [checkOnMount, checkForUpdates, platform.metadata.isTauri]);
 
   return {
     status,

app/src/hooks/useAutoUpdater.tsx

@@ -73,7 +73,7 @@ export function useAutoUpdater(options: boolean | UseAutoUpdaterOptions = false)
     }
     // Empty dependency array - only run once on mount
     // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [platform.metadata.isTauricheckOnMountcheckForUpdates]);
+  }, [checkOnMount, checkForUpdates, platform.metadata.isTauri]);
 
   // Show toast when update is available
   useEffect(() => {

app/src/lib/api/client.ts

@@ -17,6 +17,7 @@ import type {
   HistoryResponse,
   ModelDownloadRequest,
   ModelStatusListResponse,
+  PresetVoice,
   ProfileSampleResponse,
   StoryCreate,
   StoryDetailResponse,
@@ -97,6 +98,10 @@ class ApiClient {
     return this.request<VoiceProfileResponse>(`/profiles/${profileId}`);
   }
 
+  async listPresetVoices(engine: string): Promise<{ engine: string; voices: PresetVoice[] }> {
+    return this.request<{ engine: string; voices: PresetVoice[] }>(`/profiles/presets/${engine}`);
+  }
+
   async updateProfile(profileId: string, data: VoiceProfileCreate): Promise<VoiceProfileResponse> {
     return this.request<VoiceProfileResponse>(`/profiles/${profileId}`, {
       method: 'PUT',
@@ -229,6 +234,12 @@ class ApiClient {
     });
   }
 
+  async cancelGeneration(generationId: string): Promise<{ message: string }> {
+    return this.request<{ message: string }>(`/generate/${generationId}/cancel`, {
+      method: 'POST',
+    });
+  }
+
   async regenerateGeneration(generationId: string): Promise<GenerationResponse> {
     return this.request<GenerationResponse>(`/generate/${generationId}/regenerate`, {
       method: 'POST',
@@ -265,6 +276,12 @@ class ApiClient {
     });
   }
 
+  async clearFailedGenerations(): Promise<{ deleted: number }> {
+    return this.request<{ deleted: number }>(`/history/failed`, {
+      method: 'DELETE',
+    });
+  }
+
   async exportGeneration(generationId: string): Promise<Blob> {
     const url = `${this.getBaseUrl()}/history/${generationId}/export`;
     const response = await fetch(url);
@@ -373,7 +390,9 @@ class ApiClient {
     return this.request<{ path: string }>('/models/cache-dir');
   }
 
-  async migrateModels(destination: string): Promise<{ source: string; destination: string }> {
+  async migrateModels(
+    destination: string,
+  ): Promise<{ source: string; destination: string; moved: number; errors: string[] }> {
     return this.request('/models/migrate', {
       method: 'POST',
       body: JSON.stringify({ destination }),

app/src/lib/api/types.ts

@@ -1,10 +1,17 @@
 // API Types matching backend Pydantic models
 import type { LanguageCode } from '@/lib/constants/languages';
 
+export type VoiceType = 'cloned' | 'preset' | 'designed';
+
 export interface VoiceProfileCreate {
   name: string;
   description?: string;
   language: LanguageCode;
+  voice_type?: VoiceType;
+  preset_engine?: string;
+  preset_voice_id?: string;
+  design_prompt?: string;
+  default_engine?: string;
 }
 
 export interface VoiceProfileResponse {
@@ -14,12 +21,24 @@ export interface VoiceProfileResponse {
   language: string;
   avatar_path?: string;
   effects_chain?: EffectConfig[];
+  voice_type: VoiceType;
+  preset_engine?: string;
+  preset_voice_id?: string;
+  design_prompt?: string;
+  default_engine?: string;
   generation_count: number;
   sample_count: number;
   created_at: string;
   updated_at: string;
 }
 
+export interface PresetVoice {
+  voice_id: string;
+  name: string;
+  gender: 'male' | 'female';
+  language: string;
+}
+
 export interface ProfileSampleCreate {
   reference_text: string;
 }
@@ -42,8 +61,15 @@ export interface GenerationRequest {
   text: string;
   language: LanguageCode;
   seed?: number;
-  model_size?: '1.7B' | '0.6B';
-  engine?: 'qwen' | 'luxtts' | 'chatterbox' | 'chatterbox_turbo';
+  model_size?: '1.7B' | '0.6B' | '1B' | '3B';
+  engine?:
+    | 'qwen'
+    | 'qwen_custom_voice'
+    | 'luxtts'
+    | 'chatterbox'
+    | 'chatterbox_turbo'
+    | 'tada'
+    | 'kokoro';
   instruct?: string;
   max_chunk_chars?: number;
   crossfade_ms?: number;

app/src/lib/constants/languages.ts

@@ -5,6 +5,7 @@
  * LuxTTS is English-only.
  * Chatterbox Multilingual supports 23 languages.
  * Chatterbox Turbo is English-only.
+ * Kokoro supports 8 languages.
  */
 
 /** All languages that any engine supports. */
@@ -66,6 +67,9 @@ export const ENGINE_LANGUAGES: Record<string, readonly LanguageCode[]> = {
     'zh',
   ],
   chatterbox_turbo: ['en'],
+  tada: ['en', 'ar', 'zh', 'de', 'es', 'fr', 'it', 'ja', 'pl', 'pt'],
+  kokoro: ['en', 'es', 'fr', 'hi', 'it', 'pt', 'ja', 'zh'],
+  qwen_custom_voice: ['zh', 'en', 'ja', 'ko', 'de', 'fr', 'ru', 'pt', 'es', 'it'],
 } as const;
 
 /** Helper: get language options for a given engine. */

app/src/lib/hooks/useGenerationForm.ts

@@ -10,14 +10,25 @@ import { useGeneration } from '@/lib/hooks/useGeneration';
 import { useModelDownloadToast } from '@/lib/hooks/useModelDownloadToast';
 import { useGenerationStore } from '@/stores/generationStore';
 import { useServerStore } from '@/stores/serverStore';
+import { useUIStore } from '@/stores/uiStore';
 
 const generationSchema = z.object({
   text: z.string().min(1, '').max(50000),
   language: z.enum(LANGUAGE_CODES as [LanguageCode, ...LanguageCode[]]),
   seed: z.number().int().optional(),
-  modelSize: z.enum(['1.7B', '0.6B']).optional(),
+  modelSize: z.enum(['1.7B', '0.6B', '1B', '3B']).optional(),
   instruct: z.string().max(500).optional(),
-  engine: z.enum(['qwen', 'luxtts', 'chatterbox', 'chatterbox_turbo']).optional(),
+  engine: z
+    .enum([
+      'qwen',
+      'qwen_custom_voice',
+      'luxtts',
+      'chatterbox',
+      'chatterbox_turbo',
+      'tada',
+      'kokoro',
+    ])
+    .optional(),
 });
 
 export type GenerationFormValues = z.infer<typeof generationSchema>;
@@ -35,6 +46,7 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
   const maxChunkChars = useServerStore((state) => state.maxChunkChars);
   const crossfadeMs = useServerStore((state) => state.crossfadeMs);
   const normalizeAudio = useServerStore((state) => state.normalizeAudio);
+  const selectedEngine = useUIStore((state) => state.selectedEngine);
   const [downloadingModelName, setDownloadingModelName] = useState<string | null>(null);
   const [downloadingDisplayName, setDownloadingDisplayName] = useState<string | null>(null);
 
@@ -52,7 +64,7 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
       seed: undefined,
       modelSize: '1.7B',
       instruct: '',
-      engine: 'qwen',
+      engine: (selectedEngine as GenerationFormValues['engine']) || 'qwen',
       ...options.defaultValues,
     },
   });
@@ -79,7 +91,15 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
             ? 'chatterbox-tts'
             : engine === 'chatterbox_turbo'
               ? 'chatterbox-turbo'
-              : `qwen-tts-${data.modelSize}`;
+              : engine === 'tada'
+                ? data.modelSize === '3B'
+                  ? 'tada-3b-ml'
+                  : 'tada-1b'
+                : engine === 'kokoro'
+                  ? 'kokoro'
+                  : engine === 'qwen_custom_voice'
+                    ? `qwen-custom-voice-${data.modelSize}`
+                    : `qwen-tts-${data.modelSize}`;
       const displayName =
         engine === 'luxtts'
           ? 'LuxTTS'
@@ -87,9 +107,19 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
             ? 'Chatterbox TTS'
             : engine === 'chatterbox_turbo'
               ? 'Chatterbox Turbo'
-              : data.modelSize === '1.7B'
-                ? 'Qwen TTS 1.7B'
-                : 'Qwen TTS 0.6B';
+              : engine === 'tada'
+                ? data.modelSize === '3B'
+                  ? 'TADA 3B Multilingual'
+                  : 'TADA 1B'
+                : engine === 'kokoro'
+                  ? 'Kokoro 82M'
+                  : engine === 'qwen_custom_voice'
+                    ? data.modelSize === '1.7B'
+                      ? 'Qwen CustomVoice 1.7B'
+                      : 'Qwen CustomVoice 0.6B'
+                    : data.modelSize === '1.7B'
+                      ? 'Qwen TTS 1.7B'
+                      : 'Qwen TTS 0.6B';
 
       // Check if model needs downloading
       try {
@@ -104,7 +134,11 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
         console.error('Failed to check model status:', error);
       }
 
-      const isQwen = engine === 'qwen';
+      const hasModelSizes =
+        engine === 'qwen' || engine === 'qwen_custom_voice' || engine === 'tada';
+      // Only Qwen CustomVoice actually honors the instruct kwarg at model level.
+      // Base Qwen3-TTS accepts the kwarg but ignores it.
+      const supportsInstruct = engine === 'qwen_custom_voice';
       const effectsChain = options.getEffectsChain?.();
       // This now returns immediately with status="generating"
       const result = await generation.mutateAsync({
@@ -112,9 +146,9 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
         text: data.text,
         language: data.language,
         seed: data.seed,
-        model_size: isQwen ? data.modelSize : undefined,
+        model_size: hasModelSizes ? data.modelSize : undefined,
         engine,
-        instruct: isQwen ? data.instruct || undefined : undefined,
+        instruct: supportsInstruct ? data.instruct || undefined : undefined,
         max_chunk_chars: maxChunkChars,
         crossfade_ms: crossfadeMs,
         normalize: normalizeAudio,

app/src/lib/hooks/useHistory.ts

@@ -29,6 +29,17 @@ export function useDeleteGeneration() {
   });
 }
 
+export function useClearFailedGenerations() {
+  const queryClient = useQueryClient();
+
+  return useMutation({
+    mutationFn: () => apiClient.clearFailedGenerations(),
+    onSuccess: () => {
+      queryClient.invalidateQueries({ queryKey: ['history'] });
+    },
+  });
+}
+
 export function useExportGeneration() {
   const platform = usePlatform();
 

app/src/lib/hooks/useModelDownloadToast.tsx

@@ -131,7 +131,8 @@ export function useModelDownloadToast({
                 )}
               </div>
             ),
-            duration: progress.status === 'complete' || progress.status === 'error' ? 5000 : Infinity,
+            duration:
+              progress.status === 'complete' || progress.status === 'error' ? 5000 : Infinity,
           });
 
           // Close connection and dismiss toast on completion or error

app/src/lib/hooks/useSystemAudioCapture.ts

@@ -26,8 +26,24 @@ export function useSystemAudioCapture({
 
   // Check if system audio capture is supported
   useEffect(() => {
-    const supported = platform.audio.isSystemAudioSupported();
-    setIsSupported(supported);
+    let isActive = true;
+
+    void platform.audio
+      .isSystemAudioSupported()
+      .then((supported) => {
+        if (isActive) {
+          setIsSupported(supported);
+        }
+      })
+      .catch(() => {
+        if (isActive) {
+          setIsSupported(false);
+        }
+      });
+
+    return () => {
+      isActive = false;
+    };
   }, [platform]);
 
   const startRecording = useCallback(async () => {

app/src/lib/queryClient.ts

@@ -0,0 +1,19 @@
+import { QueryClient } from '@tanstack/react-query';
+
+/**
+ * Shared QueryClient instance used across the app.
+ *
+ * Extracted into its own side-effect-free module so it can be imported from
+ * both the React bootstrap (main.tsx) and non-React code (stores, utilities)
+ * without pulling in ReactDOM or other bootstrap side effects.
+ */
+export const queryClient = new QueryClient({
+  defaultOptions: {
+    queries: {
+      staleTime: 1000 * 60 * 5, // 5 minutes
+      gcTime: 1000 * 60 * 10, // 10 minutes (formerly cacheTime)
+      retry: 1,
+      refetchOnWindowFocus: false,
+    },
+  },
+});

app/src/main.tsx

@@ -1,20 +1,10 @@
-import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import { QueryClientProvider } from '@tanstack/react-query';
 // import { ReactQueryDevtools } from '@tanstack/react-query-devtools';
 import React from 'react';
 import ReactDOM from 'react-dom/client';
 import App from './App';
 import './index.css';
-
-const queryClient = new QueryClient({
-  defaultOptions: {
-    queries: {
-      staleTime: 1000 * 60 * 5, // 5 minutes
-      gcTime: 1000 * 60 * 10, // 10 minutes (formerly cacheTime)
-      retry: 1,
-      refetchOnWindowFocus: false,
-    },
-  },
-});
+import { queryClient } from './lib/queryClient';
 
 ReactDOM.createRoot(document.getElementById('root')!).render(
   <React.StrictMode>

app/src/platform/PlatformContext.tsx

@@ -9,11 +9,7 @@ export interface PlatformProviderProps {
 }
 
 export function PlatformProvider({ platform, children }: PlatformProviderProps) {
-  return (
-    <PlatformContext.Provider value={platform}>
-      {children}
-    </PlatformContext.Provider>
-  );
+  return <PlatformContext.Provider value={platform}>{children}</PlatformContext.Provider>;
 }
 
 export function usePlatform(): Platform {

app/src/platform/types.ts

@@ -42,7 +42,7 @@ export interface AudioDevice {
 }
 
 export interface PlatformAudio {
-  isSystemAudioSupported(): boolean;
+  isSystemAudioSupported(): Promise<boolean>;
   startSystemAudioCapture(maxDurationSecs: number): Promise<void>;
   stopSystemAudioCapture(): Promise<Blob>;
   listOutputDevices(): Promise<AudioDevice[]>;

app/src/stores/serverStore.ts

@@ -1,5 +1,6 @@
 import { create } from 'zustand';
 import { persist } from 'zustand/middleware';
+import { queryClient } from '@/lib/queryClient';
 
 interface ServerStore {
   serverUrl: string;
@@ -30,11 +31,25 @@ interface ServerStore {
   setCustomModelsDir: (dir: string | null) => void;
 }
 
+/**
+ * Invalidate all React Query caches so stale data from the previous
+ * server is not shown. Called when the server URL changes.
+ */
+function invalidateAllServerData() {
+  queryClient.invalidateQueries();
+}
+
 export const useServerStore = create<ServerStore>()(
   persist(
-    (set) => ({
+    (set, get) => ({
       serverUrl: 'http://127.0.0.1:17493',
-      setServerUrl: (url) => set({ serverUrl: url }),
+      setServerUrl: (url) => {
+        const prev = get().serverUrl;
+        set({ serverUrl: url });
+        if (url !== prev) {
+          invalidateAllServerData();
+        }
+      },
 
       isConnected: false,
       setIsConnected: (connected) => set({ isConnected: connected }),

app/src/stores/uiStore.ts

@@ -31,6 +31,10 @@ interface UIStore {
   selectedProfileId: string | null;
   setSelectedProfileId: (id: string | null) => void;
 
+  // Currently selected engine (synced from generation form)
+  selectedEngine: string;
+  setSelectedEngine: (engine: string) => void;
+
   // Selected voice in Voices tab inspector
   selectedVoiceId: string | null;
   setSelectedVoiceId: (id: string | null) => void;
@@ -59,6 +63,9 @@ export const useUIStore = create<UIStore>((set) => ({
   selectedProfileId: null,
   setSelectedProfileId: (id) => set({ selectedProfileId: id }),
 
+  selectedEngine: 'qwen',
+  setSelectedEngine: (engine) => set({ selectedEngine: engine }),
+
   selectedVoiceId: null,
   setSelectedVoiceId: (id) => set({ selectedVoiceId: id }),
 

backend/__init__.py

@@ -1,3 +1,3 @@
 # Backend package
 
-__version__ = "0.2.3"
+__version__ = "0.4.1"

backend/app.py

@@ -135,7 +135,7 @@ def _mount_frontend(application: FastAPI) -> None:
     async def serve_spa(full_path: str):
         file_path = (frontend_dir / full_path).resolve()
         # Guard against path traversal — only serve files inside frontend_dir
-        if full_path and file_path.is_file() and str(file_path).startswith(str(frontend_dir)):
+        if full_path and file_path.is_file() and file_path.is_relative_to(frontend_dir):
             return FileResponse(file_path)
         return FileResponse(frontend_dir / "index.html", media_type="text/html")
 
@@ -146,15 +146,36 @@ def _get_gpu_status() -> str:
     """Return a human-readable string describing GPU availability."""
     backend_type = get_backend_type()
     if torch.cuda.is_available():
+        from .backends.base import check_cuda_compatibility
+
         device_name = torch.cuda.get_device_name(0)
+        compatible, _warning = check_cuda_compatibility()
         is_rocm = hasattr(torch.version, "hip") and torch.version.hip is not None
         if is_rocm:
-            return f"ROCm ({device_name})"
-        return f"CUDA ({device_name})"
+            label = f"ROCm ({device_name})"
+        else:
+            label = f"CUDA ({device_name})"
+        if not compatible:
+            label += " [UNSUPPORTED - see logs]"
+        return label
     elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
         return "MPS (Apple Silicon)"
     elif backend_type == "mlx":
         return "Metal (Apple Silicon via MLX)"
+
+    # Intel XPU (Arc / Data Center) via IPEX
+    try:
+        import intel_extension_for_pytorch  # noqa: F401
+
+        if hasattr(torch, "xpu") and torch.xpu.is_available():
+            try:
+                xpu_name = torch.xpu.get_device_name(0)
+            except Exception:
+                xpu_name = "Intel GPU"
+            return f"XPU ({xpu_name})"
+    except ImportError:
+        pass
+
     return "None (CPU only)"
 
 
@@ -216,6 +237,13 @@ def _register_lifecycle(application: FastAPI) -> None:
         logger.info("Backend: %s", backend_type.upper())
         logger.info("GPU: %s", _get_gpu_status())
 
+        # Warn if GPU architecture is not supported by this PyTorch build
+        from .backends.base import check_cuda_compatibility
+
+        _compatible, _cuda_warning = check_cuda_compatibility()
+        if not _compatible:
+            logger.warning("GPU COMPATIBILITY: %s", _cuda_warning)
+
         from .services.cuda import check_and_update_cuda_binary
 
         create_background_task(check_and_update_cuda_binary())

backend/backends/__init__.py

@@ -163,9 +163,12 @@ _stt_backend: Optional[STTBackend] = None
 # The factory function uses this for the if/elif chain; the model configs live on the backend classes.
 TTS_ENGINES = {
     "qwen": "Qwen TTS",
+    "qwen_custom_voice": "Qwen CustomVoice",
     "luxtts": "LuxTTS",
     "chatterbox": "Chatterbox TTS",
     "chatterbox_turbo": "Chatterbox Turbo",
+    "tada": "TADA",
+    "kokoro": "Kokoro",
 }
 
 
@@ -203,6 +206,32 @@ def _get_qwen_model_configs() -> list[ModelConfig]:
     ]
 
 
+def _get_qwen_custom_voice_configs() -> list[ModelConfig]:
+    """Return Qwen CustomVoice model configs."""
+    return [
+        ModelConfig(
+            model_name="qwen-custom-voice-1.7B",
+            display_name="Qwen CustomVoice 1.7B",
+            engine="qwen_custom_voice",
+            hf_repo_id="Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
+            model_size="1.7B",
+            size_mb=3500,
+            supports_instruct=True,
+            languages=["zh", "en", "ja", "ko", "de", "fr", "ru", "pt", "es", "it"],
+        ),
+        ModelConfig(
+            model_name="qwen-custom-voice-0.6B",
+            display_name="Qwen CustomVoice 0.6B",
+            engine="qwen_custom_voice",
+            hf_repo_id="Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice",
+            model_size="0.6B",
+            size_mb=1200,
+            supports_instruct=True,
+            languages=["zh", "en", "ja", "ko", "de", "fr", "ru", "pt", "es", "it"],
+        ),
+    ]
+
+
 def _get_non_qwen_tts_configs() -> list[ModelConfig]:
     """Return model configs for non-Qwen TTS engines.
 
@@ -259,6 +288,32 @@ def _get_non_qwen_tts_configs() -> list[ModelConfig]:
             needs_trim=True,
             languages=["en"],
         ),
+        ModelConfig(
+            model_name="tada-1b",
+            display_name="TADA 1B (English)",
+            engine="tada",
+            hf_repo_id="HumeAI/tada-1b",
+            model_size="1B",
+            size_mb=4000,
+            languages=["en"],
+        ),
+        ModelConfig(
+            model_name="tada-3b-ml",
+            display_name="TADA 3B Multilingual",
+            engine="tada",
+            hf_repo_id="HumeAI/tada-3b-ml",
+            model_size="3B",
+            size_mb=8000,
+            languages=["en", "ar", "zh", "de", "es", "fr", "it", "ja", "pl", "pt"],
+        ),
+        ModelConfig(
+            model_name="kokoro",
+            display_name="Kokoro 82M",
+            engine="kokoro",
+            hf_repo_id="hexgrad/Kokoro-82M",
+            size_mb=350,
+            languages=["en", "es", "fr", "hi", "it", "pt", "ja", "zh"],
+        ),
     ]
 
 
@@ -305,12 +360,12 @@ def _get_whisper_configs() -> list[ModelConfig]:
 
 def get_all_model_configs() -> list[ModelConfig]:
     """Return the full list of model configs (TTS + STT)."""
-    return _get_qwen_model_configs() + _get_non_qwen_tts_configs() + _get_whisper_configs()
+    return _get_qwen_model_configs() + _get_qwen_custom_voice_configs() + _get_non_qwen_tts_configs() + _get_whisper_configs()
 
 
 def get_tts_model_configs() -> list[ModelConfig]:
     """Return only TTS model configs."""
-    return _get_qwen_model_configs() + _get_non_qwen_tts_configs()
+    return _get_qwen_model_configs() + _get_qwen_custom_voice_configs() + _get_non_qwen_tts_configs()
 
 
 # Lookup helpers — these replace the if/elif chains in main.py
@@ -339,10 +394,12 @@ def engine_has_model_sizes(engine: str) -> bool:
 
 
 async def load_engine_model(engine: str, model_size: str = "default") -> None:
-    """Load a model for the given engine, handling the Qwen model_size special case."""
+    """Load a model for the given engine, handling engines with multiple model sizes."""
     backend = get_tts_backend_for_engine(engine)
-    if engine == "qwen":
+    if engine in ("qwen", "qwen_custom_voice"):
         await backend.load_model_async(model_size)
+    elif engine == "tada":
+        await backend.load_model(model_size)
     else:
         await backend.load_model()
 
@@ -358,7 +415,7 @@ async def ensure_model_cached_or_raise(engine: str, model_size: str = "default")
             cfg = c
             break
 
-    if engine == "qwen":
+    if engine in ("qwen", "qwen_custom_voice", "tada"):
         if not backend._is_model_cached(model_size):
             raise HTTPException(
                 status_code=400,
@@ -393,6 +450,14 @@ def unload_model_by_config(config: ModelConfig) -> bool:
             return True
         return False
 
+    if config.engine == "qwen_custom_voice":
+        backend = get_tts_backend_for_engine(config.engine)
+        loaded_size = getattr(backend, "_current_model_size", None) or getattr(backend, "model_size", None)
+        if backend.is_loaded() and loaded_size == config.model_size:
+            backend.unload_model()
+            return True
+        return False
+
     # All other TTS engines
     backend = get_tts_backend_for_engine(config.engine)
     if backend.is_loaded():
@@ -416,6 +481,11 @@ def check_model_loaded(config: ModelConfig) -> bool:
             loaded_size = getattr(tts_model, "_current_model_size", None) or getattr(tts_model, "model_size", None)
             return tts_model.is_loaded() and loaded_size == config.model_size
 
+        if config.engine == "qwen_custom_voice":
+            backend = get_tts_backend_for_engine(config.engine)
+            loaded_size = getattr(backend, "_current_model_size", None) or getattr(backend, "model_size", None)
+            return backend.is_loaded() and loaded_size == config.model_size
+
         backend = get_tts_backend_for_engine(config.engine)
         return backend.is_loaded()
     except Exception:
@@ -433,6 +503,9 @@ def get_model_load_func(config: ModelConfig):
     if config.engine == "qwen":
         return lambda: tts.get_tts_model().load_model(config.model_size)
 
+    if config.engine == "qwen_custom_voice":
+        return lambda: get_tts_backend_for_engine(config.engine).load_model(config.model_size)
+
     return lambda: get_tts_backend_for_engine(config.engine).load_model()
 
 
@@ -490,6 +563,18 @@ def get_tts_backend_for_engine(engine: str) -> TTSBackend:
             from .chatterbox_turbo_backend import ChatterboxTurboTTSBackend
 
             backend = ChatterboxTurboTTSBackend()
+        elif engine == "tada":
+            from .hume_backend import HumeTadaBackend
+
+            backend = HumeTadaBackend()
+        elif engine == "kokoro":
+            from .kokoro_backend import KokoroTTSBackend
+
+            backend = KokoroTTSBackend()
+        elif engine == "qwen_custom_voice":
+            from .qwen_custom_voice_backend import QwenCustomVoiceBackend
+
+            backend = QwenCustomVoiceBackend()
         else:
             raise ValueError(f"Unknown TTS engine: {engine}. Supported: {list(TTS_ENGINES.keys())}")
 

backend/backends/base.py

@@ -126,6 +126,75 @@ def get_torch_device(
     return "cpu"
 
 
+def check_cuda_compatibility() -> tuple[bool, str | None]:
+    """Check if the installed PyTorch supports the current GPU's compute capability.
+
+    Returns:
+        (compatible, warning_message) — compatible is True if OK or no CUDA GPU,
+        warning_message is a human-readable string if there's a problem.
+    """
+    import torch
+
+    if not torch.cuda.is_available():
+        return True, None
+
+    major, minor = torch.cuda.get_device_capability(0)
+    capability = f"{major}.{minor}"
+    device_name = torch.cuda.get_device_name(0)
+    sm_tag = f"sm_{major}{minor}"
+
+    # torch.cuda._get_arch_list() returns the SM architectures this build
+    # was compiled for (e.g. ["sm_50", "sm_60", ..., "sm_90"]).
+    try:
+        arch_list = torch.cuda._get_arch_list()
+        if arch_list:
+            # Check for both sm_XX and compute_XX (JIT-compiled) entries
+            compute_tag = f"compute_{major}{minor}"
+            if sm_tag not in arch_list and compute_tag not in arch_list:
+                return False, (
+                    f"{device_name} (compute capability {capability} / {sm_tag}) "
+                    f"is not supported by this PyTorch build. "
+                    f"Supported architectures: {', '.join(arch_list)}. "
+                    f"Install PyTorch nightly (cu128) for newer GPU support: "
+                    f"pip install torch --index-url https://download.pytorch.org/whl/nightly/cu128"
+                )
+    except AttributeError:
+        pass
+
+    return True, None
+
+
+def empty_device_cache(device: str) -> None:
+    """
+    Free cached memory on the given device (CUDA or XPU).
+
+    Backends should call this after unloading models so VRAM is returned
+    to the OS.
+    """
+    import torch
+
+    if device == "cuda" and torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    elif device == "xpu" and hasattr(torch, "xpu"):
+        torch.xpu.empty_cache()
+
+
+def manual_seed(seed: int, device: str) -> None:
+    """
+    Set the random seed on both CPU and the active accelerator.
+
+    Covers CUDA and Intel XPU so that generation is reproducible
+    regardless of which GPU backend is in use.
+    """
+    import torch
+
+    torch.manual_seed(seed)
+    if device == "cuda" and torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+    elif device == "xpu" and hasattr(torch, "xpu"):
+        torch.xpu.manual_seed(seed)
+
+
 async def combine_voice_prompts(
     audio_paths: List[str],
     reference_texts: List[str],

backend/backends/chatterbox_backend.py

@@ -18,6 +18,8 @@ from . import TTSBackend
 from .base import (
     is_model_cached,
     get_torch_device,
+    empty_device_cache,
+    manual_seed,
     combine_voice_prompts as _combine_voice_prompts,
     model_load_progress,
     patch_chatterbox_f32,
@@ -48,7 +50,7 @@ class ChatterboxTTSBackend:
         self._model_load_lock = asyncio.Lock()
 
     def _get_device(self) -> str:
-        return get_torch_device(force_cpu_on_mac=True)
+        return get_torch_device(force_cpu_on_mac=True, allow_xpu=True)
 
     def is_loaded(self) -> bool:
         return self.model is not None
@@ -117,10 +119,7 @@ class ChatterboxTTSBackend:
             del self.model
             self.model = None
             self._device = None
-            if device == "cuda":
-                import torch
-
-                torch.cuda.empty_cache()
+            empty_device_cache(device)
             logger.info("Chatterbox unloaded")
 
     async def create_voice_prompt(
@@ -200,7 +199,7 @@ class ChatterboxTTSBackend:
             import torch
 
             if seed is not None:
-                torch.manual_seed(seed)
+                manual_seed(seed, self._device)
 
             logger.info(f"[Chatterbox] Generating: lang={language}")
 
