Merge pull request #12 from JezzWTF/codex/gen-optim

Improve streaming generation flow and CPU startup handling
2026-06-01 15:22:14 +00:00 · 2026-05-02 16:46:59 +01:00
parent a39ec536fd 0e17c60bdc
commit 0236807928
32 changed files with 1251 additions and 383 deletions
@@ -1,20 +0,0 @@
 {
  "permissions": {
    "allow": [
      "Bash(mv podcast-forge/pnpm-lock.yaml /tmp/vibepod-pnpm-lock.yaml)",
      "Bash(git mv *)",
      "Bash(mv /tmp/vibepod-pnpm-lock.yaml web/pnpm-lock.yaml)",
      "Bash(git rm *)",
      "Bash(uv lock *)",
      "Bash(pnpm install *)",
      "Bash(git add *)",
      "Bash(command -v uv)",
      "Bash(uv --version)",
      "Bash(uv sync *)",
      "Bash(pnpm --filter vibepod-web exec tsc --noEmit)",
      "Bash(xargs cat *)",
      "Bash(.venv/Scripts/python.exe -c \"import torch; print\\('torch:', torch.__version__\\); print\\('CUDA available:', torch.cuda.is_available\\(\\)\\); print\\('CUDA version:', torch.version.cuda\\)\")",
      "Bash(nvidia-smi)"
    ]
  }
 }
@@ -0,0 +1,37 @@
 root = true
 [*]
 end_of_line = lf
 charset = utf-8
 trim_trailing_whitespace = true
 insert_final_newline = true
 [*.{ts,tsx,js,jsx,mjs,cjs,mts,cts}]
 indent_style = space
 indent_size = 2
 [*.{json,jsonc}]
 indent_style = space
 indent_size = 2
 [*.{css,html}]
 indent_style = space
 indent_size = 2
 [*.{yaml,yml}]
 indent_style = space
 indent_size = 2
 [*.py]
 indent_style = space
 indent_size = 4
 [*.{toml}]
 indent_style = space
 indent_size = 4
 [*.md]
 trim_trailing_whitespace = false
 [Makefile]
 indent_style = tab
@@ -8,3 +8,42 @@ HF_TOKEN=
 # Override the HuggingFace model cache directory (optional)
 # HF_HOME=/path/to/hf-cache
 # ---------------------------------------------------------------------------
 # Runtime tuning
 # ---------------------------------------------------------------------------
 # Force the Python server device. Usually set by `pnpm dev` / `pnpm dev:cpu`.
 # VIBEPOD_DEVICE=cuda
 # VIBEPOD_DEVICE=cpu
 # CPU mode: keep async decode enabled. This overlaps acoustic decoding with
 # language-model work and measured ~20% faster on an 8-thread CPU run.
 VIBEPOD_ASYNC_DECODE=1
 # CPU mode: thread tuning. On an 8-core / 16-thread Ryzen test system,
 # 8 worker threads with 1 inter-op thread gave the best wall time, while 12
 # over-subscribed and regressed.
 # VIBEPOD_CPU_THREADS=8
 # VIBEPOD_CPU_INTEROP_THREADS=1
 # CPU mode: playback buffering. CPU generation is slower than realtime, so
 # smooth streaming needs a larger initial buffer than CUDA. Lower these for
 # faster startup if you are OK with occasional rebuffering.
 # VIBEPOD_PREBUFFER_SECS=24
 # VIBEPOD_REBUFFER_THRESHOLD_SECS=2
 # VIBEPOD_RESUME_THRESHOLD_SECS=12
 # CPU mode: dynamic INT8 quantization is enabled by default in start.sh.
 # Set to 0 if you are comparing quality/performance or debugging.
 # VIBEPOD_QUANTIZE=1
 # CUDA mode: dtype and attention selection. Defaults are bf16 + SDPA unless
 # optional FlashAttention is explicitly enabled and importable.
 # VIBEPOD_CUDA_DTYPE=bf16
 # VIBEPOD_ATTN_IMPL=sdpa
 # VIBEPOD_ENABLE_FLASH_ATTN=0
 # Debug/profiling. Keep disabled for benchmark timing; async CPU profiling
 # double-counts overlapped decode work.
 # VIBEPOD_PROFILE_GENERATION=0
@@ -0,0 +1,24 @@
 * text=auto eol=lf
 *.sh text eol=lf
 *.py text eol=lf
 *.ts text eol=lf
 *.tsx text eol=lf
 *.js text eol=lf
 *.jsx text eol=lf
 *.mjs text eol=lf
 *.cjs text eol=lf
 *.mts text eol=lf
 *.cts text eol=lf
 *.css text eol=lf
 *.html text eol=lf
 *.json text eol=lf
 *.jsonc text eol=lf
 *.yaml text eol=lf
 *.yml text eol=lf
 *.toml text eol=lf
 *.md text eol=lf
 *.mdx text eol=lf
 *.lock text eol=lf
 *.env text eol=lf
 *.env.* text eol=lf
@@ -24,3 +24,4 @@ web/node_modules/
 .DS_Store
 Thumbs.db
 .vscode/settings.json
 .claude/settings.local.json
@@ -0,0 +1,18 @@
 # Dependencies
 node_modules/
 web/node_modules/
 # Build outputs
 web/.next/
 web/tsconfig.tsbuildinfo
 web/next-env.d.ts
 # Python / server
 server/
 # Lock files
 pnpm-lock.yaml
 web/pnpm-lock.yaml
 # Generated
 web/public/
@@ -0,0 +1,8 @@
 {
  "semi": true,
  "singleQuote": false,
  "tabWidth": 2,
  "trailingComma": "es5",
  "printWidth": 100,
  "endOfLine": "lf"
 }
@@ -8,10 +8,10 @@ This file gives AI coding agents (Jules, Copilot, Claude Code, etc.) the context
 VibePod is a text-to-speech web app. It has two services that must both run for the app to work:
-| Service | Language | Entry point | Port |
+| Service    | Language                           | Entry point                     | Port |
-|---------|----------|-------------|------|
+| ---------- | ---------------------------------- | ------------------------------- | ---- |
-| **server** | Python 3.10+ (FastAPI + VibeVoice) | `server/start.sh` | 8000 |
+| **server** | Python 3.10+ (FastAPI + VibeVoice) | `server/start.sh`               | 8000 |
-| **web** | TypeScript (Next.js 15, React 19) | `pnpm --filter vibepod-web dev` | 3000 |
+| **web**    | TypeScript (Next.js 15, React 19)  | `pnpm --filter vibepod-web dev` | 3000 |
 The Next.js frontend proxies all model requests through its own API routes to the FastAPI server — it never calls the Python server directly from the browser.
@@ -51,12 +51,12 @@ pnpm build
 The `--cpu` flag in `start.sh` sets `VIBEPOD_DEVICE=cpu` and uses a separate venv (`server/.venv-cpu`) so CUDA and CPU installs never conflict. `vibevoice_server.py` reads `VIBEPOD_DEVICE` at startup via `_resolve_device()` — do not remove or rename that function.
-| Env var | Values | Set by |
+| Env var                  | Values                  | Set by                      |
-|---------|--------|--------|
+| ------------------------ | ----------------------- | --------------------------- |
-| `VIBEPOD_DEVICE` | `cpu` \| `cuda` | `server/start.sh` |
+| `VIBEPOD_DEVICE`         | `cpu` \| `cuda`         | `server/start.sh`           |
-| `UV_PROJECT_ENVIRONMENT` | `.venv-cpu` \| `.venv` | `server/start.sh` |
+| `UV_PROJECT_ENVIRONMENT` | `.venv-cpu` \| `.venv`  | `server/start.sh`           |
-| `HF_TOKEN` | HuggingFace token | Jules secret / `.env.local` |
+| `HF_TOKEN`               | HuggingFace token       | Jules secret / `.env.local` |
-| `VIBEVOICE_SERVER_URL` | `http://localhost:8000` | `.env.local` |
+| `VIBEVOICE_SERVER_URL`   | `http://localhost:8000` | `.env.local`                |
 ---
@@ -94,7 +94,9 @@ dev.sh                   Concurrent launcher (forwards flags to start.sh)
 ## API reference
 ### `GET /health`
 Returns server status. Safe to poll.
 ```json
 {
  "status": "online",
@@ -103,13 +105,17 @@ Returns server status. Safe to poll.
  "voices": ["carter", "davis", "emma", "frank", "grace", "mike"]
 }
 ```
 `status` values: `downloading` | `loading` | `online` | `error`
 ### `POST /generate`
 Streams audio as SSE events.
 ```json
 { "text": "Hello world", "speaker": "carter", "cfg_scale": 1.5, "inference_steps": 10 }
 ```
 Event types: `audio_chunk` (base64 float32 PCM) | `complete` | `error` | `cancelled`
 ---
@@ -117,12 +123,14 @@ Event types: `audio_chunk` (base64 float32 PCM) | `complete` | `error` | `cancel
 ## Do / Don't
 **Do:**
 - Use `pnpm dev:cpu` in Jules — never plain `pnpm dev`
 - Run `git checkout server/uv.lock` if uv rewrites it during setup
 - Keep `_resolve_device()` in `vibevoice_server.py` — it's the CPU/CUDA switching logic
 - Test server changes against `GET /health` and `POST /generate`
 **Don't:**
 - Run `uv sync` without `UV_PROJECT_ENVIRONMENT=.venv-cpu` in the Jules sandbox
 - Install Python packages with pip
 - Modify `server/uv.lock` manually
@@ -173,16 +173,21 @@ The shape language is a hybrid of structural precision and tactile softness.
 ## Components
 ### Card Containers
 The fundamental building block of the UI. Every distinct section (Script, Player, Controls, Logs) is housed in a card featuring the `card-bg`, a 1px `border`, and `rounded-xl` corners. The internal layout always features an uppercase teal header for immediate section identification.
 ### Primary Action Buttons
 Used for high-leverage actions like "Generate Audio" and "Play/Pause." These buttons utilize the `gradient-primary-dim` background, bold white text, and emit a soft teal glow to draw the eye and signify their importance.
 ### Range Sliders
 Custom-styled input ranges replace default browser styles. The tracks are muted and slim, while the thumbs are bright teal, fully rounded, and emit a glow that intensifies on hover, providing a premium, tactile scrubbing experience.
 ### Status Indicators & Logs
 A critical component of the application. Status badges utilize a minimalist pill shape with a pulsing ring animation to indicate active server processing. The log panel explicitly uses monospace typography and color-codes messages (green for success, red for error, white for neutral) to provide a terminal-like readout of the backend systems.
 ### Gradients
 Gradients are used purposefully to indicate progress, activity, or brand presence. The primary gradient (`135deg` from teal to violet) is used for branding (the logo icon and text) and primary buttons. Horizontal gradients (`90deg`) are used dynamically in progress bars to represent the flow of data over time (e.g., loading, downloading, and audio generation).
@@ -14,12 +14,12 @@ The Next.js app proxies audio generation requests to the FastAPI server, keeping
 ## Prerequisites
-| Tool | Install |
+| Tool                               | Install                             |
-|------|---------|
+| ---------------------------------- | ----------------------------------- |
-| [Node.js 20+](https://nodejs.org) | `winget install OpenJS.NodeJS.LTS` |
+| [Node.js 20+](https://nodejs.org)  | `winget install OpenJS.NodeJS.LTS`  |
-| [pnpm](https://pnpm.io) | `npm i -g pnpm` |
+| [pnpm](https://pnpm.io)            | `npm i -g pnpm`                     |
 | [Python 3.10+](https://python.org) | `winget install Python.Python.3.13` |
-| [uv](https://docs.astral.sh/uv/) | `winget install astral-sh.uv` |
+| [uv](https://docs.astral.sh/uv/)   | `winget install astral-sh.uv`       |
 ## Getting started
@@ -50,10 +50,10 @@ The frontend shows a loading indicator while the model downloads. Once the serve
 VibePod maintains two completely separate Python virtual environments so CUDA and CPU torch installs never conflict:
-| Mode | Command | venv | torch source |
+| Mode           | Command        | venv               | torch source            |
-|------|---------|------|--------------|
+| -------------- | -------------- | ------------------ | ----------------------- |
-| CUDA (default) | `pnpm dev` | `server/.venv` | PyTorch CUDA 12.4 index |
+| CUDA (default) | `pnpm dev`     | `server/.venv`     | PyTorch CUDA 12.4 index |
-| CPU-only | `pnpm dev:cpu` | `server/.venv-cpu` | PyPI (CPU wheel) |
+| CPU-only       | `pnpm dev:cpu` | `server/.venv-cpu` | PyPI (CPU wheel)        |
 On first run, each mode creates its own venv automatically. You can switch between them freely — they are fully independent. The active device is reported by the `/health` endpoint as `"device": "cpu"` or `"device": "cuda"`.
