Use uv for Python management in CI and build script

- CI: install uv via astral-sh/setup-uv, use uv to install Python
  and run the build script (replaces setup-python which fails on
  self-hosted macOS runners)
- build_sidecar.py: auto-detects uv and uses it for venv creation
  and package installation (much faster), falls back to standard
  venv + pip when uv is not available

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.claude/settings.local.json

@@ -0,0 +1,21 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(git init:*)",
+      "Bash(git:*)",
+      "WebSearch",
+      "Bash(npm create:*)",
+      "Bash(cp:*)",
+      "Bash(npm install:*)",
+      "Bash(/home/jknapp/.cargo/bin/cargo test:*)",
+      "Bash(ruff:*)",
+      "Bash(npm run:*)",
+      "Bash(npx svelte-check:*)",
+      "Bash(pip install:*)",
+      "Bash(python3:*)",
+      "Bash(/home/jknapp/.cargo/bin/cargo check:*)",
+      "Bash(cargo check:*)",
+      "Bash(npm ls:*)"
+    ]
+  }
+}

.gitea/workflows/build.yml

@@ -0,0 +1,191 @@
+name: Build & Release
+
+on:
+  push:
+    branches: [main]
+    tags: ["v*"]
+  pull_request:
+    branches: [main]
+
+env:
+  PYTHON_VERSION: "3.11"
+  NODE_VERSION: "20"
+
+jobs:
+  build-sidecar:
+    name: Build sidecar (${{ matrix.target }})
+    runs-on: ${{ matrix.runner }}
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - runner: ubuntu-latest
+            target: x86_64-unknown-linux-gnu
+            platform: linux
+          - runner: windows-latest
+            target: x86_64-pc-windows-msvc
+            platform: windows
+          - runner: macos-latest
+            target: aarch64-apple-darwin
+            platform: macos
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Set up Python
+        run: uv python install ${{ env.PYTHON_VERSION }}
+
+      - name: Build sidecar
+        working-directory: python
+        run: uv run --python ${{ env.PYTHON_VERSION }} python build_sidecar.py --cpu-only
+
+      - name: Upload sidecar artifact
+        uses: actions/upload-artifact@v3
+        with:
+          name: sidecar-${{ matrix.target }}
+          path: python/dist/voice-to-notes-sidecar/
+          retention-days: 7
+
+  build-tauri:
+    name: Build app (${{ matrix.target }})
+    needs: build-sidecar
+    runs-on: ${{ matrix.runner }}
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - runner: ubuntu-latest
+            target: x86_64-unknown-linux-gnu
+            platform: linux
+          - runner: windows-latest
+            target: x86_64-pc-windows-msvc
+            platform: windows
+          - runner: macos-latest
+            target: aarch64-apple-darwin
+            platform: macos
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: ${{ env.NODE_VERSION }}
+
+      - name: Install Rust stable
+        uses: dtolnay/rust-toolchain@stable
+
+      - name: Install system dependencies (Linux)
+        if: matrix.platform == 'linux'
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y libgtk-3-dev libwebkit2gtk-4.1-dev libappindicator3-dev librsvg2-dev patchelf
+
+      - name: Install system dependencies (macOS)
+        if: matrix.platform == 'macos'
+        run: |
+          brew install --quiet create-dmg || true
+
+      - name: Download sidecar artifact
+        uses: actions/download-artifact@v3
+        with:
+          name: sidecar-${{ matrix.target }}
+          path: src-tauri/binaries/
+
+      - name: Make sidecar executable (Unix)
+        if: matrix.platform != 'windows'
+        run: chmod +x src-tauri/binaries/voice-to-notes-sidecar-${{ matrix.target }}
+
+      - name: Install npm dependencies
+        run: npm ci
+
+      - name: Build Tauri app
+        run: npm run tauri build
+        env:
+          TAURI_SIGNING_PRIVATE_KEY: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY }}
+          TAURI_CONFIG: '{"bundle":{"externalBin":["binaries/voice-to-notes-sidecar"]}}'
+
+      - name: Upload app artifacts (Linux)
+        if: matrix.platform == 'linux'
+        uses: actions/upload-artifact@v3
+        with:
+          name: app-${{ matrix.target }}
+          path: |
+            src-tauri/target/release/bundle/deb/*.deb
+            src-tauri/target/release/bundle/appimage/*.AppImage
+          retention-days: 30
+
+      - name: Upload app artifacts (Windows)
+        if: matrix.platform == 'windows'
+        uses: actions/upload-artifact@v3
+        with:
+          name: app-${{ matrix.target }}
+          path: |
+            src-tauri/target/release/bundle/msi/*.msi
+            src-tauri/target/release/bundle/nsis/*.exe
+          retention-days: 30
+
+      - name: Upload app artifacts (macOS)
+        if: matrix.platform == 'macos'
+        uses: actions/upload-artifact@v3
+        with:
+          name: app-${{ matrix.target }}
+          path: |
+            src-tauri/target/release/bundle/dmg/*.dmg
+            src-tauri/target/release/bundle/macos/*.app
+          retention-days: 30
+
+  release:
+    name: Create Release
+    needs: build-tauri
+    if: github.ref == 'refs/heads/main'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install required tools
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y jq curl
+
+      - name: Download all app artifacts
+        uses: actions/download-artifact@v3
+        with:
+          path: artifacts/
+
+      - name: Generate release tag
+        id: tag
+        run: echo "tag=build-$(date +%Y%m%d-%H%M%S)" >> $GITHUB_OUTPUT
+
+      - name: Create release
+        env:
+          BUILD_TOKEN: ${{ secrets.BUILD_TOKEN }}
+          TAG: ${{ steps.tag.outputs.tag }}
+        run: |
+          # Create the release
+          RELEASE_ID=$(curl -s -X POST \
+            -H "Authorization: token ${BUILD_TOKEN}" \
+            -H "Content-Type: application/json" \
+            -d "{\"tag_name\": \"${TAG}\", \"name\": \"Voice to Notes ${TAG}\", \"body\": \"Automated build from main branch.\", \"draft\": false, \"prerelease\": true}" \
+            "${GITHUB_SERVER_URL}/api/v1/repos/${GITHUB_REPOSITORY}/releases" | jq -r '.id')
+
+          echo "Release ID: ${RELEASE_ID}"
+
+          if [ "${RELEASE_ID}" = "null" ] || [ -z "${RELEASE_ID}" ]; then
+            echo "ERROR: Failed to create release. Check BUILD_TOKEN permissions."
+            exit 1
+          fi
+
+          # Upload all artifacts
+          find artifacts/ -type f \( -name "*.deb" -o -name "*.AppImage" -o -name "*.msi" -o -name "*.exe" -o -name "*.dmg" \) | while read file; do
+            filename=$(basename "$file")
+            echo "Uploading ${filename}..."
+            curl -s -X POST \
+              -H "Authorization: token ${BUILD_TOKEN}" \
+              -H "Content-Type: application/octet-stream" \
+              --data-binary "@${file}" \
+              "${GITHUB_SERVER_URL}/api/v1/repos/${GITHUB_REPOSITORY}/releases/${RELEASE_ID}/assets?name=${filename}"
+          done