@@ -220,10 +219,7 @@ class ChatterboxTTSBackend:
             else:
                 audio = np.asarray(wav, dtype=np.float32)
 
-            sample_rate = (
-                getattr(self.model, "sr", None)
-                or getattr(self.model, "sample_rate", 24000)
-            )
+            sample_rate = getattr(self.model, "sr", None) or getattr(self.model, "sample_rate", 24000)
 
             return audio, sample_rate
 

backend/backends/chatterbox_turbo_backend.py

@@ -18,6 +18,8 @@ from . import TTSBackend
 from .base import (
     is_model_cached,
     get_torch_device,
+    empty_device_cache,
+    manual_seed,
     combine_voice_prompts as _combine_voice_prompts,
     model_load_progress,
     patch_chatterbox_f32,
@@ -48,7 +50,7 @@ class ChatterboxTurboTTSBackend:
         self._model_load_lock = asyncio.Lock()
 
     def _get_device(self) -> str:
-        return get_torch_device(force_cpu_on_mac=True)
+        return get_torch_device(force_cpu_on_mac=True, allow_xpu=True)
 
     def is_loaded(self) -> bool:
         return self.model is not None
@@ -116,10 +118,7 @@ class ChatterboxTurboTTSBackend:
             del self.model
             self.model = None
             self._device = None
-            if device == "cuda":
-                import torch
-
-                torch.cuda.empty_cache()
+            empty_device_cache(device)
             logger.info("Chatterbox Turbo unloaded")
 
     async def create_voice_prompt(
@@ -181,7 +180,7 @@ class ChatterboxTurboTTSBackend:
             import torch
 
             if seed is not None:
-                torch.manual_seed(seed)
+                manual_seed(seed, self._device)
 
             logger.info("[Chatterbox Turbo] Generating (English)")
 
@@ -200,10 +199,7 @@ class ChatterboxTurboTTSBackend:
             else:
                 audio = np.asarray(wav, dtype=np.float32)
 
-            sample_rate = (
-                getattr(self.model, "sr", None)
-                or getattr(self.model, "sample_rate", 24000)
-            )
+            sample_rate = getattr(self.model, "sr", None) or getattr(self.model, "sample_rate", 24000)
 
             return audio, sample_rate
 

backend/backends/hume_backend.py

@@ -0,0 +1,346 @@
+"""
+HumeAI TADA TTS backend implementation.
+
+Wraps HumeAI's TADA (Text-Acoustic Dual Alignment) model for
+high-quality voice cloning. Two model variants:
+  - tada-1b: English-only, ~2B params (Llama 3.2 1B base)
+  - tada-3b-ml: Multilingual, ~4B params (Llama 3.2 3B base)
+
+Both use a shared encoder/codec (HumeAI/tada-codec). The encoder
+produces 1:1 aligned token embeddings from reference audio, and the
+causal LM generates speech via flow-matching diffusion.
+
+24kHz output, bf16 inference on CUDA, fp32 on CPU.
+"""
+
+import asyncio
+import logging
+import threading
+from typing import ClassVar, List, Optional, Tuple
+
+import numpy as np
+
+from . import TTSBackend
+from .base import (
+    is_model_cached,
+    get_torch_device,
+    empty_device_cache,
+    manual_seed,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+)
+from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
+
+logger = logging.getLogger(__name__)
+
+# HuggingFace repos
+TADA_CODEC_REPO = "HumeAI/tada-codec"
+TADA_1B_REPO = "HumeAI/tada-1b"
+TADA_3B_ML_REPO = "HumeAI/tada-3b-ml"
+
+TADA_MODEL_REPOS = {
+    "1B": TADA_1B_REPO,
+    "3B": TADA_3B_ML_REPO,
+}
+
+# Key weight files for cache detection
+_TADA_MODEL_WEIGHT_FILES = [
+    "model.safetensors",
+]
+
+_TADA_CODEC_WEIGHT_FILES = [
+    "encoder/model.safetensors",
+]
+
+
+class HumeTadaBackend:
+    """HumeAI TADA TTS backend for high-quality voice cloning."""
+
+    _load_lock: ClassVar[threading.Lock] = threading.Lock()
+
+    def __init__(self):
+        self.model = None
+        self.encoder = None
+        self.model_size = "1B"  # default to 1B
+        self._device = None
+        self._model_load_lock = asyncio.Lock()
+
+    def _get_device(self) -> str:
+        # Force CPU on macOS — MPS has issues with flow matching
+        # and large vocab lm_head (>65536 output channels)
+        return get_torch_device(force_cpu_on_mac=True, allow_xpu=True)
+
+    def is_loaded(self) -> bool:
+        return self.model is not None
+
+    def _get_model_path(self, model_size: str = "1B") -> str:
+        return TADA_MODEL_REPOS.get(model_size, TADA_1B_REPO)
+
+    def _is_model_cached(self, model_size: str = "1B") -> bool:
+        repo = TADA_MODEL_REPOS.get(model_size, TADA_1B_REPO)
+        model_cached = is_model_cached(repo, required_files=_TADA_MODEL_WEIGHT_FILES)
+        codec_cached = is_model_cached(TADA_CODEC_REPO, required_files=_TADA_CODEC_WEIGHT_FILES)
+        return model_cached and codec_cached
+
+    async def load_model(self, model_size: str = "1B") -> None:
+        """Load the TADA model and encoder."""
+        if self.model is not None and self.model_size == model_size:
+            return
+        async with self._model_load_lock:
+            if self.model is not None and self.model_size == model_size:
+                return
+            # Unload existing model if switching sizes
+            if self.model is not None:
+                self.unload_model()
+            self.model_size = model_size
+            await asyncio.to_thread(self._load_model_sync, model_size)
+
+    def _load_model_sync(self, model_size: str = "1B"):
+        """Synchronous model loading with progress tracking."""
+        model_name = f"tada-{model_size.lower()}"
+        is_cached = self._is_model_cached(model_size)
+        repo = TADA_MODEL_REPOS.get(model_size, TADA_1B_REPO)
+
+        with model_load_progress(model_name, is_cached):
+            # Install DAC shim before importing tada — tada's encoder/decoder
+            # import dac.nn.layers.Snake1d which requires the descript-audio-codec
+            # package.  The real package pulls in onnx/tensorboard/matplotlib via
+            # descript-audiotools, so we use a lightweight shim instead.
+            from ..utils.dac_shim import install_dac_shim
+
+            install_dac_shim()
+
+            import torch
+            from huggingface_hub import snapshot_download
+
+            device = self._get_device()
+            self._device = device
+            logger.info(f"Loading HumeAI TADA {model_size} on {device}...")
+
+            # Download codec (encoder + decoder) if not cached
+            logger.info("Downloading TADA codec...")
+            snapshot_download(
+                repo_id=TADA_CODEC_REPO,
+                token=None,
+                allow_patterns=["*.safetensors", "*.json", "*.txt", "*.bin"],
+            )
+
+            # Download model weights if not cached
+            logger.info(f"Downloading TADA {model_size} model...")
+            snapshot_download(
+                repo_id=repo,
+                token=None,
+                allow_patterns=["*.safetensors", "*.json", "*.txt", "*.bin", "*.model"],
+            )
+
+            # TADA hardcodes "meta-llama/Llama-3.2-1B" as the tokenizer
+            # source in its Aligner and TadaForCausalLM.from_pretrained().
+            # That repo is gated (requires Meta license acceptance).
+            # Download the tokenizer from an ungated mirror and get its
+            # local cache path so we can point TADA at it directly.
+            logger.info("Downloading Llama tokenizer (ungated mirror)...")
+            tokenizer_path = snapshot_download(
+                repo_id="unsloth/Llama-3.2-1B",
+                token=None,
+                allow_patterns=["tokenizer*", "special_tokens*"],
+            )
+
+            # Determine dtype — use bf16 on CUDA/XPU for ~50% memory savings
+            if device == "cuda" and torch.cuda.is_bf16_supported():
+                model_dtype = torch.bfloat16
+            elif device == "xpu":
+                # Intel Arc (Alchemist+) supports bf16 natively
+                model_dtype = torch.bfloat16
+            else:
+                model_dtype = torch.float32
+
+            # Patch the Aligner config class to use the local tokenizer
+            # path instead of the gated "meta-llama/Llama-3.2-1B" default.
+            # This avoids monkey-patching AutoTokenizer.from_pretrained
+            # which corrupts the classmethod descriptor for other engines.
+            from tada.modules.aligner import AlignerConfig
+
+            AlignerConfig.tokenizer_name = tokenizer_path
+
+            # Load encoder (only needed for voice prompt encoding)
+            from tada.modules.encoder import Encoder
+
+            logger.info("Loading TADA encoder...")
+            self.encoder = Encoder.from_pretrained(TADA_CODEC_REPO, subfolder="encoder").to(device)
+            self.encoder.eval()
+
+            # Load the causal LM (includes decoder for wav generation).
+            # TadaForCausalLM.from_pretrained() calls
+            #   getattr(config, "tokenizer_name", "meta-llama/Llama-3.2-1B")
+            # which hits the gated repo. Pre-load the config from HF,
+            # inject the local tokenizer path, then pass it in.
+            from tada.modules.tada import TadaForCausalLM, TadaConfig
+
+            logger.info(f"Loading TADA {model_size} model...")
+            config = TadaConfig.from_pretrained(repo)
+            config.tokenizer_name = tokenizer_path
+            self.model = TadaForCausalLM.from_pretrained(repo, config=config, torch_dtype=model_dtype).to(device)
+            self.model.eval()
+
+        logger.info(f"HumeAI TADA {model_size} loaded successfully on {device}")
+
+    def unload_model(self) -> None:
+        """Unload model and encoder to free memory."""
+        if self.model is not None:
+            del self.model
+            self.model = None
+        if self.encoder is not None:
+            del self.encoder
+            self.encoder = None
+
+        device = self._device
+        self._device = None
+
+        if device:
+            empty_device_cache(device)
+
+        logger.info("HumeAI TADA unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> Tuple[dict, bool]:
+        """
+        Create voice prompt from reference audio using TADA's encoder.
+
+        TADA's encoder performs forced alignment between audio and text tokens,
+        producing an EncoderOutput with 1:1 token-audio alignment. If no
+        reference_text is provided, the encoder uses built-in ASR (English only).
+
+        We serialize the EncoderOutput to a dict for caching.
+        """
+        await self.load_model(self.model_size)
+
+        cache_key = ("tada_" + get_cache_key(audio_path, reference_text)) if use_cache else None
+
+        if cache_key:
+            cached = get_cached_voice_prompt(cache_key)
+            if cached is not None and isinstance(cached, dict):
+                return cached, True
+
+        def _encode_sync():
+            import torch
+            import soundfile as sf
+
+            device = self._device
+
+            # Load audio with soundfile (torchaudio 2.10+ requires torchcodec)
+            audio_np, sr = sf.read(str(audio_path), dtype="float32")
+            audio = torch.from_numpy(audio_np).float()
+            if audio.ndim == 1:
+                audio = audio.unsqueeze(0)  # (samples,) -> (1, samples)
+            else:
+                audio = audio.T  # (samples, channels) -> (channels, samples)
+            audio = audio.to(device)
+
+            # Encode with forced alignment
+            text_arg = [reference_text] if reference_text else None
+            prompt = self.encoder(audio, text=text_arg, sample_rate=sr)
+
+            # Serialize EncoderOutput to a dict of CPU tensors for caching
+            prompt_dict = {}
+            for field_name in prompt.__dataclass_fields__:
+                val = getattr(prompt, field_name)
+                if isinstance(val, torch.Tensor):
+                    prompt_dict[field_name] = val.detach().cpu()
+                elif isinstance(val, list):
+                    prompt_dict[field_name] = val
+                elif isinstance(val, (int, float)):
+                    prompt_dict[field_name] = val
+                else:
+                    prompt_dict[field_name] = val
+            return prompt_dict
+
+        encoded = await asyncio.to_thread(_encode_sync)
+
+        if cache_key:
+            cache_voice_prompt(cache_key, encoded)
+
+        return encoded, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: List[str],
+        reference_texts: List[str],
+    ) -> Tuple[np.ndarray, str]:
+        return await _combine_voice_prompts(audio_paths, reference_texts, sample_rate=24000)
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> Tuple[np.ndarray, int]:
+        """
+        Generate audio from text using HumeAI TADA.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Serialized EncoderOutput dict from create_voice_prompt()
+            language: Language code (en, ar, de, es, fr, it, ja, pl, pt, zh)
+            seed: Random seed for reproducibility
+            instruct: Not supported by TADA (ignored)
+
+        Returns:
+            Tuple of (audio_array, sample_rate=24000)
+        """
+        await self.load_model(self.model_size)
+
+        def _generate_sync():
+            import torch
+            from tada.modules.encoder import EncoderOutput
+
+            if seed is not None:
+                manual_seed(seed, self._device)
+
+            device = self._device
+
+            # Reconstruct EncoderOutput from the cached dict
+            restored = {}
+            for k, v in voice_prompt.items():
+                if isinstance(v, torch.Tensor):
+                    # Move to device and match model dtype for float tensors
+                    if v.is_floating_point():
+                        model_dtype = next(self.model.parameters()).dtype
+                        restored[k] = v.to(device=device, dtype=model_dtype)
+                    else:
+                        restored[k] = v.to(device=device)
+                else:
+                    restored[k] = v
+
+            prompt = EncoderOutput(**restored)
+
+            # For non-English with the 3B-ML model, we could reload the
+            # encoder with the language-specific aligner. However, the
+            # generation itself is language-agnostic — only the encoder's
+            # aligner changes. Since we encode at create_voice_prompt time,
+            # the language is already baked in. For simplicity, we don't
+            # reload the encoder here.
+
+            logger.info(f"[TADA] Generating ({language}), text length: {len(text)}")
+
+            output = self.model.generate(
+                prompt=prompt,
+                text=text,
+            )
+
+            # output.audio is a list of tensors (one per batch item)
+            if output.audio and output.audio[0] is not None:
+                audio_tensor = output.audio[0]
+                audio = audio_tensor.detach().cpu().numpy().squeeze().astype(np.float32)
+            else:
+                logger.warning("[TADA] Generation produced no audio")
+                audio = np.zeros(24000, dtype=np.float32)
+
+            return audio, 24000
+
+        return await asyncio.to_thread(_generate_sync)

backend/backends/kokoro_backend.py

@@ -0,0 +1,288 @@
+"""
+Kokoro TTS backend implementation.
+
+Wraps the Kokoro-82M model for fast, lightweight text-to-speech.
+82M parameters, CPU realtime, 24kHz output, Apache 2.0 license.
+
+Kokoro uses pre-built voice style vectors (not traditional zero-shot cloning
+from arbitrary audio). Voice prompts are stored as deferred references to
+HF-hosted voice .pt files.
+
+Languages supported (via misaki G2P):
+  - American English (a), British English (b)
+  - Spanish (e), French (f), Hindi (h), Italian (i), Portuguese (p)
+  - Japanese (j) — requires misaki[ja]
+  - Chinese (z) — requires misaki[zh]
+"""
+
+import asyncio
+import logging
+import os
+from typing import Optional
+
+import numpy as np
+
+from . import TTSBackend
+from .base import (
+    get_torch_device,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+)
+
+logger = logging.getLogger(__name__)
+
+# HuggingFace repo for model + voice detection
+KOKORO_HF_REPO = "hexgrad/Kokoro-82M"
+KOKORO_SAMPLE_RATE = 24000
+
+# Default voice if none specified
+KOKORO_DEFAULT_VOICE = "af_heart"
+
+# All available Kokoro voices: (voice_id, display_name, gender, lang_code)
+KOKORO_VOICES = [
+    # American English female
+    ("af_alloy", "Alloy", "female", "en"),
+    ("af_aoede", "Aoede", "female", "en"),
+    ("af_bella", "Bella", "female", "en"),
+    ("af_heart", "Heart", "female", "en"),
+    ("af_jessica", "Jessica", "female", "en"),
+    ("af_kore", "Kore", "female", "en"),
+    ("af_nicole", "Nicole", "female", "en"),
+    ("af_nova", "Nova", "female", "en"),
+    ("af_river", "River", "female", "en"),
+    ("af_sarah", "Sarah", "female", "en"),
+    ("af_sky", "Sky", "female", "en"),
+    # American English male
+    ("am_adam", "Adam", "male", "en"),
+    ("am_echo", "Echo", "male", "en"),
+    ("am_eric", "Eric", "male", "en"),
+    ("am_fenrir", "Fenrir", "male", "en"),
+    ("am_liam", "Liam", "male", "en"),
+    ("am_michael", "Michael", "male", "en"),
+    ("am_onyx", "Onyx", "male", "en"),
+    ("am_puck", "Puck", "male", "en"),
+    ("am_santa", "Santa", "male", "en"),
+    # British English female
+    ("bf_alice", "Alice", "female", "en"),
+    ("bf_emma", "Emma", "female", "en"),
+    ("bf_isabella", "Isabella", "female", "en"),
+    ("bf_lily", "Lily", "female", "en"),
+    # British English male
+    ("bm_daniel", "Daniel", "male", "en"),
+    ("bm_fable", "Fable", "male", "en"),
+    ("bm_george", "George", "male", "en"),
+    ("bm_lewis", "Lewis", "male", "en"),
+    # Spanish
+    ("ef_dora", "Dora", "female", "es"),
+    ("em_alex", "Alex", "male", "es"),
+    ("em_santa", "Santa", "male", "es"),
+    # French
+    ("ff_siwis", "Siwis", "female", "fr"),
+    # Hindi
+    ("hf_alpha", "Alpha", "female", "hi"),
+    ("hf_beta", "Beta", "female", "hi"),
+    ("hm_omega", "Omega", "male", "hi"),
+    ("hm_psi", "Psi", "male", "hi"),
+    # Italian
+    ("if_sara", "Sara", "female", "it"),
+    ("im_nicola", "Nicola", "male", "it"),
+    # Japanese
+    ("jf_alpha", "Alpha", "female", "ja"),
+    ("jf_gongitsune", "Gongitsune", "female", "ja"),
+    ("jf_nezumi", "Nezumi", "female", "ja"),
+    ("jf_tebukuro", "Tebukuro", "female", "ja"),
+    ("jm_kumo", "Kumo", "male", "ja"),
+    # Portuguese
+    ("pf_dora", "Dora", "female", "pt"),
+    ("pm_alex", "Alex", "male", "pt"),
+    ("pm_santa", "Santa", "male", "pt"),
+    # Chinese
+    ("zf_xiaobei", "Xiaobei", "female", "zh"),
+    ("zf_xiaoni", "Xiaoni", "female", "zh"),
+    ("zf_xiaoxiao", "Xiaoxiao", "female", "zh"),
+    ("zf_xiaoyi", "Xiaoyi", "female", "zh"),
+]
+
+# Map our ISO language codes to Kokoro lang_code characters
+LANG_CODE_MAP = {
+    "en": "a",  # American English
+    "es": "e",
+    "fr": "f",
+    "hi": "h",
+    "it": "i",
+    "pt": "p",
+    "ja": "j",
+    "zh": "z",
+}
+
+
+class KokoroTTSBackend:
+    """Kokoro-82M TTS backend — tiny, fast, CPU-friendly."""
+
+    def __init__(self):
+        self._model = None
+        self._pipelines: dict = {}  # lang_code -> KPipeline
+        self._device: Optional[str] = None
+        self.model_size = "default"
+
+    def _get_device(self) -> str:
+        """Select device. Kokoro supports CUDA and CPU. MPS needs fallback env var."""
+        device = get_torch_device(allow_mps=False)
+        # Kokoro can use MPS but requires PYTORCH_ENABLE_MPS_FALLBACK=1
+        # For now, skip MPS to avoid user confusion — CPU is already realtime
+        return device
+
+    @property
+    def device(self) -> str:
+        if self._device is None:
+            self._device = self._get_device()
+        return self._device
+
+    def is_loaded(self) -> bool:
+        return self._model is not None
+
+    def _get_model_path(self, model_size: str) -> str:
+        return KOKORO_HF_REPO
+
+    def _is_model_cached(self, model_size: str = "default") -> bool:
+        """Check if Kokoro model files are cached locally."""
+        from .base import is_model_cached
+
+        return is_model_cached(
+            KOKORO_HF_REPO,
+            required_files=["config.json", "kokoro-v1_0.pth"],
+        )
+
+    async def load_model(self, model_size: str = "default") -> None:
+        """Load the Kokoro model."""
+        if self._model is not None:
+            return
+        await asyncio.to_thread(self._load_model_sync)
+
+    def _load_model_sync(self):
+        """Synchronous model loading."""
+        model_name = "kokoro"
+        is_cached = self._is_model_cached()
+
+        with model_load_progress(model_name, is_cached):
+            from kokoro import KModel
+
+            device = self.device
+            logger.info(f"Loading Kokoro-82M on {device}...")
+
+            self._model = KModel(repo_id=KOKORO_HF_REPO).to(device).eval()
+
+        logger.info("Kokoro-82M loaded successfully")
+
+    def _get_pipeline(self, lang_code: str):
+        """Get or create a KPipeline for the given language code."""
+        kokoro_lang = LANG_CODE_MAP.get(lang_code, "a")
+
+        if kokoro_lang not in self._pipelines:
+            from kokoro import KPipeline
+
+            # Create pipeline with our existing model (no redundant model loading)
+            self._pipelines[kokoro_lang] = KPipeline(
+                lang_code=kokoro_lang,
+                repo_id=KOKORO_HF_REPO,
+                model=self._model,
+            )
+
+        return self._pipelines[kokoro_lang]
+
+    def unload_model(self) -> None:
+        """Unload model to free memory."""
+        if self._model is not None:
+            del self._model
+            self._model = None
+            self._pipelines.clear()
+
+            import torch
+
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+            logger.info("Kokoro unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> tuple[dict, bool]:
+        """
+        Create voice prompt for Kokoro.
+
+        Kokoro doesn't do traditional voice cloning from arbitrary audio.
+        When called for a cloned profile (fallback), uses the default voice.
+        For preset profiles, the voice_prompt dict is built by the profile
+        service and bypasses this method entirely.
+        """
+        return {
+            "voice_type": "preset",
+            "preset_engine": "kokoro",
+            "preset_voice_id": KOKORO_DEFAULT_VOICE,
+        }, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: list[str],
+        reference_texts: list[str],
+    ) -> tuple[np.ndarray, str]:
+        """Combine voice prompts — uses base implementation for audio concatenation."""
+        return await _combine_voice_prompts(
+            audio_paths, reference_texts, sample_rate=KOKORO_SAMPLE_RATE
+        )
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> tuple[np.ndarray, int]:
+        """
+        Generate audio from text using Kokoro.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Dict with kokoro_voice key
+            language: Language code
+            seed: Random seed for reproducibility
+            instruct: Not supported by Kokoro (ignored)
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model()
+
+        voice_name = voice_prompt.get("preset_voice_id") or voice_prompt.get("kokoro_voice") or KOKORO_DEFAULT_VOICE
+
+        def _generate_sync():
+            import torch
+
+            if seed is not None:
+                torch.manual_seed(seed)
+                if torch.cuda.is_available():
+                    torch.cuda.manual_seed(seed)
+
+            pipeline = self._get_pipeline(language)
+
+            # Generate all chunks and concatenate
+            audio_chunks = []
+            for result in pipeline(text, voice=voice_name, speed=1.0):
+                if result.audio is not None:
+                    chunk = result.audio
+                    if isinstance(chunk, torch.Tensor):
+                        chunk = chunk.detach().cpu().numpy()
+                    audio_chunks.append(chunk.squeeze())
+
+            if not audio_chunks:
+                # Return 1 second of silence as fallback
+                return np.zeros(KOKORO_SAMPLE_RATE, dtype=np.float32), KOKORO_SAMPLE_RATE
+
+            audio = np.concatenate(audio_chunks)
+            return audio.astype(np.float32), KOKORO_SAMPLE_RATE
+
+        return await asyncio.to_thread(_generate_sync)

backend/backends/luxtts_backend.py

@@ -12,7 +12,14 @@ from typing import Optional, Tuple
 import numpy as np
 
 from . import TTSBackend
-from .base import is_model_cached, get_torch_device, combine_voice_prompts as _combine_voice_prompts, model_load_progress
+from .base import (
+    is_model_cached,
+    get_torch_device,
+    empty_device_cache,
+    manual_seed,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+)
 from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
 
 logger = logging.getLogger(__name__)
@@ -30,7 +37,7 @@ class LuxTTSBackend:
         self._device = None
 
     def _get_device(self) -> str:
-        return get_torch_device(allow_mps=True)
+        return get_torch_device(allow_mps=True, allow_xpu=True)
 
     def is_loaded(self) -> bool:
         return self.model is not None
@@ -69,9 +76,12 @@ class LuxTTSBackend:
 
             if device == "cpu":
                 import os
+
                 threads = os.cpu_count() or 4
                 self.model = LuxTTS(
-                    model_path=LUXTTS_HF_REPO, device="cpu", threads=min(threads, 8),
+                    model_path=LUXTTS_HF_REPO,
+                    device="cpu",
+                    threads=min(threads, 8),
                 )
             else:
                 self.model = LuxTTS(model_path=LUXTTS_HF_REPO, device=device)
@@ -81,12 +91,12 @@ class LuxTTSBackend:
     def unload_model(self) -> None:
         """Unload model to free memory."""
         if self.model is not None:
+            device = self.device
             del self.model
             self.model = None
+            self._device = None
 
-            import torch
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+            empty_device_cache(device)
 
             logger.info("LuxTTS unloaded")
 
@@ -154,12 +164,8 @@ class LuxTTSBackend:
         await self.load_model()
 
         def _generate_sync():
-            import torch
-
             if seed is not None:
-                torch.manual_seed(seed)
-                if torch.cuda.is_available():
-                    torch.cuda.manual_seed(seed)
+                manual_seed(seed, self.device)
 
             wav = self.model.generate_speech(
                 text=text,

backend/backends/mlx_backend.py

@@ -6,7 +6,6 @@ from typing import Optional, List, Tuple
 import asyncio
 import logging
 import numpy as np
-import os
 from pathlib import Path
 
 logger = logging.getLogger(__name__)
@@ -21,6 +20,7 @@ ensure_original_qwen_config_cached()
 from . import TTSBackend, STTBackend, LANGUAGE_CODE_TO_NAME, WHISPER_HF_REPOS
 from .base import is_model_cached, combine_voice_prompts as _combine_voice_prompts, model_load_progress
 from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
+from ..utils.hf_offline_patch import force_offline_if_cached
 
 
 class MLXTTSBackend:
@@ -96,32 +96,13 @@ class MLXTTSBackend:
         model_name = f"qwen-tts-{model_size}"
         is_cached = self._is_model_cached(model_size)
 
-        # Force offline mode when cached to avoid network requests
-        original_hf_hub_offline = os.environ.get("HF_HUB_OFFLINE")
-        if is_cached:
-            os.environ["HF_HUB_OFFLINE"] = "1"
-            logger.info("[PATCH] Model %s is cached, forcing HF_HUB_OFFLINE=1 to avoid network requests", model_size)
+        with model_load_progress(model_name, is_cached):
+            from mlx_audio.tts import load
 
-        try:
-            with model_load_progress(model_name, is_cached):
-                from mlx_audio.tts import load
+            logger.info("Loading MLX TTS model %s...", model_size)
 
-                logger.info("Loading MLX TTS model %s...", model_size)
-
-                try:
-                    self.model = load(model_path)
-                except Exception as load_error:
-                    if is_cached and "offline" in str(load_error).lower():
-                        logger.warning("[PATCH] Offline load failed, trying with network: %s", load_error)
-                        os.environ.pop("HF_HUB_OFFLINE", None)
-                        self.model = load(model_path)
-                    else:
-                        raise
-        finally:
-            if original_hf_hub_offline is not None:
-                os.environ["HF_HUB_OFFLINE"] = original_hf_hub_offline
-            else:
-                os.environ.pop("HF_HUB_OFFLINE", None)
+            with force_offline_if_cached(is_cached, model_name):
+                self.model = load(model_path)
 
         self._current_model_size = model_size
         self.model_size = model_size
@@ -329,7 +310,9 @@ class MLXSTTBackend:
 
             model_name = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
             logger.info("Loading MLX Whisper model %s...", model_size)
-            self.model = load(model_name)
+
+            with force_offline_if_cached(is_cached, progress_model_name):
+                self.model = load(model_name)
 
         self.model_size = model_size
         logger.info("MLX Whisper model %s loaded successfully", model_size)

backend/backends/pytorch_backend.py

@@ -14,11 +14,14 @@ from . import TTSBackend, STTBackend, LANGUAGE_CODE_TO_NAME, WHISPER_HF_REPOS
 from .base import (
     is_model_cached,
     get_torch_device,
+    empty_device_cache,
+    manual_seed,
     combine_voice_prompts as _combine_voice_prompts,
     model_load_progress,
 )
 from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
 from ..utils.audio import load_audio
+from ..utils.hf_offline_patch import force_offline_if_cached
 
 
 class PyTorchTTSBackend:
@@ -96,18 +99,28 @@ class PyTorchTTSBackend:
             model_path = self._get_model_path(model_size)
             logger.info("Loading TTS model %s on %s...", model_size, self.device)
 