@@ -74,11 +74,11 @@ pnpm build            # Production build of the frontend
 Copy `.env.example` to `.env.local` and set:
-| Variable | Default | Description |
+| Variable               | Default                 | Description                                               |
-|----------|---------|-------------|
+| ---------------------- | ----------------------- | --------------------------------------------------------- |
 | `VIBEVOICE_SERVER_URL` | `http://localhost:8000` | URL the Next.js API routes use to reach the Python server |
-| `HF_TOKEN` | — | HuggingFace token (required if the model repo is gated) |
+| `HF_TOKEN`             | —                       | HuggingFace token (required if the model repo is gated)   |
-| `HF_HOME` | — | Override the HuggingFace model cache directory |
+| `HF_HOME`              | —                       | Override the HuggingFace model cache directory            |
 ## Project structure
@@ -107,11 +107,11 @@ server/
 ## Generation parameters
-| Parameter | Range | Default | Effect |
+| Parameter         | Range                                               | Default  | Effect                                         |
-|-----------|-------|---------|--------|
+| ----------------- | --------------------------------------------------- | -------- | ---------------------------------------------- |
-| `speaker` | `carter`, `davis`, `emma`, `frank`, `grace`, `mike` | `carter` | Voice preset used for the generated audio |
+| `speaker`         | `carter`, `davis`, `emma`, `frank`, `grace`, `mike` | `carter` | Voice preset used for the generated audio      |
-| `cfg_scale` | 0.5 – 4.0 | 1.5 | Higher = more expressive guidance |
+| `cfg_scale`       | 0.5 – 4.0                                           | 1.5      | Higher = more expressive guidance              |
-| `inference_steps` | 5 – 20 | 10 | More steps = higher quality, slower generation |
+| `inference_steps` | 5 – 20                                              | 10       | More steps = higher quality, slower generation |
 ## How it works
@@ -8,7 +8,14 @@
    "dev:cpu": "bash dev.sh --cpu",
    "dev:server": "bash server/start.sh",
    "dev:server:cpu": "bash server/start.sh --cpu",
-    "dev:web": "pnpm --filter vibepod-web dev"
+    "dev:web": "pnpm --filter vibepod-web dev",
    "format": "prettier --write . && cd server && uv run ruff format .",
    "format:check": "prettier --check . && cd server && uv run ruff format --check .",
    "lint:server": "cd server && uv run ruff check .",
    "lint:server:fix": "cd server && uv run ruff check --fix ."
  },
  "devDependencies": {
    "prettier": "^3.5.3"
  },
  "packageManager": "pnpm@10.33.2+sha512.a90faf6feeab71ad6c6e57f94e0fe1a12f5dcc22cd754db40ae9593eb6a3e0b6b12e3540218bb37ae083404b1f2ce6db2a4121e979829b4aff94b99f49da1cf8"
 }
@@ -6,7 +6,11 @@ settings:
 importers:
-  .: {}
+  .:
    devDependencies:
      prettier:
        specifier: ^3.5.3
        version: 3.8.3
  web:
    dependencies:
@@ -516,6 +520,11 @@ packages:
    resolution: {integrity: sha512-W62t/Se6rA0Az3DfCL0AqJwXuKwBeYg6nOaIgzP+xZ7N5BFCI7DYi1qs6ygUYT6rvfi6t9k65UMLJC+PHZpDAA==}
    engines: {node: ^10 || ^12 || >=14}
  prettier@3.8.3:
    resolution: {integrity: sha512-7igPTM53cGHMW8xWuVTydi2KO233VFiTNyF5hLJqpilHfmn8C8gPf+PS7dUT64YcXFbiMGZxS9pCSxL/Dxm/Jw==}
    engines: {node: '>=14'}
    hasBin: true
  react-dom@19.1.0:
    resolution: {integrity: sha512-Xs1hdnE+DyKgeHJeJznQmYMIBG3TKIHJJT95Q58nHLSrElKlGQqDTR2HQ9fx5CN/Gk6Vh/kupBTDLU11/nDk/g==}
    peerDependencies:
@@ -917,6 +926,8 @@ snapshots:
      picocolors: 1.1.1
      source-map-js: 1.2.1
  prettier@3.8.3: {}
  react-dom@19.1.0(react@19.1.0):
    dependencies:
      react: 19.1.0
@@ -1,2 +1,2 @@
 packages:
-  - 'web'
+  - "web"
@@ -39,6 +39,13 @@ VibePod Studio will turn generated audio from a one-shot download into a reusabl
 - Add project save/load, autosave, and recoverable render jobs.
 - Prepare the audio pipeline for queueing longer renders outside the request lifecycle.
 ## Later: VibeVoice Performance Research
 - Move the current VibePod hot-path monkey patches into the `JezzWTF/VibeVoice` fork once the feature direction has settled.
 - Add clearer generation profiling for overlapped CPU work, especially decode wait time versus total acoustic decode time.
 - Prototype batched positive/negative CFG TTS LM inference behind an opt-in flag and benchmark it against the current sequential path on CPU and CUDA.
 - Keep experimental performance work isolated from user-facing feature work unless it shows a clear speedup without audio quality regressions.
 ## Foundation Work Needed First
 - Persist generated outputs with stable IDs.
@@ -0,0 +1 @@
 3.12
@@ -30,15 +30,12 @@ def download() -> str:
        from huggingface_hub import snapshot_download
    except ImportError:
        print(
-            "ERROR: huggingface_hub is not installed.\n"
+            "ERROR: huggingface_hub is not installed.\nRun: pip install huggingface_hub",
            "Run: pip install huggingface_hub",
            file=sys.stderr,
        )
        sys.exit(1)
-    token: str | None = os.environ.get("HF_TOKEN") or os.environ.get(
+    token: str | None = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
        "HUGGINGFACE_TOKEN"
    )
    print(f"Checking / downloading model: {MODEL_ID}")
    print("(This may take several minutes on first run — the model is ~1 GB)")
@@ -8,7 +8,8 @@ dependencies = [
    # To switch back to CPU-only, remove the [tool.uv.sources] torch entry below.
    "torch>=2.0.0",
    # VibeVoice custom model + processor classes (not yet in upstream transformers)
-    "vibevoice @ git+https://github.com/microsoft/VibeVoice.git",
+    # Uses JezzWTF/VibeVoice fork so VibePod-specific optimisations land here.
    "vibevoice @ git+https://github.com/JezzWTF/VibeVoice.git",
    # Exact version required by vibevoice's streaming TTS module
    "transformers==4.51.3",
    "fastapi>=0.111.0",
@@ -18,10 +19,36 @@ dependencies = [
    "huggingface_hub>=0.23.0",
 ]
 [dependency-groups]
 dev = [
    "ruff>=0.11.0",
 ]
 # No build-system — this is a scripts project, not an installable package.
 # Lock file is committed so installs are reproducible.
 # Run `uv lock --upgrade` to bump dependencies.
 [tool.ruff]
 line-length = 100
 indent-width = 4
 target-version = "py310"
 exclude = [".git", ".venv", ".venv-cpu", "__pycache__"]
 [tool.ruff.lint]
 select = ["E", "F", "UP", "B", "SIM", "I"]
 ignore = [
    "E501",  # line-too-long — handled by formatter
    "B905",  # zip() without strict= — existing code, lengths are known-correct
 ]
 [tool.ruff.lint.per-file-ignores]
 "download_model.py" = ["T201"]  # allow print statements in CLI scripts
 [tool.ruff.format]
 quote-style = "double"
 indent-style = "space"
 line-ending = "lf"
 [[tool.uv.index]]
 name = "pytorch-cu124"
 url = "https://download.pytorch.org/whl/cu124"
@@ -7,6 +7,11 @@
 #   ./start.sh          — CUDA mode (default, uses PyTorch CUDA 12.4 wheel, venv: .venv)
 #   ./start.sh --cpu    — CPU-only mode (uses PyPI CPU torch wheel, venv: .venv-cpu)
 #
 # Optional CUDA acceleration:
 #   VIBEPOD_ENABLE_FLASH_ATTN=1 ./start.sh
 #   Installs a pre-built flash-attn wheel when the CUDA venv uses Python 3.12,
 #   torch 2.6.0, and CUDA 12.4 on Windows. Other platforms fall back to SDPA.
 #
 # The two modes maintain completely separate virtual environments so their torch
 # installations never conflict. UV_PROJECT_ENVIRONMENT tells uv which venv to use;
 # --no-sources skips [tool.uv.sources] so the CPU run pulls the default PyPI torch wheel.
@@ -51,6 +56,19 @@ if ! command -v uv &>/dev/null; then
    exit 1
 fi
 validate_flash_attn() {
    uv run python -c "import flash_attn; import triton; import transformers.modeling_utils" &>/dev/null
 }
 remove_broken_flash_attn() {
    if uv run python -c "import importlib.util; raise SystemExit(0 if importlib.util.find_spec('flash_attn') else 1)" &>/dev/null; then
        if ! validate_flash_attn; then
            echo "    Installed flash-attn is not usable in this environment; removing it."
            uv pip uninstall flash-attn
        fi
    fi
 }
 # ---------------------------------------------------------------------------
 # 2. Sync Python environment
 #    CPU mode:  use .venv-cpu and skip [tool.uv.sources] so uv pulls the
@@ -61,10 +79,64 @@ echo ""
 if $CPU_MODE; then
    echo "--> Syncing CPU Python environment (.venv-cpu)..."
    export UV_PROJECT_ENVIRONMENT=".venv-cpu"
    LOCK_BACKUP=""
    if [[ -f uv.lock ]]; then
        LOCK_BACKUP="$(mktemp)"
        cp uv.lock "$LOCK_BACKUP"
    fi
    uv sync --no-sources
    if [[ -n "$LOCK_BACKUP" ]]; then
        cp "$LOCK_BACKUP" uv.lock
        rm -f "$LOCK_BACKUP"
    fi
 else
    echo "--> Syncing CUDA Python environment (.venv)..."
    uv sync
    remove_broken_flash_attn
    if [[ "${VIBEPOD_ENABLE_FLASH_ATTN:-0}" == "1" ]]; then
        echo ""
        echo "--> Checking optional FlashAttention wheel..."
        if validate_flash_attn; then
            echo "    flash-attn already installed and importable."
        else
            PY_TAG="$(uv run python -c "import sys; print(f'cp{sys.version_info.major}{sys.version_info.minor}')")"
            TORCH_TAG="$(uv run python -c "import torch; print(torch.__version__.split('+', 1)[0])")"
            CUDA_TAG="$(uv run python -c "import torch; print('cu' + torch.version.cuda.replace('.', ''))")"
            FLASH_ATTN_WHEEL_URL=""
            if [[ "$PY_TAG" == "cp312" && "$TORCH_TAG" == "2.6.0" && "$CUDA_TAG" == "cu124" ]]; then
                case "$(uname -s)" in
                    MINGW*|CYGWIN*|MSYS*)
                        FLASH_ATTN_WHEEL_URL="https://huggingface.co/lldacing/flash-attention-windows-wheel/resolve/main/flash_attn-2.7.4%2Bcu124torch2.6.0cxx11abiFALSE-cp312-cp312-win_amd64.whl"
                        echo "    Installing flash-attn for Python 3.12, torch 2.6.0, CUDA 12.4 (Windows)..."
                        ;;
                    *)
                        echo "    No pre-built flash-attn wheel available for this platform ($(uname -s))."
                        echo "    Continuing with PyTorch SDPA attention."
                        ;;
                esac
            else
                echo "    No known wheel for Python tag $PY_TAG, torch $TORCH_TAG, CUDA $CUDA_TAG."
                echo "    Continuing with PyTorch SDPA attention."
            fi
            if [[ -n "$FLASH_ATTN_WHEEL_URL" ]]; then
                if uv pip install "$FLASH_ATTN_WHEEL_URL"; then
                    if validate_flash_attn; then
                        echo "    flash-attn import check passed."
                    else
                        echo "    flash-attn import check failed; removing it and continuing with SDPA."