-            if self.device == "cpu":
-                self.model = Qwen3TTSModel.from_pretrained(
-                    model_path,
-                    torch_dtype=torch.float32,
-                    low_cpu_mem_usage=False,
-                )
-            else:
-                self.model = Qwen3TTSModel.from_pretrained(
-                    model_path,
-                    device_map=self.device,
-                    torch_dtype=torch.bfloat16,
-                )
+            # Route both HF Hub and Transformers through a single cache root.
+            # On Windows local setups, model assets can otherwise split between
+            # .hf-cache/hub and .hf-cache/transformers, causing speech_tokenizer
+            # and preprocessor_config.json to fail to resolve during load.
+            from huggingface_hub import constants as hf_constants
+            tts_cache_dir = hf_constants.HF_HUB_CACHE
+
+            with force_offline_if_cached(is_cached, model_name):
+                if self.device == "cpu":
+                    self.model = Qwen3TTSModel.from_pretrained(
+                        model_path,
+                        cache_dir=tts_cache_dir,
+                        torch_dtype=torch.float32,
+                        low_cpu_mem_usage=False,
+                    )
+                else:
+                    self.model = Qwen3TTSModel.from_pretrained(
+                        model_path,
+                        cache_dir=tts_cache_dir,
+                        device_map=self.device,
+                        torch_dtype=torch.bfloat16,
+                    )
 
         self._current_model_size = model_size
         self.model_size = model_size
@@ -120,8 +133,7 @@ class PyTorchTTSBackend:
             self.model = None
             self._current_model_size = None
 
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+            empty_device_cache(self.device)
 
             logger.info("TTS model unloaded")
 
@@ -213,9 +225,7 @@ class PyTorchTTSBackend:
             """Run synchronous generation in thread pool."""
             # Set seed if provided
             if seed is not None:
-                torch.manual_seed(seed)
-                if torch.cuda.is_available():
-                    torch.cuda.manual_seed(seed)
+                manual_seed(seed, self.device)
 
             # Generate audio - this is the blocking operation
             wavs, sample_rate = self.model.generate_voice_clone(
@@ -282,8 +292,9 @@ class PyTorchSTTBackend:
             model_name = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
             logger.info("Loading Whisper model %s on %s...", model_size, self.device)
 
-            self.processor = WhisperProcessor.from_pretrained(model_name)
-            self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
+            with force_offline_if_cached(is_cached, progress_model_name):
+                self.processor = WhisperProcessor.from_pretrained(model_name)
+                self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
 
         self.model.to(self.device)
         self.model_size = model_size
@@ -297,8 +308,7 @@ class PyTorchSTTBackend:
             self.model = None
             self.processor = None
 
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+            empty_device_cache(self.device)
 
             logger.info("Whisper model unloaded")
 

backend/backends/qwen_custom_voice_backend.py

@@ -0,0 +1,210 @@
+"""
+Qwen3-TTS CustomVoice backend implementation.
+
+Wraps the Qwen3-TTS-12Hz CustomVoice model for preset-speaker TTS with
+instruction-based style control. Uses the same qwen_tts library as the
+Base model (pytorch_backend.py) but loads a different checkpoint and
+calls generate_custom_voice() instead of generate_voice_clone().
+
+Key differences from the Base engine:
+  - Uses preset speakers (9 built-in voices) instead of zero-shot cloning
+  - Supports instruct parameter for tone/emotion/prosody control
+  - Two model sizes: 1.7B and 0.6B
+
+Languages supported: zh, en, ja, ko, de, fr, ru, pt, es, it
+"""
+
+import asyncio
+import logging
+from typing import Optional
+
+import numpy as np
+import torch
+
+from . import TTSBackend, LANGUAGE_CODE_TO_NAME
+from .base import (
+    is_model_cached,
+    get_torch_device,
+    combine_voice_prompts as _combine_voice_prompts,
+    model_load_progress,
+)
+
+logger = logging.getLogger(__name__)
+
+# ── Preset speakers ──────────────────────────────────────────────────
+
+# (speaker_id, display_name, gender, native_language_code, description)
+QWEN_CUSTOM_VOICES = [
+    ("Vivian", "Vivian", "female", "zh", "Bright, slightly edgy young female voice"),
+    ("Serena", "Serena", "female", "zh", "Warm, gentle young female voice"),
+    ("Uncle_Fu", "Uncle Fu", "male", "zh", "Seasoned male voice with a low, mellow timbre"),
+    ("Dylan", "Dylan", "male", "zh", "Youthful Beijing male voice with a clear, natural timbre"),
+    ("Eric", "Eric", "male", "zh", "Lively Chengdu male voice with a slightly husky brightness"),
+    ("Ryan", "Ryan", "male", "en", "Dynamic male voice with strong rhythmic drive"),
+    ("Aiden", "Aiden", "male", "en", "Sunny American male voice with a clear midrange"),
+    ("Ono_Anna", "Ono Anna", "female", "ja", "Playful Japanese female voice with a light, nimble timbre"),
+    ("Sohee", "Sohee", "female", "ko", "Warm Korean female voice with rich emotion"),
+]
+
+QWEN_CV_DEFAULT_SPEAKER = "Ryan"
+
+# HuggingFace repo IDs per model size
+QWEN_CV_HF_REPOS = {
+    "1.7B": "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
+    "0.6B": "Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice",
+}
+
+
+class QwenCustomVoiceBackend:
+    """Qwen3-TTS CustomVoice backend — preset speakers with instruct control."""
+
+    def __init__(self, model_size: str = "1.7B"):
+        self.model = None
+        self.model_size = model_size
+        self.device = self._get_device()
+        self._current_model_size: Optional[str] = None
+
+    def _get_device(self) -> str:
+        return get_torch_device(allow_xpu=True, allow_directml=True)
+
+    def is_loaded(self) -> bool:
+        return self.model is not None
+
+    def _get_model_path(self, model_size: str) -> str:
+        if model_size not in QWEN_CV_HF_REPOS:
+            raise ValueError(f"Unknown model size: {model_size}")
+        return QWEN_CV_HF_REPOS[model_size]
+
+    def _is_model_cached(self, model_size: Optional[str] = None) -> bool:
+        size = model_size or self.model_size
+        return is_model_cached(self._get_model_path(size))
+
+    async def load_model_async(self, model_size: Optional[str] = None) -> None:
+        if model_size is None:
+            model_size = self.model_size
+
+        if self.model is not None and self._current_model_size == model_size:
+            return
+
+        if self.model is not None and self._current_model_size != model_size:
+            self.unload_model()
+
+        await asyncio.to_thread(self._load_model_sync, model_size)
+
+    # Alias for compatibility with the TTSBackend protocol
+    load_model = load_model_async
+
+    def _load_model_sync(self, model_size: str) -> None:
+        model_name = f"qwen-custom-voice-{model_size}"
+        is_cached = self._is_model_cached(model_size)
+
+        with model_load_progress(model_name, is_cached):
+            from qwen_tts import Qwen3TTSModel
+
+            model_path = self._get_model_path(model_size)
+            logger.info("Loading Qwen CustomVoice %s on %s...", model_size, self.device)
+
+            if self.device == "cpu":
+                self.model = Qwen3TTSModel.from_pretrained(
+                    model_path,
+                    torch_dtype=torch.float32,
+                    low_cpu_mem_usage=False,
+                )
+            else:
+                self.model = Qwen3TTSModel.from_pretrained(
+                    model_path,
+                    device_map=self.device,
+                    torch_dtype=torch.bfloat16,
+                )
+
+        self._current_model_size = model_size
+        self.model_size = model_size
+        logger.info("Qwen CustomVoice %s loaded successfully", model_size)
+
+    def unload_model(self) -> None:
+        if self.model is not None:
+            del self.model
+            self.model = None
+            self._current_model_size = None
+
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+            logger.info("Qwen CustomVoice unloaded")
+
+    async def create_voice_prompt(
+        self,
+        audio_path: str,
+        reference_text: str,
+        use_cache: bool = True,
+    ) -> tuple[dict, bool]:
+        """
+        Create voice prompt for CustomVoice.
+
+        CustomVoice doesn't use reference audio — it uses preset speakers.
+        When called for a cloned profile (fallback), uses the default speaker.
+        For preset profiles, the voice_prompt dict is built by the profile
+        service and bypasses this method entirely.
+        """
+        return {
+            "voice_type": "preset",
+            "preset_engine": "qwen_custom_voice",
+            "preset_voice_id": QWEN_CV_DEFAULT_SPEAKER,
+        }, False
+
+    async def combine_voice_prompts(
+        self,
+        audio_paths: list[str],
+        reference_texts: list[str],
+    ) -> tuple[np.ndarray, str]:
+        return await _combine_voice_prompts(audio_paths, reference_texts)
+
+    async def generate(
+        self,
+        text: str,
+        voice_prompt: dict,
+        language: str = "en",
+        seed: Optional[int] = None,
+        instruct: Optional[str] = None,
+    ) -> tuple[np.ndarray, int]:
+        """
+        Generate audio using Qwen CustomVoice.
+
+        Args:
+            text: Text to synthesize
+            voice_prompt: Dict with preset_voice_id (speaker name)
+            language: Language code (zh, en, ja, ko, etc.)
+            seed: Random seed for reproducibility
+            instruct: Natural language instruction for style control
+                      (e.g. "Speak in an angry tone", "Very happy")
+
+        Returns:
+            Tuple of (audio_array, sample_rate)
+        """
+        await self.load_model_async(None)
+
+        speaker = voice_prompt.get("preset_voice_id") or QWEN_CV_DEFAULT_SPEAKER
+
+        def _generate_sync():
+            if seed is not None:
+                torch.manual_seed(seed)
+                if torch.cuda.is_available():
+                    torch.cuda.manual_seed(seed)
+
+            lang_name = LANGUAGE_CODE_TO_NAME.get(language, "auto")
+
+            kwargs = {
+                "text": text,
+                "language": lang_name.capitalize() if lang_name != "auto" else "Auto",
+                "speaker": speaker,
+            }
+
+            # Only pass instruct if non-empty
+            if instruct:
+                kwargs["instruct"] = instruct
+
+            wavs, sample_rate = self.model.generate_custom_voice(**kwargs)
+            return wavs[0], sample_rate
+
+        audio, sample_rate = await asyncio.to_thread(_generate_sync)
+        return audio, sample_rate

backend/build_binary.py

@@ -34,9 +34,15 @@ def build_server(cuda=False):
     binary_name = "voicebox-server-cuda" if cuda else "voicebox-server"
 
     # PyInstaller arguments
+    # CUDA builds use --onedir so we can split the output into two archives:
+    #   1. Server core (~200-400MB) — versioned with the app
+    #   2. CUDA libs (~2GB) — versioned independently (only redownloaded on
+    #      CUDA toolkit / torch major version changes)
+    # CPU builds remain --onefile for simplicity.
+    pack_mode = "--onedir" if cuda else "--onefile"
     args = [
         "server.py",  # Use server.py as entry point instead of main.py
-        "--onefile",
+        pack_mode,
         "--name",
         binary_name,
     ]
@@ -46,6 +52,29 @@ def build_server(cuda=False):
     if platform.system() == "Windows":
         args.append("--noconsole")
 
+    # numpy 2.x / torch ABI mismatch fix: install memmove fallback for
+    # torch.from_numpy() before the app starts. Runtime hooks run after
+    # FrozenImporter is registered so frozen torch/numpy are importable.
+    # Paths are passed relative to backend_dir because os.chdir(backend_dir)
+    # runs before PyInstaller. Absolute paths would get baked into the
+    # generated .spec, breaking reproducible builds on other machines / CI.
+    args.extend(
+        [
+            "--runtime-hook",
+            "pyi_rth_numpy_compat.py",
+            # Stub torch.compiler.disable before transformers imports
+            # flex_attention, which otherwise triggers torch._dynamo →
+            # torch._numpy._ufuncs and crashes at module load under
+            # PyInstaller. See pyi_rth_torch_compiler_disable.py.
+            "--runtime-hook",
+            "pyi_rth_torch_compiler_disable.py",
+            # Per-module collection overrides (e.g. forcing scipy.stats._distn_infrastructure
+            # to bundle .py source alongside .pyc so the runtime hook can source-patch it).
+            "--additional-hooks-dir",
+            "pyi_hooks",
+        ]
+    )
+
     # Add local qwen_tts path if specified (for editable installs)
     qwen_tts_path = os.getenv("QWEN_TTS_PATH")
     if qwen_tts_path and Path(qwen_tts_path).exists():
@@ -80,6 +109,8 @@ def build_server(cuda=False):
             "--hidden-import",
             "backend.backends.pytorch_backend",
             "--hidden-import",
+            "backend.backends.qwen_custom_voice_backend",
+            "--hidden-import",
             "backend.utils.audio",
             "--hidden-import",
             "backend.utils.cache",
@@ -107,6 +138,11 @@ def build_server(cuda=False):
             "backend.backends.chatterbox_backend",
             "--hidden-import",
             "backend.backends.chatterbox_turbo_backend",
+            # chatterbox multilingual uses spacy_pkuseg for Chinese word
+            # segmentation, which ships pickled dict files (dicts/default.pkl)
+            # and native .so extensions that --hidden-import alone won't bundle.
+            "--collect-all",
+            "spacy_pkuseg",
             "--hidden-import",
             "backend.backends.luxtts_backend",
             "--hidden-import",
@@ -165,9 +201,9 @@ def build_server(cuda=False):
             "tqdm",
             "--hidden-import",
             "requests",
-            "--collect-submodules",
-            "qwen_tts",
-            "--collect-data",
+            # qwen_tts uses inspect.getsource() at runtime to locate
+            # modeling_qwen3_tts.py — needs physical .py source files bundled
+            "--collect-all",
             "qwen_tts",
             # Fix for pkg_resources and jaraco namespace packages
             "--hidden-import",
@@ -186,6 +222,73 @@ def build_server(cuda=False):
             # needed by LuxTTS for text-to-phoneme conversion
             "--collect-all",
             "piper_phonemize",
+            # HumeAI TADA — speech-language model using Llama + flow matching
+            "--hidden-import",
+            "backend.backends.hume_backend",
+            "--hidden-import",
+            "tada",
+            "--hidden-import",
+            "tada.modules",
+            "--hidden-import",
+            "tada.modules.tada",
+            "--hidden-import",
+            "tada.modules.encoder",
+            "--hidden-import",
+            "tada.modules.decoder",
+            "--hidden-import",
+            "tada.modules.aligner",
+            "--hidden-import",
+            "tada.modules.acoustic_spkr_verf",
+            "--hidden-import",
+            "tada.nn",
+            "--hidden-import",
+            "tada.nn.vibevoice",
+            "--hidden-import",
+            "tada.utils",
+            "--hidden-import",
+            "tada.utils.gray_code",
+            "--hidden-import",
+            "tada.utils.text",
+            # DAC shim — provides dac.nn.layers.Snake1d without the real
+            # descript-audio-codec package (which pulls onnx/tensorboard via
+            # descript-audiotools). The shim is in backend/utils/dac_shim.py.
+            "--hidden-import",
+            "backend.utils.dac_shim",
+            "--hidden-import",
+            "torchaudio",
+            "--collect-submodules",
+            "tada",
+            # Kokoro 82M — lightweight TTS engine using misaki G2P
+            # collect-all is required because transformers introspects .py source
+            # files at runtime (e.g. _can_set_attn_implementation opens the class
+            # file); hidden-import alone only bundles bytecode.
+            "--hidden-import",
+            "backend.backends.kokoro_backend",
+            "--collect-all",
+            "kokoro",
+            # misaki ships G2P data files (dictionaries, phoneme tables)
+            # that must be bundled for espeak/en/ja/zh G2P to work
+            "--collect-all",
+            "misaki",
+            # language_tags ships JSON data files (index.json etc.) loaded at
+            # runtime via: misaki → phonemizer → segments → csvw → language_tags
+            "--collect-all",
+            "language_tags",
+            # espeakng_loader ships the entire espeak-ng-data directory (369 files)
+            # loaded at import time by misaki.espeak via get_data_path()
+            "--collect-all",
+            "espeakng_loader",
+            # spacy en_core_web_sm model — misaki.en tries to spacy.cli.download()
+            # at runtime if not found, which calls pip as a subprocess and crashes
+            # the frozen binary. Bundle the model so spacy.util.is_package() passes.
+            "--collect-all",
+            "en_core_web_sm",
+            "--copy-metadata",
+            "en_core_web_sm",
+            "--hidden-import",
+            "en_core_web_sm",
+            "--hidden-import",
+            "loguru",
         ]
     )
 
@@ -328,7 +431,7 @@ def build_server(cuda=False):
                     "torchvision",
                     "torchaudio",
                     "--index-url",
-                    "https://download.pytorch.org/whl/cu126",
+                    "https://download.pytorch.org/whl/cu128",
                     "--force-reinstall",
                     "-q",
                 ],

backend/config.py

@@ -19,7 +19,22 @@ if _custom_models_dir:
     logger.info("Model download path set to: %s", _custom_models_dir)
 
 # Default data directory (used in development)
-_data_dir = Path("data")
+_data_dir = Path("data").resolve()
+
+
+def _path_relative_to_any_data_dir(path: Path) -> Path | None:
+    """Extract the path within a data dir from an absolute or relative path."""
+    parts = path.parts
+    for idx, part in enumerate(parts):
+        if part != "data":
+            continue
+
+        tail = parts[idx + 1 :]
+        if tail:
+            return Path(*tail)
+        return Path()
+
+    return None
 
 
 def set_data_dir(path: str | Path):
@@ -30,9 +45,9 @@ def set_data_dir(path: str | Path):
         path: Path to the data directory
     """
     global _data_dir
-    _data_dir = Path(path)
+    _data_dir = Path(path).resolve()
     _data_dir.mkdir(parents=True, exist_ok=True)
-    logger.info("Data directory set to: %s", _data_dir.absolute())
+    logger.info("Data directory set to: %s", _data_dir)
 
 
 def get_data_dir() -> Path:
@@ -45,6 +60,46 @@ def get_data_dir() -> Path:
     return _data_dir
 
 
+def to_storage_path(path: str | Path) -> str:
+    """Convert a filesystem path to a DB-safe path relative to the data dir."""
+    resolved_path = Path(path).resolve()
+
+    relative_to_any_data_dir = _path_relative_to_any_data_dir(resolved_path)
+    if relative_to_any_data_dir is not None:
+        return str(relative_to_any_data_dir)
+
+    try:
+        return str(resolved_path.relative_to(_data_dir))
+    except ValueError:
+        return str(resolved_path)
+
+
+def resolve_storage_path(path: str | Path | None) -> Path | None:
+    """Resolve a DB-stored path against the configured data dir."""
+    if path is None:
+        return None
+
+    stored_path = Path(path)
+    if stored_path.is_absolute():
+        rebased_path = _path_relative_to_any_data_dir(stored_path)
+        if rebased_path is not None:
+            candidate = (_data_dir / rebased_path).resolve()
+            if candidate.exists() or not stored_path.exists():
+                return candidate
+
+        return stored_path
+
+    # 0.3.0 records sometimes stored relative paths with the data-dir name
+    # baked in (e.g. "data/profiles/..."). Joining those directly with
+    # _data_dir produces a spurious "<data_dir>/data/profiles/..." nest.
+    if stored_path.parts and stored_path.parts[0] == "data":
+        stored_path = (
+            Path(*stored_path.parts[1:]) if len(stored_path.parts) > 1 else Path()
+        )
+
+    return (_data_dir / stored_path).resolve()
+
+
 def get_db_path() -> Path:
     """Get database file path."""
     return _data_dir / "voicebox.db"

backend/database/migrations.py

@@ -34,6 +34,7 @@ def run_migrations(engine) -> None:
     _migrate_generations(engine, inspector, tables)
     _migrate_effect_presets(engine, inspector, tables)
     _migrate_generation_versions(engine, inspector, tables)
+    _normalize_storage_paths(engine, tables)
 
 
 # -- helpers ---------------------------------------------------------------
@@ -134,6 +135,17 @@ def _migrate_profiles(engine, inspector, tables: set[str]) -> None:
         _add_column(engine, "profiles", "avatar_path VARCHAR", "avatar_path")
     if "effects_chain" not in columns:
         _add_column(engine, "profiles", "effects_chain TEXT", "effects_chain")
+    # Voice type system — v0.3.x
+    if "voice_type" not in columns:
+        _add_column(engine, "profiles", "voice_type VARCHAR DEFAULT 'cloned'", "voice_type")
+    if "preset_engine" not in columns:
+        _add_column(engine, "profiles", "preset_engine VARCHAR", "preset_engine")
+    if "preset_voice_id" not in columns:
+        _add_column(engine, "profiles", "preset_voice_id VARCHAR", "preset_voice_id")
+    if "design_prompt" not in columns:
+        _add_column(engine, "profiles", "design_prompt TEXT", "design_prompt")
+    if "default_engine" not in columns:
+        _add_column(engine, "profiles", "default_engine VARCHAR", "default_engine")
 
 
 def _migrate_generations(engine, inspector, tables: set[str]) -> None:
@@ -168,3 +180,47 @@ def _migrate_generation_versions(engine, inspector, tables: set[str]) -> None:
     columns = _get_columns(inspector, "generation_versions")
     if "source_version_id" not in columns:
         _add_column(engine, "generation_versions", "source_version_id VARCHAR", "source_version_id")
+
+
+def _normalize_storage_paths(engine, tables: set[str]) -> None:
+    """Normalize stored file paths to be relative to the configured data dir."""
+    from pathlib import Path
+
+    from ..config import get_data_dir, to_storage_path, resolve_storage_path
+
+    data_dir = get_data_dir()
+
+    path_columns = [
+        ("generations", "audio_path"),
+        ("generation_versions", "audio_path"),
+        ("profile_samples", "audio_path"),
+        ("profiles", "avatar_path"),
+    ]
+
+    total_fixed = 0
+    with engine.connect() as conn:
+        for table, column in path_columns:
+            if table not in tables:
+                continue
+            rows = conn.execute(
+                text(f"SELECT id, {column} FROM {table} WHERE {column} IS NOT NULL")
+            ).fetchall()
+            for row_id, path_val in rows:
+                if not path_val:
+                    continue
+                p = Path(path_val)
+                resolved = resolve_storage_path(p)
+                if resolved is None:
+                    continue
+
+                normalized = to_storage_path(resolved)
+
+                if normalized != path_val:
+                    conn.execute(
+                        text(f"UPDATE {table} SET {column} = :path WHERE id = :id"),
+                        {"path": normalized, "id": row_id},
+                    )
+                    total_fixed += 1
+        if total_fixed > 0:
+            conn.commit()
+            logger.info("Normalized %d stored file paths", total_fixed)

backend/database/models.py

@@ -10,7 +10,13 @@ Base = declarative_base()
 
 
 class VoiceProfile(Base):
-    """Voice profile."""
+    """Voice profile.
+
+    voice_type discriminates three flavours:
+      - "cloned"   — traditional reference-audio profiles (all cloning engines)
+      - "preset"   — engine-specific pre-built voice (e.g. Kokoro voices)
+      - "designed"  — text-described voice (e.g. Qwen CustomVoice, future)
+    """
 
     __tablename__ = "profiles"
 
@@ -20,6 +26,14 @@ class VoiceProfile(Base):
     language = Column(String, default="en")
     avatar_path = Column(String, nullable=True)
     effects_chain = Column(Text, nullable=True)
+
+    # Voice type system — added v0.3.x
+    voice_type = Column(String, default="cloned")  # "cloned" | "preset" | "designed"
+    preset_engine = Column(String, nullable=True)   # e.g. "kokoro" — only for preset
+    preset_voice_id = Column(String, nullable=True)  # e.g. "am_adam" — only for preset
+    design_prompt = Column(Text, nullable=True)      # text description — only for designed
+    default_engine = Column(String, nullable=True)   # auto-selected engine, locked for preset
+
     created_at = Column(DateTime, default=datetime.utcnow)
     updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
 

backend/database/seed.py

@@ -3,7 +3,8 @@
 import json
 import logging
 import uuid
-from pathlib import Path
+
+from .. import config
 
 logger = logging.getLogger(__name__)
 
@@ -25,7 +26,8 @@ def backfill_generation_versions(SessionLocal, Generation, GenerationVersion) ->
         for gen in generations:
             if gen.id in existing_version_gen_ids:
                 continue
-            if not Path(gen.audio_path).exists():
+            resolved_audio_path = config.resolve_storage_path(gen.audio_path)
+            if resolved_audio_path is None or not resolved_audio_path.exists():
                 continue
             version = GenerationVersion(
                 id=str(uuid.uuid4()),

backend/models.py

@@ -15,6 +15,11 @@ class VoiceProfileCreate(BaseModel):
     language: str = Field(
         default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he|ar|da|el|fi|hi|ms|nl|no|pl|sv|sw|tr)$"
     )
+    voice_type: Optional[str] = Field(default="cloned", pattern="^(cloned|preset|designed)$")
+    preset_engine: Optional[str] = Field(None, max_length=50)
+    preset_voice_id: Optional[str] = Field(None, max_length=100)
+    design_prompt: Optional[str] = Field(None, max_length=2000)
+    default_engine: Optional[str] = Field(None, max_length=50)
 
 
 class VoiceProfileResponse(BaseModel):
@@ -26,6 +31,11 @@ class VoiceProfileResponse(BaseModel):
     language: str
     avatar_path: Optional[str] = None
     effects_chain: Optional[List["EffectConfig"]] = None
+    voice_type: str = "cloned"
+    preset_engine: Optional[str] = None
+    preset_voice_id: Optional[str] = None
+    design_prompt: Optional[str] = None
+    default_engine: Optional[str] = None
     generation_count: int = 0
     sample_count: int = 0
     created_at: datetime
@@ -66,9 +76,9 @@ class GenerationRequest(BaseModel):
     text: str = Field(..., min_length=1, max_length=50000)
     language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he|ar|da|el|fi|hi|ms|nl|no|pl|sv|sw|tr)$")
     seed: Optional[int] = Field(None, ge=0)
-    model_size: Optional[str] = Field(default="1.7B", pattern="^(1\\.7B|0\\.6B)$")
+    model_size: Optional[str] = Field(default="1.7B", pattern="^(1\\.7B|0\\.6B|1B|3B)$")
     instruct: Optional[str] = Field(None, max_length=500)
-    engine: Optional[str] = Field(default="qwen", pattern="^(qwen|luxtts|chatterbox|chatterbox_turbo)$")
+    engine: Optional[str] = Field(default="qwen", pattern="^(qwen|qwen_custom_voice|luxtts|chatterbox|chatterbox_turbo|tada|kokoro)$")
     max_chunk_chars: int = Field(
         default=800, ge=100, le=5000, description="Max characters per chunk for long text splitting"
     )
@@ -172,6 +182,7 @@ class HealthResponse(BaseModel):
     vram_used_mb: Optional[float] = None
     backend_type: Optional[str] = None  # Backend type (mlx or pytorch)
     backend_variant: Optional[str] = None  # Binary variant (cpu or cuda)
+    gpu_compatibility_warning: Optional[str] = None  # Warning if GPU arch unsupported
 
 
 class DirectoryCheck(BaseModel):

backend/pyi_hooks/hook-scipy.stats._distn_infrastructure.py

@@ -0,0 +1,12 @@
+"""
+Force scipy.stats._distn_infrastructure to be bundled with its .py source file
+alongside the .pyc bytecode.
+
+The runtime hook in backend/pyi_rth_torch_compiler_disable.py patches this
+module's source at load time (the module has a `del obj` at line 369 that
+raises NameError under PyInstaller's frozen importer). That patch reads the
+source via loader.get_source(), which only works if the .py file was
+actually collected into the bundle.
+"""
+
+module_collection_mode = "pyz+py"

backend/pyi_hooks/hook-transformers.masking_utils.py

@@ -0,0 +1,11 @@
+"""
+Force transformers.masking_utils to be bundled with its .py source alongside
+the .pyc bytecode so the runtime hook in
+backend/pyi_rth_torch_compiler_disable.py can source-patch it.
+
+The patch forces the torch<2.6 code path, bypassing `with TransformGetItemToIndex()`
+which our torch._dynamo no-op stub can't implement for real — the real context
+manager uses dynamo graph transforms to avoid `.item()` calls inside vmap.
+"""
+
+module_collection_mode = "pyz+py"

backend/pyi_rth_numpy_compat.py

@@ -0,0 +1,95 @@
+"""
+PyInstaller runtime hook: numpy 2.x / torch ABI mismatch fix.
+
+Problem
+-------
+torch is compiled against numpy 1.x headers. numpy 2.x changed the version
+number returned by PyArray_GetNDArrayCVersion() (0x01000009 → 0x02000000),
+so torch's is_numpy_available() returns False and every torch.from_numpy()
+call raises:
+
+    RuntimeError: Numpy is not available
+
+This surfaces as:
+
+    ValueError: Unable to create tensor, you should probably activate
+    padding with 'padding=True'
+
+during TTS generation (EncodecFeatureExtractor → BatchFeature.convert_to_tensors).
+
+Fix
+---
+Runtime hooks execute after PyInstaller's FrozenImporter is registered, so
+frozen torch/numpy are importable here. We start a background thread that
+waits for torch to finish loading then wraps torch.from_numpy with a ctypes
+memmove fallback that bypasses the C-level numpy ABI check entirely.
+
+This approach works with any numpy version and is safer than binary-patching
+libtorch_python.dylib (which risks PyArray_Descr struct layout mismatches).
+"""
+
+import sys
+import threading
+
+
+def _patch_torch_from_numpy():
+    import time
+
+    for _ in range(7200):  # poll up to 360 s at 50 ms intervals
+        time.sleep(0.05)
+        torch = sys.modules.get("torch")
+        if torch is None or not hasattr(torch, "from_numpy"):
+            continue
+        if getattr(torch, "_vb_from_numpy_patched", False):
+            return
+        try:
+            import ctypes
+            import numpy as np
+
+            _orig = torch.from_numpy
+
+            # Explicit numpy → torch dtype map. Silent fallback to float32 on
+            # unknown dtypes would reinterpret the memcpy'd bytes as fp32 and
+            # silently corrupt data (e.g. fp16 tensors from some TTS engines),
+            # so we raise instead.
+            dtype_map = {
+                "float16": _t.float16,
+                "float32": _t.float32,
+                "float64": _t.float64,
+                "int8": _t.int8,
+                "int16": _t.int16,
+                "int32": _t.int32,
+                "int64": _t.int64,
+                "uint8": _t.uint8,
+                "bool": _t.bool,
+                "complex64": _t.complex64,
+                "complex128": _t.complex128,
+            }
+
+            def _safe_from_numpy(
+                arr, _orig=_orig, _c=ctypes, _np=np, _t=torch, _map=dtype_map
+            ):
+                try:
+                    return _orig(arr)
+                except RuntimeError:
+                    a = _np.ascontiguousarray(arr)
+                    key = str(a.dtype)
+                    if key not in _map:
+                        raise TypeError(
+                            f"pyi_rth_numpy_compat: unsupported numpy dtype "
+                            f"{key!r} in torch.from_numpy fallback; add an "
+                            f"explicit mapping rather than silently copying "
+                            f"bytes into the wrong dtype."
+                        )
+                    out = _t.empty(list(a.shape), dtype=_map[key])
+                    _c.memmove(out.data_ptr(), a.ctypes.data, a.nbytes)
+                    return out
+
+            torch.from_numpy = _safe_from_numpy
+            torch._vb_from_numpy_patched = True
+        except Exception:
+            pass
+        return
+
+
+threading.Thread(target=_patch_torch_from_numpy, daemon=True).start()

backend/pyi_rth_torch_compiler_disable.py

@@ -0,0 +1,540 @@
+"""
+PyInstaller runtime hook: stub torch._dynamo to a no-op module.
+
+Problem
+-------
+transformers triggers torch._dynamo import at module-load time (not just
+when torch.compile is called) via class-body decorators:
+
+    transformers/modeling_utils.py:1984
+        @torch._dynamo.allow_in_graph
+        class PreTrainedModel(...)
+
+    transformers/integrations/flex_attention.py:61
+        @torch.compiler.disable(recursive=False)
+        class WrappedFlexAttention...
+
+The attribute access triggers torch.__getattr__ -> importlib.import_module
+-> torch._dynamo -> torch._dynamo.utils imports torch._numpy ->
+torch._numpy._ndarray imports torch._numpy._ufuncs, which crashes under
+PyInstaller with:
+
+    File "torch/_numpy/_ufuncs.py", line 235, in <module>
+        vars()[name] = deco_binary_ufunc(ufunc)
+    NameError: name 'name' is not defined
+
+(The module-level `for name in _binary: vars()[name] = ...` pattern works
+in a regular venv but fails in the PyInstaller bundle. Root cause is in
+PyInstaller's importer / bytecode pipeline and not easily fixed upstream.)
+
+Surfaces as Kokoro failing to load when `from transformers import AlbertModel`
+trips the decorator chain.
+
+Fix
+---
+voicebox never uses torch.compile / torch._dynamo for inference, so we
+replace torch._dynamo with a no-op stub module before transformers is
+imported. Any attribute access on the stub returns a pass-through callable,
+so `@torch._dynamo.allow_in_graph`, `torch._dynamo.is_compiling()`,
+`torch._dynamo.mark_static_address(...)`, etc. all work.
+
+This hook is pure sys.modules manipulation — we deliberately do NOT import
+torch here. Runtime hooks run before the app starts and before
+pyi_rth_numpy_compat has had a chance to patch torch.from_numpy (it runs
+in a background thread, waiting for torch to appear in sys.modules).
+Eager-importing torch at hook time would trip the numpy ABI issue and
+kill the server process at startup.
+
+torch.compiler.disable does not need a separate stub: its implementation
+is effectively `import torch._dynamo; return torch._dynamo.disable(...)`,
+and since our stub is in sys.modules, that call resolves to our no-op
+_NoopDecorator pass-through.
+"""
+
+import os
+import sys
+import tempfile
+import types
+
+
+# Diagnostics — log hook activity to a file alongside the bundle so we can
+# see what's happening when the server is run as a sidecar (no stdout for
+# runtime hook prints). Safe no-op if the file can't be written.
+_DIAG_PATH = os.path.join(tempfile.gettempdir(), "voicebox_rt_hook.log")
+
+
+def _diag(msg: str) -> None:
+    try:
+        with open(_DIAG_PATH, "a", encoding="utf-8") as f:
+            f.write(msg + "\n")
+    except Exception:
+        pass
+
+
+_HOOK_VERSION = "v6-masking-utils-finder"
+_diag(f"=== runtime hook load @ pid={os.getpid()} version={_HOOK_VERSION} ===")
+
+
+class _NoopDecorator:
+    """Multi-role no-op: decorator, falsey predicate, and context manager.
+
+    Returned from calls like `torch._dynamo.disable()` (decorator),
+    `torch._dynamo.is_compiling()` (predicate used in `if not ...`), and
+    `with torch._dynamo._trace_wrapped_higher_order_op.TransformGetItemToIndex():`
+    (context manager used to scope an fx graph transformation).
+
+    By implementing __call__, __bool__, __enter__, __exit__, and __iter__ we
+    cover every use pattern we've seen transformers/torch use on a stubbed
+    object. Anything we haven't covered will raise a clearer error than a
+    silent wrong-result.
+    """
+
+    __slots__ = ()
+
+    def __call__(self, fn=None, *args, **kwargs):
+        return fn
+
+    def __bool__(self) -> bool:
+        return False
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        return False  # don't suppress exceptions
+
+    def __iter__(self):
+        return iter(())
+
+
+_noop_decorator_singleton = _NoopDecorator()
+
+
+def _noop_callable(*args, **kwargs):
+    # Direct-decorator use: @torch._dynamo.foo (no parens) — fn is positional
+    if len(args) == 1 and callable(args[0]) and not kwargs:
+        return args[0]
+    # Side-effect call with non-callable arg(s), e.g. mark_static_address(tensor)
+    return _noop_decorator_singleton
+
+
+class _NoopDynamoModule(types.ModuleType):
+    """Permissive stub: every attribute is a pass-through callable.
+
+    Covers attributes transformers hits at import time (allow_in_graph) and
+    runtime (is_compiling, mark_static_address, reset, disable, ...).
+
+    Dunder attributes (__file__, __spec__, __loader__, ...) raise
+    AttributeError so probes like inspect.getmodule() — which does
+    `hasattr(m, '__file__')` then `os.path.normpath(m.__file__)` — see the
+    module as having no source file and fall through to its normal
+    handling, instead of receiving a function and blowing up.
+    """
+
+    def __getattr__(self, name: str):
+        if name.startswith("__") and name.endswith("__"):
+            raise AttributeError(name)
+        return _noop_callable
+
+
+class _DynamoLoader:
+    """Loader used by _DynamoMetaPathFinder to materialise stub submodules."""
+
+    def create_module(self, spec):
+        return _NoopDynamoModule(spec.name)
+
+    def exec_module(self, module):
+        # Mark every stub submodule as a package so deeper submodule imports
+        # (`from torch._dynamo.X.Y import Z`) keep working.
+        module.__path__ = []
+
+
+class _DynamoMetaPathFinder:
+    """Resolve any `torch._dynamo.X[.Y...]` import to a no-op stub module.
+
+    Without this, `from torch._dynamo._trace_wrapped_higher_order_op import X`
+    fails even with torch._dynamo pre-populated in sys.modules — Python's
+    import machinery checks the parent's __path__ and then looks up the
+    child, and we need to provide both.
+    """
+
+    def find_spec(self, fullname, path=None, target=None):
+        if fullname == "torch._dynamo":
+            return None  # handled by the pre-populated sys.modules entry
+        if not fullname.startswith("torch._dynamo."):
+            return None
+        from importlib.machinery import ModuleSpec
+
+        return ModuleSpec(fullname, _DynamoLoader(), is_package=True)
+
+
+class _TransformersStubFinder:
+    """Replace specific transformers submodules with no-op stubs.
+
+    Two modules are targeted:
+
+    1. transformers.utils.auto_docstring
+       The real @auto_docstring decorator loads
+       transformers.models.auto.modeling_auto just to build example docstrings,
+       which drags in GenerationMixin -> candidate_generator -> sklearn.metrics
+       -> scipy.stats._distn_infrastructure and trips (2) below. Docstrings
+       aren't functional for inference, so a pass-through decorator is safe.
+
+    2. transformers.generation.candidate_generator
+       Imported at module scope by transformers.generation.utils. It does
+       `from sklearn.metrics import roc_curve` at module load, which triggers:
+
+            File "scipy/stats/_distn_infrastructure.py", line 369, in <module>
+            NameError: name 'obj' is not defined
+
+       This is a PyInstaller-specific module-load bug (same class as the
+       torch._numpy._ufuncs crash) where a module-level `for obj in [s for s
+       in dir() if ...]` loop evaluates to empty in the bundle, leaving `obj`
+       unbound before `del obj`.
+
+       The exports (AssistedCandidateGenerator, EarlyExitCandidateGenerator,
+       etc.) are speculative-decoding helpers voicebox's TTS engines do not
+       use; a no-op stub module satisfies the imports.
+    """
+
+    _STUBBED_MODULES = frozenset(
+        {
+            "transformers.utils.auto_docstring",
+            "transformers.generation.candidate_generator",
+        }
+    )
+
+    def find_spec(self, fullname, path=None, target=None):
+        if fullname not in self._STUBBED_MODULES:
+            return None
+        from importlib.machinery import ModuleSpec
+
+        return ModuleSpec(fullname, _NoopStubLoader(), is_package=False)
+
+
+class _NoopStubLoader:
+    def create_module(self, spec):
+        return _NoopDynamoModule(spec.name)
+
+    def exec_module(self, module):
+        # _NoopDynamoModule.__getattr__ already answers every non-dunder
+        # attribute with a pass-through callable, which satisfies
+        # `from stubbed_module import X` for any X.
+        pass
+
+
+def _patch_scipy_distn_source(source: str) -> str:
+    """Replace the unsafe `del obj` with a no-op that survives when obj is unbound.
+
+    Returns the input unchanged if the target line isn't found (e.g. scipy
+    version has changed).
+    """
+    target = "\ndel obj\n"
+    replacement = "\nglobals().pop('obj', None)\n"
+    if target in source:
+        return source.replace(target, replacement, 1)
+    return source
+
+
+def _patch_masking_utils_source(source: str) -> str:
+    """Force torch<2.6 code path in transformers.masking_utils.
+
+    The torch>=2.6 path uses `with TransformGetItemToIndex():` to allow
+    `.item()` calls inside vmap. That context manager is implemented via
+    torch._dynamo graph transforms, which our stub doesn't reproduce — it's
+    a no-op. The inner `_vmap_for_bhqkv` then crashes with:
+
+        RuntimeError: vmap: It looks like you're calling .item() on a Tensor.
+
+    Forcing the torch<2.6 flag off selects sdpa_mask_older_torch which uses
+    a different vmap pattern that does not hit .item() and does not need
+    TransformGetItemToIndex.
+    """
+    target = 'is_torch_greater_or_equal("2.6", accept_dev=True)'
+    # Find the specific line that assigns _is_torch_greater_or_equal_than_2_6
+    if "_is_torch_greater_or_equal_than_2_6 = " + target in source:
+        return source.replace(
+            "_is_torch_greater_or_equal_than_2_6 = " + target,
+            "_is_torch_greater_or_equal_than_2_6 = False",
+            1,
+        )
+    return source
+
+
+class _SourcePatchingFinder:
+    """Generic delegate-and-wrap meta-path finder that patches a module's
+    source before exec'ing.
+
+    Subclasses declare `target` (module fullname) and `patch` (str->str).
+    Requires the target module's .py source to be bundled (use a PyInstaller
+    hook setting module_collection_mode = "pyz+py").
+    """
+
+    target: str
+    patch_fn: callable = None
+
+    def find_spec(self, fullname, path=None, target=None):
+        if fullname != self.target:
+            return None
+        for finder in sys.meta_path:
+            if finder is self:
+                continue
+            find = getattr(finder, "find_spec", None)
+            if find is None:
+                continue
+            try:
+                real_spec = find(fullname, path, target)
+            except Exception:
+                continue
+            if real_spec is None or real_spec.loader is None:
+                continue
+            real_spec.loader = _SourcePatchLoader(real_spec.loader, self.patch_fn)
+            return real_spec
+        return None
+
+
+class _SourcePatchLoader:
+    """Delegate loader that reads source via get_source, applies a patch, and
+    compile/exec's the patched text into module.__dict__.
+    """
+
+    def __init__(self, inner, patch_fn):
+        self._inner = inner
+        self._patch_fn = patch_fn
+
+    def __getattr__(self, name):
+        return getattr(self._inner, name)
+
+    def create_module(self, spec):
+        return self._inner.create_module(spec)
+
+    def exec_module(self, module):
+        source = None
+        try:
+            source = self._inner.get_source(module.__name__)
+        except Exception as e:
+            _diag(f"[source-patch] get_source({module.__name__}) failed: {e!r}")
+
+        if not source:
+            _diag(
+                f"[source-patch] no source for {module.__name__}; "
+                "falling back to inner exec_module (patch NOT applied)"
+            )
+            self._inner.exec_module(module)
+            return
+
+        patched = self._patch_fn(source)
+        _diag(
+            f"[source-patch] {module.__name__}: "
+            f"patched={patched is not source}, len={len(patched)}"
+        )
+        spec = module.__spec__
+        if spec is not None and spec.submodule_search_locations is not None:
+            module.__path__ = spec.submodule_search_locations
+        filename = getattr(self._inner, "path", module.__name__)
+        exec(compile(patched, filename, "exec"), module.__dict__)
+        _diag(f"[source-patch] {module.__name__} OK")
+
+
+class _MaskingUtilsFinder(_SourcePatchingFinder):
+    target = "transformers.masking_utils"
+    patch_fn = staticmethod(_patch_masking_utils_source)
+
+
+class _ScipyDistnPatchingFinder:
+    """Delegate-and-wrap finder for scipy.stats._distn_infrastructure.
+
+    That module ends with:
+
+        for obj in [s for s in dir() if s.startswith('_doc_')]:
+            exec('del ' + obj)
+        del obj
+
+    In the PyInstaller bundle the list comprehension evaluates to empty
+    (module-level dir() under the frozen importer returns a different scope
+    than CPython's normal module-exec path — same class of bug as the
+    torch._numpy._ufuncs crash). The for loop body doesn't run, `obj` is
+    never bound, and the trailing `del obj` raises NameError at module load.
+
+    This kills every downstream module: librosa (needed by nearly every TTS
+    engine for mel filters) -> scipy.signal -> scipy.stats -> here.
+
+    Workaround: delegate to the real loader, but pre-bind `obj = None` in the
+    module namespace before its bytecode runs. If the for loop executes, each
+    iteration overwrites the sentinel via STORE_NAME (normal behaviour). If it
+    doesn't, `del obj` removes the sentinel and module load succeeds. The
+    `_doc_*` cleanup this line was meant to do is purely cosmetic — those vars
+    stay in the module namespace but nothing references them after this point.
+    """
+
+    _TARGET = "scipy.stats._distn_infrastructure"
+
+    def find_spec(self, fullname, path=None, target=None):
+        if fullname != self._TARGET:
+            return None
+        _diag(f"[scipy-finder] match: {fullname}, path={path!r}")
+        # Delegate to the other finders to locate the real spec
+        for finder in sys.meta_path:
+            if finder is self:
+                continue
+            find = getattr(finder, "find_spec", None)
+            if find is None:
+                continue
+            try:
+                real_spec = find(fullname, path, target)
+            except Exception as e:
+                _diag(f"[scipy-finder] inner finder {type(finder).__name__} raised: {e}")
+                continue
+            if real_spec is None:
+                continue
+            if real_spec.loader is None:
+                _diag(f"[scipy-finder] {type(finder).__name__} returned spec with loader=None")
+                continue
+            _diag(
+                f"[scipy-finder] wrapped loader from "
+                f"{type(finder).__name__} -> {type(real_spec.loader).__name__}"
+            )
+            real_spec.loader = _ScipyDistnPrebindLoader(real_spec.loader)
+            return real_spec
+        _diag("[scipy-finder] NO inner finder returned a spec")
+        return None
+
+
+class _ScipyDistnPrebindLoader:
+    """Thin wrapper that pre-binds `obj = None` before delegating to the
+    real PyInstaller loader.
+
+    Every other attribute/method delegates to the inner loader — PyiFrozenLoader
+    is a rich FileLoader/ExecutionLoader with get_code/get_source/get_filename/
+    is_package/get_resource_reader/etc., any of which Python's import machinery
+    or 3rd-party code may call on spec.loader. Forwarding via __getattr__
+    avoids breaking any of those paths (and preserves @_check_name contracts
+    because the decorated methods run on the inner instance where self.name
+    matches spec.name).
+    """
+
+    def __init__(self, inner):
+        self._inner = inner
+
+    def __getattr__(self, name):
+        # __getattr__ fires only for attrs not already on self, so delegate
+        # everything that isn't create_module/exec_module (or __getattr__/init).
+        return getattr(self._inner, name)
+
+    def create_module(self, spec):
+        return self._inner.create_module(spec)
+
+    def exec_module(self, module):
+        # Compile scipy's module source with the problematic line patched.
+        #
+        # The real module ends with:
+        #     for obj in [s for s in dir() if s.startswith('_doc_')]:
+        #         exec('del ' + obj)
+        #     del obj
+        #
+        # Under PyInstaller's frozen importer, `del obj` raises NameError
+        # even when we pre-populate module.__dict__['obj'] — the pre-compiled
+        # .pyc bytecode interacts with the frame setup differently than a
+        # fresh compile() from source. Easiest robust fix: read the source
+        # and replace `del obj` with a safe variant before compiling.
+        #
+        # Requires the .py source to be bundled alongside the .pyc — see
+        # backend/pyi_hooks/hook-scipy.stats._distn_infrastructure.py.
+        source = None
+        try:
+            source = self._inner.get_source(module.__name__)
+        except Exception as e:
+            _diag(f"[scipy-loader] get_source failed: {e!r}")
+
+        if source:
+            patched = _patch_scipy_distn_source(source)
+            _diag(
+                f"[scipy-loader] source-patch path: patched={patched is not source}, "
+                f"len={len(patched)}"
+            )
+            spec = module.__spec__
+            if spec is not None and spec.submodule_search_locations is not None:
+                module.__path__ = spec.submodule_search_locations
+            filename = getattr(self._inner, "path", module.__name__)
+            bytecode = compile(patched, filename, "exec")
+            try:
+                exec(bytecode, module.__dict__)
+            except Exception as e:
+                _diag(f"[scipy-loader] patched exec raised {type(e).__name__}: {e!r}")
+                raise
+            _diag(f"[scipy-loader] exec_module {module.__name__} OK (source-patched)")
+            return
+
+        # No source available — fall back to the pre-bind approach. This is
+        # best-effort; if the frozen .pyc really does see a different `obj`
+        # slot, this will still crash, but we've done all we can without
+        # source.
+        _diag("[scipy-loader] no source available; falling back to pre-bind")
+        module.__dict__["obj"] = None
+        self._inner.exec_module(module)
+
+
+def _install_dynamo_stub() -> None:
+    stub = _NoopDynamoModule("torch._dynamo")
+    # Mark as a package so `from torch._dynamo.X import Y` imports work
+    # (Python's import machinery checks parent.__path__ before looking up
+    # the child).
+    stub.__path__ = []
+    # torch._dynamo.config is accessed as a nested attribute namespace
+    # (e.g. `torch._dynamo.config.capture_scalar_outputs = True`), so use
+    # a permissive module so any attr read returns a no-op and sets succeed.
+    stub.config = _NoopDynamoModule("torch._dynamo.config")
+    stub.config.__path__ = []
+    sys.modules["torch._dynamo"] = stub
+    sys.modules["torch._dynamo.config"] = stub.config
+
+    # Finders:
+    #  - torch._dynamo.* submodules -> no-op stubs
+    #  - transformers.utils.auto_docstring and
+    #    transformers.generation.candidate_generator -> no-op stubs (both
+    #    paths reach sklearn -> scipy.stats which trips a separate crash)
+    #  - scipy.stats._distn_infrastructure -> real load with `obj` pre-bound,
+    #    so librosa -> scipy.signal -> scipy.stats loads cleanly
+    for _FinderCls in (
+        _DynamoMetaPathFinder,
+        _TransformersStubFinder,
+        _ScipyDistnPatchingFinder,
+        _MaskingUtilsFinder,
+    ):
+        try:
+            sys.meta_path.insert(0, _FinderCls())
+            _diag(f"installed finder: {_FinderCls.__name__}")
+        except Exception as e:
+            _diag(f"FAILED to install {_FinderCls.__name__}: {e!r}")
+    _diag(
+        "final sys.meta_path head: "
+        + ", ".join(type(f).__name__ for f in sys.meta_path[:6])
+    )
+
+    # If torch is already imported, also set the attribute on the package so
+    # `torch._dynamo` resolves to our stub without triggering torch.__getattr__
+    # (which would lazy-import the real module and crash).
+    torch_mod = sys.modules.get("torch")
+    if torch_mod is not None:
+        torch_mod._dynamo = stub
+
+
+try:
+    _install_dynamo_stub()
+except Exception as _e:
+    # Best effort. If this fails the original NameError will surface when
+    # transformers imports — no worse than not patching at all.
+    _diag(f"_install_dynamo_stub FAILED: {_e!r}")
+
+# NOTE: we deliberately do NOT import torch or torch.compiler here.
+# Runtime hooks run before the app starts and before pyi_rth_numpy_compat
+# has had a chance to patch torch.from_numpy (it runs in a background
+# thread, waiting for torch to appear in sys.modules). Importing torch
+# eagerly at hook time would trip the numpy ABI issue and kill the
+# server process at startup.
+#
+# torch.compiler.disable does not need an explicit stub: its
+# implementation is effectively `import torch._dynamo; return
+# torch._dynamo.disable(fn, recursive, reason=reason)`, and since our
+# stub is installed in sys.modules, that call resolves to our no-op
+# _NoopDecorator pass-through.

backend/requirements-mlx.txt

@@ -2,4 +2,14 @@
 # These should only be installed on aarch64-apple-darwin platforms
 
 mlx>=0.30.0
-mlx-audio>=0.3.1
+
+# NOTE: mlx-audio is intentionally not listed here. From 0.3.1 onward it
+# declares `transformers==5.0.0rc3` / `>=5.0.0`, which conflicts with the
+# `transformers<=4.57.6` cap in requirements.txt and breaks CI's clean
+# resolver. The mlx-audio API surface we use (mlx_audio.tts.load,
+# mlx_audio.stt.load) works fine on transformers 4.57.x in practice.
+#
+# Install it via `pip install --no-deps mlx-audio==0.4.1` after this file
+# (see .github/workflows/release.yml). All other mlx-audio runtime deps
+# (huggingface_hub, librosa, miniaudio, mlx-lm, numba, numpy, protobuf,
+# pyloudnorm, sounddevice, tqdm) are already in requirements.txt.

backend/requirements.txt

@@ -8,7 +8,7 @@ sqlalchemy>=2.0.0
 alembic>=1.13.0
 
 # ML models
-torch>=2.1.0
+torch>=2.2.0
 transformers>=4.36.0,<=4.57.6
 accelerate>=0.26.0
 huggingface_hub>=0.20.0
@@ -33,10 +33,24 @@ s3tokenizer
 spacy-pkuseg
 pyloudnorm
 
+# HumeAI TADA sub-dependencies (hume-tada itself is installed
+# --no-deps in the setup script because it pins torch>=2.7,<2.8.
+# descript-audio-codec is NOT installed — it pulls onnx/tensorboard
+# via descript-audiotools.  A lightweight shim in utils/dac_shim.py
+# provides the only class TADA uses: Snake1d.)
+torchaudio
+
+# Kokoro TTS (lightweight 82M-param engine)
+kokoro>=0.9.4
+misaki[en,ja,zh]>=0.9.4
+# spacy model for misaki English G2P — must be pre-installed or misaki
+# tries spacy.cli.download() at runtime which crashes frozen builds
+en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl
+
 # Audio processing
 librosa>=0.10.0
 soundfile>=0.12.0
-numpy>=1.24.0
+numpy>=1.24.0,<2.0
 numba>=0.60.0,<0.61.0
 pedalboard>=0.9.0
 

backend/routes/audio.py

@@ -1,12 +1,10 @@
 """Audio file serving endpoints."""
 
-from pathlib import Path
-
 from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import FileResponse
 from sqlalchemy.orm import Session
 
-from .. import models
+from .. import config, models
 from ..services import history
 from ..database import get_db
 
@@ -22,8 +20,8 @@ async def get_version_audio(version_id: str, db: Session = Depends(get_db)):
     if not version:
         raise HTTPException(status_code=404, detail="Version not found")
 
-    audio_path = Path(version.audio_path)
-    if not audio_path.exists():
+    audio_path = config.resolve_storage_path(version.audio_path)
+    if audio_path is None or not audio_path.exists():
         raise HTTPException(status_code=404, detail="Audio file not found")
 
     return FileResponse(
@@ -40,8 +38,8 @@ async def get_audio(generation_id: str, db: Session = Depends(get_db)):
     if not generation:
         raise HTTPException(status_code=404, detail="Generation not found")
 
-    audio_path = Path(generation.audio_path)
-    if not audio_path.exists():
+    audio_path = config.resolve_storage_path(generation.audio_path)
+    if audio_path is None or not audio_path.exists():
         raise HTTPException(status_code=404, detail="Audio file not found")
 
     return FileResponse(
@@ -60,8 +58,8 @@ async def get_sample_audio(sample_id: str, db: Session = Depends(get_db)):
     if not sample:
         raise HTTPException(status_code=404, detail="Sample not found")
 
-    audio_path = Path(sample.audio_path)
-    if not audio_path.exists():
+    audio_path = config.resolve_storage_path(sample.audio_path)
+    if audio_path is None or not audio_path.exists():
         raise HTTPException(status_code=404, detail="Audio file not found")
 
     return FileResponse(

backend/routes/effects.py

@@ -3,7 +3,6 @@
 import asyncio
 import io
 import uuid
-from pathlib import Path
 
 from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import StreamingResponse
@@ -41,10 +40,11 @@ async def preview_effects(
     all_versions = versions_mod.list_versions(generation_id, db)
     clean_version = next((v for v in all_versions if v.effects_chain is None), None)
     source_path = clean_version.audio_path if clean_version else gen.audio_path
-    if not source_path or not Path(source_path).exists():
+    resolved_source_path = config.resolve_storage_path(source_path)
+    if resolved_source_path is None or not resolved_source_path.exists():
         raise HTTPException(status_code=404, detail="Source audio file not found")
 
-    audio, sample_rate = await asyncio.to_thread(load_audio, source_path)
+    audio, sample_rate = await asyncio.to_thread(load_audio, str(resolved_source_path))
     processed = await asyncio.to_thread(apply_effects, audio, sample_rate, chain_dicts)
 
     import soundfile as sf
@@ -193,10 +193,11 @@ async def apply_effects_to_generation(
             source_path = clean_version.audio_path
             source_version_id = clean_version.id
 
-    if not source_path or not Path(source_path).exists():
+    resolved_source_path = config.resolve_storage_path(source_path)
+    if resolved_source_path is None or not resolved_source_path.exists():
         raise HTTPException(status_code=404, detail="Source audio file not found")
 
-    audio, sample_rate = await asyncio.to_thread(load_audio, source_path)
+    audio, sample_rate = await asyncio.to_thread(load_audio, str(resolved_source_path))
     processed_audio = await asyncio.to_thread(apply_effects, audio, sample_rate, chain_dicts)
 
     version_id = str(uuid.uuid4())
@@ -208,7 +209,7 @@ async def apply_effects_to_generation(
     version = versions_mod.create_version(
         generation_id=generation_id,
         label=label,
-        audio_path=str(processed_path),
+        audio_path=config.to_storage_path(processed_path),
         db=db,
         effects_chain=chain_dicts,
         is_default=data.set_as_default,

backend/routes/generations.py

@@ -14,12 +14,16 @@ from .. import models
 from ..services import history, profiles, tts
 from ..database import Generation as DBGeneration, VoiceProfile as DBVoiceProfile, get_db
 from ..services.generation import run_generation
-from ..services.task_queue import enqueue_generation
+from ..services.task_queue import cancel_generation as cancel_generation_job, enqueue_generation
 from ..utils.tasks import get_task_manager
 
 router = APIRouter()
 