                        uv pip uninstall flash-attn
                    fi
                else
                    echo "    flash-attn wheel install failed; continuing with SDPA."
                fi
            fi
        fi
    fi
 fi
 # ---------------------------------------------------------------------------
@@ -78,11 +150,28 @@ export PYTHONUTF8=1
 if $CPU_MODE; then
    export VIBEPOD_DEVICE="cpu"
    export UV_PROJECT_ENVIRONMENT=".venv-cpu"
    if [[ -z "${VIBEPOD_CPU_THREADS:-}" ]]; then
        VIBEPOD_CPU_THREADS="$(uv run --no-sync --no-sources python -c "import os; print(max(1, (os.cpu_count() or 2) // 2))")"
        export VIBEPOD_CPU_THREADS
    fi
    export OMP_NUM_THREADS="${OMP_NUM_THREADS:-$VIBEPOD_CPU_THREADS}"
    export MKL_NUM_THREADS="${MKL_NUM_THREADS:-$VIBEPOD_CPU_THREADS}"
    # Dynamic INT8 quantization — on by default for CPU (~22% faster, prediction_head
    # excluded automatically to avoid regression on small fixed-size tensors).
    # Set VIBEPOD_QUANTIZE=0 to disable if you notice audio quality differences.
    export VIBEPOD_QUANTIZE="${VIBEPOD_QUANTIZE:-1}"
    # Optional CPU flags:
    #   VIBEPOD_ASYNC_DECODE=0  Disable async decode/tts_lm overlap (on by default)
    #   VIBEPOD_CPU_BF16=1      Force bfloat16 weights (auto-detected via AVX512_BF16)
    #   VIBEPOD_COMPILE=1       torch.compile hot paths (ineffective for autoregressive
    #                           models on CPU — not recommended, kept for experimentation)
    UV_RUN_ARGS=(--no-sync --no-sources)
 else
    export VIBEPOD_DEVICE="cuda"
    UV_RUN_ARGS=()
 fi
-exec uv run uvicorn vibevoice_server:app \
+exec uv run "${UV_RUN_ARGS[@]}" uvicorn vibevoice_server:app \
    --host 127.0.0.1 \
    --port 8000 \
    --log-level info \
@@ -1479,7 +1479,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.1.0.70"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "(python_full_version < '3.11' and sys_platform == 'emscripten') or (python_full_version < '3.11' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
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@@ -1490,7 +1490,7 @@ name = "nvidia-cufft-cu12"
 version = "11.2.1.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(python_full_version < '3.11' and sys_platform == 'emscripten') or (python_full_version < '3.11' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
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@@ -1509,9 +1509,9 @@ name = "nvidia-cusolver-cu12"
 version = "11.6.1.9"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "(python_full_version < '3.11' and sys_platform == 'emscripten') or (python_full_version < '3.11' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
-    { name = "nvidia-cusparse-cu12" },
+    { name = "nvidia-cusparse-cu12", marker = "(python_full_version < '3.11' and sys_platform == 'emscripten') or (python_full_version < '3.11' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(python_full_version < '3.11' and sys_platform == 'emscripten') or (python_full_version < '3.11' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
 ]
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@@ -1522,7 +1522,7 @@ name = "nvidia-cusparse-cu12"
 version = "12.3.1.170"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(python_full_version < '3.11' and sys_platform == 'emscripten') or (python_full_version < '3.11' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
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 [[package]]
 name = "safehttpx"
 version = "0.1.7"
@@ -3100,6 +3125,11 @@ dependencies = [
    { name = "vibevoice" },
 ]
 [package.dev-dependencies]
 dev = [
    { name = "ruff" },
 ]
 [package.metadata]
 requires-dist = [
    { name = "fastapi", specifier = ">=0.111.0" },
@@ -3109,13 +3139,16 @@ requires-dist = [
    { name = "torch", specifier = ">=2.0.0", index = "https://download.pytorch.org/whl/cu124" },
    { name = "transformers", specifier = "==4.51.3" },
    { name = "uvicorn", extras = ["standard"], specifier = ">=0.29.0" },
-    { name = "vibevoice", git = "https://github.com/microsoft/VibeVoice.git" },
+    { name = "vibevoice", git = "https://github.com/JezzWTF/VibeVoice.git" },
 ]
 [package.metadata.requires-dev]
 dev = [{ name = "ruff", specifier = ">=0.11.0" }]
 [[package]]
 name = "vibevoice"
 version = "1.0.0"
-source = { git = "https://github.com/microsoft/VibeVoice.git#e73d1e17c3754f046352014856a922f8208fb5d3" }
+source = { git = "https://github.com/JezzWTF/VibeVoice.git#fe832f20e3d1638594f551a08f02253f14408dbd" }
 dependencies = [
    { name = "absl-py" },
    { name = "accelerate" },
@@ -20,17 +20,22 @@ Device selection:
 import asyncio
 import base64
 import concurrent.futures
 import copy
 import functools
 import importlib.util
 import json
 import logging
 import os
 import platform
 import threading
 import time
 import types
 import urllib.request
 from collections.abc import AsyncGenerator
 from contextlib import asynccontextmanager
 from pathlib import Path
-from typing import AsyncGenerator, Literal, Optional
+from typing import Literal
 import torch
 from fastapi import FastAPI, HTTPException, Request
@@ -46,8 +51,7 @@ SAMPLE_RATE = 24_000
 VOICES_DIR = Path(__file__).parent / "voices" / "streaming_model"
 VOICE_BASE_URL = (
-    "https://raw.githubusercontent.com/microsoft/VibeVoice/main"
+    "https://raw.githubusercontent.com/microsoft/VibeVoice/main/demo/voices/streaming_model"
    "/demo/voices/streaming_model"
 )
 EN_VOICES: dict[str, str] = {
@@ -62,6 +66,11 @@ DEFAULT_SPEAKER = "carter"
 _IGNORE_PATTERNS = ["*.msgpack", "flax_model*", "tf_model*", "rust_model*", "*.ot"]
 # ── Pipeline executor ──────────────────────────────────────────────────────────
 # Overlaps acoustic_decode with forward_tts_lm on a background thread (1 worker).
 _decode_executor: concurrent.futures.ThreadPoolExecutor | None = None
 # ── Device selection ────────────────────────────────────────────────────────────
 # VIBEPOD_DEVICE env var is set by start.sh based on the --cpu / --cuda flag.
 # Falls back to auto-detection if not set.
@@ -106,6 +115,38 @@ def _env_float(name: str, default: float) -> float:
        return default
 def _cpu_supports_bf16() -> bool:
    """Return True if the CPU has AVX512_BF16 hardware support."""
    return (
        hasattr(torch, "cpu")
        and hasattr(torch.cpu, "is_avx512_bf16_supported")
        and torch.cpu.is_avx512_bf16_supported()
    )
 def _configure_cpu_runtime() -> dict[str, object]:
    logical_cpus = os.cpu_count() or 1
    default_threads = max(1, logical_cpus // 2) if platform.system() == "Windows" else logical_cpus
    intra_threads = _env_int("VIBEPOD_CPU_THREADS", default_threads)
    interop_threads = _env_int("VIBEPOD_CPU_INTEROP_THREADS", 1)
    mkldnn_enabled = os.environ.get("VIBEPOD_CPU_MKLDNN", "1").strip() != "0"
    torch.set_num_threads(max(1, intra_threads))
    try:
        torch.set_num_interop_threads(max(1, interop_threads))
    except RuntimeError as exc:
        logger.warning("Could not set CPU inter-op threads: %s", exc)
    torch.backends.mkldnn.enabled = mkldnn_enabled
    return {
        "logical_cpus": logical_cpus,
        "threads": torch.get_num_threads(),
        "interop_threads": torch.get_num_interop_threads(),
        "mkldnn_available": torch.backends.mkldnn.is_available(),
        "mkldnn_enabled": torch.backends.mkldnn.enabled,
    }
 # ── Global state ────────────────────────────────────────────────────────────────
 ModelStatus = Literal["downloading", "loading", "online", "error"]
@@ -114,7 +155,7 @@ _processor = None
 _model = None
 _device: str = "cpu"
 _model_status: ModelStatus = "loading"
-_model_error: Optional[str] = None
+_model_error: str | None = None
 _voice_presets: dict[str, object] = {}
 _load_lock = threading.Lock()
 _generation_lock = asyncio.Lock()
@@ -161,9 +202,7 @@ def _is_model_cached() -> bool:
    try:
        from huggingface_hub import snapshot_download
-        snapshot_download(
+        snapshot_download(MODEL_ID, local_files_only=True, ignore_patterns=_IGNORE_PATTERNS)
            MODEL_ID, local_files_only=True, ignore_patterns=_IGNORE_PATTERNS
        )
        return True
    except Exception:
        return False
@@ -172,9 +211,7 @@ def _is_model_cached() -> bool:
 def _download_model() -> None:
    from huggingface_hub import snapshot_download
-    token: Optional[str] = os.environ.get("HF_TOKEN") or os.environ.get(
+    token: str | None = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
        "HUGGINGFACE_TOKEN"
    )
    DlTqdm = _make_dl_tqdm()
    logger.info("Model not cached — downloading %s...", MODEL_ID)
    snapshot_download(
@@ -188,7 +225,7 @@ def _download_model() -> None:
 def _download_voices() -> None:
    VOICES_DIR.mkdir(parents=True, exist_ok=True)
-    for name, filename in EN_VOICES.items():
+    for _name, filename in EN_VOICES.items():
        dest = VOICES_DIR / filename
        if not dest.exists():
            url = f"{VOICE_BASE_URL}/{filename}"
@@ -211,8 +248,56 @@ def _init_processor():
 def _init_model(device: str):
    logger.info("Loading model on %s...", device)
-    load_dtype = torch.bfloat16 if device == "cuda" else torch.float32
+    if device == "cuda":
-    attn_impl = "flash_attention_2" if device == "cuda" else "sdpa"
+        torch.set_float32_matmul_precision("high")
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True
        torch.backends.cudnn.benchmark = True
        torch.backends.cuda.enable_flash_sdp(True)
        torch.backends.cuda.enable_mem_efficient_sdp(True)
        torch.backends.cuda.enable_math_sdp(True)
        torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
        logger.info(
            "PyTorch SDPA backends: flash=%s, mem_efficient=%s, math=%s",
            torch.backends.cuda.flash_sdp_enabled(),
            torch.backends.cuda.mem_efficient_sdp_enabled(),
            torch.backends.cuda.math_sdp_enabled(),
        )
    elif device == "cpu":
        torch.set_float32_matmul_precision("medium")
        logger.info("CPU runtime configuration: %s", _configure_cpu_runtime())
    cuda_dtype = os.environ.get("VIBEPOD_CUDA_DTYPE", "bf16").lower()
    if device == "cuda" and cuda_dtype == "fp16":
        load_dtype = torch.float16
    elif device == "cuda":
        load_dtype = torch.bfloat16
    else:
        cpu_bf16_env = os.environ.get("VIBEPOD_CPU_BF16", "auto").lower()
        if cpu_bf16_env == "1":
            load_dtype = torch.bfloat16
            logger.info("CPU BF16 forced via VIBEPOD_CPU_BF16=1")
        elif cpu_bf16_env == "0":
            load_dtype = torch.float32
            logger.info("CPU float32 forced via VIBEPOD_CPU_BF16=0")
        elif _cpu_supports_bf16():
            load_dtype = torch.bfloat16
            logger.info("AVX512_BF16 detected — loading model in bfloat16")
        else:
            load_dtype = torch.float32
            logger.info("No AVX512_BF16 — using float32 (set VIBEPOD_CPU_BF16=1 to override)")
    logger.info("Loading model weights with dtype %s", load_dtype)
    requested_attn_impl = os.environ.get("VIBEPOD_ATTN_IMPL", "auto").lower()
    has_flash_attn = importlib.util.find_spec("flash_attn") is not None
    if requested_attn_impl in {"eager", "sdpa"}:
        attn_impl = requested_attn_impl
    elif requested_attn_impl == "flash_attention_2":
        attn_impl = "flash_attention_2" if has_flash_attn else "sdpa"
    else:
        attn_impl = "flash_attention_2" if device == "cuda" and has_flash_attn else "sdpa"
    logger.info("Using Transformers attention implementation: %s", attn_impl)
    if device == "cuda" and not has_flash_attn:
        logger.info("flash_attn is not installed; using PyTorch SDPA attention.")
    from vibevoice.modular.modeling_vibevoice_streaming_inference import (
        VibeVoiceStreamingForConditionalGenerationInference,
@@ -225,9 +310,13 @@ def _init_model(device: str):
            device_map=device,
            attn_implementation=attn_impl,
        )
-    except Exception:
+    except Exception as exc:
        if attn_impl == "sdpa":
            raise
        logger.warning(
-            "Model load with %s failed; falling back to sdpa", attn_impl, exc_info=True
+            "Model load with %s failed (%s); falling back to sdpa",
            attn_impl,
            exc,
        )
        model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
            MODEL_ID,
@@ -237,10 +326,272 @@ def _init_model(device: str):
        )
    model.eval()
    if device == "cpu":
        model = _apply_cpu_optimizations(model)
    model.set_ddpm_inference_steps(num_steps=_config["default_inference_steps"])
    _install_generation_optimizations(model)
    if device == "cpu":
        # Must run after _install_generation_optimizations so the async wrapper
        # sits outside the profiling wrapper (VibeVoice calls async → profiling → real decode).
        _install_cpu_pipeline_optimizations(model)
    return model
 def _apply_cpu_optimizations(model: object) -> object:
    """Apply optional post-load CPU optimizations. Returns (possibly new) model object."""
    do_quantize = os.environ.get("VIBEPOD_QUANTIZE", "0") == "1"
    do_compile = os.environ.get("VIBEPOD_COMPILE", "0") == "1"
    if do_quantize:
        logger.info("Applying dynamic INT8 quantization to Linear layers...")
        try:
            import torch.ao.quantization
            # The diffusion prediction_head operates on small fixed-size tensors where
            # INT8 pack/unpack overhead exceeds the matmul savings (~+20% regression in
            # testing). Save and restore it so it stays in float32.
            saved_prediction_head = None
            if hasattr(model, "model") and hasattr(model.model, "prediction_head"):
                saved_prediction_head = model.model.prediction_head
                del model.model.prediction_head
            model = torch.ao.quantization.quantize_dynamic(
                model, {torch.nn.Linear}, dtype=torch.qint8
            )
            if saved_prediction_head is not None:
                model.model.prediction_head = saved_prediction_head
                logger.info(
                    "Dynamic INT8 quantization applied (prediction_head excluded — stays float32)."