 
+def _resolve_generation_engine(data: models.GenerationRequest, profile) -> str:
+    return data.engine or getattr(profile, "default_engine", None) or getattr(profile, "preset_engine", None) or "qwen"
+
+
 @router.post("/generate", response_model=models.GenerationResponse)
 async def generate_speech(
     data: models.GenerationRequest,
@@ -35,7 +39,12 @@ async def generate_speech(
 
     from ..backends import engine_has_model_sizes
 
-    engine = data.engine or "qwen"
+    engine = _resolve_generation_engine(data, profile)
+    try:
+        profiles.validate_profile_engine(profile, engine)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
     model_size = (data.model_size or "1.7B") if engine_has_model_sizes(engine) else None
 
     generation = await history.create_generation(
@@ -73,6 +82,7 @@ async def generate_speech(
                 pass
 
     enqueue_generation(
+        generation_id,
         run_generation(
             generation_id=generation_id,
             profile_id=data.profile_id,
@@ -118,6 +128,7 @@ async def retry_generation(generation_id: str, db: Session = Depends(get_db)):
     )
 
     enqueue_generation(
+        generation_id,
         run_generation(
             generation_id=generation_id,
             profile_id=gen.profile_id,
@@ -161,6 +172,7 @@ async def regenerate_generation(generation_id: str, db: Session = Depends(get_db
     version_id = str(uuid.uuid4())
 
     enqueue_generation(
+        generation_id,
         run_generation(
             generation_id=generation_id,
             profile_id=gen.profile_id,
@@ -178,6 +190,34 @@ async def regenerate_generation(generation_id: str, db: Session = Depends(get_db
     return models.GenerationResponse.model_validate(gen)
 
 
+@router.post("/generate/{generation_id}/cancel")
+async def cancel_generation(generation_id: str, db: Session = Depends(get_db)):
+    """Cancel a queued or running generation."""
+    gen = db.query(DBGeneration).filter_by(id=generation_id).first()
+    if not gen:
+        raise HTTPException(status_code=404, detail="Generation not found")
+
+    if (gen.status or "completed") not in ("loading_model", "generating"):
+        raise HTTPException(status_code=400, detail="Only active generations can be cancelled")
+
+    cancellation_state = cancel_generation_job(generation_id)
+    if cancellation_state is None:
+        raise HTTPException(status_code=409, detail="Generation is no longer cancellable")
+
+    if cancellation_state == "queued":
+        task_manager = get_task_manager()
+        task_manager.complete_generation(generation_id)
+        await history.update_generation_status(
+            generation_id=generation_id,
+            status="failed",
+            db=db,
+            error="Generation cancelled",
+        )
+        return {"message": "Queued generation cancelled"}
+
+    return {"message": "Generation cancellation requested"}
+
+
 @router.get("/generate/{generation_id}/status")
 async def get_generation_status(generation_id: str, db: Session = Depends(get_db)):
     """SSE endpoint that streams generation status updates."""
@@ -230,7 +270,11 @@ async def stream_speech(
     if not profile:
         raise HTTPException(status_code=404, detail="Profile not found")
 
-    engine = data.engine or "qwen"
+    engine = _resolve_generation_engine(data, profile)
+    try:
+        profiles.validate_profile_engine(profile, engine)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
     tts_model = get_tts_backend_for_engine(engine)
     model_size = data.model_size or "1.7B"
 
@@ -263,6 +307,22 @@ async def stream_speech(
         trim_fn=trim_fn,
     )
 
+    effects_chain_config = None
+    if data.effects_chain is not None:
+        effects_chain_config = [e.model_dump() for e in data.effects_chain]
+    elif profile.effects_chain:
+        import json as _json
+
+        try:
+            effects_chain_config = _json.loads(profile.effects_chain)
+        except Exception:
+            effects_chain_config = None
+
+    if effects_chain_config:
+        from ..utils.effects import apply_effects
+
+        audio = apply_effects(audio, sample_rate, effects_chain_config)
+
     if data.normalize:
         from ..utils.audio import normalize_audio
 

backend/routes/health.py

@@ -93,6 +93,12 @@ async def health():
     except ImportError:
         pass
 
+    gpu_compat_warning = None
+    if has_cuda:
+        from ..backends.base import check_cuda_compatibility
+
+        _compatible, gpu_compat_warning = check_cuda_compatibility()
+
     gpu_available = has_cuda or has_mps or has_xpu or has_directml or backend_type == "mlx"
 
     gpu_type = None
@@ -110,6 +116,11 @@ async def health():
     vram_used = None
     if has_cuda:
         vram_used = torch.cuda.memory_allocated() / 1024 / 1024
+    elif has_xpu:
+        try:
+            vram_used = torch.xpu.memory_allocated() / 1024 / 1024
+        except Exception:
+            pass  # memory_allocated() may not be available on all IPEX versions
 
     model_loaded = False
     model_size = None
@@ -162,7 +173,11 @@ async def health():
         gpu_type=gpu_type,
         vram_used_mb=vram_used,
         backend_type=backend_type,
-        backend_variant=os.environ.get("VOICEBOX_BACKEND_VARIANT", "cuda" if torch.cuda.is_available() else "cpu"),
+        backend_variant=os.environ.get(
+            "VOICEBOX_BACKEND_VARIANT",
+            "cuda" if torch.cuda.is_available() else ("xpu" if has_xpu else "cpu"),
+        ),
+        gpu_compatibility_warning=gpu_compat_warning,
     )
 
 

backend/routes/history.py

@@ -1,13 +1,12 @@
 """Generation history endpoints."""
 
 import io
-from pathlib import Path
 
 from fastapi import APIRouter, Depends, File, HTTPException, UploadFile
 from fastapi.responses import FileResponse, StreamingResponse
 from sqlalchemy.orm import Session
 
-from .. import models
+from .. import config, models
 from ..services import export_import, history
 from ..app import safe_content_disposition
 from ..database import Generation as DBGeneration, VoiceProfile as DBVoiceProfile, get_db
@@ -63,6 +62,13 @@ async def import_generation(
         raise HTTPException(status_code=500, detail=str(e))
 
 
+@router.delete("/history/failed")
+async def clear_failed_generations(db: Session = Depends(get_db)):
+    """Delete every generation with status='failed'. Used by the UI's 'Clear failed' button (#410)."""
+    count = await history.delete_failed_generations(db)
+    return {"deleted": count}
+
+
 @router.get("/history/{generation_id}", response_model=models.HistoryResponse)
 async def get_generation(
     generation_id: str,
@@ -90,6 +96,11 @@ async def get_generation(
         duration=gen.duration,
         seed=gen.seed,
         instruct=gen.instruct,
+        engine=gen.engine or "qwen",
+        model_size=gen.model_size,
+        status=gen.status or "completed",
+        error=gen.error,
+        is_favorited=bool(gen.is_favorited),
         created_at=gen.created_at,
     )
 
@@ -162,8 +173,8 @@ async def export_generation_audio(
     if not generation.audio_path:
         raise HTTPException(status_code=404, detail="Generation has no audio file")
 
-    audio_path = Path(generation.audio_path)
-    if not audio_path.is_file():
+    audio_path = config.resolve_storage_path(generation.audio_path)
+    if audio_path is None or not audio_path.is_file():
         raise HTTPException(status_code=404, detail="Audio file not found")
 
     safe_text = "".join(c for c in generation.text[:30] if c.isalnum() or c in (" ", "-", "_")).strip()

backend/routes/models.py

@@ -135,14 +135,15 @@ async def migrate_models(request: models.ModelMigrateRequest):
     if destination.resolve().is_relative_to(source.resolve()):
         raise HTTPException(status_code=400, detail="Destination cannot be inside the current cache directory")
 
+    progress_manager = get_progress_manager()
     model_dirs = [d for d in source.iterdir() if d.name.startswith("models--") and d.is_dir()]
     if not model_dirs:
+        progress_manager.update_progress("migration", 1, 1, status="complete")
+        progress_manager.mark_complete("migration")
         return {"moved": 0, "errors": [], "source": str(source), "destination": str(destination)}
 
     destination.mkdir(parents=True, exist_ok=True)
 
-    progress_manager = get_progress_manager()
-
     same_fs = False
     try:
         same_fs = source.stat().st_dev == destination.stat().st_dev

backend/routes/profiles.py

@@ -1,6 +1,8 @@
 """Voice profile endpoints."""
 
 import io
+import json as _json
+import logging
 import tempfile
 from datetime import datetime
 from pathlib import Path
@@ -15,6 +17,8 @@ from ..database import VoiceProfile as DBVoiceProfile, get_db
 from ..services import channels, export_import, profiles
 from ..services.profiles import _profile_to_response
 
+logger = logging.getLogger(__name__)
+
 router = APIRouter()
 
 
@@ -62,6 +66,46 @@ async def import_profile(
         raise HTTPException(status_code=500, detail=str(e))
 
 
+# ── Preset Voice Endpoints ───────────────────────────────────────────
+# These MUST be declared before /profiles/{profile_id} to avoid the
+# wildcard swallowing "presets" as a profile_id.
+
+
+@router.get("/profiles/presets/{engine}")
+async def list_preset_voices(engine: str):
+    """List available preset voices for an engine."""
+    if engine == "kokoro":
+        from ..backends.kokoro_backend import KOKORO_VOICES
+
+        return {
+            "engine": engine,
+            "voices": [
+                {
+                    "voice_id": vid,
+                    "name": name,
+                    "gender": gender,
+                    "language": lang,
+                }
+                for vid, name, gender, lang in KOKORO_VOICES
+            ],
+        }
+    if engine == "qwen_custom_voice":
+        from ..backends.qwen_custom_voice_backend import QWEN_CUSTOM_VOICES
+
+        return {
+            "engine": engine,
+            "voices": [
+                {
+                    "voice_id": speaker_id,
+                    "name": display_name,
+                    "gender": gender,
+                    "language": lang,
+                }
+                for speaker_id, display_name, gender, lang, _desc in QWEN_CUSTOM_VOICES
+            ],
+        }
+    return {"engine": engine, "voices": []}
+
 @router.get("/profiles/{profile_id}", response_model=models.VoiceProfileResponse)
 async def get_profile(
     profile_id: str,
@@ -215,8 +259,8 @@ async def get_profile_avatar(
     if not profile.avatar_path:
         raise HTTPException(status_code=404, detail="No avatar found for this profile")
 
-    avatar_path = Path(profile.avatar_path)
-    if not avatar_path.exists():
+    avatar_path = config.resolve_storage_path(profile.avatar_path)
+    if avatar_path is None or not avatar_path.exists():
         raise HTTPException(status_code=404, detail="Avatar file not found")
 
     return FileResponse(avatar_path)
@@ -297,8 +341,6 @@ async def update_profile_effects(
     db: Session = Depends(get_db),
 ):
     """Set or clear the default effects chain for a voice profile."""
-    import json as _json
-
     profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
     if not profile:
         raise HTTPException(status_code=404, detail="Profile not found")

backend/server.py

@@ -105,6 +105,11 @@ def _start_parent_watchdog(parent_pid, data_dir=None):
     This is the clean shutdown mechanism: instead of the Tauri app trying to
     forcefully kill the server (which spawns console windows on Windows),
     the server monitors its parent and shuts itself down gracefully.
+
+    The Tauri app writes a .keep-running sentinel file to data_dir before
+    exiting when "remain running after close" is enabled. This is a reliable
+    fallback for the HTTP /watchdog/disable request, which can race with
+    process exit on Windows.
     """
     import os
     import signal
@@ -164,6 +169,19 @@ def _start_parent_watchdog(parent_pid, data_dir=None):
         if not alive:
             watchdog_logger.warning(f"Parent PID {parent_pid} not found on first check — disabling watchdog")
             return
+        # Clear any stale .keep-running sentinel from a previous session. The
+        # sentinel is only removed by the watchdog when it's consumed during a
+        # grace period; if the HTTP /watchdog/disable path wins the race on a
+        # "keep running" exit, the sentinel is left on disk. Wipe it here so a
+        # future session can't inherit that stale signal.
+        if data_dir:
+            stale = os.path.join(data_dir, ".keep-running")
+            if os.path.exists(stale):
+                try:
+                    os.remove(stale)
+                    watchdog_logger.info("Removed stale .keep-running sentinel from previous session")
+                except OSError as e:
+                    watchdog_logger.warning(f"Failed to remove stale sentinel: {e}")
         while True:
             if _watchdog_disabled:
                 watchdog_logger.info("Watchdog disabled (keep server running), stopping monitor")
@@ -178,6 +196,18 @@ def _start_parent_watchdog(parent_pid, data_dir=None):
                 if _watchdog_disabled:
                     watchdog_logger.info("Watchdog was disabled during grace period, keeping server alive")
                     return
+                # Check for sentinel file written by Tauri before exit.
+                # This catches the case where the HTTP disable request
+                # didn't arrive before the parent process died (common
+                # on Windows where process teardown is fast).
+                sentinel = os.path.join(data_dir, ".keep-running") if data_dir else None
+                if sentinel and os.path.exists(sentinel):
+                    watchdog_logger.info("Found .keep-running sentinel file, keeping server alive")
+                    try:
+                        os.remove(sentinel)
+                    except OSError:
+                        pass
+                    return
                 watchdog_logger.info("Watchdog still enabled after grace period, shutting down server...")
                 if sys.platform == "win32":
                     # sys.exit triggers SystemExit, allowing uvicorn to run

backend/services/cuda.py

@@ -1,16 +1,23 @@
 """
-CUDA backend binary download, assembly, and verification.
+CUDA backend download, assembly, and verification.
 
-Downloads split parts of the CUDA-enabled voicebox-server binary from
-GitHub Releases, reassembles them, verifies integrity via SHA-256,
-and places the binary in the app's data directory for use on next
-backend restart.
+Downloads two archives from GitHub Releases:
+  1. Server core (voicebox-server-cuda.tar.gz) — the exe + non-NVIDIA deps,
+     versioned with the app.
+  2. CUDA libs (cuda-libs-{version}.tar.gz) — NVIDIA runtime libraries,
+     versioned independently (only redownloaded on CUDA toolkit bump).
+
+Both archives are extracted into {data_dir}/backends/cuda/ which forms the
+complete PyInstaller --onedir directory structure that torch expects.
 """
 
+import asyncio
 import hashlib
+import json
 import logging
 import os
 import sys
+import tarfile
 from pathlib import Path
 from typing import Optional
 
@@ -24,6 +31,16 @@ GITHUB_RELEASES_URL = "https://github.com/jamiepine/voicebox/releases/download"
 
 PROGRESS_KEY = "cuda-backend"
 
+# The current expected CUDA libs version.  Bump this when we change the
+# CUDA toolkit version or torch's CUDA dependency changes (e.g. cu126 -> cu128).
+CUDA_LIBS_VERSION = "cu128-v1"
+
+# Prevents concurrent download_cuda_binary() calls from racing on the same
+# temp file.  The auto-update background task and the manual HTTP endpoint
+# can both invoke download_cuda_binary(); without this lock the progress-
+# manager status check is a TOCTOU race.
+_download_lock = asyncio.Lock()
+
 
 def get_backends_dir() -> Path:
     """Directory where downloaded backend binaries are stored."""
@@ -32,21 +49,46 @@ def get_backends_dir() -> Path:
     return d
 
 
-def get_cuda_binary_name() -> str:
-    """Platform-specific CUDA binary filename."""
+def get_cuda_dir() -> Path:
+    """Directory where the CUDA backend (onedir) is extracted."""
+    d = get_backends_dir() / "cuda"
+    d.mkdir(parents=True, exist_ok=True)
+    return d
+
+
+def get_cuda_exe_name() -> str:
+    """Platform-specific CUDA executable filename."""
     if sys.platform == "win32":
         return "voicebox-server-cuda.exe"
     return "voicebox-server-cuda"
 
 
 def get_cuda_binary_path() -> Optional[Path]:
-    """Return path to CUDA binary if it exists."""
-    p = get_backends_dir() / get_cuda_binary_name()
+    """Return path to the CUDA executable if it exists inside the onedir."""
+    p = get_cuda_dir() / get_cuda_exe_name()
     if p.exists():
         return p
     return None
 
 
+def get_cuda_libs_manifest_path() -> Path:
+    """Path to the cuda-libs.json manifest inside the CUDA dir."""
+    return get_cuda_dir() / "cuda-libs.json"
+
+
+def get_installed_cuda_libs_version() -> Optional[str]:
+    """Read the installed CUDA libs version from cuda-libs.json, or None."""
+    manifest_path = get_cuda_libs_manifest_path()
+    if not manifest_path.exists():
+        return None
+    try:
+        data = json.loads(manifest_path.read_text())
+        return data.get("version")
+    except Exception as e:
+        logger.warning(f"Could not read cuda-libs.json: {e}")
+        return None
+
+
 def is_cuda_active() -> bool:
     """Check if the current process is the CUDA binary.
 
@@ -60,140 +102,252 @@ def get_cuda_status() -> dict:
     progress_manager = get_progress_manager()
     cuda_path = get_cuda_binary_path()
     progress = progress_manager.get_progress(PROGRESS_KEY)
+    cuda_libs_version = get_installed_cuda_libs_version()
 
     return {
         "available": cuda_path is not None,
         "active": is_cuda_active(),
         "binary_path": str(cuda_path) if cuda_path else None,
+        "cuda_libs_version": cuda_libs_version,
         "downloading": progress is not None and progress.get("status") == "downloading",
         "download_progress": progress,
     }
 
 
-async def download_cuda_binary(version: Optional[str] = None):
-    """Download the CUDA backend binary from GitHub Releases.
+def _needs_server_download(version: Optional[str] = None) -> bool:
+    """Check if the server core archive needs to be (re)downloaded."""
+    cuda_path = get_cuda_binary_path()
+    if not cuda_path:
+        return True
+    # Check if the binary version matches the expected app version
+    installed = get_cuda_binary_version()
+    expected = version or __version__
+    if expected.startswith("v"):
+        expected = expected[1:]
+    return installed != expected
 
-    Downloads split parts listed in a manifest file, concatenates them,
-    and verifies the SHA-256 checksum for integrity. Atomic write
-    (temp file -> rename).
+
+def _needs_cuda_libs_download() -> bool:
+    """Check if the CUDA libs archive needs to be (re)downloaded."""
+    installed = get_installed_cuda_libs_version()
+    if installed is None:
+        return True
+    return installed != CUDA_LIBS_VERSION
+
+
+async def _download_and_extract_archive(
+    client,
+    url: str,
+    sha256_url: Optional[str],
+    dest_dir: Path,
+    label: str,
+    progress_offset: int,
+    total_size: int,
+):
+    """Download a .tar.gz archive and extract it into dest_dir.
 
     Args:
-        version: Version tag (e.g. "v0.2.0"). Defaults to current app version.
+        client: httpx.AsyncClient
+        url: URL of the .tar.gz archive
+        sha256_url: URL of the .sha256 checksum file (optional)
+        dest_dir: Directory to extract into
+        label: Human-readable label for progress updates
+        progress_offset: Byte offset for progress reporting (when downloading
+            multiple archives sequentially)
+        total_size: Total bytes across all downloads (for progress bar)
     """
+    progress = get_progress_manager()
+    temp_path = dest_dir / f".download-{label.replace(' ', '-')}.tmp"
+
+    # Clean up leftover partial download
+    if temp_path.exists():
+        temp_path.unlink()
+
+    # Fetch expected checksum (fail-fast: never extract an unverified archive)
+    expected_sha = None
+    if sha256_url:
+        try:
+            sha_resp = await client.get(sha256_url)
+            sha_resp.raise_for_status()
+            expected_sha = sha_resp.text.strip().split()[0]
+            logger.info(f"{label}: expected SHA-256: {expected_sha[:16]}...")
+        except Exception as e:
+            raise RuntimeError(f"{label}: failed to fetch checksum from {sha256_url}") from e
+
+    # Stream download, verify, and extract — always clean up temp file
+    downloaded = 0
+    try:
+        async with client.stream("GET", url) as response:
+            response.raise_for_status()
+            with open(temp_path, "wb") as f:
+                async for chunk in response.aiter_bytes(chunk_size=1024 * 1024):
+                    f.write(chunk)
+                    downloaded += len(chunk)
+                    progress.update_progress(
+                        PROGRESS_KEY,
+                        current=progress_offset + downloaded,
+                        total=total_size,
+                        filename=f"Downloading {label}",
+                        status="downloading",
+                    )
+
+        # Verify integrity
+        if expected_sha:
+            progress.update_progress(
+                PROGRESS_KEY,
+                current=progress_offset + downloaded,
+                total=total_size,
+                filename=f"Verifying {label}...",
+                status="downloading",
+            )
+            sha256 = hashlib.sha256()
+            with open(temp_path, "rb") as f:
+                while True:
+                    data = f.read(1024 * 1024)
+                    if not data:
+                        break
+                    sha256.update(data)
+            actual = sha256.hexdigest()
+            if actual != expected_sha:
+                raise ValueError(
+                    f"{label} integrity check failed: expected {expected_sha[:16]}..., got {actual[:16]}..."
+                )
+            logger.info(f"{label}: integrity verified")
+
+        # Extract (use data filter for path traversal protection on Python 3.12+)
+        progress.update_progress(
+            PROGRESS_KEY,
+            current=progress_offset + downloaded,
+            total=total_size,
+            filename=f"Extracting {label}...",
+            status="downloading",
+        )
+        with tarfile.open(temp_path, "r:gz") as tar:
+            if sys.version_info >= (3, 12):
+                tar.extractall(path=dest_dir, filter="data")
+            else:
+                tar.extractall(path=dest_dir)
+
+        logger.info(f"{label}: extracted to {dest_dir}")
+    finally:
+        if temp_path.exists():
+            temp_path.unlink()
+    return downloaded
+
+
+async def download_cuda_binary(version: Optional[str] = None):
+    """Download the CUDA backend (server core + CUDA libs if needed).
+
+    Downloads both archives from GitHub Releases, extracts them into
+    {data_dir}/backends/cuda/, and writes the cuda-libs.json manifest.
+
+    Only downloads what's needed:
+    - Server core: always redownloaded (versioned with app)
+    - CUDA libs: only if missing or version mismatch
+
+    Args:
+        version: Version tag (e.g. "v0.3.0"). Defaults to current app version.
+    """
+    if _download_lock.locked():
+        logger.info("CUDA download already in progress, skipping duplicate request")
+        return
+    async with _download_lock:
+        await _download_cuda_binary_locked(version)
+
+
+async def _download_cuda_binary_locked(version: Optional[str] = None):
+    """Inner implementation of download_cuda_binary, called under _download_lock."""
     import httpx
 
     if version is None:
         version = f"v{__version__}"
 
     progress = get_progress_manager()
-    binary_name = get_cuda_binary_name()
-    dest_dir = get_backends_dir()
-    final_path = dest_dir / binary_name
-    temp_path = dest_dir / f"{binary_name}.download"
+    cuda_dir = get_cuda_dir()
 
-    # Clean up any leftover partial download
-    if temp_path.exists():
-        temp_path.unlink()
+    need_server = _needs_server_download(version)
+    need_libs = _needs_cuda_libs_download()
 
-    logger.info(f"Starting CUDA backend download for {version}")
+    if not need_server and not need_libs:
+        logger.info("CUDA backend is up to date, nothing to download")
+        return
+
+    logger.info(
+        f"Starting CUDA backend download for {version} "
+        f"(server={'yes' if need_server else 'cached'}, "
+        f"libs={'yes' if need_libs else 'cached'})"
+    )
     progress.update_progress(
-        PROGRESS_KEY, current=0, total=0,
-        filename="Fetching manifest...", status="downloading",
+        PROGRESS_KEY,
+        current=0,
+        total=0,
+        filename="Preparing download...",
+        status="downloading",
     )
 
     base_url = f"{GITHUB_RELEASES_URL}/{version}"
-    stem = Path(binary_name).stem  # voicebox-server-cuda
+    server_archive = "voicebox-server-cuda.tar.gz"
+    libs_archive = f"cuda-libs-{CUDA_LIBS_VERSION}.tar.gz"
 
     try:
         async with httpx.AsyncClient(follow_redirects=True, timeout=30.0) as client:
-            # Fetch the manifest (list of split part filenames)
-            manifest_url = f"{base_url}/{stem}.manifest"
-            manifest_resp = await client.get(manifest_url)
-            manifest_resp.raise_for_status()
-            parts = [p.strip() for p in manifest_resp.text.strip().splitlines() if p.strip()]
-
-            if not parts:
-                raise ValueError("Empty manifest — no split parts found")
-
-            logger.info(f"Found {len(parts)} split parts to download")
-
-            # Fetch expected checksum (optional — for integrity verification)
-            expected_sha = None
-            try:
-                sha_url = f"{base_url}/{stem}.sha256"
-                sha_resp = await client.get(sha_url)
-                if sha_resp.status_code == 200:
-                    # Format: "sha256hex  filename\n"
-                    expected_sha = sha_resp.text.strip().split()[0]
-                    logger.info(f"Expected SHA-256: {expected_sha[:16]}...")
-            except Exception as e:
-                logger.warning(f"Could not fetch checksum file — skipping verification: {e}")
-
-            # Get total size across all parts by issuing HEAD requests
+            # Estimate total download size
             total_size = 0
-            for part_name in parts:
+            if need_server:
                 try:
-                    head_resp = await client.head(f"{base_url}/{part_name}")
-                    content_length = int(head_resp.headers.get("content-length", 0))
-                    total_size += content_length
+                    head = await client.head(f"{base_url}/{server_archive}")
+                    total_size += int(head.headers.get("content-length", 0))
                 except Exception:
                     pass
+            if need_libs:
+                try:
+                    head = await client.head(f"{base_url}/{libs_archive}")
+                    total_size += int(head.headers.get("content-length", 0))
+                except Exception:
+                    pass
+
             logger.info(f"Total download size: {total_size / 1024 / 1024:.1f} MB")
 
-            # Download and concatenate parts
-            total_downloaded = 0
-            with open(temp_path, "wb") as f:
-                for i, part_name in enumerate(parts):
-                    part_url = f"{base_url}/{part_name}"
-                    logger.info(f"Downloading part {i + 1}/{len(parts)}: {part_name}")
+            offset = 0
 
-                    async with client.stream("GET", part_url) as response:
-                        response.raise_for_status()
-                        async for chunk in response.aiter_bytes(chunk_size=1024 * 1024):
-                            f.write(chunk)
-                            total_downloaded += len(chunk)
-                            progress.update_progress(
-                                PROGRESS_KEY, current=total_downloaded, total=total_size,
-                                filename=f"Downloading CUDA backend ({i + 1}/{len(parts)})",
-                                status="downloading",
-                            )
-
-        # Verify integrity if checksum was available
-        if expected_sha:
-            progress.update_progress(
-                PROGRESS_KEY, current=total_downloaded, total=total_downloaded,
-                filename="Verifying integrity...", status="downloading",
-            )
-            sha256 = hashlib.sha256()
-            with open(temp_path, "rb") as f:
-                while True:
-                    chunk = f.read(1024 * 1024)
-                    if not chunk:
-                        break
-                    sha256.update(chunk)
-
-            actual = sha256.hexdigest()
-            if actual != expected_sha:
-                raise ValueError(
-                    f"Integrity check failed: expected {expected_sha[:16]}..., "
-                    f"got {actual[:16]}..."
+            # Download server core
+            if need_server:
+                server_downloaded = await _download_and_extract_archive(
+                    client,
+                    url=f"{base_url}/{server_archive}",
+                    sha256_url=f"{base_url}/{server_archive}.sha256",
+                    dest_dir=cuda_dir,
+                    label="CUDA server",
+                    progress_offset=offset,
+                    total_size=total_size,
                 )
-            logger.info(f"Integrity verified: {actual[:16]}...")
+                offset += server_downloaded
 
-        # Atomic move into place (replace handles existing target on all platforms)
-        temp_path.replace(final_path)
+                # Make executable on Unix
+                exe_path = cuda_dir / get_cuda_exe_name()
+                if sys.platform != "win32" and exe_path.exists():
+                    exe_path.chmod(0o755)
 
-        # Make executable on Unix
-        if sys.platform != "win32":
-            final_path.chmod(0o755)
+            # Download CUDA libs
+            if need_libs:
+                await _download_and_extract_archive(
+                    client,
+                    url=f"{base_url}/{libs_archive}",
+                    sha256_url=f"{base_url}/{libs_archive}.sha256",
+                    dest_dir=cuda_dir,
+                    label="CUDA libraries",
+                    progress_offset=offset,
+                    total_size=total_size,
+                )
 
-        logger.info(f"CUDA backend downloaded to {final_path}")
+                # Write local cuda-libs.json manifest
+                manifest = {"version": CUDA_LIBS_VERSION}
+                get_cuda_libs_manifest_path().write_text(json.dumps(manifest, indent=2) + "\n")
+
+        logger.info(f"CUDA backend ready at {cuda_dir}")
         progress.mark_complete(PROGRESS_KEY)
 
     except Exception as e:
-        # Clean up on failure
-        if temp_path.exists():
-            temp_path.unlink()
         logger.error(f"CUDA backend download failed: {e}")
         progress.mark_error(PROGRESS_KEY, str(e))
         raise
@@ -202,15 +356,19 @@ async def download_cuda_binary(version: Optional[str] = None):
 def get_cuda_binary_version() -> Optional[str]:
     """Get the version of the installed CUDA binary, or None if not installed."""
     import subprocess
+
     cuda_path = get_cuda_binary_path()
     if not cuda_path:
         return None
     try:
         result = subprocess.run(
             [str(cuda_path), "--version"],
-            capture_output=True, text=True, timeout=30,
+            capture_output=True,
+            text=True,
+            timeout=30,
+            cwd=str(cuda_path.parent),  # Run from the onedir directory
         )
-        # Output format: "voicebox-server 0.2.0"
+        # Output format: "voicebox-server 0.3.0"
         for line in result.stdout.strip().splitlines():
             if "voicebox-server" in line:
                 return line.split()[-1]
@@ -222,26 +380,29 @@ def get_cuda_binary_version() -> Optional[str]:
 async def check_and_update_cuda_binary():
     """Check if the CUDA binary is outdated and auto-download if so.
 
-    Called on server startup. If a CUDA binary exists but its version
-    doesn't match the current app version, triggers a background download
-    of the updated CUDA binary. The download progress is visible to the
-    frontend via the existing SSE progress endpoint.
+    Called on server startup. Checks both server version and CUDA libs
+    version. Downloads only what's needed.
     """
     cuda_path = get_cuda_binary_path()
     if not cuda_path:
         return  # No CUDA binary installed, nothing to update
 
-    cuda_version = get_cuda_binary_version()
-    current_version = __version__
+    need_server = _needs_server_download()
+    need_libs = _needs_cuda_libs_download()
 
-    if cuda_version == current_version:
-        logger.info(f"CUDA binary is up to date (v{current_version})")
+    if not need_server and not need_libs:
+        logger.info(f"CUDA binary is up to date (server=v{__version__}, libs={get_installed_cuda_libs_version()})")
         return
 
-    logger.info(
-        f"CUDA binary version mismatch: binary=v{cuda_version}, app=v{current_version}. "
-        f"Auto-downloading updated CUDA backend..."
-    )
+    reasons = []
+    if need_server:
+        cuda_version = get_cuda_binary_version()
+        reasons.append(f"server v{cuda_version} != v{__version__}")
+    if need_libs:
+        installed_libs = get_installed_cuda_libs_version()
+        reasons.append(f"libs {installed_libs} != {CUDA_LIBS_VERSION}")
+
+    logger.info(f"CUDA backend needs update ({', '.join(reasons)}). Auto-downloading...")
 
     try:
         await download_cuda_binary()
@@ -250,10 +411,12 @@ async def check_and_update_cuda_binary():
 
 
 async def delete_cuda_binary() -> bool:
-    """Delete the downloaded CUDA binary. Returns True if deleted."""
-    path = get_cuda_binary_path()
-    if path and path.exists():
-        path.unlink()
-        logger.info(f"Deleted CUDA binary: {path}")
+    """Delete the downloaded CUDA backend directory. Returns True if deleted."""
+    import shutil
+
+    cuda_dir = get_cuda_dir()
+    if cuda_dir.exists() and any(cuda_dir.iterdir()):
+        shutil.rmtree(cuda_dir)
+        logger.info(f"Deleted CUDA backend directory: {cuda_dir}")
         return True
     return False

backend/services/effects.py

@@ -11,6 +11,8 @@ from typing import List, Optional
 from sqlalchemy.orm import Session
 from sqlalchemy.exc import IntegrityError
 
+from ..utils.effects import validate_effects_chain
+
 from ..database import EffectPreset as DBEffectPreset
 from ..models import EffectPresetResponse, EffectPresetCreate, EffectPresetUpdate, EffectConfig
 
@@ -52,7 +54,6 @@ def get_preset_by_name(name: str, db: Session) -> Optional[EffectPresetResponse]
 
 def create_preset(data: EffectPresetCreate, db: Session) -> EffectPresetResponse:
     """Create a new user effect preset."""
-    from .utils.effects import validate_effects_chain
 
     chain_dicts = [e.model_dump() for e in data.effects_chain]
     error = validate_effects_chain(chain_dicts)
@@ -94,7 +95,6 @@ def update_preset(preset_id: str, data: EffectPresetUpdate, db: Session) -> Opti
     if data.description is not None:
         preset.description = data.description
     if data.effects_chain is not None:
-        from .utils.effects import validate_effects_chain
 
         chain_dicts = [e.model_dump() for e in data.effects_chain]
         error = validate_effects_chain(chain_dicts)

backend/services/export_import.py

@@ -73,8 +73,8 @@ def export_profile_to_zip(profile_id: str, db: Session) -> bytes:
         # Check if profile has avatar
         has_avatar = False
         if profile.avatar_path:
-            avatar_path = Path(profile.avatar_path)
-            if avatar_path.exists():
+            avatar_path = config.resolve_storage_path(profile.avatar_path)
+            if avatar_path is not None and avatar_path.exists():
                 has_avatar = True
                 # Add avatar to ZIP root with original extension
                 avatar_ext = avatar_path.suffix
@@ -98,7 +98,9 @@ def export_profile_to_zip(profile_id: str, db: Session) -> bytes:
 
         for sample in samples:
             # Get filename from audio_path (should be {sample_id}.wav)
-            audio_path = Path(sample.audio_path)
+            audio_path = config.resolve_storage_path(sample.audio_path)
+            if audio_path is None:
+                raise ValueError(f"Audio file not found: {sample.audio_path}")
             filename = audio_path.name
 
             # Read audio file
@@ -279,7 +281,7 @@ def export_generation_to_zip(generation_id: str, db: Session) -> bytes:
         # Build version manifest entries
         version_entries = []
         for v in versions:
-            v_path = Path(v.audio_path)
+            v_path = config.resolve_storage_path(v.audio_path)
             effects_chain = None
             if v.effects_chain:
                 effects_chain = json.loads(v.effects_chain)
@@ -314,14 +316,14 @@ def export_generation_to_zip(generation_id: str, db: Session) -> bytes:
         
         # Add all version audio files
         for v in versions:
-            v_path = Path(v.audio_path)
-            if v_path.exists():
+            v_path = config.resolve_storage_path(v.audio_path)
+            if v_path is not None and v_path.exists():
                 zip_file.write(v_path, f"audio/{v_path.name}")
 
         # Fallback: if no versions exist, include the generation's main audio
         if not versions:
-            audio_path = Path(generation.audio_path)
-            if audio_path.exists():
+            audio_path = config.resolve_storage_path(generation.audio_path)
+            if audio_path is not None and audio_path.exists():
                 zip_file.write(audio_path, f"audio/{audio_path.name}")
     
     zip_buffer.seek(0)
@@ -426,7 +428,7 @@ async def import_generation_from_zip(file_bytes: bytes, db: Session) -> dict:
                     profile_id=profile_id,
                     text=generation_data["text"],
                     language=generation_data["language"],
-                    audio_path=str(audio_dest),
+                    audio_path=config.to_storage_path(audio_dest),
                     duration=generation_data["duration"],
                     seed=generation_data.get("seed"),
                     instruct=generation_data.get("instruct"),

backend/services/generation.py

@@ -16,6 +16,7 @@ Mode differences:
 
 from __future__ import annotations
 
+import asyncio
 import traceback
 from typing import Literal, Optional
 
@@ -126,6 +127,13 @@ async def run_generation(
             duration=duration,
         )
 
+    except asyncio.CancelledError:
+        await history.update_generation_status(
+            generation_id=generation_id,
+            status="failed",
+            db=bg_db,
+            error="Generation cancelled",
+        )
     except Exception as e:
         traceback.print_exc()
         await history.update_generation_status(
@@ -163,7 +171,7 @@ def _save_generate(
     versions_mod.create_version(
         generation_id=generation_id,
         label="original",
-        audio_path=str(clean_audio_path),
+        audio_path=config.to_storage_path(clean_audio_path),
         db=db,
         effects_chain=None,
         is_default=not has_effects,
@@ -174,6 +182,8 @@ def _save_generate(
     if has_effects:
         from ..utils.effects import apply_effects, validate_effects_chain
 
+        assert effects_chain is not None
+
         error_msg = validate_effects_chain(effects_chain)
         if error_msg:
             import logging
@@ -189,13 +199,13 @@ def _save_generate(
             versions_mod.create_version(
                 generation_id=generation_id,
                 label="version-2",
-                audio_path=str(processed_path),
+                audio_path=config.to_storage_path(processed_path),
                 db=db,
                 effects_chain=effects_chain,
                 is_default=True,
             )
 
-    return final_audio_path
+    return config.to_storage_path(final_audio_path)
 
 
 def _save_retry(
@@ -211,7 +221,7 @@ def _save_retry(
     """
     audio_path = config.get_generations_dir() / f"{generation_id}.wav"
     save_audio(audio, str(audio_path), sample_rate)
-    return str(audio_path)
+    return config.to_storage_path(audio_path)
 
 
 def _save_regenerate(
@@ -244,10 +254,10 @@ def _save_regenerate(
     versions_mod.create_version(
         generation_id=generation_id,
         label=label,
-        audio_path=str(audio_path),
+        audio_path=config.to_storage_path(audio_path),
         db=db,
         effects_chain=None,
         is_default=True,
     )
 
-    return str(audio_path)
+    return config.to_storage_path(audio_path)

backend/services/history.py

@@ -253,8 +253,8 @@ async def delete_generation(
 
     # Delete main audio file (if not already removed by version cleanup)
     if generation.audio_path:
-        audio_path = Path(generation.audio_path)
-        if audio_path.exists():
+        audio_path = config.resolve_storage_path(generation.audio_path)
+        if audio_path is not None and audio_path.exists():
             audio_path.unlink()
 
     # Delete from database
@@ -264,6 +264,43 @@ async def delete_generation(
     return True
 
 
+async def delete_failed_generations(db: Session) -> int:
+    """
+    Delete every generation whose status is 'failed'.
+
+    Used by the "Clear failed" action in the UI so users can tidy up
+    history after the model wasn't loaded, the app was closed mid-run,
+    or a generation otherwise errored out (see issue #410).
+
+    Returns:
+        Number of generations deleted.
+    """
+    from . import versions as versions_mod
+
+    failed = db.query(DBGeneration).filter(DBGeneration.status == "failed").all()
+    count = 0
+    for generation in failed:
+        # Clean up version files/rows first.
+        versions_mod.delete_versions_for_generation(generation.id, db)
+
+        # Remove the main audio file if it somehow made it to disk.
+        if generation.audio_path:
+            audio_path = config.resolve_storage_path(generation.audio_path)
+            if audio_path is not None and audio_path.exists():
+                try:
+                    audio_path.unlink()
+                except OSError:
+                    # Best-effort cleanup — don't abort the whole sweep
+                    # if a single file can't be removed.
+                    pass
+
+        db.delete(generation)
+        count += 1
+
+    db.commit()
+    return count
+
+
 async def delete_generations_by_profile(
     profile_id: str,
     db: Session,
@@ -282,9 +319,13 @@ async def delete_generations_by_profile(
     
     count = 0
     for generation in generations:
+        # Delete associated version files and rows first
+        from . import versions as versions_mod
+        versions_mod.delete_versions_for_generation(generation.id, db)
+
         # Delete audio file
-        audio_path = Path(generation.audio_path)
-        if audio_path.exists():
+        audio_path = config.resolve_storage_path(generation.audio_path)
+        if audio_path is not None and audio_path.exists():
             audio_path.unlink()
         
         # Delete from database

backend/services/profiles.py

@@ -1,33 +1,30 @@
-"""
-Voice profile management module.
-"""
+"""Voice profile management module."""
 
-from typing import List, Optional
-from datetime import datetime
-import uuid
+import json as _json
+import logging
 import shutil
+import uuid
+from datetime import datetime
 from pathlib import Path
-from sqlalchemy.orm import Session
-from sqlalchemy import func, select
 
+from sqlalchemy import func
+from sqlalchemy.orm import Session
+
+from .. import config
+from ..database import Generation as DBGeneration, ProfileSample as DBProfileSample, VoiceProfile as DBVoiceProfile
 from ..models import (
+    EffectConfig,
+    ProfileSampleResponse,
     VoiceProfileCreate,
     VoiceProfileResponse,
-    ProfileSampleCreate,
-    ProfileSampleResponse,
 )
-from ..database import (
-    VoiceProfile as DBVoiceProfile,
-    ProfileSample as DBProfileSample,
-    Generation as DBGeneration,
-)
-from ..models import EffectConfig
-from ..utils.audio import validate_reference_audio, validate_and_load_reference_audio, load_audio, save_audio
-from ..utils.images import validate_image, process_avatar
+from ..utils.audio import save_audio, validate_and_load_reference_audio
 from ..utils.cache import _get_cache_dir, clear_profile_cache
-from .tts import get_tts_model
-from .. import config
-import json as _json
+from ..utils.images import process_avatar, validate_image
+
+logger = logging.getLogger(__name__)
+
+CLONING_ENGINES = {"qwen", "luxtts", "chatterbox", "chatterbox_turbo", "tada"}
 
 
 def _profile_to_response(
@@ -52,6 +49,11 @@ def _profile_to_response(
         language=profile.language,
         avatar_path=profile.avatar_path,
         effects_chain=effects_chain,
+        voice_type=getattr(profile, "voice_type", None) or "cloned",
+        preset_engine=getattr(profile, "preset_engine", None),
+        preset_voice_id=getattr(profile, "preset_voice_id", None),
+        design_prompt=getattr(profile, "design_prompt", None),
+        default_engine=getattr(profile, "default_engine", None),
         generation_count=generation_count,
         sample_count=sample_count,
         created_at=profile.created_at,
@@ -59,6 +61,79 @@ def _profile_to_response(
     )
 
 
+def _get_preset_voice_ids(engine: str) -> set[str]:
+    if engine == "kokoro":
+        from ..backends.kokoro_backend import KOKORO_VOICES
+
+        return {voice_id for voice_id, _name, _gender, _lang in KOKORO_VOICES}
+
+    if engine == "qwen_custom_voice":
+        from ..backends.qwen_custom_voice_backend import QWEN_CUSTOM_VOICES
+
+        return {voice_id for voice_id, _name, _gender, _lang, _desc in QWEN_CUSTOM_VOICES}
+
+    return set()
+
+
+def _validate_profile_fields(
+    *,
+    voice_type: str,
+    preset_engine: str | None,
+    preset_voice_id: str | None,
+    design_prompt: str | None,
+    default_engine: str | None,
+) -> str | None:
+    if voice_type == "preset":
+        if not preset_engine or not preset_voice_id:
+            return "Preset profiles require both preset_engine and preset_voice_id"
+        if default_engine and default_engine != preset_engine:
+            return "Preset profiles must use their preset_engine as default_engine"
+
+        available_voice_ids = _get_preset_voice_ids(preset_engine)
+        if available_voice_ids and preset_voice_id not in available_voice_ids:
+            return f"Preset voice '{preset_voice_id}' is not valid for engine '{preset_engine}'"
+        return None
+
+    if voice_type == "designed":
+        if not design_prompt or not design_prompt.strip():
+            return "Designed profiles require a design_prompt"
+        if preset_engine or preset_voice_id:
+            return "Designed profiles cannot set preset_engine or preset_voice_id"
+        return None
+
+    if preset_engine or preset_voice_id:
+        return "Cloned profiles cannot set preset_engine or preset_voice_id"
+    if design_prompt:
+        return "Cloned profiles cannot set design_prompt"
+    if default_engine and default_engine not in CLONING_ENGINES:
+        return f"Cloned profiles cannot use default engine '{default_engine}'"
+    return None
+
+
+def validate_profile_engine(profile, engine: str) -> None:
+    voice_type = getattr(profile, "voice_type", None) or "cloned"
+
+    if voice_type == "preset":
+        preset_engine = getattr(profile, "preset_engine", None)
+        preset_voice_id = getattr(profile, "preset_voice_id", None)
+        if not preset_engine or not preset_voice_id:
+            raise ValueError(f"Preset profile {profile.id} is missing preset engine metadata")
+        if preset_engine != engine:
+            raise ValueError(
+                f"Preset profile {profile.id} only supports engine '{preset_engine}', not '{engine}'"
+            )
+        return
+
+    if voice_type == "designed":
+        design_prompt = getattr(profile, "design_prompt", None)
+        if not design_prompt or not design_prompt.strip():
+            raise ValueError(f"Designed profile {profile.id} is missing design_prompt")
+        return
+
+    if engine not in CLONING_ENGINES:
+        raise ValueError(f"Engine '{engine}' does not support cloned voice profiles")
+
+
 async def create_profile(
     data: VoiceProfileCreate,
     db: Session,
@@ -80,11 +155,32 @@ async def create_profile(
     if existing_profile:
         raise ValueError(f"A profile with the name '{data.name}' already exists. Please choose a different name.")
 
+    # Auto-set default_engine for preset profiles
+    default_engine = data.default_engine
+    voice_type = data.voice_type or "cloned"
+    if voice_type == "preset" and data.preset_engine and not default_engine:
+        default_engine = data.preset_engine
+
+    validation_error = _validate_profile_fields(
+        voice_type=voice_type,
+        preset_engine=data.preset_engine,
+        preset_voice_id=data.preset_voice_id,
+        design_prompt=data.design_prompt,
+        default_engine=default_engine,
+    )
+    if validation_error:
+        raise ValueError(validation_error)
+
     db_profile = DBVoiceProfile(
         id=str(uuid.uuid4()),
         name=data.name,
         description=data.description,
         language=data.language,
+        voice_type=voice_type,
+        preset_engine=data.preset_engine,
+        preset_voice_id=data.preset_voice_id,
+        design_prompt=data.design_prompt,
+        default_engine=default_engine,
         created_at=datetime.utcnow(),
         updated_at=datetime.utcnow(),
     )
@@ -140,7 +236,7 @@ async def add_profile_sample(
     db_sample = DBProfileSample(
         id=sample_id,
         profile_id=profile_id,
-        audio_path=str(dest_path),
+        audio_path=config.to_storage_path(dest_path),
         reference_text=reference_text,
     )
 
@@ -161,7 +257,7 @@ async def add_profile_sample(
 async def get_profile(
     profile_id: str,
     db: Session,
-) -> Optional[VoiceProfileResponse]:
+) -> VoiceProfileResponse | None:
     """
     Get a voice profile by ID.
 
@@ -182,7 +278,7 @@ async def get_profile(
 async def get_profile_samples(
     profile_id: str,
     db: Session,
-) -> List[ProfileSampleResponse]:
+) -> list[ProfileSampleResponse]:
     """
     Get all samples for a profile.
 
@@ -197,7 +293,7 @@ async def get_profile_samples(
     return [ProfileSampleResponse.model_validate(s) for s in samples]
 
 
-async def list_profiles(db: Session) -> List[VoiceProfileResponse]:
+async def list_profiles(db: Session) -> list[VoiceProfileResponse]:
     """
     List all voice profiles with generation and sample counts.
 
@@ -238,7 +334,7 @@ async def update_profile(
     profile_id: str,
     data: VoiceProfileCreate,
     db: Session,
-) -> Optional[VoiceProfileResponse]:
+) -> VoiceProfileResponse | None:
     """
     Update a voice profile.
 
@@ -262,9 +358,27 @@ async def update_profile(
         if existing_profile:
             raise ValueError(f"A profile with the name '{data.name}' already exists. Please choose a different name.")
 
+    voice_type = getattr(profile, "voice_type", None) or "cloned"
+    preset_engine = getattr(profile, "preset_engine", None)
+    preset_voice_id = getattr(profile, "preset_voice_id", None)
+    design_prompt = getattr(profile, "design_prompt", None)
+    default_engine = data.default_engine if data.default_engine is not None else getattr(profile, "default_engine", None)
+
+    validation_error = _validate_profile_fields(
+        voice_type=voice_type,
+        preset_engine=preset_engine,
+        preset_voice_id=preset_voice_id,
+        design_prompt=design_prompt,
+        default_engine=default_engine,
+    )
+    if validation_error:
+        raise ValueError(validation_error)
+
     profile.name = data.name
     profile.description = data.description
     profile.language = data.language
+    if data.default_engine is not None:
+        profile.default_engine = data.default_engine or None  # empty string → NULL
     profile.updated_at = datetime.utcnow()
 
     db.commit()
@@ -327,8 +441,8 @@ async def delete_profile_sample(
     # Store profile_id before deleting
     profile_id = sample.profile_id
 
-    audio_path = Path(sample.audio_path)
-    if audio_path.exists():
+    audio_path = config.resolve_storage_path(sample.audio_path)
+    if audio_path is not None and audio_path.exists():
         audio_path.unlink()
 
     db.delete(sample)
@@ -345,7 +459,7 @@ async def update_profile_sample(
     sample_id: str,
     reference_text: str,
     db: Session,
-) -> Optional[ProfileSampleResponse]:
+) -> ProfileSampleResponse | None:
     """
     Update a profile sample's reference text.
 
@@ -382,19 +496,57 @@ async def create_voice_prompt_for_profile(
     engine: str = "qwen",
 ) -> dict:
     """
-    Create a combined voice prompt from all samples in a profile.
+    Create a voice prompt from a profile.
+
+    For cloned profiles: combines all audio samples into a voice prompt.
+    For preset profiles: returns the engine-specific preset voice reference.
+    For designed profiles: returns the text design prompt (future).
 
     Args:
         profile_id: Profile ID
         db: Database session
         use_cache: Whether to use cached prompts
-        engine: TTS engine to create prompt for ("qwen" or "luxtts")
+        engine: TTS engine to create prompt for
 
     Returns:
         Voice prompt dictionary
     """
     from ..backends import get_tts_backend_for_engine
 
+    profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
+    if not profile:
+        raise ValueError(f"Profile not found: {profile_id}")
+
+    voice_type = getattr(profile, "voice_type", None) or "cloned"
+    validate_profile_engine(profile, engine)
+
+    # ── Preset profiles: return engine-specific voice reference ──
+    if voice_type == "preset":
+        if not profile.preset_engine or not profile.preset_voice_id:
+            raise ValueError(f"Preset profile {profile_id} is missing preset engine metadata")
+        if profile.preset_engine != engine:
+            raise ValueError(
+                f"Preset profile {profile_id} only supports engine '{profile.preset_engine}', not '{engine}'"
+            )
+        return {
+            "voice_type": "preset",
+            "preset_engine": profile.preset_engine,
+            "preset_voice_id": profile.preset_voice_id,
+        }
+
+    # ── Designed profiles: return text description (future) ──
+    if voice_type == "designed":
+        if not profile.design_prompt or not profile.design_prompt.strip():
+            raise ValueError(f"Designed profile {profile_id} is missing design_prompt")
+        return {
+            "voice_type": "designed",
+            "design_prompt": profile.design_prompt,
+        }
+
+    if engine not in CLONING_ENGINES:
+        raise ValueError(f"Engine '{engine}' does not support cloned voice profiles")
+
+    # ── Cloned profiles: create from audio samples ──
     samples = db.query(DBProfileSample).filter_by(profile_id=profile_id).all()
 
     if not samples:
@@ -404,40 +556,48 @@ async def create_voice_prompt_for_profile(
 
     if len(samples) == 1:
         sample = samples[0]
+        sample_audio_path = config.resolve_storage_path(sample.audio_path)
+        if sample_audio_path is None:
+            raise ValueError(f"Sample audio not found for profile {profile_id}")
         voice_prompt, _ = await tts_model.create_voice_prompt(
-            sample.audio_path,
+            str(sample_audio_path),
             sample.reference_text,
             use_cache=use_cache,
         )
         return voice_prompt
-    else:
-        audio_paths = [s.audio_path for s in samples]
-        reference_texts = [s.reference_text for s in samples]
 
-        combined_audio, combined_text = await tts_model.combine_voice_prompts(
-            audio_paths,
-            reference_texts,
-        )
+    audio_paths = []
+    for sample in samples:
+        sample_audio_path = config.resolve_storage_path(sample.audio_path)
+        if sample_audio_path is None:
+            raise ValueError(f"Sample audio not found for profile {profile_id}")
+        audio_paths.append(str(sample_audio_path))
+    reference_texts = [s.reference_text for s in samples]
 
-        # Save combined audio to cache directory (persistent)
-        # Create a hash of sample IDs to identify this specific combination
-        import hashlib
+    combined_audio, combined_text = await tts_model.combine_voice_prompts(
+        audio_paths,
+        reference_texts,
+    )
 
-        sample_ids_str = "-".join(sorted([s.id for s in samples]))
-        combination_hash = hashlib.md5(sample_ids_str.encode()).hexdigest()[:12]
+    # Save combined audio to cache directory (persistent)
+    # Create a hash of sample IDs to identify this specific combination
+    import hashlib
 
-        cache_dir = _get_cache_dir()
-        cache_dir.mkdir(parents=True, exist_ok=True)
-        combined_path = cache_dir / f"combined_{profile_id}_{combination_hash}.wav"
+    sample_ids_str = "-".join(sorted([s.id for s in samples]))
+    combination_hash = hashlib.md5(sample_ids_str.encode()).hexdigest()[:12]
 
-        save_audio(combined_audio, str(combined_path), 24000)
+    cache_dir = _get_cache_dir()
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    combined_path = cache_dir / f"combined_{profile_id}_{combination_hash}.wav"
 
-        voice_prompt, _ = await tts_model.create_voice_prompt(
-            str(combined_path),
-            combined_text,
-            use_cache=use_cache,
-        )
-        return voice_prompt
+    save_audio(combined_audio, str(combined_path), 24000)
+
+    voice_prompt, _ = await tts_model.create_voice_prompt(
+        str(combined_path),
+        combined_text,
+        use_cache=use_cache,
+    )
+    return voice_prompt
 
 
 async def upload_avatar(
@@ -465,8 +625,8 @@ async def upload_avatar(
         raise ValueError(error_msg)
 
     if profile.avatar_path:
-        old_avatar = Path(profile.avatar_path)
-        if old_avatar.exists():
+        old_avatar = config.resolve_storage_path(profile.avatar_path)
+        if old_avatar is not None and old_avatar.exists():
             old_avatar.unlink()
 
     # Determine file extension from uploaded file
@@ -487,7 +647,7 @@ async def upload_avatar(
 
     process_avatar(image_path, str(output_path))
 
-    profile.avatar_path = str(output_path)
+    profile.avatar_path = config.to_storage_path(output_path)
     profile.updated_at = datetime.utcnow()
 
     db.commit()
@@ -514,8 +674,8 @@ async def delete_avatar(
     if not profile or not profile.avatar_path:
         return False
 
-    avatar_path = Path(profile.avatar_path)
-    if avatar_path.exists():
+    avatar_path = config.resolve_storage_path(profile.avatar_path)
+    if avatar_path is not None and avatar_path.exists():
         avatar_path.unlink()
 
     profile.avatar_path = None

backend/services/stories.py

@@ -10,6 +10,7 @@ from pathlib import Path
 from sqlalchemy.orm import Session
 from sqlalchemy import func
 
+from .. import config
 from ..models import (
     StoryCreate,
     StoryResponse,
@@ -483,13 +484,15 @@ async def split_story_item(
     Returns:
         List of two updated item details (original and new) or None if not found/invalid
     """
-    # Get the item
+    # Get the item with a row lock to prevent concurrent splits on the
+    # same clip (e.g. from rapid double-clicks racing each other).
     item = (
         db.query(DBStoryItem)
         .filter_by(
             id=item_id,
             story_id=story_id,
         )
+        .with_for_update()
         .first()
     )
     if not item:
@@ -826,8 +829,8 @@ async def export_story_audio(
             if version:
                 resolved_audio_path = version.audio_path
 
-        audio_path = Path(resolved_audio_path)
-        if not audio_path.exists():
+        audio_path = config.resolve_storage_path(resolved_audio_path)
+        if audio_path is None or not audio_path.exists():
             continue
 
         try:

backend/services/task_queue.py

@@ -5,12 +5,27 @@ to avoid GPU contention.
 
 import asyncio
 import traceback
+from dataclasses import dataclass
+from typing import Coroutine, Literal
 
 # Keep references to fire-and-forget background tasks to prevent GC
 _background_tasks: set = set()
 
+
+@dataclass
+class GenerationJob:
+    """Queued generation work plus the generation ID it belongs to."""
+
+    generation_id: str
+    coro: Coroutine
+
+
 # Generation queue — serializes TTS inference to avoid GPU contention
 _generation_queue: asyncio.Queue = None  # type: ignore  # initialized at startup
+_generation_worker_task: asyncio.Task | None = None
+_queued_generation_ids: set[str] = set()
+_running_generation_tasks: dict[str, asyncio.Task] = {}
+_cancelled_generation_ids: set[str] = set()
 
 
 def create_background_task(coro) -> asyncio.Task:
@@ -24,25 +39,70 @@ def create_background_task(coro) -> asyncio.Task:
 async def _generation_worker():
     """Worker that processes generation tasks one at a time."""
     while True:
-        coro = await _generation_queue.get()
+        job = await _generation_queue.get()
         try:
-            await coro
+            if job.generation_id in _cancelled_generation_ids:
+                _cancelled_generation_ids.discard(job.generation_id)
+                job.coro.close()
+                continue
+
+            task = asyncio.create_task(job.coro)
+            _running_generation_tasks[job.generation_id] = task
+            _queued_generation_ids.discard(job.generation_id)
+            try:
+                await task
+            except asyncio.CancelledError:
+                if not task.cancelled():
+                    raise
         except Exception:
             traceback.print_exc()
         finally:
+            _running_generation_tasks.pop(job.generation_id, None)
+            _queued_generation_ids.discard(job.generation_id)
             _generation_queue.task_done()
 
 
-def enqueue_generation(coro):
+def enqueue_generation(generation_id: str, coro):
     """Add a generation coroutine to the serial queue."""
-    _generation_queue.put_nowait(coro)
+    if _generation_queue is None:
+        raise RuntimeError("Generation queue has not been initialized")
+
+    _queued_generation_ids.add(generation_id)
+    _generation_queue.put_nowait(GenerationJob(generation_id=generation_id, coro=coro))
 
 
-def init_queue():
+def cancel_generation(generation_id: str) -> Literal["queued", "running"] | None:
+    """Cancel a queued or running generation if it is still active."""
+    running_task = _running_generation_tasks.get(generation_id)
+    if running_task is not None:
+        running_task.cancel()
+        return "running"
+
+    if generation_id in _queued_generation_ids:
+        _queued_generation_ids.discard(generation_id)
+        _cancelled_generation_ids.add(generation_id)
+        return "queued"
+
+    return None
+
+
+def init_queue(force: bool = False):
     """Initialize the generation queue and start the worker.
 