                )
            else:
                logger.info("Dynamic INT8 quantization applied.")
        except Exception as exc:
            logger.warning("Dynamic quantization failed: %s — skipping", exc)
    if do_compile:
        # torch.compile with inductor on CPU is ineffective for autoregressive TTS:
        # each token step produces a unique input shape, so every step triggers a new
        # kernel compile event rather than reusing compiled code.  Kept as an escape
        # hatch but not recommended.
        compile_mode = os.environ.get("VIBEPOD_COMPILE_MODE", "reduce-overhead")
        logger.info(
            "torch.compile enabled (mode=%s) — NOTE: limited benefit for autoregressive"
            " models on CPU due to dynamic sequence lengths.",
            compile_mode,
        )
        _compile_targets: list[tuple[str, object, str, bool]] = [
            ("forward_tts_lm", model, "forward_tts_lm", True),
        ]
        if hasattr(model, "model"):
            inner = model.model
            if hasattr(inner, "prediction_head"):
                _compile_targets.append(("prediction_head", inner, "prediction_head", False))
            if hasattr(inner, "acoustic_tokenizer") and hasattr(inner.acoustic_tokenizer, "decode"):
                _compile_targets.append(
                    ("acoustic_tokenizer.decode", inner.acoustic_tokenizer, "decode", False)
                )
        for label, obj, attr, dynamic in _compile_targets:
            try:
                compiled = torch.compile(
                    getattr(obj, attr),
                    backend="inductor",
                    mode=compile_mode,
                    dynamic=dynamic,
                )
                setattr(obj, attr, compiled)
                logger.info("  compiled: %s", label)
            except Exception as exc:
                logger.warning("  torch.compile failed for %s: %s — skipping", label, exc)
    return model
 def _install_generation_optimizations(model: object) -> None:
    """Patch VibeVoice hot paths without changing model quality settings."""
    def profile_enabled() -> bool:
        return os.environ.get("VIBEPOD_PROFILE_GENERATION", "0") == "1"
    def profile_sync() -> None:
        if torch.cuda.is_available():
            torch.cuda.synchronize()
    def profile_record(self, key: str, elapsed: float) -> None:
        stats = getattr(self, "_vibepod_profile", None)
        if stats is None:
            stats = {}
            self._vibepod_profile = stats
        bucket = stats.setdefault(key, {"count": 0, "seconds": 0.0})
        bucket["count"] += 1
        bucket["seconds"] += elapsed
    def timed_method(self, key: str, fn, *args, **kwargs):
        if not profile_enabled():
            return fn(*args, **kwargs)
        profile_sync()
        started = time.perf_counter()
        result = fn(*args, **kwargs)
        profile_sync()
        profile_record(self, key, time.perf_counter() - started)
        return result
    def prepare_noise_scheduler(self):
        scheduler = self.model.noise_scheduler
        cache_key = self.ddpm_inference_steps
        cache = getattr(self, "_vibepod_scheduler_cache", {})
        cached = cache.get(cache_key)
        if cached is None:
            scheduler.set_timesteps(self.ddpm_inference_steps)
            cached = {
                "num_inference_steps": scheduler.num_inference_steps,
                "timesteps": scheduler.timesteps,
                "sigmas": scheduler.sigmas,
            }
            cache[cache_key] = cached
            self._vibepod_scheduler_cache = cache
        else:
            scheduler.num_inference_steps = cached["num_inference_steps"]
            scheduler.timesteps = cached["timesteps"]
            scheduler.sigmas = cached["sigmas"]
            scheduler.model_outputs = [None] * scheduler.config.solver_order
            scheduler.lower_order_nums = 0
            scheduler._step_index = None
            scheduler._begin_index = None
        return scheduler
    def sample_speech_tokens_optimized(self, condition, neg_condition, cfg_scale=3.0):
        scheduler = prepare_noise_scheduler(self)
        condition = torch.cat([condition, neg_condition], dim=0).to(
            self.model.prediction_head.device
        )
        batch_size = condition.shape[0] // 2
        speech = torch.randn(batch_size, self.config.acoustic_vae_dim).to(condition)
        t_batch_cache_key = (
            self.ddpm_inference_steps,
            condition.device.type,
            condition.device.index,
            condition.dtype,
            batch_size,
        )
        t_batch_cache = getattr(self, "_vibepod_t_batch_cache", {})
        t_batches = t_batch_cache.get(t_batch_cache_key)
        if t_batches is None or len(t_batches) != len(scheduler.timesteps):
            t_batches = [
                t.repeat(condition.shape[0]).to(device=condition.device, dtype=condition.dtype)
                for t in scheduler.timesteps
            ]
            t_batch_cache[t_batch_cache_key] = t_batches
            self._vibepod_t_batch_cache = t_batch_cache
        for t, t_batch in zip(scheduler.timesteps, t_batches):
            if batch_size == 1:
                combined = speech.expand(condition.shape[0], -1)
            else:
                combined = torch.cat([speech, speech], dim=0)
            if profile_enabled():
                profile_sync()
                started = time.perf_counter()
            eps = self.model.prediction_head(combined, t_batch, condition=condition)
            if profile_enabled():
                profile_sync()
                profile_record(self, "diffusion_prediction_head", time.perf_counter() - started)
            cond_eps, uncond_eps = torch.split(eps, batch_size, dim=0)
            guided_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
            if profile_enabled():
                started = time.perf_counter()
            speech = scheduler.step(guided_eps, t, speech).prev_sample
            if profile_enabled():
                profile_record(self, "diffusion_scheduler_step", time.perf_counter() - started)
        return speech
    forward_lm = model.forward_lm
    forward_tts_lm = model.forward_tts_lm
    acoustic_decode = model.model.acoustic_tokenizer.decode
    def forward_lm_profiled(*args, **kwargs):
        return timed_method(model, "forward_lm", forward_lm, *args, **kwargs)
    def forward_tts_lm_profiled(*args, **kwargs):
        return timed_method(model, "forward_tts_lm", forward_tts_lm, *args, **kwargs)
    def acoustic_decode_profiled(*args, **kwargs):
        return timed_method(model, "acoustic_decode", acoustic_decode, *args, **kwargs)
    model.forward_lm = forward_lm_profiled
    model.forward_tts_lm = forward_tts_lm_profiled
    model.model.acoustic_tokenizer.decode = acoustic_decode_profiled
    model.sample_speech_tokens = types.MethodType(sample_speech_tokens_optimized, model)
    logger.info("Installed VibeVoice generation hot-path optimizations.")
 def _install_cpu_pipeline_optimizations(model: object) -> None:
    """Attach the decode executor to the model for the optimised generate() loop.
    The JezzWTF/VibeVoice fork's generate() checks for two optional attributes:
      model._vibepod_decode_executor  — ThreadPoolExecutor (1 worker) that
          overlaps acoustic_decode with acoustic_connector + forward_tts_lm.
          Profiling showed this hides ~72s of decode cost behind tts_lm work,
          capturing ~96% of the theoretical overlap savings.
      model._vibepod_cfg_executor     — intentionally NOT set. Parallel pos/neg
          forward_tts_lm via a second thread causes MKL OpenMP thread-pool
          contention on CPU: both threads compete for the same OMP worker pool,
          making each call slower rather than faster. Net effect: ~6% regression.
          The hook remains in the fork for potential GPU or future use.
    Attributes default to None, so the fork's generate() falls back to the
    original sequential behaviour on CUDA or any non-VibePod install.
    """
    global _decode_executor
    if os.environ.get("VIBEPOD_ASYNC_DECODE", "1") != "1":
        logger.info("CPU async decode disabled via VIBEPOD_ASYNC_DECODE=0.")
        return
    _decode_executor = concurrent.futures.ThreadPoolExecutor(
        max_workers=1, thread_name_prefix="vibepod-decode"
    )
    model._vibepod_decode_executor = _decode_executor
    logger.info(
        "CPU pipeline: decode executor attached — acoustic_decode overlaps "
        "tts_lm. Disable with VIBEPOD_ASYNC_DECODE=0."
    )
 def _model_float_dtype() -> torch.dtype:
    try:
        return next(_model.parameters()).dtype
    except StopIteration:
        return torch.float32
 def _move_cached_prompt(value: object, device: str, dtype: torch.dtype) -> object:
    if torch.is_tensor(value):
        if torch.is_floating_point(value):
            return value.to(device=device, dtype=dtype)
        return value.to(device=device)
    if isinstance(value, dict):
        for k in list(value.keys()):
            value[k] = _move_cached_prompt(value[k], device, dtype)
        return value
    if isinstance(value, list):
        return [_move_cached_prompt(v, device, dtype) for v in value]
    if isinstance(value, tuple):
        return tuple(_move_cached_prompt(v, device, dtype) for v in value)
    if hasattr(value, "key_cache") and hasattr(value, "value_cache"):
        value.key_cache = [_move_cached_prompt(t, device, dtype) for t in value.key_cache]
        value.value_cache = [_move_cached_prompt(t, device, dtype) for t in value.value_cache]
    return value
 def _load_voice_presets(device: str) -> dict[str, object]:
    presets = {}
    for name, filename in EN_VOICES.items():
@@ -273,19 +624,33 @@ def _load_model_sync() -> None:
            is_cpu = _device == "cpu"
            _config["device"] = _device
            _config["chunk_accum"] = _env_int("VIBEPOD_CHUNK_ACCUM", 4 if is_cpu else 1)
-            _config["prebuffer_secs"] = _env_float("VIBEPOD_PREBUFFER_SECS", 5.0 if is_cpu else 2.0)
+            _config["prebuffer_secs"] = _env_float(
-            _config["rebuffer_threshold_secs"] = _env_float("VIBEPOD_REBUFFER_THRESHOLD_SECS", 1.0 if is_cpu else 0.4)
+                "VIBEPOD_PREBUFFER_SECS", 24.0 if is_cpu else 5.0
-            _config["resume_threshold_secs"] = _env_float("VIBEPOD_RESUME_THRESHOLD_SECS", 2.5 if is_cpu else 1.5)
+            )
-            _config["default_inference_steps"] = _env_int("VIBEPOD_DEFAULT_INFERENCE_STEPS", 8 if is_cpu else 10)
+            _config["rebuffer_threshold_secs"] = _env_float(
                "VIBEPOD_REBUFFER_THRESHOLD_SECS", 2.0 if is_cpu else 1.0
            )
            _config["resume_threshold_secs"] = _env_float(
                "VIBEPOD_RESUME_THRESHOLD_SECS", 12.0 if is_cpu else 3.0
            )
            _config["default_inference_steps"] = _env_int(
                "VIBEPOD_DEFAULT_INFERENCE_STEPS", 8 if is_cpu else 10
            )
            if is_cpu:
                logical_cpus = os.cpu_count() or 1
                _config["cpu_threads"] = _env_int(
                    "VIBEPOD_CPU_THREADS",
                    max(1, logical_cpus // 2) if platform.system() == "Windows" else logical_cpus,
                )
                _config["cpu_interop_threads"] = _env_int("VIBEPOD_CPU_INTEROP_THREADS", 1)
                _config["cpu_mkldnn"] = os.environ.get("VIBEPOD_CPU_MKLDNN", "1").strip() != "0"
            _processor = _init_processor()
            _model = _init_model(_device)
            _voice_presets = _load_voice_presets(_device)
            _model_status = "online"
-            logger.info(
+            logger.info("Model ready on %s. Voices: %s", _device, list(_voice_presets.keys()))
                "Model ready on %s. Voices: %s", _device, list(_voice_presets.keys())
            )
            logger.info("Configuration: %s", _config)
        except Exception as exc:
@@ -302,6 +667,8 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
    thread = threading.Thread(target=_load_model_sync, daemon=True, name="model-loader")
    thread.start()
    yield
    if _decode_executor is not None:
        _decode_executor.shutdown(wait=False)
 app = FastAPI(title="VibePod TTS Server", version="0.1.0", lifespan=lifespan)
@@ -314,7 +681,7 @@ class GenerateRequest(BaseModel):
    text: str = Field(..., min_length=1, max_length=10_000)
    speaker: str = Field(default=DEFAULT_SPEAKER)
    cfg_scale: float = Field(default=1.5, ge=0.5, le=4.0)
-    inference_steps: Optional[int] = Field(default=None, ge=5, le=20)
+    inference_steps: int | None = Field(default=None, ge=5, le=20)
    @field_validator("text")
    @classmethod
@@ -353,8 +720,8 @@ async def health() -> dict:
 def _sync_generate(
    req: GenerateRequest,
-    streamer: Optional[object] = None,
+    streamer: object | None = None,
-    cancel_event: Optional[threading.Event] = None,
+    cancel_event: threading.Event | None = None,
 ) -> str:
    """Blocking inference. Returns the speaker used.
    Runs in a thread-pool executor — do not call from the event loop directly.
@@ -364,10 +731,17 @@ def _sync_generate(
        raise RuntimeError("Generation cancelled.")
    speaker = req.speaker if req.speaker in _voice_presets else DEFAULT_SPEAKER
-    voice_preset = copy.deepcopy(_voice_presets[speaker])
+    model_dtype = _model_float_dtype()
    voice_preset = _move_cached_prompt(copy.deepcopy(_voice_presets[speaker]), _device, model_dtype)
-    steps = req.inference_steps if req.inference_steps is not None else _config["default_inference_steps"]
+    steps = (
        req.inference_steps
        if req.inference_steps is not None
        else _config["default_inference_steps"]
    )
    _model.set_ddpm_inference_steps(num_steps=steps)
    if os.environ.get("VIBEPOD_PROFILE_GENERATION", "0") == "1":
        _model._vibepod_profile = {}
    inputs = _processor.process_input_with_cached_prompt(
        text=req.text,
@@ -380,19 +754,20 @@ def _sync_generate(
        if torch.is_tensor(v):
            inputs[k] = v.to(_device)
-    outputs = _model.generate(
+    with torch.inference_mode():
-        **inputs,
+        _model.generate(
-        max_new_tokens=None,
+            **inputs,
-        cfg_scale=req.cfg_scale,
+            max_new_tokens=None,
-        tokenizer=_processor.tokenizer,
+            cfg_scale=req.cfg_scale,
-        generation_config={"do_sample": False},
+            tokenizer=_processor.tokenizer,
-        verbose=True,
+            generation_config={"do_sample": False},
-        all_prefilled_outputs=copy.deepcopy(voice_preset),
+            verbose=False,
-        audio_streamer=streamer,
+            show_progress_bar=False,
-    )
+            return_speech=False,
-
+            stop_check_fn=cancel_event.is_set if cancel_event else None,
-    if not outputs.speech_outputs or outputs.speech_outputs[0] is None:
+            all_prefilled_outputs=voice_preset,
-        raise ValueError("Model returned no audio output.")
+            audio_streamer=streamer,
        )
    return speaker
@@ -401,6 +776,22 @@ def _sse(event: dict) -> str:
    return f"data: {json.dumps(event)}\n\n"
 def _generation_profile() -> dict[str, dict[str, float]] | None:
    if os.environ.get("VIBEPOD_PROFILE_GENERATION", "0") != "1":
        return None
    stats = getattr(_model, "_vibepod_profile", None)
    if not stats:
        return {}
    return {
        key: {
            "count": value["count"],
            "seconds": round(value["seconds"], 3),
            "avg_ms": round(value["seconds"] * 1000 / value["count"], 3) if value["count"] else 0.0,
        }
        for key, value in sorted(stats.items())
    }
@app.post("/generate")
 async def generate(req: GenerateRequest, request: Request) -> StreamingResponse:
    if _model_status != "online":
@@ -417,28 +808,62 @@ async def generate(req: GenerateRequest, request: Request) -> StreamingResponse:
        )
    async def event_stream() -> AsyncGenerator[str, None]:
-        from vibevoice.modular.streamer import AsyncAudioStreamer
+        class NonBlockingAudioStreamer:
            """Async streamer that keeps GPU->CPU copies out of the model thread."""
            def __init__(self, batch_size: int, stop_signal: object = None) -> None:
                self.batch_size = batch_size
                self.stop_signal = stop_signal
                self.audio_queues = [asyncio.Queue() for _ in range(batch_size)]
                self.finished_flags = [False for _ in range(batch_size)]
                self.loop = asyncio.get_running_loop()
            def put(self, audio_chunks: torch.Tensor, sample_indices: torch.Tensor) -> None:
                for i, sample_idx in enumerate(sample_indices):
                    idx = sample_idx.item()
                    if idx < self.batch_size and not self.finished_flags[idx]:
                        self.loop.call_soon_threadsafe(
                            self.audio_queues[idx].put_nowait,
                            audio_chunks[i].detach(),
                        )
            def end(self, sample_indices: torch.Tensor | None = None) -> None:
                if sample_indices is None:
                    indices_to_end = range(self.batch_size)
                else:
                    indices_to_end = [s.item() if torch.is_tensor(s) else s for s in sample_indices]
                for idx in indices_to_end:
                    if idx < self.batch_size and not self.finished_flags[idx]:
                        self.loop.call_soon_threadsafe(
                            self.audio_queues[idx].put_nowait, self.stop_signal
                        )
                        self.finished_flags[idx] = True
        start = time.monotonic()
-        streamer = AsyncAudioStreamer(batch_size=1)
+        streamer = NonBlockingAudioStreamer(batch_size=1)
        cancel_event = threading.Event()
        accum_size = max(1, _config["chunk_accum"])
        accumulated_chunks = []
        chunk_count = 0
        audio_samples = 0
        first_chunk_at: float | None = None
        last_chunk_at: float | None = None
        max_chunk_gap = 0.0
        speaker = req.speaker if req.speaker in _voice_presets else DEFAULT_SPEAKER
        async with _generation_lock:
            loop = asyncio.get_event_loop()
            future = loop.run_in_executor(
                None, functools.partial(_sync_generate, req, streamer, cancel_event)
            )
            future.add_done_callback(lambda _: streamer.end())
            # Drain audio chunks as they arrive from the diffusion head.
            # stop_signal=None is the default sentinel that ends the queue.
            while True:
                try:
-                    chunk = await asyncio.wait_for(
+                    chunk = await asyncio.wait_for(streamer.audio_queues[0].get(), timeout=120.0)
                        streamer.audio_queues[0].get(), timeout=120.0
                    )
                except asyncio.TimeoutError:
                    cancel_event.set()
                    future.cancel()
@@ -454,17 +879,45 @@ async def generate(req: GenerateRequest, request: Request) -> StreamingResponse:
                if chunk is None:  # stop signal
                    break
-                accumulated_chunks.append(chunk.detach().cpu().float())
+                accumulated_chunks.append(chunk.detach())
                if len(accumulated_chunks) >= accum_size:
-                    combined = torch.cat(accumulated_chunks, dim=0)
+                    now = time.monotonic()
                    if first_chunk_at is None:
                        first_chunk_at = now
                    if last_chunk_at is not None:
                        max_chunk_gap = max(max_chunk_gap, now - last_chunk_at)
                    last_chunk_at = now
                    combined = (
                        torch.cat(accumulated_chunks, dim=0)
                        .detach()
                        .to("cpu", dtype=torch.float32)
                        .contiguous()
                    )
                    chunk_count += 1
                    audio_samples += combined.numel()
                    pcm_b64 = base64.b64encode(combined.numpy().tobytes()).decode()
                    yield _sse({"type": "audio_chunk", "data": pcm_b64})
                    accumulated_chunks = []
            # Flush any remaining chunks
            if accumulated_chunks:
-                combined = torch.cat(accumulated_chunks, dim=0)
+                now = time.monotonic()
                if first_chunk_at is None:
                    first_chunk_at = now
                if last_chunk_at is not None:
                    max_chunk_gap = max(max_chunk_gap, now - last_chunk_at)
                last_chunk_at = now
                combined = (
                    torch.cat(accumulated_chunks, dim=0)
                    .detach()
                    .to("cpu", dtype=torch.float32)
                    .contiguous()
                )
                chunk_count += 1
                audio_samples += combined.numel()
                pcm_b64 = base64.b64encode(combined.numpy().tobytes()).decode()
                yield _sse({"type": "audio_chunk", "data": pcm_b64})
@@ -479,17 +932,40 @@ async def generate(req: GenerateRequest, request: Request) -> StreamingResponse:
                yield _sse(
                    {
                        "type": "error",
-                        "message": "Internal server error during generation.",
+                        "message": f"Generation failed: {exc}",
                    }
                )
                return
        elapsed = round(time.monotonic() - start, 1)
        audio_secs = audio_samples / SAMPLE_RATE
        realtime_factor = audio_secs / elapsed if elapsed > 0 else None
        profile = _generation_profile()
        if profile is not None:
            logger.info("Generation profile: %s", profile)
        logger.info("Generation complete in %.1fs", elapsed)
-        yield _sse({"type": "complete", "elapsed": elapsed, "speaker": speaker})
+        complete_event = {
            "type": "complete",
            "elapsed": elapsed,
            "speaker": speaker,
            "audio_secs": round(audio_secs, 2),
            "realtime_factor": round(realtime_factor, 3) if realtime_factor is not None else None,
            "chunks": chunk_count,
            "first_chunk_secs": round(first_chunk_at - start, 2)
            if first_chunk_at is not None
            else None,
            "max_chunk_gap_secs": round(max_chunk_gap, 2),
        }
        if profile is not None:
            complete_event["profile"] = profile
        yield _sse(complete_event)
    return StreamingResponse(
        event_stream(),
        media_type="text/event-stream",
-        headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
+        headers={
            "Cache-Control": "no-cache, no-transform",
            "X-Accel-Buffering": "no",
            "X-Content-Type-Options": "nosniff",
        },
    )
@@ -1,10 +1,13 @@
 import { NextRequest, NextResponse } from "next/server";
 export const dynamic = "force-dynamic";
 export const runtime = "nodejs";
 export async function POST(request: NextRequest) {
  const pythonServerUrl = process.env.VIBEVOICE_SERVER_URL ?? "http://localhost:8000";
  try {
-    const body = await request.json() as {
+    const body = (await request.json()) as {
      text: string;
      speaker?: string;
      cfg_scale?: number;
@@ -24,6 +27,7 @@ export async function POST(request: NextRequest) {
        cfg_scale: body.cfg_scale ?? 1.5,
        inference_steps: body.inference_steps ?? 10,
      }),
      signal: request.signal,
    });
    if (!upstream.ok) {
@@ -36,8 +40,9 @@ export async function POST(request: NextRequest) {
      status: 200,
      headers: {
        "Content-Type": "text/event-stream",
-        "Cache-Control": "no-cache",
+        "Cache-Control": "no-cache, no-transform",
-        "Connection": "keep-alive",
+        Connection: "keep-alive",
        "X-Content-Type-Options": "nosniff",
        "X-Accel-Buffering": "no",
      },
    });
@@ -4,8 +4,7 @@ const OFFLINE_RESPONSE = { status: "offline" };
 const COMMON_OPTIONS = { headers: { "Cache-Control": "no-store" } };
 export async function GET() {
-  const pythonServerUrl =
+  const pythonServerUrl = process.env.VIBEVOICE_SERVER_URL ?? "http://localhost:8000";
    process.env.VIBEVOICE_SERVER_URL ?? "http://localhost:8000";
  try {
    const res = await fetch(`${pythonServerUrl}/health`, {
@@ -27,6 +26,7 @@ export async function GET() {
          message: data.message,
          progress: data.progress ?? null,
          voices: data.voices ?? [],
          config: data.config ?? null,
        },
        COMMON_OPTIONS
      );
@@ -12,8 +12,10 @@
  --muted: #64748b;
  --success: #22c55e;
  --error: #ef4444;
-  --font-sans: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
+  --font-sans:
-  --font-mono: ui-monospace, SFMono-Regular, "SF Mono", Menlo, Consolas, "Liberation Mono", monospace;
+    ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
  --font-mono:
    ui-monospace, SFMono-Regular, "SF Mono", Menlo, Consolas, "Liberation Mono", monospace;
 }
@theme inline {
@@ -69,19 +69,39 @@ type AppAction =
 function reducer(state: AppState, action: AppAction): AppState {
  switch (action.type) {
-    case "SET_SCRIPT":         return { ...state, script: action.payload };
+    case "SET_SCRIPT":
-    case "SET_SPEAKER":        return { ...state, speaker: action.payload };
+      return { ...state, script: action.payload };
-    case "SET_CFG_SCALE":      return { ...state, cfgScale: action.payload };