     Must be called once during application startup (inside a running event loop).
     """
-    global _generation_queue
+    global _generation_queue, _generation_worker_task
+    global _queued_generation_ids, _running_generation_tasks, _cancelled_generation_ids
+
+    if _generation_worker_task is not None and not _generation_worker_task.done():
+        if not force:
+            return
+        _generation_worker_task.cancel()
+        for task in list(_running_generation_tasks.values()):
+            task.cancel()
+
     _generation_queue = asyncio.Queue()
-    create_background_task(_generation_worker())
+    _queued_generation_ids = set()
+    _running_generation_tasks = {}
+    _cancelled_generation_ids = set()
+    _generation_worker_task = create_background_task(_generation_worker())

backend/services/versions.py

@@ -158,8 +158,8 @@ def delete_version(version_id: str, db: Session) -> bool:
     gen_id = version.generation_id
 
     # Delete audio file
-    audio_path = Path(version.audio_path)
-    if audio_path.exists():
+    audio_path = config.resolve_storage_path(version.audio_path)
+    if audio_path is not None and audio_path.exists():
         audio_path.unlink()
 
     db.delete(version)
@@ -193,8 +193,8 @@ def delete_versions_for_generation(generation_id: str, db: Session) -> int:
     )
     count = 0
     for v in versions:
-        audio_path = Path(v.audio_path)
-        if audio_path.exists():
+        audio_path = config.resolve_storage_path(v.audio_path)
+        if audio_path is not None and audio_path.exists():
             audio_path.unlink()
         db.delete(v)
         count += 1

backend/tests/E2E_MODEL_TEST_DESIGN.md

@@ -0,0 +1,220 @@
+# End-to-End Model Generation Test — Design
+
+## Goal
+
+A single script, runnable on macOS and Windows, that exercises every TTS model against the **frozen PyInstaller binary** (not the dev server), captures per-model pass/fail and error messages, and exits non-zero if any model fails. Generation is strictly sequential — one model loaded at a time.
+
+## Test matrix (10 runs)
+
+Derived from `backend/backends/__init__.py:185-316`. Each row maps to one `POST /generate` call.
+
+| # | engine                | model_size | profile kind | notes |
+|---|-----------------------|------------|--------------|-------|
+| 1 | `qwen`                | `1.7B`     | cloned       | reference audio required |
+| 2 | `qwen`                | `0.6B`     | cloned       | |
+| 3 | `qwen_custom_voice`   | `1.7B`     | preset       | `preset_voice_id="Ryan"` |
+| 4 | `qwen_custom_voice`   | `0.6B`     | preset       | `preset_voice_id="Ryan"` |
+| 5 | `luxtts`              | —          | cloned       | English only |
+| 6 | `chatterbox`          | —          | cloned       | |
+| 7 | `chatterbox_turbo`    | —          | cloned       | English only |
+| 8 | `tada`                | `1B`       | cloned       | tada-1b, English only |
+| 9 | `tada`                | `3B`       | cloned       | tada-3b-ml, multilingual |
+| 10| `kokoro`              | —          | preset       | `preset_voice_id="af_heart"` |
+
+Cloned engines (1, 2, 5, 6, 7, 8, 9) share **one** profile created once with the reference WAV. Preset profiles are created separately, one for kokoro and one for qwen_custom_voice.
+
+Language for every run: `en` (covers every engine's supported set).
+
+## End-to-end flow
+
+```
+1. Resolve paths          → find binary, build if missing
+2. Launch binary          → spawn with --port --data-dir --parent-pid
+3. Wait for /health       → poll until status=="healthy" or 120s timeout
+4. Create profiles        → 1 cloned + 2 preset, via /profiles (+ /samples)
+5. For each (engine, model_size) in matrix:
+     a. Check cache       → GET /models/status → cached? short timeout : long
+     b. POST /generate    → get generation_id
+     c. Stream /status    → consume SSE until completed/failed/timeout
+     d. Record result     → {engine, model_size, status, duration, error, elapsed}
+6. Write results          → JSON + Markdown table to ./results/
+7. Shutdown binary        → SIGTERM, fall back to kill, verify port freed
+8. Exit code              → 0 if all passed, 1 otherwise
+```
+
+## Binary resolution
+
+Search order — **first hit wins**:
+
+| Platform | Path | Build type |
+|----------|------|------------|
+| macOS    | `backend/dist/voicebox-server-cuda/voicebox-server-cuda` | onedir (CUDA, rarely on Mac) |
+| macOS    | `backend/dist/voicebox-server`                          | onefile (CPU) |
+| Windows  | `backend\dist\voicebox-server-cuda\voicebox-server-cuda.exe` | onedir (CUDA) |
+| Windows  | `backend\dist\voicebox-server.exe`                      | onefile (CPU) |
+
+If none exist, run `python backend/build_binary.py` and wait for it to finish (can take 5-20 min). Fail with a clear error if the build itself fails. `--skip-build` flag forces "error out if no binary" instead of building.
+
+## Spawn command
+
+Mirrors Tauri's launch in `tauri/src-tauri/src/main.rs:369-388`:
+
+```
+<binary> --host 127.0.0.1 --port <free-port> --data-dir <tempdir> --parent-pid <test-pid>
+```
+
+- **Port**: bind to `0` first in Python to grab a free port, then pass that number.
+- **Data dir**: `tempfile.mkdtemp(prefix="voicebox-e2e-")`. Deleted after the run unless `--keep-data-dir`. Profiles and generated WAVs land here.
+- **Parent PID**: current Python PID — ensures the backend dies if the test crashes (watchdog in `server.py:102-224`).
+- **stdout/stderr**: tee to both a log file in `./results/server-<timestamp>.log` and a rolling in-memory buffer. On model failure, last 100 lines of the buffer are attached to that model's error record.
+
+## Profile setup
+
+One cloned profile shared across all cloning engines:
+
+```http
+POST /profiles
+{
+  "name": "e2e-cloned",
+  "voice_type": "cloned",
+  "language": "en"
+}
+```
+
+Then:
+
+```http
+POST /profiles/{id}/samples   (multipart)
+  file: <reference WAV>
+  reference_text: <exact transcription>
+```
+
+Two preset profiles:
+
+```http
+POST /profiles
+{ "name": "e2e-kokoro",  "voice_type": "preset", "language": "en",
+  "preset_engine": "kokoro",            "preset_voice_id": "af_heart" }
+
+POST /profiles
+{ "name": "e2e-qwen-cv", "voice_type": "preset", "language": "en",
+  "preset_engine": "qwen_custom_voice", "preset_voice_id": "Ryan" }
+```
+
+## Generation request (per matrix row)
+
+```http
+POST /generate
+{
+  "profile_id": "<appropriate profile>",
+  "text": "The quick brown fox jumps over the lazy dog.",
+  "language": "en",
+  "engine": "<engine>",
+  "model_size": "<size or omitted>",
+  "seed": 42,
+  "normalize": true
+}
+```
+
+Response `id` feeds into the SSE status loop (`GET /generate/{id}/status`, `routes/generations.py:190-227`). Loop reads lines until a payload with `status in ("completed", "failed")` arrives, then breaks.
+
+## Timeout strategy (split)
+
+Check `GET /models/status` for the target model **before** generation:
+
+| Cached? | Per-model timeout | Rationale |
+|---------|-------------------|-----------|
+| Yes     | **3 minutes**     | Inference only; generous for CPU builds |
+| No      | **20 minutes**    | First-run HF download up to 8 GB (tada-3b-ml) |
+
+On timeout: cancel the SSE stream, mark the row `timeout`, and continue to the next row. Don't abort the whole run on one timeout.
+
+## Result format
+
+`./results/e2e-<platform>-<arch>-<timestamp>.json`:
+
+```json
+{
+  "platform": "darwin-arm64",
+  "binary": "/abs/path/voicebox-server",
+  "binary_size_mb": 612,
+  "started_at": "2026-04-16T12:34:56Z",
+  "finished_at": "...",
+  "results": [
+    {
+      "engine": "qwen",
+      "model_size": "1.7B",
+      "status": "passed|failed|timeout",
+      "generation_id": "...",
+      "was_cached": true,
+      "elapsed_seconds": 12.4,
+      "audio_duration": 3.1,
+      "audio_path": "/tmp/.../gen.wav",
+      "error": null,
+      "server_log_tail": null
+    }
+  ]
+}
+```
+
+Companion `./results/e2e-<...>.md`:
+
+```
+# Voicebox E2E — darwin-arm64 — 2026-04-16 12:34
+
+| Engine              | Size | Status | Elapsed | Error |
+|---------------------|------|--------|---------|-------|
+| qwen                | 1.7B | PASS   |  12.4s  |       |
+| qwen                | 0.6B | FAIL   |   4.1s  | CUDA OOM: ... |
+...
+```
+
+## CLI flags
+
+```
+python -m backend.tests.test_all_models_e2e [flags]
+
+--binary PATH           Use this binary instead of auto-detecting
+--skip-build            Error if no binary found (no auto-build)
+--reference-wav PATH    Reference audio (default: backend/tests/fixtures/reference_voice.wav)
+--reference-text STR    Transcription (default: read from fixtures/reference_voice.txt)
+--only ENGINE[,...]     Run only these engines (e.g. kokoro,qwen)
+--skip ENGINE[,...]     Skip these engines
+--keep-data-dir         Don't delete tempdir after run
+--timeout-cached SEC    Override 180
+--timeout-download SEC  Override 1200
+--port N                Override auto-picked port
+--output-dir PATH       Default: backend/tests/results/
+```
+
+## File layout
+
+```
+backend/tests/
+├── E2E_MODEL_TEST_DESIGN.md       (this file)
+├── test_all_models_e2e.py         (main script, ~400-500 LoC)
+├── fixtures/
+│   ├── reference_voice.wav        (user-provided, ~5-15s clean speech)
+│   └── reference_voice.txt        (exact transcription)
+└── results/                       (gitignored)
+    ├── e2e-darwin-arm64-<ts>.json
+    ├── e2e-darwin-arm64-<ts>.md
+    └── server-<ts>.log
+```
+
+The script uses only stdlib + `httpx` (or `requests`) + `sseclient-py` — all already in `backend/requirements.txt`. No pytest to keep it invocable as a single command on fresh checkouts.
+
+## Safety & cleanup
+
+- Always kill the spawned binary in a `try/finally`. On Windows, `taskkill /F /T` the whole tree (Tauri does the same).
+- Verify the port is free on shutdown (Tauri port-reuse check in `main.rs:114-186` could otherwise pick up a ghost).
+- Don't touch the user's HF cache by default — let the server use `HF_HUB_CACHE` / `VOICEBOX_MODELS_DIR`. Passing `--isolated-cache` would point both env vars at the tempdir for a true cold-start run (opt-in only; would re-download every time).
+
+## Non-goals
+
+- Not validating audio quality (no WER, no waveform comparison). Pass = "endpoint returned `completed` and produced a non-empty WAV".
+- Not testing STT (Whisper), effects chains, channels, or streaming endpoints.
+- Not running on CI today — human-invoked on dev machines. CI integration is a follow-up once the script is stable.
+- No model unload between runs — models stay loaded; server manages its own eviction.
+- No version-drift check on the binary.
+- No `instruct` parameter exercised on qwen_custom_voice runs.

backend/tests/fixtures/README.md

@@ -0,0 +1,16 @@
+# E2E Test Fixtures
+
+Place two files here before running `test_all_models_e2e.py`:
+
+- `reference_voice.wav` — a clean speech sample, mono, 16–24 kHz, ~5–15 seconds.
+- `reference_voice.txt` — the **exact** transcription of the WAV (single line, no trailing newline required).
+
+These are used to create a cloned voice profile for every cloning-capable engine (qwen, luxtts, chatterbox, chatterbox_turbo, tada). Keep them out of version control if they contain personal audio — this directory is not gitignored by default, so add them to `.gitignore` locally if needed.
+
+You can point the test at different files with:
+
+```
+python backend/tests/test_all_models_e2e.py \
+  --reference-wav /path/to/your.wav \
+  --reference-text "exact transcription here"
+```

backend/tests/test_all_models_e2e.py

@@ -0,0 +1,630 @@
+"""
+End-to-end model generation test.
+
+Exercises every TTS model against the frozen PyInstaller binary, captures
+per-model pass/fail, and writes a JSON + Markdown report.
+
+Usage:
+    python backend/tests/test_all_models_e2e.py [flags]
+
+See E2E_MODEL_TEST_DESIGN.md for the full design.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import platform
+import shutil
+import signal
+import socket
+import subprocess
+import sys
+import tempfile
+import threading
+import time
+from collections import deque
+from dataclasses import asdict, dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional
+
+import httpx
+
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+BACKEND_DIR = REPO_ROOT / "backend"
+DIST_DIR = BACKEND_DIR / "dist"
+FIXTURES_DIR = Path(__file__).resolve().parent / "fixtures"
+RESULTS_DIR = Path(__file__).resolve().parent / "results"
+
+
+# ── Test matrix ──────────────────────────────────────────────────────
+
+@dataclass(frozen=True)
+class MatrixRow:
+    label: str            # human-readable (appears in report)
+    engine: str           # /generate engine
+    model_size: Optional[str]  # /generate model_size (None = omit)
+    profile_kind: str     # "cloned" | "preset_kokoro" | "preset_qwen_cv"
+    model_name: str       # /models/status key for cache lookup
+
+
+MATRIX: list[MatrixRow] = [
+    MatrixRow("qwen 1.7B",              "qwen",              "1.7B", "cloned",          "qwen-tts-1.7B"),
+    MatrixRow("qwen 0.6B",              "qwen",              "0.6B", "cloned",          "qwen-tts-0.6B"),
+    MatrixRow("qwen_custom_voice 1.7B", "qwen_custom_voice", "1.7B", "preset_qwen_cv",  "qwen-custom-voice-1.7B"),
+    MatrixRow("qwen_custom_voice 0.6B", "qwen_custom_voice", "0.6B", "preset_qwen_cv",  "qwen-custom-voice-0.6B"),
+    MatrixRow("luxtts",                 "luxtts",            None,   "cloned",          "luxtts"),
+    MatrixRow("chatterbox",             "chatterbox",        None,   "cloned",          "chatterbox-tts"),
+    MatrixRow("chatterbox_turbo",       "chatterbox_turbo",  None,   "cloned",          "chatterbox-turbo"),
+    MatrixRow("tada 1B",                "tada",              "1B",   "cloned",          "tada-1b"),
+    MatrixRow("tada 3B",                "tada",              "3B",   "cloned",          "tada-3b-ml"),
+    MatrixRow("kokoro",                 "kokoro",            None,   "preset_kokoro",   "kokoro"),
+]
+
+TEXT = "The quick brown fox jumps over the lazy dog."
+DEFAULT_TIMEOUT_CACHED = 180
+DEFAULT_TIMEOUT_DOWNLOAD = 1200
+HEALTH_TIMEOUT = 120
+
+
+# ── Result record ────────────────────────────────────────────────────
+
+@dataclass
+class ModelResult:
+    label: str
+    engine: str
+    model_size: Optional[str]
+    status: str                      # "passed" | "failed" | "timeout"
+    was_cached: Optional[bool] = None
+    generation_id: Optional[str] = None
+    elapsed_seconds: float = 0.0
+    audio_duration: Optional[float] = None
+    audio_path: Optional[str] = None
+    audio_bytes: Optional[int] = None
+    error: Optional[str] = None
+    http_status: Optional[int] = None
+    server_log_tail: Optional[list[str]] = None
+
+
+# ── Binary resolution ────────────────────────────────────────────────
+
+def find_binary() -> Optional[Path]:
+    """Return the first existing binary in priority order, or None."""
+    is_win = platform.system() == "Windows"
+    exe = ".exe" if is_win else ""
+    candidates = [
+        DIST_DIR / "voicebox-server-cuda" / f"voicebox-server-cuda{exe}",
+        DIST_DIR / f"voicebox-server{exe}",
+    ]
+    for c in candidates:
+        if c.exists() and c.is_file():
+            return c
+    return None
+
+
+def build_binary() -> Path:
+    """Invoke build_binary.py and return the resulting binary path."""
+    print("[build] No frozen binary found — invoking build_binary.py (this may take 5-20 minutes)...", flush=True)
+    script = BACKEND_DIR / "build_binary.py"
+    result = subprocess.run(
+        [sys.executable, str(script)],
+        cwd=str(BACKEND_DIR),
+    )
+    if result.returncode != 0:
+        raise RuntimeError(f"build_binary.py exited with code {result.returncode}")
+    found = find_binary()
+    if found is None:
+        raise RuntimeError("build_binary.py finished but no binary was found in backend/dist/")
+    return found
+
+
+# ── Server spawn + log capture ───────────────────────────────────────
+
+class ServerProcess:
+    def __init__(self, binary: Path, port: int, data_dir: Path, log_path: Path):
+        self.binary = binary
+        self.port = port
+        self.data_dir = data_dir
+        self.log_path = log_path
+        self.proc: Optional[subprocess.Popen] = None
+        self._log_buffer: deque[str] = deque(maxlen=500)
+        self._reader_thread: Optional[threading.Thread] = None
+
+    def start(self) -> None:
+        args = [
+            str(self.binary),
+            "--host", "127.0.0.1",
+            "--port", str(self.port),
+            "--data-dir", str(self.data_dir),
+            "--parent-pid", str(os.getpid()),
+        ]
+        print(f"[spawn] {' '.join(args)}", flush=True)
+        self._log_fh = open(self.log_path, "w", encoding="utf-8", errors="replace")
+        # Combine stderr into stdout so we get a single ordered stream.
+        self.proc = subprocess.Popen(
+            args,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            bufsize=1,
+            text=True,
+            errors="replace",
+        )
+        self._reader_thread = threading.Thread(target=self._pump_logs, daemon=True)
+        self._reader_thread.start()
+
+    def _pump_logs(self) -> None:
+        assert self.proc is not None and self.proc.stdout is not None
+        for line in self.proc.stdout:
+            self._log_buffer.append(line.rstrip("\n"))
+            self._log_fh.write(line)
+            self._log_fh.flush()
+
+    def log_tail(self, n: int = 100) -> list[str]:
+        tail = list(self._log_buffer)[-n:]
+        return tail
+
+    def is_alive(self) -> bool:
+        return self.proc is not None and self.proc.poll() is None
+
+    def stop(self) -> None:
+        if self.proc is None:
+            return
+        if self.proc.poll() is not None:
+            return
+        try:
+            if platform.system() == "Windows":
+                subprocess.run(
+                    ["taskkill", "/F", "/T", "/PID", str(self.proc.pid)],
+                    capture_output=True,
+                )
+            else:
+                self.proc.send_signal(signal.SIGTERM)
+        except Exception as e:
+            print(f"[shutdown] signal failed: {e}", flush=True)
+        try:
+            self.proc.wait(timeout=10)
+        except subprocess.TimeoutExpired:
+            print("[shutdown] server didn't exit cleanly, killing", flush=True)
+            self.proc.kill()
+            try:
+                self.proc.wait(timeout=5)
+            except subprocess.TimeoutExpired:
+                pass
+        if self._reader_thread is not None:
+            self._reader_thread.join(timeout=2)
+        try:
+            self._log_fh.close()
+        except Exception:
+            pass
+
+
+def pick_free_port() -> int:
+    s = socket.socket()
+    s.bind(("127.0.0.1", 0))
+    port = s.getsockname()[1]
+    s.close()
+    return port
+
+
+# ── HTTP helpers ─────────────────────────────────────────────────────
+
+def wait_for_health(base_url: str, server: ServerProcess, timeout: int) -> None:
+    deadline = time.time() + timeout
+    with httpx.Client(timeout=5.0) as client:
+        while time.time() < deadline:
+            if not server.is_alive():
+                raise RuntimeError("Server process exited before becoming healthy")
+            try:
+                r = client.get(f"{base_url}/health")
+                if r.status_code == 200 and r.json().get("status") == "healthy":
+                    return
+            except httpx.HTTPError:
+                pass
+            time.sleep(1.0)
+    raise TimeoutError(f"Server did not become healthy within {timeout}s")
+
+
+def get_model_cached(client: httpx.Client, base_url: str, model_name: str) -> Optional[bool]:
+    try:
+        r = client.get(f"{base_url}/models/status", timeout=30.0)
+        r.raise_for_status()
+        for m in r.json().get("models", []):
+            if m.get("model_name") == model_name:
+                return bool(m.get("downloaded"))
+    except httpx.HTTPError:
+        return None
+    return None
+
+
+def create_cloned_profile(client: httpx.Client, base_url: str, wav_path: Path, reference_text: str) -> str:
+    r = client.post(f"{base_url}/profiles", json={
+        "name": "e2e-cloned",
+        "voice_type": "cloned",
+        "language": "en",
+    })
+    r.raise_for_status()
+    profile_id = r.json()["id"]
+
+    with open(wav_path, "rb") as f:
+        r = client.post(
+            f"{base_url}/profiles/{profile_id}/samples",
+            files={"file": (wav_path.name, f, "audio/wav")},
+            data={"reference_text": reference_text},
+            timeout=120.0,
+        )
+    r.raise_for_status()
+    return profile_id
+
+
+def create_preset_profile(client: httpx.Client, base_url: str, name: str, engine: str, voice_id: str) -> str:
+    r = client.post(f"{base_url}/profiles", json={
+        "name": name,
+        "voice_type": "preset",
+        "language": "en",
+        "preset_engine": engine,
+        "preset_voice_id": voice_id,
+    })
+    r.raise_for_status()
+    return r.json()["id"]
+
+
+def run_one_generation(
+    client: httpx.Client,
+    base_url: str,
+    row: MatrixRow,
+    profile_id: str,
+    timeout_s: int,
+) -> tuple[str, dict]:
+    """Start a generation and stream its status until done/failed/timeout.
+
+    Returns (status, payload) where status is "completed" | "failed" | "timeout".
+    """
+    body = {
+        "profile_id": profile_id,
+        "text": TEXT,
+        "language": "en",
+        "engine": row.engine,
+        "seed": 42,
+        "normalize": True,
+    }
+    if row.model_size is not None:
+        body["model_size"] = row.model_size
+
+    r = client.post(f"{base_url}/generate", json=body, timeout=30.0)
+    r.raise_for_status()
+    gen = r.json()
+    gen_id = gen["id"]
+
+    deadline = time.time() + timeout_s
+    last_payload: dict = gen
+    status_url = f"{base_url}/generate/{gen_id}/status"
+
+    while time.time() < deadline:
+        remaining = max(1.0, deadline - time.time())
+        try:
+            with client.stream("GET", status_url, timeout=httpx.Timeout(remaining + 5, read=remaining + 5)) as resp:
+                resp.raise_for_status()
+                for line in resp.iter_lines():
+                    if not line or not line.startswith("data: "):
+                        continue
+                    try:
+                        payload = json.loads(line[6:])
+                    except json.JSONDecodeError:
+                        continue
+                    last_payload = payload
+                    status = payload.get("status")
+                    if status == "not_found":
+                        return "failed", {"error": "generation not found", **payload}
+                    if status in ("completed", "failed"):
+                        return status, payload
+                    if time.time() >= deadline:
+                        break
+        except httpx.HTTPError:
+            time.sleep(1.0)
+            continue
+
+    return "timeout", last_payload
+
+
+def fetch_audio_info(
+    client: httpx.Client, base_url: str, generation_id: str, data_dir: Path
+) -> tuple[Optional[str], Optional[int]]:
+    """Return (audio_path, audio_bytes) for a completed generation.
+
+    Server stores audio_path relative to data_dir; resolve it to get a size.
+    """
+    try:
+        r = client.get(f"{base_url}/history/{generation_id}", timeout=10.0)
+        if r.status_code != 200:
+            return None, None
+        data = r.json()
+        audio_path = data.get("audio_path")
+        if not audio_path:
+            return None, None
+        p = Path(audio_path)
+        if not p.is_absolute():
+            p = data_dir / p
+        if p.exists():
+            return str(p), p.stat().st_size
+        return audio_path, None
+    except httpx.HTTPError:
+        return None, None
+
+
+# ── Report writers ───────────────────────────────────────────────────
+
+def write_reports(
+    output_dir: Path,
+    binary: Path,
+    started_at: datetime,
+    finished_at: datetime,
+    results: list[ModelResult],
+) -> tuple[Path, Path]:
+    output_dir.mkdir(parents=True, exist_ok=True)
+    plat = f"{platform.system().lower()}-{platform.machine().lower()}"
+    ts = started_at.strftime("%Y%m%d-%H%M%S")
+    json_path = output_dir / f"e2e-{plat}-{ts}.json"
+    md_path = output_dir / f"e2e-{plat}-{ts}.md"
+
+    doc = {
+        "platform": plat,
+        "binary": str(binary),
+        "binary_size_mb": round(binary.stat().st_size / (1024 * 1024), 1) if binary.exists() else None,
+        "started_at": started_at.isoformat(),
+        "finished_at": finished_at.isoformat(),
+        "elapsed_seconds": (finished_at - started_at).total_seconds(),
+        "results": [asdict(r) for r in results],
+    }
+    json_path.write_text(json.dumps(doc, indent=2))
+
+    lines = [
+        f"# Voicebox E2E — {plat} — {started_at.strftime('%Y-%m-%d %H:%M UTC')}",
+        "",
+        f"Binary: `{binary}`  ",
+        f"Elapsed: {doc['elapsed_seconds']:.1f}s",
+        "",
+        "| Model | Status | Cached | Elapsed | Audio | Error |",
+        "|-------|--------|--------|---------|-------|-------|",
+    ]
+    for r in results:
+        status_icon = {"passed": "PASS", "failed": "FAIL", "timeout": "TIMEOUT"}.get(r.status, r.status.upper())
+        cached = "yes" if r.was_cached else ("no" if r.was_cached is False else "?")
+        audio_col = f"{r.audio_duration:.2f}s" if r.audio_duration else ("—" if r.status != "passed" else "?")
+        error_col = (r.error or "").replace("\n", " ")[:120]
+        lines.append(f"| {r.label} | {status_icon} | {cached} | {r.elapsed_seconds:.1f}s | {audio_col} | {error_col} |")
+
+    failed_rows = [r for r in results if r.status != "passed"]
+    if failed_rows:
+        lines.append("")
+        lines.append("## Failures")
+        for r in failed_rows:
+            lines.append("")
+            lines.append(f"### {r.label} — {r.status}")
+            if r.error:
+                lines.append("")
+                lines.append("```")
+                lines.append(r.error)
+                lines.append("```")
+            if r.server_log_tail:
+                lines.append("")
+                lines.append("<details><summary>server log (last lines)</summary>")
+                lines.append("")
+                lines.append("```")
+                lines.extend(r.server_log_tail)
+                lines.append("```")
+                lines.append("</details>")
+
+    md_path.write_text("\n".join(lines) + "\n")
+    return json_path, md_path
+
+
+# ── Main ─────────────────────────────────────────────────────────────
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Voicebox E2E model generation test")
+    p.add_argument("--binary", type=Path, help="Path to voicebox-server binary (overrides auto-detect)")
+    p.add_argument("--skip-build", action="store_true", help="Error if binary missing instead of building")
+    p.add_argument(
+        "--reference-wav",
+        type=Path,
+        default=FIXTURES_DIR / "reference_voice.wav",
+        help="Reference audio for cloning engines",
+    )
+    p.add_argument(
+        "--reference-text",
+        help="Transcription of reference-wav (default: read from fixtures/reference_voice.txt)",
+    )
+    p.add_argument("--only", help="Comma-separated engines to run (e.g. kokoro,qwen)")
+    p.add_argument("--skip", help="Comma-separated engines to skip")
+    p.add_argument("--keep-data-dir", action="store_true", help="Don't delete tempdir after run")
+    p.add_argument("--timeout-cached", type=int, default=DEFAULT_TIMEOUT_CACHED)
+    p.add_argument("--timeout-download", type=int, default=DEFAULT_TIMEOUT_DOWNLOAD)
+    p.add_argument("--port", type=int, help="Override auto-picked port")
+    p.add_argument("--output-dir", type=Path, default=RESULTS_DIR)
+    return p.parse_args()
+
+
+def filter_matrix(args: argparse.Namespace) -> list[MatrixRow]:
+    only = set(x.strip() for x in args.only.split(",")) if args.only else None
+    skip = set(x.strip() for x in args.skip.split(",")) if args.skip else set()
+    rows = []
+    for r in MATRIX:
+        if only is not None and r.engine not in only:
+            continue
+        if r.engine in skip:
+            continue
+        rows.append(r)
+    return rows
+
+
+def resolve_reference(args: argparse.Namespace) -> tuple[Path, str]:
+    wav = args.reference_wav
+    if not wav.exists():
+        raise FileNotFoundError(
+            f"Reference WAV not found: {wav}\n"
+            f"Place a sample at {FIXTURES_DIR / 'reference_voice.wav'} or pass --reference-wav.\n"
+            f"See backend/tests/fixtures/README.md."
+        )
+    if args.reference_text:
+        text = args.reference_text
+    else:
+        txt_path = wav.with_suffix(".txt")
+        if not txt_path.exists():
+            raise FileNotFoundError(
+                f"Reference transcription not found: {txt_path}\n"
+                f"Create it next to the WAV, or pass --reference-text."
+            )
+        text = txt_path.read_text().strip()
+    if not text:
+        raise ValueError("Reference transcription is empty")
+    return wav, text
+
+
+def main() -> int:
+    args = parse_args()
+    rows = filter_matrix(args)
+    if not rows:
+        print("No rows selected after --only/--skip filtering", file=sys.stderr)
+        return 2
+
+    # Binary
+    binary = args.binary or find_binary()
+    if binary is None:
+        if args.skip_build:
+            print("No frozen binary found and --skip-build set. Run: python backend/build_binary.py", file=sys.stderr)
+            return 2
+        binary = build_binary()
+    if not binary.exists():
+        print(f"Binary path does not exist: {binary}", file=sys.stderr)
+        return 2
+    print(f"[binary] {binary}", flush=True)
+
+    # Reference audio (only required if any cloning row is in the matrix)
+    needs_reference = any(r.profile_kind == "cloned" for r in rows)
+    ref_wav: Optional[Path] = None
+    ref_text: Optional[str] = None
+    if needs_reference:
+        try:
+            ref_wav, ref_text = resolve_reference(args)
+        except (FileNotFoundError, ValueError) as e:
+            print(f"[fixture] {e}", file=sys.stderr)
+            return 2
+        print(f"[fixture] reference WAV:  {ref_wav}", flush=True)
+        print(f"[fixture] reference text: {ref_text!r}", flush=True)
+
+    # Tempdir + log path
+    data_dir = Path(tempfile.mkdtemp(prefix="voicebox-e2e-"))
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    ts = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S")
+    log_path = args.output_dir / f"server-{ts}.log"
+
+    port = args.port or pick_free_port()
+    base_url = f"http://127.0.0.1:{port}"
+
+    server = ServerProcess(binary=binary, port=port, data_dir=data_dir, log_path=log_path)
+    started_at = datetime.now(timezone.utc)
+    results: list[ModelResult] = []
+
+    try:
+        server.start()
+        print(f"[health] waiting for {base_url}/health ...", flush=True)
+        wait_for_health(base_url, server, HEALTH_TIMEOUT)
+        print("[health] ready", flush=True)
+
+        with httpx.Client(timeout=30.0) as client:
+            # Profile setup (only create what's needed)
+            cloned_profile_id: Optional[str] = None
+            kokoro_profile_id: Optional[str] = None
+            qwen_cv_profile_id: Optional[str] = None
+            needed_kinds = {r.profile_kind for r in rows}
+            if "cloned" in needed_kinds:
+                assert ref_wav is not None and ref_text is not None
+                print("[profile] creating cloned profile...", flush=True)
+                cloned_profile_id = create_cloned_profile(client, base_url, ref_wav, ref_text)
+            if "preset_kokoro" in needed_kinds:
+                print("[profile] creating kokoro preset...", flush=True)
+                kokoro_profile_id = create_preset_profile(client, base_url, "e2e-kokoro", "kokoro", "af_heart")
+            if "preset_qwen_cv" in needed_kinds:
+                print("[profile] creating qwen_custom_voice preset...", flush=True)
+                qwen_cv_profile_id = create_preset_profile(client, base_url, "e2e-qwen-cv", "qwen_custom_voice", "Ryan")
+
+            profile_lookup = {
+                "cloned": cloned_profile_id,
+                "preset_kokoro": kokoro_profile_id,
+                "preset_qwen_cv": qwen_cv_profile_id,
+            }
+
+            # Matrix loop
+            for row in rows:
+                print(f"\n[run] {row.label} (engine={row.engine}, size={row.model_size})", flush=True)
+                profile_id = profile_lookup[row.profile_kind]
+                assert profile_id is not None
+                was_cached = get_model_cached(client, base_url, row.model_name)
+                timeout_s = args.timeout_cached if was_cached else args.timeout_download
+                print(f"[run] cached={was_cached} timeout={timeout_s}s", flush=True)
+
+                t0 = time.time()
+                result = ModelResult(
+                    label=row.label,
+                    engine=row.engine,
+                    model_size=row.model_size,
+                    status="failed",
+                    was_cached=was_cached,
+                )
+                try:
+                    status, payload = run_one_generation(client, base_url, row, profile_id, timeout_s)
+                    result.status = "passed" if status == "completed" else status
+                    result.generation_id = payload.get("id")
+                    result.audio_duration = payload.get("duration")
+                    result.error = payload.get("error")
+                    if status == "completed" and result.generation_id:
+                        audio_path, audio_bytes = fetch_audio_info(
+                            client, base_url, result.generation_id, data_dir
+                        )
+                        result.audio_path = audio_path
+                        result.audio_bytes = audio_bytes
+                        if audio_bytes is not None and audio_bytes == 0:
+                            result.status = "failed"
+                            result.error = (result.error or "") + " (audio file is empty)"
+                except httpx.HTTPStatusError as e:
+                    result.status = "failed"
+                    result.http_status = e.response.status_code
+                    try:
+                        detail = e.response.json().get("detail")
+                    except Exception:
+                        detail = e.response.text
+                    result.error = f"HTTP {e.response.status_code}: {detail}"
+                except Exception as e:
+                    result.status = "failed"
+                    result.error = f"{type(e).__name__}: {e}"
+
+                result.elapsed_seconds = round(time.time() - t0, 2)
+                if result.status != "passed":
+                    result.server_log_tail = server.log_tail(100)
+                print(f"[run] {row.label} → {result.status} in {result.elapsed_seconds}s"
+                      + (f" ({result.error})" if result.error else ""), flush=True)
+                results.append(result)
+    finally:
+        finished_at = datetime.now(timezone.utc)
+        server.stop()
+        if not args.keep_data_dir:
+            shutil.rmtree(data_dir, ignore_errors=True)
+        else:
+            print(f"[cleanup] keeping data dir: {data_dir}", flush=True)
+
+    json_path, md_path = write_reports(args.output_dir, binary, started_at, finished_at, results)
+    print(f"\n[report] {json_path}")
+    print(f"[report] {md_path}")
+    print(f"[report] server log: {log_path}")
+
+    passed = sum(1 for r in results if r.status == "passed")
+    failed = len(results) - passed
+    print(f"\n== {passed} passed, {failed} failed ==")
+    return 0 if failed == 0 else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