+    case "SET_SPEAKER":
-    case "SET_INFERENCE_STEPS": return { ...state, inferenceSteps: action.payload };
+      return { ...state, speaker: action.payload };
-    case "SET_PREBUFFER_SECS": return { ...state, prebufferSecs: action.payload };
+    case "SET_CFG_SCALE":
-    case "SET_REBUFFER_THRESHOLD": return { ...state, rebufferThresholdSecs: action.payload };
+      return { ...state, cfgScale: action.payload };
-    case "SET_RESUME_THRESHOLD": return { ...state, resumeThresholdSecs: action.payload };
+    case "SET_INFERENCE_STEPS":
      return { ...state, inferenceSteps: action.payload };
    case "SET_PREBUFFER_SECS":
      return { ...state, prebufferSecs: action.payload };
    case "SET_REBUFFER_THRESHOLD":
      return { ...state, rebufferThresholdSecs: action.payload };
    case "SET_RESUME_THRESHOLD":
      return { ...state, resumeThresholdSecs: action.payload };
    case "START_GENERATION":
-      return { ...state, isGenerating: true, audioUrl: null, logs: [], genElapsed: 0, genPct: null };
+      return {
        ...state,
        isGenerating: true,
        audioUrl: null,
        logs: [],
        genElapsed: 0,
        genPct: null,
      };
    case "GEN_PROGRESS":
      return { ...state, genElapsed: action.elapsed, genPct: action.pct };
    case "GENERATION_SUCCESS":
-      return { ...state, isGenerating: false, genElapsed: 0, genPct: null, audioUrl: action.payload };
+      return {
        ...state,
        isGenerating: false,
        genElapsed: 0,
        genPct: null,
        audioUrl: action.payload,
      };
    case "GENERATION_CANCELLED":
    case "GENERATION_ERROR":
      return { ...state, isGenerating: false, genElapsed: 0, genPct: null };
@@ -89,21 +109,27 @@ function reducer(state: AppState, action: AppAction): AppState {
      return { ...state, logs: [...state.logs, action.payload] };
    case "SET_SERVER_STATUS": {
      const isNewConfig = !state.serverConfig && action.payload.config;
-      const deviceChanged = !!(state.serverConfig && action.payload.config && state.serverConfig.device !== action.payload.config.device);
+      const deviceChanged = !!(
        state.serverConfig &&
        action.payload.config &&
        state.serverConfig.device !== action.payload.config.device
      );
-      const nextSteps = (isNewConfig || deviceChanged)
+      const nextSteps =
        isNewConfig || deviceChanged
          ? action.payload.config!.default_inference_steps
          : state.inferenceSteps;
-      const nextPrebuffer = (isNewConfig || deviceChanged)
+      const nextPrebuffer =
-          ? action.payload.config!.prebuffer_secs
+        isNewConfig || deviceChanged ? action.payload.config!.prebuffer_secs : state.prebufferSecs;
          : state.prebufferSecs;
-      const nextRebuffer = (isNewConfig || deviceChanged)
+      const nextRebuffer =
        isNewConfig || deviceChanged
          ? action.payload.config!.rebuffer_threshold_secs
          : state.rebufferThresholdSecs;
-      const nextResume = (isNewConfig || deviceChanged)
+      const nextResume =
        isNewConfig || deviceChanged
          ? action.payload.config!.resume_threshold_secs
          : state.resumeThresholdSecs;
@@ -121,7 +147,8 @@ function reducer(state: AppState, action: AppAction): AppState {
        resumeThresholdSecs: nextResume,
      };
    }
-    default: return state;
+    default:
      return state;
  }
 }
@@ -130,9 +157,9 @@ const initialState: AppState = {
  speaker: "carter",
  cfgScale: 1.5,
  inferenceSteps: 10,
-  prebufferSecs: 2.0,
+  prebufferSecs: 5.0,
-  rebufferThresholdSecs: 0.4,
+  rebufferThresholdSecs: 1.0,
-  resumeThresholdSecs: 1.5,
+  resumeThresholdSecs: 3.0,
  isGenerating: false,
  genElapsed: 0,
  genPct: null,
@@ -213,7 +240,10 @@ export default function HomePage() {
    }
    poll();
-    return () => { cancelled = true; clearTimeout(timeoutId); };
+    return () => {
      cancelled = true;
      clearTimeout(timeoutId);
    };
  }, []);
  const handleGenerate = useCallback(async () => {
@@ -241,7 +271,6 @@ export default function HomePage() {
      <Header />
      <main className="flex-1 container mx-auto px-4 py-6 max-w-6xl">
        <div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
          {/* Left: script + audio player */}
          <div className="lg:col-span-2 flex flex-col gap-6">
            <TextInputPanel
@@ -261,12 +290,16 @@ export default function HomePage() {
              onCfgScaleChange={(v) => dispatch({ type: "SET_CFG_SCALE", payload: v })}
              inferenceSteps={state.inferenceSteps}
              onInferenceStepsChange={(v) => dispatch({ type: "SET_INFERENCE_STEPS", payload: v })}
-            prebufferSecs={state.prebufferSecs}
+              prebufferSecs={state.prebufferSecs}
-            onPrebufferSecsChange={(v) => dispatch({ type: "SET_PREBUFFER_SECS", payload: v })}
+              onPrebufferSecsChange={(v) => dispatch({ type: "SET_PREBUFFER_SECS", payload: v })}
-            rebufferThresholdSecs={state.rebufferThresholdSecs}
+              rebufferThresholdSecs={state.rebufferThresholdSecs}
-            onRebufferThresholdChange={(v) => dispatch({ type: "SET_REBUFFER_THRESHOLD", payload: v })}
+              onRebufferThresholdChange={(v) =>
-            resumeThresholdSecs={state.resumeThresholdSecs}
+                dispatch({ type: "SET_REBUFFER_THRESHOLD", payload: v })
-            onResumeThresholdChange={(v) => dispatch({ type: "SET_RESUME_THRESHOLD", payload: v })}
+              }
              resumeThresholdSecs={state.resumeThresholdSecs}
              onResumeThresholdChange={(v) =>
                dispatch({ type: "SET_RESUME_THRESHOLD", payload: v })
              }
              onGenerate={handleGenerate}
              onStop={stop}
              onPauseStream={pauseStream}
@@ -281,7 +314,6 @@ export default function HomePage() {
            />
            <StatusLog messages={state.logs} />
          </div>
        </div>
      </main>
    </div>
@@ -14,15 +14,8 @@ function formatTime(seconds: number): string {
 }
 export default function AudioPlayer({ audioUrl }: AudioPlayerProps) {
-  const {
+  const { isPlaying, currentTime, duration, volume, toggle, seek, setVolume } =
-    isPlaying,
+    useAudioPlayer(audioUrl);
    currentTime,
    duration,
    volume,
    toggle,
    seek,
    setVolume,
  } = useAudioPlayer(audioUrl);
  if (!audioUrl) return null;
@@ -56,12 +49,10 @@ export default function AudioPlayer({ audioUrl }: AudioPlayerProps) {
            background: "rgba(45, 212, 191, 0.05)",
          }}
          onMouseEnter={(e) => {
-            (e.currentTarget as HTMLButtonElement).style.background =
+            (e.currentTarget as HTMLButtonElement).style.background = "rgba(45, 212, 191, 0.15)";
              "rgba(45, 212, 191, 0.15)";
          }}
          onMouseLeave={(e) => {
-            (e.currentTarget as HTMLButtonElement).style.background =
+            (e.currentTarget as HTMLButtonElement).style.background = "rgba(45, 212, 191, 0.05)";
              "rgba(45, 212, 191, 0.05)";
          }}
        >
          <svg
@@ -115,27 +106,18 @@ export default function AudioPlayer({ audioUrl }: AudioPlayerProps) {
          onClick={toggle}
          className="w-10 h-10 rounded-full flex items-center justify-center transition-transform active:scale-95 cursor-pointer"
          style={{
-            background:
+            background: "linear-gradient(135deg, var(--accent-teal-dim), var(--accent-violet-dim))",
              "linear-gradient(135deg, var(--accent-teal-dim), var(--accent-violet-dim))",
            boxShadow: "0 4px 12px rgba(45, 212, 191, 0.3)",
          }}
          aria-label={isPlaying ? "Pause" : "Play"}
        >
          {isPlaying ? (
-            <svg
+            <svg className="w-4 h-4 text-white" viewBox="0 0 24 24" fill="currentColor">
              className="w-4 h-4 text-white"
              viewBox="0 0 24 24"
              fill="currentColor"
            >
              <rect x="6" y="4" width="4" height="16" />
              <rect x="14" y="4" width="4" height="16" />
            </svg>
          ) : (
-            <svg
+            <svg className="w-4 h-4 text-white" viewBox="0 0 24 24" fill="currentColor">
              className="w-4 h-4 text-white"
              viewBox="0 0 24 24"
              fill="currentColor"
            >
              <polygon points="5 3 19 12 5 21 5 3" />
            </svg>
          )}
@@ -143,9 +125,7 @@ export default function AudioPlayer({ audioUrl }: AudioPlayerProps) {
        {/* Duration info */}
        <div className="flex-1 flex items-center gap-1 text-sm">
-          <span style={{ color: "var(--foreground)" }}>
+          <span style={{ color: "var(--foreground)" }}>{formatTime(currentTime)}</span>
            {formatTime(currentTime)}
          </span>
          <span style={{ color: "var(--muted)" }}>/</span>
          <span style={{ color: "var(--muted)" }}>{formatTime(duration)}</span>
        </div>
@@ -36,18 +36,27 @@ const STATUS_CONFIG: Record<
  Exclude<ServerStatus, "online">,
  { color: string; label: (p: DownloadProgress | null) => string }
 > = {
-  offline:     { color: "var(--error)",   label: () => "Server offline — waiting for connection..." },
+  offline: { color: "var(--error)", label: () => "Server offline — waiting for connection..." },
-  downloading: { color: "#60a5fa",        label: (p) => p && p.total > 0 ? `Downloading model... (${p.done} / ${p.total} files)` : "Downloading model (~1 GB)..." },
+  downloading: {
-  loading:     { color: "#fbbf24",        label: () => "Loading model into memory..." },
+    color: "#60a5fa",
-  error:       { color: "var(--error)",   label: () => "Server error — check the terminal for details." },
+    label: (p) =>
      p && p.total > 0
        ? `Downloading model... (${p.done} / ${p.total} files)`
        : "Downloading model (~1 GB)...",
  },
  loading: { color: "#fbbf24", label: () => "Loading model into memory..." },
  error: { color: "var(--error)", label: () => "Server error — check the terminal for details." },
 };
 function SpinnerIcon() {
  return (
    <svg className="animate-spin w-4 h-4" viewBox="0 0 24 24" fill="none">
      <circle className="opacity-25" cx="12" cy="12" r="10" stroke="currentColor" strokeWidth="4" />
-      <path className="opacity-75" fill="currentColor" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4z" />
+      <path
        className="opacity-75"
        fill="currentColor"
        d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4z"
      />
    </svg>
  );
 }
@@ -146,7 +155,10 @@ export default function GenerationControls({
          onChange={(e) => onCfgScaleChange(parseFloat(e.target.value))}
          className="w-full"
        />
-        <div className="flex items-center justify-between text-xs" style={{ color: "var(--muted)" }}>
+        <div
          className="flex items-center justify-between text-xs"
          style={{ color: "var(--muted)" }}
        >
          <span>Flat (0.5)</span>
          <span>CFG Scale</span>
          <span>Expressive (4.0)</span>
@@ -157,7 +169,7 @@ export default function GenerationControls({
      <div className="flex flex-col gap-2">
        <div className="flex items-center justify-between">
          <label className="text-sm font-medium" style={{ color: "var(--foreground)" }}>
-            Quality vs Speed
+            Speed vs Quality
          </label>
          <span
            className="text-sm font-mono px-2 py-0.5 rounded"
@@ -176,7 +188,10 @@ export default function GenerationControls({
          className="w-full"
          style={{ "--thumb-color": "var(--accent-violet)" } as React.CSSProperties}
        />
-        <div className="flex items-center justify-between text-xs" style={{ color: "var(--muted)" }}>
+        <div
          className="flex items-center justify-between text-xs"
          style={{ color: "var(--muted)" }}
        >
          <span>Faster (5)</span>
          <span>Diffusion Steps</span>
          <span>Better (20)</span>
@@ -207,7 +222,11 @@ export default function GenerationControls({
      </div>
      {showAdvanced && (
-        <div id="advanced-buffering-panel" className="flex flex-col gap-4 pl-2 border-l" style={{ borderColor: "var(--border)" }}>
+        <div
          id="advanced-buffering-panel"
          className="flex flex-col gap-4 pl-2 border-l"
          style={{ borderColor: "var(--border)" }}
        >
          {/* Pre-buffer */}
          <div className="flex flex-col gap-2">
            <div className="flex items-center justify-between">