backend/tests/test_audio_preprocess.py

@@ -0,0 +1,112 @@
+"""
+Unit tests for reference-audio preprocessing.
+
+Covers :func:`backend.utils.audio.preprocess_reference_audio` and
+:func:`backend.utils.audio.validate_and_load_reference_audio`.
+"""
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+import soundfile as sf
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from utils.audio import (  # noqa: E402
+    preprocess_reference_audio,
+    validate_and_load_reference_audio,
+)
+
+
+SR = 24000
+
+
+def _tone(duration_s: float, amp: float = 0.3, freq: float = 220.0) -> np.ndarray:
+    n = int(duration_s * SR)
+    t = np.arange(n, dtype=np.float32) / SR
+    return (amp * np.sin(2 * np.pi * freq * t)).astype(np.float32)
+
+
+def test_peak_cap_scales_hot_input():
+    audio = _tone(3.0, amp=0.99)
+    out = preprocess_reference_audio(audio, SR)
+    assert np.abs(out).max() <= 0.951
+
+
+def test_peak_cap_leaves_moderate_input_untouched():
+    audio = _tone(3.0, amp=0.5)
+    out = preprocess_reference_audio(audio, SR)
+    assert np.isclose(np.abs(out).max(), 0.5, atol=1e-3)
+
+
+def test_dc_offset_removed():
+    audio = _tone(3.0, amp=0.3) + 0.1
+    out = preprocess_reference_audio(audio, SR)
+    assert abs(float(np.mean(out))) < 1e-3
+
+
+def test_silence_is_trimmed_with_padding_kept():
+    silence = np.zeros(int(SR * 1.0), dtype=np.float32)
+    speech = _tone(3.0, amp=0.3)
+    audio = np.concatenate([silence, speech, silence])
+    out = preprocess_reference_audio(audio, SR)
+    # Most of the 2s of leading/trailing silence should be gone, but the
+    # 3s of speech plus ~200ms of padding should remain.
+    assert len(audio) - len(out) >= SR, "expected >=1s of silence trimmed"
+    assert len(out) >= int(3.0 * SR), "speech body should be preserved"
+
+
+def test_clean_audio_is_not_padded_past_original_length():
+    # Well-recorded audio with no edge silence shouldn't get longer after
+    # preprocessing — otherwise a 29.9 s upload could be pushed past the
+    # 30 s max_duration ceiling downstream.
+    audio = _tone(3.0, amp=0.3)
+    out = preprocess_reference_audio(audio, SR)
+    assert len(out) <= len(audio)
+
+
+def test_empty_input_returns_empty():
+    out = preprocess_reference_audio(np.zeros(0, dtype=np.float32), SR)
+    assert out.size == 0
+
+
+def test_validate_accepts_previously_rejected_hot_file(tmp_path):
+    audio = _tone(3.0, amp=0.995)
+    path = tmp_path / "hot.wav"
+    sf.write(str(path), audio, SR)
+
+    ok, err, out_audio, out_sr = validate_and_load_reference_audio(str(path))
+
+    assert ok, f"expected pass, got error: {err}"
+    assert out_audio is not None
+    assert out_sr == SR
+    assert np.abs(out_audio).max() <= 0.951
+
+
+def test_validate_still_rejects_silent_input(tmp_path):
+    audio = np.zeros(int(SR * 3.0), dtype=np.float32)
+    path = tmp_path / "silent.wav"
+    sf.write(str(path), audio, SR)
+
+    ok, err, _, _ = validate_and_load_reference_audio(str(path))
+
+    assert not ok
+    assert err is not None
+    assert "too short" in err.lower() or "quiet" in err.lower()
+
+
+def test_validate_rejects_too_short(tmp_path):
+    audio = _tone(0.5, amp=0.3)
+    path = tmp_path / "short.wav"
+    sf.write(str(path), audio, SR)
+
+    ok, err, _, _ = validate_and_load_reference_audio(str(path))
+
+    assert not ok
+    assert "too short" in (err or "").lower()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

backend/tests/test_task_queue_cancellation.py

@@ -0,0 +1,54 @@
+import asyncio
+
+import pytest
+
+from backend.services import task_queue
+
+
+@pytest.mark.asyncio
+async def test_cancel_queued_generation_skips_execution():
+    task_queue.init_queue(force=True)
+
+    running_started = asyncio.Event()
+    release_running = asyncio.Event()
+    queued_ran = asyncio.Event()
+
+    async def running_job():
+        running_started.set()
+        await release_running.wait()
+
+    async def queued_job():
+        queued_ran.set()
+
+    task_queue.enqueue_generation("gen-running", running_job())
+    await asyncio.wait_for(running_started.wait(), timeout=1)
+
+    task_queue.enqueue_generation("gen-queued", queued_job())
+    assert task_queue.cancel_generation("gen-queued") == "queued"
+
+    release_running.set()
+    await asyncio.sleep(0.1)
+
+    assert not queued_ran.is_set()
+
+
+@pytest.mark.asyncio
+async def test_cancel_running_generation_cancels_task():
+    task_queue.init_queue(force=True)
+
+    running_started = asyncio.Event()
+    running_cancelled = asyncio.Event()
+
+    async def running_job():
+        running_started.set()
+        try:
+            await asyncio.Event().wait()
+        except asyncio.CancelledError:
+            running_cancelled.set()
+            raise
+
+    task_queue.enqueue_generation("gen-running", running_job())
+    await asyncio.wait_for(running_started.wait(), timeout=1)
+
+    assert task_queue.cancel_generation("gen-running") == "running"
+    await asyncio.wait_for(running_cancelled.wait(), timeout=1)

backend/utils/audio.py

@@ -199,6 +199,66 @@ def trim_tts_output(
     return trimmed
 
 
+def preprocess_reference_audio(
+    audio: np.ndarray,
+    sample_rate: int,
+    peak_target: float = 0.95,
+    trim_top_db: float = 40.0,
+    edge_padding_ms: int = 100,
+) -> np.ndarray:
+    """
+    Clean up a reference-audio sample before validation/storage.
+
+    Removes DC offset, trims leading/trailing silence, and caps the peak so a
+    slightly-hot recording doesn't get rejected downstream as "clipping". The
+    goal is to accept reasonable real-world recordings — not to repair badly
+    distorted ones. True clipping artifacts inside the waveform can't be
+    recovered by peak scaling and will still sound bad.
+
+    Args:
+        audio: Mono audio array.
+        sample_rate: Sample rate of ``audio`` in Hz.
+        peak_target: Peak amplitude cap in [0, 1]. Applied only if the input
+            peak exceeds this value.
+        trim_top_db: Silence threshold for edge trimming, in dB below peak.
+            40 dB sits below normal speech dynamic range (≈30 dB) so soft
+            trailing syllables are preserved, while still catching obvious
+            leading/trailing silence. Lower values are more aggressive;
+            librosa's own default is 60.
+        edge_padding_ms: Milliseconds of padding to add back at each edge
+            *only if* trimming shortened the waveform, so TTS engines have a
+            brief silence to anchor on without ever making the output longer
+            than the input.
+
+    Returns:
+        Preprocessed audio array (float32).
+    """
+    audio = audio.astype(np.float32, copy=False)
+
+    if audio.size == 0:
+        return audio
+
+    audio = audio - float(np.mean(audio))
+
+    trimmed, _ = librosa.effects.trim(audio, top_db=trim_top_db)
+    if 0 < trimmed.size < audio.size:
+        pad_each = int(sample_rate * edge_padding_ms / 1000)
+        # Never pad past the original length — for near-max-duration uploads
+        # an unconditional pad would push them over the 30 s ceiling and
+        # trigger a spurious "too long" rejection.
+        headroom = (audio.size - trimmed.size) // 2
+        pad = min(pad_each, max(headroom, 0))
+        if pad > 0:
+            trimmed = np.pad(trimmed, (pad, pad), mode="constant")
+        audio = trimmed
+
+    peak = float(np.abs(audio).max())
+    if peak > peak_target and peak > 0:
+        audio = audio * (peak_target / peak)
+
+    return audio
+
+
 def validate_reference_audio(
     audio_path: str,
     min_duration: float = 2.0,
@@ -207,13 +267,13 @@ def validate_reference_audio(
 ) -> Tuple[bool, Optional[str]]:
     """
     Validate reference audio for voice cloning.
-    
+
     Args:
         audio_path: Path to audio file
         min_duration: Minimum duration in seconds
         max_duration: Maximum duration in seconds
         min_rms: Minimum RMS level
-        
+
     Returns:
         Tuple of (is_valid, error_message)
     """
@@ -231,26 +291,28 @@ def validate_and_load_reference_audio(
 ) -> Tuple[bool, Optional[str], Optional[np.ndarray], Optional[int]]:
     """
     Validate and load reference audio in a single pass.
-    
+
+    Applies :func:`preprocess_reference_audio` before checks so that
+    slightly-hot recordings aren't rejected as clipping. Duration and RMS
+    checks run on the preprocessed waveform.
+
     Returns:
         Tuple of (is_valid, error_message, audio_array, sample_rate)
     """
     try:
         audio, sr = load_audio(audio_path)
+        audio = preprocess_reference_audio(audio, sr)
         duration = len(audio) / sr
-        
+
         if duration < min_duration:
             return False, f"Audio too short (minimum {min_duration} seconds)", None, None
         if duration > max_duration:
             return False, f"Audio too long (maximum {max_duration} seconds)", None, None
-        
+
         rms = np.sqrt(np.mean(audio**2))
         if rms < min_rms:
             return False, "Audio is too quiet or silent", None, None
-        
-        if np.abs(audio).max() > 0.99:
-            return False, "Audio is clipping (reduce input gain)", None, None
-        
+
         return True, None, audio, sr
     except Exception as e:
         return False, f"Error validating audio: {str(e)}", None, None

backend/utils/cache.py

@@ -64,7 +64,7 @@ def get_cached_voice_prompt(
     cache_file = _get_cache_dir() / f"{cache_key}.prompt"
     if cache_file.exists():
         try:
-            prompt = torch.load(cache_file)
+            prompt = torch.load(cache_file, weights_only=True)
             _memory_cache[cache_key] = prompt
             return prompt
         except Exception:

backend/utils/dac_shim.py

@@ -0,0 +1,95 @@
+"""
+Minimal shim for descript-audio-codec (DAC).
+
+TADA only imports Snake1d from dac.nn.layers and dac.model.dac.
+The real DAC package pulls in descript-audiotools which depends on
+onnx, tensorboard, protobuf, matplotlib, pystoi, etc. — none of
+which are needed for TADA's runtime use of Snake1d.
+
+This shim provides the exact Snake1d implementation (MIT-licensed,
+from https://github.com/descriptinc/descript-audio-codec) so we can
+avoid the entire audiotools dependency chain.
+
+If the real DAC package is installed, this module is never used —
+Python's import system will find the site-packages version first.
+Install this shim only when descript-audio-codec is NOT installed.
+"""
+
+import sys
+import types
+
+import torch
+import torch.nn as nn
+
+
+# ── Snake activation (from dac/nn/layers.py) ────────────────────────
+
+# NOTE: The original DAC code uses @torch.jit.script here for a 1.4x
+# speedup.  We omit it because TorchScript calls inspect.getsource()
+# which fails inside a PyInstaller frozen binary (no .py source files).
+def snake(x: torch.Tensor, alpha: torch.Tensor) -> torch.Tensor:
+    shape = x.shape
+    x = x.reshape(shape[0], shape[1], -1)
+    x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
+    x = x.reshape(shape)
+    return x
+
+
+class Snake1d(nn.Module):
+    def __init__(self, channels: int):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones(1, channels, 1))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return snake(x, self.alpha)
+
+
+# ── Register as dac.nn.layers and dac.model.dac ─────────────────────
+
+def install_dac_shim() -> None:
+    """Register fake dac package modules in sys.modules.
+
+    Only installs the shim if 'dac' is not already importable
+    (i.e. the real descript-audio-codec is not installed).
+    """
+    try:
+        import dac  # noqa: F401  — real package exists, do nothing
+        return
+    except ImportError:
+        pass
+
+    # Create the module tree: dac -> dac.nn -> dac.nn.layers
+    #                              -> dac.model -> dac.model.dac
+    dac_pkg = types.ModuleType("dac")
+    dac_pkg.__path__ = []  # make it a package
+    dac_pkg.__package__ = "dac"
+
+    dac_nn = types.ModuleType("dac.nn")
+    dac_nn.__path__ = []
+    dac_nn.__package__ = "dac.nn"
+
+    dac_nn_layers = types.ModuleType("dac.nn.layers")
+    dac_nn_layers.__package__ = "dac.nn"
+    dac_nn_layers.Snake1d = Snake1d
+    dac_nn_layers.snake = snake
+
+    dac_model = types.ModuleType("dac.model")
+    dac_model.__path__ = []
+    dac_model.__package__ = "dac.model"
+
+    dac_model_dac = types.ModuleType("dac.model.dac")
+    dac_model_dac.__package__ = "dac.model"
+    dac_model_dac.Snake1d = Snake1d
+
+    # Wire up submodules
+    dac_pkg.nn = dac_nn
+    dac_pkg.model = dac_model
+    dac_nn.layers = dac_nn_layers
+    dac_model.dac = dac_model_dac
+
+    # Register in sys.modules
+    sys.modules["dac"] = dac_pkg
+    sys.modules["dac.nn"] = dac_nn
+    sys.modules["dac.nn.layers"] = dac_nn_layers
+    sys.modules["dac.model"] = dac_model
+    sys.modules["dac.model.dac"] = dac_model_dac

backend/utils/hf_offline_patch.py

@@ -1,17 +1,64 @@
 """Monkey-patch huggingface_hub to force offline mode with cached models.
 
-Prevents mlx_audio from making network requests when models are already
-downloaded. Must be imported BEFORE mlx_audio.
+Prevents mlx_audio / transformers from making network requests when models
+are already downloaded. Must be imported BEFORE mlx_audio.
 """
 
 import logging
 import os
+from contextlib import contextmanager
 from pathlib import Path
 from typing import Optional, Union
 
 logger = logging.getLogger(__name__)
 
 
+@contextmanager
+def force_offline_if_cached(is_cached: bool, model_label: str = ""):
+    """Context manager that sets ``HF_HUB_OFFLINE=1`` while loading a cached model.
+
+    If *is_cached* is ``False`` the block runs normally (network allowed).
+    If the offline load raises an error containing "offline" we automatically
+    retry with network access so a partially-cached model still works.
+
+    Args:
+        is_cached: Whether the model weights are already on disk.
+        model_label: Human-readable name used in log messages.
+    """
+    if not is_cached:
+        yield
+        return
+
+    original_value = os.environ.get("HF_HUB_OFFLINE")
+    os.environ["HF_HUB_OFFLINE"] = "1"
+    logger.info(
+        "[offline-guard] %s is cached — forcing HF_HUB_OFFLINE=1",
+        model_label or "model",
+    )
+
+    try:
+        yield
+    except Exception as exc:
+        if "offline" in str(exc).lower():
+            logger.warning(
+                "[offline-guard] Offline load failed for %s, retrying with network: %s",
+                model_label or "model",
+                exc,
+            )
+            # Restore original env and retry — caller must wrap the load
+            # inside force_offline_if_cached so retrying here isn't possible.
+            # Instead, propagate a flag via the exception so the caller can
+            # decide.  For simplicity we just let it fall through to the
+            # finally block and re-raise.
+            raise
+        raise
+    finally:
+        if original_value is not None:
+            os.environ["HF_HUB_OFFLINE"] = original_value
+        else:
+            os.environ.pop("HF_HUB_OFFLINE", None)
+
+
 def patch_huggingface_hub_offline():
     """Monkey-patch huggingface_hub to force offline mode."""
     try:

backend/voicebox-server.spec

@@ -1,13 +1,11 @@
 # -*- mode: python ; coding: utf-8 -*-
-from PyInstaller.utils.hooks import collect_data_files
 from PyInstaller.utils.hooks import collect_submodules
 from PyInstaller.utils.hooks import collect_all
 from PyInstaller.utils.hooks import copy_metadata
 
 datas = []
 binaries = []
-hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.services.profiles', 'backend.services.history', 'backend.services.tts', 'backend.services.transcribe', 'backend.utils.platform_detect', 'backend.backends', 'backend.backends.pytorch_backend', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.services.cuda', 'backend.services.effects', 'backend.utils.effects', 'backend.services.versions', 'pedalboard', 'chatterbox', 'chatterbox.tts_turbo', 'chatterbox.mtl_tts', 'backend.backends.chatterbox_backend', 'backend.backends.chatterbox_turbo_backend', 'backend.backends.luxtts_backend', 'zipvoice', 'zipvoice.luxvoice', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'requests', 'pkg_resources.extern', 'backend.backends.mlx_backend', 'mlx', 'mlx.core', 'mlx.nn', 'mlx_audio', 'mlx_audio.tts', 'mlx_audio.stt']
-datas += collect_data_files('qwen_tts')
+hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.services.profiles', 'backend.services.history', 'backend.services.tts', 'backend.services.transcribe', 'backend.utils.platform_detect', 'backend.backends', 'backend.backends.pytorch_backend', 'backend.backends.qwen_custom_voice_backend', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.services.cuda', 'backend.services.effects', 'backend.utils.effects', 'backend.services.versions', 'pedalboard', 'chatterbox', 'chatterbox.tts_turbo', 'chatterbox.mtl_tts', 'backend.backends.chatterbox_backend', 'backend.backends.chatterbox_turbo_backend', 'backend.backends.luxtts_backend', 'zipvoice', 'zipvoice.luxvoice', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'requests', 'pkg_resources.extern', 'backend.backends.hume_backend', 'tada', 'tada.modules', 'tada.modules.tada', 'tada.modules.encoder', 'tada.modules.decoder', 'tada.modules.aligner', 'tada.modules.acoustic_spkr_verf', 'tada.nn', 'tada.nn.vibevoice', 'tada.utils', 'tada.utils.gray_code', 'tada.utils.text', 'backend.utils.dac_shim', 'torchaudio', 'backend.backends.kokoro_backend', 'en_core_web_sm', 'loguru', 'backend.backends.mlx_backend', 'mlx', 'mlx.core', 'mlx.nn', 'mlx_audio', 'mlx_audio.tts', 'mlx_audio.stt']
 datas += copy_metadata('qwen-tts')
 datas += copy_metadata('requests')
 datas += copy_metadata('transformers')
@@ -15,10 +13,13 @@ datas += copy_metadata('huggingface-hub')
 datas += copy_metadata('tokenizers')
 datas += copy_metadata('safetensors')
 datas += copy_metadata('tqdm')
-hiddenimports += collect_submodules('qwen_tts')
+datas += copy_metadata('en_core_web_sm')
 hiddenimports += collect_submodules('jaraco')
+hiddenimports += collect_submodules('tada')
 hiddenimports += collect_submodules('mlx')
 hiddenimports += collect_submodules('mlx_audio')
+tmp_ret = collect_all('spacy_pkuseg')
+datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
 tmp_ret = collect_all('zipvoice')
 datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
 tmp_ret = collect_all('linacodec')
@@ -27,12 +28,24 @@ tmp_ret = collect_all('lazy_loader')
 datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
 tmp_ret = collect_all('librosa')
 datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
+tmp_ret = collect_all('qwen_tts')
+datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
 tmp_ret = collect_all('inflect')
 datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
 tmp_ret = collect_all('perth')
 datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
 tmp_ret = collect_all('piper_phonemize')
 datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
+tmp_ret = collect_all('kokoro')
+datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
+tmp_ret = collect_all('misaki')
+datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
+tmp_ret = collect_all('language_tags')
+datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
+tmp_ret = collect_all('espeakng_loader')
+datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
+tmp_ret = collect_all('en_core_web_sm')
+datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
 tmp_ret = collect_all('mlx')
 datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
 tmp_ret = collect_all('mlx_audio')
@@ -45,9 +58,9 @@ a = Analysis(
     binaries=binaries,
     datas=datas,
     hiddenimports=hiddenimports,
-    hookspath=[],
+    hookspath=['pyi_hooks'],
     hooksconfig={},
-    runtime_hooks=[],
+    runtime_hooks=['pyi_rth_numpy_compat.py', 'pyi_rth_torch_compiler_disable.py'],
     excludes=['nvidia', 'nvidia.cublas', 'nvidia.cuda_cupti', 'nvidia.cuda_nvrtc', 'nvidia.cuda_runtime', 'nvidia.cudnn', 'nvidia.cufft', 'nvidia.curand', 'nvidia.cusolver', 'nvidia.cusparse', 'nvidia.nccl', 'nvidia.nvjitlink', 'nvidia.nvtx'],
     noarchive=False,
     optimize=0,

bun.lock

@@ -17,7 +17,7 @@
     },
     "app": {
       "name": "@voicebox/app",
-      "version": "0.2.0",
+      "version": "0.4.1",
       "dependencies": {
         "@dnd-kit/core": "^6.3.1",
         "@dnd-kit/sortable": "^10.0.0",
@@ -72,9 +72,10 @@
     },
     "landing": {
       "name": "@voicebox/landing",
-      "version": "0.2.0",
+      "version": "0.4.1",
       "dependencies": {
         "@fontsource/space-grotesk": "^5.2.10",
+        "@icons-pack/react-simple-icons": "^13.13.0",
         "@radix-ui/react-separator": "^1.1.8",
         "@radix-ui/react-slot": "^1.2.4",
         "autoprefixer": "^10.4.17",
@@ -100,7 +101,7 @@
     },
     "tauri": {
       "name": "@voicebox/tauri",
-      "version": "0.2.0",
+      "version": "0.4.1",
       "dependencies": {
         "@tauri-apps/api": "^2.0.0",
         "@tauri-apps/plugin-dialog": "^2.0.0",
@@ -123,7 +124,7 @@
     },
     "web": {
       "name": "@voicebox/web",
-      "version": "0.2.0",
+      "version": "0.4.1",
       "dependencies": {
         "@tanstack/react-query": "^5.0.0",
         "react": "^18.3.0",
@@ -287,6 +288,8 @@
 
     "@humanwhocodes/object-schema": ["@humanwhocodes/[email protected]", "", {}, "sha512-93zYdMES/c1D69yZiKDBj0V24vqNzB/koF26KPaagAfd3P/4gUlh3Dys5ogAK+Exi9QyzlD8x/08Zt7wIKcDcA=="],
 
+    "@icons-pack/react-simple-icons": ["@icons-pack/[email protected]", "", { "peerDependencies": { "react": "^16.13 || ^17 || ^18 || ^19" } }, "sha512-B5HhQMIpcSH4z8IZ8HFhD59CboHceKYMpPC9kAwGyKntvPdyJJv26DLu4Z1wAjcCLyrJhf11tMhiQGom9Rxb9g=="],
+
     "@img/colour": ["@img/[email protected]", "", {}, "sha512-A5P/LfWGFSl6nsckYtjw9da+19jB8hkJ6ACTGcDfEJ0aE+l2n2El7dsVM7UVHZQ9s2lmYMWlrS21YLy2IR1LUw=="],
 
     "@img/sharp-darwin-arm64": ["@img/[email protected]", "", { "optionalDependencies": { "@img/sharp-libvips-darwin-arm64": "1.2.4" }, "os": "darwin", "cpu": "arm64" }, "sha512-imtQ3WMJXbMY4fxb/Ndp6HBTNVtWCUI0WdobyheGf5+ad6xX8VIDO8u2xE4qc/fr08CKG/7dDseFtn6M6g/r3w=="],