@@ -221,7 +240,7 @@ export default function GenerationControls({
            <input
              type="range"
              min={0.5}
-              max={10.0}
+              max={30.0}
              step={0.5}
              value={prebufferSecs}
              onChange={(e) => onPrebufferSecsChange(parseFloat(e.target.value))}
@@ -232,7 +251,11 @@ export default function GenerationControls({
          {/* Re-buffer threshold */}
          <div className="flex flex-col gap-2">
            <div className="flex items-center justify-between">
-              <label htmlFor="rebuffer-threshold" className="text-xs font-medium" style={{ color: "var(--foreground)" }}>
+              <label
                htmlFor="rebuffer-threshold"
                className="text-xs font-medium"
                style={{ color: "var(--foreground)" }}
              >
                Re-buffer Threshold
              </label>
              <span className="text-xs font-mono" style={{ color: "var(--accent-teal)" }}>
@@ -260,7 +283,11 @@ export default function GenerationControls({
          {/* Resume threshold */}
          <div className="flex flex-col gap-2">
            <div className="flex items-center justify-between">
-              <label htmlFor="resume-threshold" className="text-xs font-medium" style={{ color: "var(--foreground)" }}>
+              <label
                htmlFor="resume-threshold"
                className="text-xs font-medium"
                style={{ color: "var(--foreground)" }}
              >
                Resume Threshold
              </label>
              <span className="text-xs font-mono" style={{ color: "var(--accent-teal)" }}>
@@ -271,7 +298,7 @@ export default function GenerationControls({
              id="resume-threshold"
              type="range"
              min={0.5}
-              max={5.0}
+              max={30.0}
              step={0.1}
              value={resumeThresholdSecs}
              onChange={(e) => {
@@ -302,7 +329,10 @@ export default function GenerationControls({
          </div>
          {serverStatus === "downloading" && (
-            <div className="w-full rounded-full h-1.5 overflow-hidden" style={{ background: "var(--border)" }}>
+            <div
              className="w-full rounded-full h-1.5 overflow-hidden"
              style={{ background: "var(--border)" }}
            >
              <div
                className="h-1.5 rounded-full transition-all duration-500"
                style={{
@@ -315,10 +345,16 @@ export default function GenerationControls({
          )}
          {serverStatus === "loading" && (
-            <div className="w-full rounded-full h-1.5 overflow-hidden" style={{ background: "var(--border)" }}>
+            <div
              className="w-full rounded-full h-1.5 overflow-hidden"
              style={{ background: "var(--border)" }}
            >
              <div
                className="h-1.5 rounded-full animate-pulse"
-                style={{ width: "60%", background: "linear-gradient(90deg, #fbbf24, var(--accent-teal))" }}
+                style={{
                  width: "60%",
                  background: "linear-gradient(90deg, #fbbf24, var(--accent-teal))",
                }}
              />
            </div>
          )}
@@ -328,11 +364,17 @@ export default function GenerationControls({
      {/* Generation progress bar */}
      {isGenerating && (
        <div className="flex flex-col gap-1.5">
-          <div className="flex items-center justify-between text-xs" style={{ color: "var(--muted)" }}>
+          <div
            className="flex items-center justify-between text-xs"
            style={{ color: "var(--muted)" }}
          >
            <span>{genElapsed}s elapsed</span>
            <span>{genPct !== null ? `${genPct}%` : "starting..."}</span>
          </div>
-          <div className="w-full rounded-full h-1.5 overflow-hidden" style={{ background: "var(--border)" }}>
+          <div
            className="w-full rounded-full h-1.5 overflow-hidden"
            style={{ background: "var(--border)" }}
          >
            <div
              className="h-1.5 rounded-full transition-all duration-500"
              style={{
@@ -355,7 +397,8 @@ export default function GenerationControls({
            buttonDisabled
              ? { background: "var(--border)", color: "var(--muted)" }
              : {
-                  background: "linear-gradient(135deg, var(--accent-teal-dim), var(--accent-violet-dim))",
+                  background:
                    "linear-gradient(135deg, var(--accent-teal-dim), var(--accent-violet-dim))",
                  color: "#fff",
                  boxShadow: "0 4px 15px rgba(45, 212, 191, 0.2)",
                }
@@ -373,7 +416,13 @@ export default function GenerationControls({
            </>
          ) : (
            <>
-              <svg className="w-4 h-4" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2">
+              <svg
                className="w-4 h-4"
                viewBox="0 0 24 24"
                fill="none"
                stroke="currentColor"
                strokeWidth="2"
              >
                <polygon points="5 3 19 12 5 21 5 3" />
              </svg>
              Generate Audio
@@ -6,8 +6,8 @@ type ServerStatus = "checking" | "downloading" | "loading" | "online" | "error"
 type Device = "cpu" | "cuda" | null;
 // Polling intervals: poll quickly until the server is online, then slow down.
-const FAST_INTERVAL_MS = 3000;   // while checking / loading
+const FAST_INTERVAL_MS = 3000; // while checking / loading
-const SLOW_INTERVAL_MS = 30000;  // once online
+const SLOW_INTERVAL_MS = 30000; // once online
 export default function Header() {
  const [status, setStatus] = useState<ServerStatus>("checking");
@@ -31,7 +31,10 @@ export default function Header() {
          intervalRef.current = setInterval(checkHealth, SLOW_INTERVAL_MS);
        }
        // Switch to fast polling if we detect the server went offline/loading
-        if ((newStatus === "offline" || newStatus === "downloading" || newStatus === "loading") && intervalRef.current) {
+        if (
          (newStatus === "offline" || newStatus === "downloading" || newStatus === "loading") &&
          intervalRef.current
        ) {
          clearInterval(intervalRef.current);
          intervalRef.current = setInterval(checkHealth, FAST_INTERVAL_MS);
        }
@@ -95,23 +98,20 @@ export default function Header() {
  const cfg = statusConfig[status];
  // Device badge — only shown once the server is online and device is known
-  const deviceBadge = status === "online" && device ? (
+  const deviceBadge =
-    <span
+    status === "online" && device ? (
-      className="px-2 py-0.5 rounded-full text-xs font-semibold tracking-wide uppercase"
+      <span
-      style={{
+        className="px-2 py-0.5 rounded-full text-xs font-semibold tracking-wide uppercase"
-        background: device === "cuda"
+        style={{
-          ? "var(--accent-violet-dim)"
+          background: device === "cuda" ? "var(--accent-violet-dim)" : "var(--accent-teal-dim)",
-          : "var(--accent-teal-dim)",
+          color: device === "cuda" ? "var(--accent-violet)" : "var(--accent-teal)",
-        color: device === "cuda"
+          border: `1px solid ${device === "cuda" ? "var(--accent-violet-dim)" : "var(--accent-teal-dim)"}`,
-          ? "var(--accent-violet)"
+        }}
-          : "var(--accent-teal)",
+        title={device === "cuda" ? "Running on NVIDIA GPU" : "Running on CPU"}
-        border: `1px solid ${device === "cuda" ? "var(--accent-violet-dim)" : "var(--accent-teal-dim)"}`,
+      >
-      }}
+        {device.toUpperCase()}
-      title={device === "cuda" ? "Running on NVIDIA GPU" : "Running on CPU"}
+      </span>
-    >
+    ) : null;
      {device.toUpperCase()}
    </span>
  ) : null;
  return (
    <header
@@ -136,8 +136,7 @@ export default function Header() {
            <h1
              className="text-xl font-bold tracking-tight"
              style={{
-                background:
+                background: "linear-gradient(135deg, var(--accent-teal), var(--accent-violet))",
                  "linear-gradient(135deg, var(--accent-teal), var(--accent-violet))",
                WebkitBackgroundClip: "text",
                WebkitTextFillColor: "transparent",
              }}
@@ -167,9 +166,7 @@ export default function Header() {
                className={`animate-ping absolute inline-flex h-full w-full rounded-full opacity-75 ${cfg.color}`}
              />
            )}
-            <span
+            <span className={`relative inline-flex rounded-full h-2 w-2 ${cfg.color}`} />
              className={`relative inline-flex rounded-full h-2 w-2 ${cfg.color}`}
            />
          </span>
          <span style={{ color: "var(--foreground)" }}>{cfg.label}</span>
        </div>
@@ -47,8 +47,7 @@ export default function StatusLog({ messages }: StatusLogProps) {
        ) : (
          messages.map((msg, i) => {
            const isError =
-              msg.toLowerCase().includes("error") ||
+              msg.toLowerCase().includes("error") || msg.toLowerCase().includes("failed");
              msg.toLowerCase().includes("failed");
            const isSuccess =
              msg.toLowerCase().includes("done") ||
              msg.toLowerCase().includes("complete") ||
@@ -15,10 +15,7 @@ interface TextInputPanelProps {
  onChange: (text: string) => void;
 }
-export default function TextInputPanel({
+export default function TextInputPanel({ value, onChange }: TextInputPanelProps) {
  value,
  onChange,
 }: TextInputPanelProps) {
  const charCount = value.length;
  const wordCount = value.trim() === "" ? 0 : value.trim().split(/\s+/).length;
@@ -43,15 +40,12 @@ export default function TextInputPanel({
              color: "var(--muted)",
            }}
            onMouseEnter={(e) => {
-              (e.target as HTMLButtonElement).style.color =
+              (e.target as HTMLButtonElement).style.color = "var(--accent-violet)";
-                "var(--accent-violet)";
+              (e.target as HTMLButtonElement).style.borderColor = "var(--accent-violet)";
              (e.target as HTMLButtonElement).style.borderColor =
                "var(--accent-violet)";
            }}
            onMouseLeave={(e) => {
              (e.target as HTMLButtonElement).style.color = "var(--muted)";
-              (e.target as HTMLButtonElement).style.borderColor =
+              (e.target as HTMLButtonElement).style.borderColor = "var(--border)";
                "var(--border)";
            }}
          >
            Load sample script
@@ -69,8 +63,7 @@ export default function TextInputPanel({
            }}
            onMouseLeave={(e) => {
              (e.target as HTMLButtonElement).style.color = "var(--muted)";
-              (e.target as HTMLButtonElement).style.borderColor =
+              (e.target as HTMLButtonElement).style.borderColor = "var(--border)";
                "var(--border)";
            }}
          >
            Clear
@@ -98,10 +91,7 @@ export default function TextInputPanel({
        }}
      />
-      <div
+      <div className="flex items-center justify-between text-xs" style={{ color: "var(--muted)" }}>
        className="flex items-center justify-between text-xs"
        style={{ color: "var(--muted)" }}
      >
        <span>
          {wordCount} word{wordCount !== 1 ? "s" : ""}
        </span>
@@ -55,16 +55,12 @@ export function useAudioPlayer(audioUrl: string | null) {
      () => setState((prev) => ({ ...prev, isPlaying: false, currentTime: 0 })),
      { signal }
    );
-    audio.addEventListener(
+    audio.addEventListener("play", () => setState((prev) => ({ ...prev, isPlaying: true })), {
-      "play",
+      signal,
-      () => setState((prev) => ({ ...prev, isPlaying: true })),
+    });
-      { signal }
+    audio.addEventListener("pause", () => setState((prev) => ({ ...prev, isPlaying: false })), {
-    );
+      signal,
-    audio.addEventListener(
+    });
      "pause",
      () => setState((prev) => ({ ...prev, isPlaying: false })),
      { signal }
    );
    return () => {
      audio.pause();
@@ -3,9 +3,10 @@
 import { useCallback, useEffect, useRef, useState } from "react";
 const SAMPLE_RATE = 24_000;
-const DEFAULT_PREBUFFER_SECS = 2.0;
+const DEFAULT_PREBUFFER_SECS = 5.0;
-const DEFAULT_REBUFFER_THRESHOLD_SECS = 0.4;
+const DEFAULT_REBUFFER_THRESHOLD_SECS = 1.0;
-const DEFAULT_RESUME_THRESHOLD_SECS = 1.5;
+const DEFAULT_RESUME_THRESHOLD_SECS = 3.0;
 const MAX_ADAPTIVE_RESUME_SECS = 30.0;
 interface GenerateOptions {
  text: string;
@@ -91,7 +92,7 @@ export function useStreamingGeneration({
  let resumeThresholdSecs = rawResumeThresholdSecs;
  if (resumeThresholdSecs <= rebufferThresholdSecs) {
    console.warn(
-      `[useStreamingGeneration] resumeThresholdSecs (${resumeThresholdSecs}) must be greater than rebufferThresholdSecs (${rebufferThresholdSecs}). Clamping resumeThresholdSecs to ${rebufferThresholdSecs + 0.5}.`,
+      `[useStreamingGeneration] resumeThresholdSecs (${resumeThresholdSecs}) must be greater than rebufferThresholdSecs (${rebufferThresholdSecs}). Clamping resumeThresholdSecs to ${rebufferThresholdSecs + 0.5}.`
    );
    resumeThresholdSecs = rebufferThresholdSecs + 0.5;
  }
@@ -104,6 +105,10 @@ export function useStreamingGeneration({
  const isAutoBufferingRef = useRef(false);
  const isUserPausedRef = useRef(false);
  const audioUrlRef = useRef<string | null>(null);
  const firstChunkSeenRef = useRef(false);
  const underrunCountRef = useRef(0);
  const totalAudioSamplesRef = useRef(0);
  const adaptiveResumeSecsRef = useRef(DEFAULT_RESUME_THRESHOLD_SECS);
  const revokeCurrentUrl = useCallback(() => {
    if (audioUrlRef.current) {
@@ -122,8 +127,12 @@ export function useStreamingGeneration({
    hasStartedPlaybackRef.current = false;
    isAutoBufferingRef.current = false;
    isUserPausedRef.current = false;
    firstChunkSeenRef.current = false;
    underrunCountRef.current = 0;
    totalAudioSamplesRef.current = 0;
    adaptiveResumeSecsRef.current = resumeThresholdSecs;
    setIsStreamPaused(false);
-  }, []);
+  }, [resumeThresholdSecs]);
  useEffect(() => {
    return () => {
@@ -153,139 +162,178 @@ export function useStreamingGeneration({
    hasStartedPlaybackRef.current = true;
  }, [enqueue]);
-  const handleAudioChunk = useCallback((chunk: Float32Array<ArrayBuffer>) => {
+  const handleAudioChunk = useCallback(
-    const ctx = audioCtxRef.current;
+    (chunk: Float32Array<ArrayBuffer>) => {
-    if (!ctx) return;
+      const ctx = audioCtxRef.current;
      if (!ctx) return;
-    chunksRef.current.push(chunk);
+      chunksRef.current.push(chunk);
      totalAudioSamplesRef.current += chunk.length;
-    if (!hasStartedPlaybackRef.current) {
+      if (!firstChunkSeenRef.current) {
-      const bufferedSecs = chunksRef.current.reduce((sum, c) => sum + c.length, 0) / SAMPLE_RATE;
+        firstChunkSeenRef.current = true;
-      if (bufferedSecs >= prebufferSecs) {
+        onLog("First audio chunk received");
        flushBufferedAudio();
      }
      return;
    }
    enqueue(ctx, chunk);
    if (isUserPausedRef.current) return;
    const ahead = nextStartTimeRef.current - ctx.currentTime;
    if (ctx.state === "running" && ahead < rebufferThresholdSecs) {
      ctx.suspend().catch(() => {});
      isAutoBufferingRef.current = true;
    } else if (
      ctx.state === "suspended" &&
      isAutoBufferingRef.current &&
      ahead >= resumeThresholdSecs
    ) {
      ctx.resume().catch(() => {});
      isAutoBufferingRef.current = false;
    }
  }, [enqueue, flushBufferedAudio, prebufferSecs, rebufferThresholdSecs, resumeThresholdSecs]);
  const generate = useCallback(async (options: GenerateOptions) => {
    if (!options.text.trim()) return;
    resetPlayback();
    revokeCurrentUrl();
    audioCtxRef.current = new AudioContext({ sampleRate: SAMPLE_RATE });
    const controller = new AbortController();
    abortRef.current = controller;
    onStart();
    onLog(`Voice: ${options.speaker}`);
    onLog(`CFG ${options.cfgScale.toFixed(1)}, steps ${options.inferenceSteps}`);
    const startedAt = Date.now();
    const timerId = window.setInterval(() => {
      onProgress((Date.now() - startedAt) / 1000, null);
    }, 500);
    try {
      const res = await fetch("/api/generate", {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({
          text: options.text,
          speaker: options.speaker,
          cfg_scale: options.cfgScale,
          inference_steps: options.inferenceSteps,
        }),
        signal: controller.signal,
      });
      if (!res.ok || !res.body) {
        const err = await res.json().catch(() => ({})) as { error?: string };
        throw new Error(err.error ?? `HTTP ${res.status}`);
      }
-      const reader = res.body.getReader();
+      if (!hasStartedPlaybackRef.current) {
-      const decoder = new TextDecoder();
+        const bufferedSecs = chunksRef.current.reduce((sum, c) => sum + c.length, 0) / SAMPLE_RATE;
-      let buffer = "";
+        if (bufferedSecs >= prebufferSecs) {
          onLog(`Playback started after ${bufferedSecs.toFixed(1)}s buffered`);
          flushBufferedAudio();
        }
        return;
      }
-      while (true) {
+      enqueue(ctx, chunk);
-        const { done, value } = await reader.read();
+      if (isUserPausedRef.current) return;
        if (done) break;
-        buffer += decoder.decode(value, { stream: true });
+      const ahead = nextStartTimeRef.current - ctx.currentTime;
-        const lines = buffer.split("\n");
+      if (ctx.state === "running" && !isAutoBufferingRef.current && ahead < rebufferThresholdSecs) {
-        buffer = lines.pop() ?? "";
+        isAutoBufferingRef.current = true;
        underrunCountRef.current += 1;
        adaptiveResumeSecsRef.current = Math.min(
          MAX_ADAPTIVE_RESUME_SECS,
          Math.max(resumeThresholdSecs, prebufferSecs + underrunCountRef.current * 2)
        );
        ctx.suspend().catch(() => {});
        onLog(
          `Buffer underrun ${underrunCountRef.current}; refilling to ${adaptiveResumeSecsRef.current.toFixed(1)}s`
        );
      } else if (isAutoBufferingRef.current && ahead >= adaptiveResumeSecsRef.current) {
        isAutoBufferingRef.current = false;
        ctx.resume().catch(() => {});
        onLog(`Buffer recovered with ${ahead.toFixed(1)}s queued`);
      }
    },
    [enqueue, flushBufferedAudio, onLog, prebufferSecs, rebufferThresholdSecs, resumeThresholdSecs]
  );
-        for (const line of lines) {
+  const generate = useCallback(
-          if (!line.startsWith("data: ")) continue;
+    async (options: GenerateOptions) => {
-          const event = JSON.parse(line.slice(6)) as {
+      if (!options.text.trim()) return;
            type: "audio_chunk" | "complete" | "error" | "cancelled";
            data?: string;
            elapsed?: number;
            message?: string;
          };
-          if (event.type === "audio_chunk" && event.data) {
+      resetPlayback();
-            handleAudioChunk(decodeFloat32Chunk(event.data));
+      revokeCurrentUrl();
-          } else if (event.type === "complete") {
+      audioCtxRef.current = new AudioContext({ sampleRate: SAMPLE_RATE });
-            if (!hasStartedPlaybackRef.current) {
+
-              flushBufferedAudio();
+      const controller = new AbortController();
      abortRef.current = controller;
      onStart();
      onLog(`Voice: ${options.speaker}`);
      onLog(`CFG ${options.cfgScale.toFixed(1)}, steps ${options.inferenceSteps}`);
      const startedAt = Date.now();
      const timerId = window.setInterval(() => {
        onProgress((Date.now() - startedAt) / 1000, null);
      }, 500);
      try {
        const res = await fetch("/api/generate", {
          method: "POST",
          headers: { "Content-Type": "application/json" },
          body: JSON.stringify({
            text: options.text,
            speaker: options.speaker,
            cfg_scale: options.cfgScale,
            inference_steps: options.inferenceSteps,
          }),
          signal: controller.signal,
        });
        if (!res.ok || !res.body) {
          const err = (await res.json().catch(() => ({}))) as { error?: string };
          throw new Error(err.error ?? `HTTP ${res.status}`);
        }
        const reader = res.body.getReader();
        const decoder = new TextDecoder();
        let buffer = "";
        while (true) {
          const { done, value } = await reader.read();
          if (done) break;
          buffer += decoder.decode(value, { stream: true });
          const lines = buffer.split("\n");
          buffer = lines.pop() ?? "";
          for (const line of lines) {
            if (!line.startsWith("data: ")) continue;
            const event = JSON.parse(line.slice(6)) as {
              type: "audio_chunk" | "complete" | "error" | "cancelled";
              data?: string;
              elapsed?: number;
              audio_secs?: number;
              realtime_factor?: number | null;
              chunks?: number;
              first_chunk_secs?: number | null;
              max_chunk_gap_secs?: number;
              message?: string;
            };
            if (event.type === "audio_chunk" && event.data) {
              handleAudioChunk(decodeFloat32Chunk(event.data));
            } else if (event.type === "complete") {
              if (!hasStartedPlaybackRef.current) {
                flushBufferedAudio();
              } else if (isAutoBufferingRef.current) {
                isAutoBufferingRef.current = false;
                audioCtxRef.current?.resume().catch(() => {});
              }
              const wavBlob = buildWav(mergeFloat32Arrays(chunksRef.current), SAMPLE_RATE);
              const audioUrl = URL.createObjectURL(wavBlob);
              audioUrlRef.current = audioUrl;
              const kb = (wavBlob.size / 1024).toFixed(0);
              const audioSecs = event.audio_secs ?? totalAudioSamplesRef.current / SAMPLE_RATE;
              const realtimeFactor =
                event.realtime_factor ??
                (event.elapsed && event.elapsed > 0 ? audioSecs / event.elapsed : null);
              const speedText =
                realtimeFactor === null ? "" : ` - ${realtimeFactor.toFixed(2)}x realtime`;
              onLog(
                `Done in ${event.elapsed}s - ${audioSecs.toFixed(1)}s audio${speedText} - ${kb} KB`
              );
              if (event.chunks && event.first_chunk_secs !== undefined) {
                onLog(
                  `Stream: first chunk ${event.first_chunk_secs}s, ${event.chunks} chunks, max gap ${event.max_chunk_gap_secs}s`
                );
              }
              onSuccess(audioUrl);
            } else if (event.type === "cancelled") {
              throw new DOMException("Generation cancelled", "AbortError");
            } else if (event.type === "error") {
              throw new Error(event.message ?? "Generation failed");
            }
            const wavBlob = buildWav(mergeFloat32Arrays(chunksRef.current), SAMPLE_RATE);
            const audioUrl = URL.createObjectURL(wavBlob);
            audioUrlRef.current = audioUrl;
            const kb = (wavBlob.size / 1024).toFixed(0);
            onLog(`Done in ${event.elapsed}s - ${kb} KB`);
            onSuccess(audioUrl);
          } else if (event.type === "cancelled") {
            throw new DOMException("Generation cancelled", "AbortError");
          } else if (event.type === "error") {
            throw new Error(event.message ?? "Generation failed");
          }
        }
      } catch (err) {
        if (err instanceof Error && err.name === "AbortError") {
          onLog("Cancelled.");
          onCancel();
        } else {
          const message = err instanceof Error ? err.message : "Unknown error";
          onLog(`Error: ${message}`);
          onError();
        }
      } finally {
        window.clearInterval(timerId);
        abortRef.current = null;
      }
-    } catch (err) {
+    },
-      if (err instanceof Error && err.name === "AbortError") {
+    [
-        onLog("Cancelled.");
+      flushBufferedAudio,
-        onCancel();
+      handleAudioChunk,
-      } else {
+      onCancel,
-        const message = err instanceof Error ? err.message : "Unknown error";
+      onError,
-        onLog(`Error: ${message}`);
+      onLog,
-        onError();
+      onProgress,
-      }
+      onStart,
-    } finally {
+      onSuccess,
-      window.clearInterval(timerId);
+      resetPlayback,
-      abortRef.current = null;
+      revokeCurrentUrl,
-    }
+    ]
-  }, [
+  );
    flushBufferedAudio,
    handleAudioChunk,
    onCancel,
    onError,
    onLog,
    onProgress,
    onStart,
    onSuccess,
    resetPlayback,
    revokeCurrentUrl,
  ]);
  const pauseStream = useCallback(() => {
    isUserPausedRef.current = true;
@@ -4,8 +4,10 @@
  "private": true,
  "scripts": {
    "dev": "next dev --turbopack",
-    "build": "next build --turbopack",
+    "build": "next build",
-    "start": "next start"
+    "start": "next start",
    "format": "prettier --write .",
    "format:check": "prettier --check ."
  },
  "dependencies": {
    "next": "15.5.15",