.gitignore

@@ -46,3 +46,9 @@ Thumbs.db
 *.ogg
 *.flac
 !test/fixtures/*
+
+# Sidecar build artifacts
+src-tauri/binaries/*
+!src-tauri/binaries/.gitkeep
+python/dist/
+python/build/

CLAUDE.md

@@ -8,7 +8,7 @@ Desktop app for transcribing audio/video with speaker identification. Runs local
 - **ML pipeline:** Python sidecar process (faster-whisper, pyannote.audio, wav2vec2)
 - **Database:** SQLite (via rusqlite in Rust)
 - **Local AI:** Bundled llama-server (llama.cpp) — default, no install needed
-- **Cloud AI providers:** LiteLLM, OpenAI, Anthropic (optional, user-configured)
+- **Cloud AI providers:** OpenAI, Anthropic, OpenAI-compatible endpoints (optional, user-configured)
 - **Caption export:** pysubs2 (Python)
 - **Audio UI:** wavesurfer.js
 - **Transcript editor:** TipTap (ProseMirror)
@@ -40,7 +40,13 @@ docs/                   # Architecture and design documents
 - Database: UUIDs as primary keys (TEXT type in SQLite)
 - All timestamps in milliseconds (integer) relative to media file start
 
+## Distribution
+- Python sidecar is frozen via PyInstaller into a standalone binary for distribution
+- Tauri bundles the sidecar via `externalBin` — no Python required for end users
+- CI/CD builds on Gitea Actions (Linux, Windows, macOS ARM)
+- Dev mode uses system Python (`VOICE_TO_NOTES_DEV=1` or debug builds)
+
 ## Platform Targets
-- Linux (primary development target)
+- Linux x86_64 (primary development target)
-- Windows (must work, tested before release)
+- Windows x86_64
-- macOS (future, not yet targeted)
+- macOS aarch64 (Apple Silicon)

README.md

@@ -2,28 +2,90 @@
 
 A desktop application that transcribes audio/video recordings with speaker identification, producing editable transcriptions with synchronized audio playback.
 
-## Goals
+## Features
 
-- **Speech-to-Text Transcription** — Accurately convert spoken audio from recordings into text
+- **Speech-to-Text Transcription** — Accurate transcription via faster-whisper (Whisper models) with word-level timestamps
-- **Speaker Identification (Diarization)** — Detect and distinguish between different speakers in a conversation
+- **Speaker Identification (Diarization)** — Detect and distinguish between speakers using pyannote.audio
-- **Speaker Naming** — Assign and persist speaker names/IDs across the transcription
+- **Synchronized Playback** — Click any word to seek to that point in the audio (Web Audio API for instant playback)
-- **Synchronized Playback** — Click any transcribed text segment to play back the corresponding audio for review and correction
+- **AI Integration** — Ask questions about your transcript via OpenAI, Anthropic, or any OpenAI-compatible API (LiteLLM proxies, Ollama, vLLM)
-- **Export Formats**
+- **Export Formats** — SRT, WebVTT, ASS captions, plain text, and Markdown with speaker labels
-  - Closed captioning files (SRT, VTT) for video
+- **Cross-Platform** — Builds for Linux, Windows, and macOS (Apple Silicon)
-  - Plain text documents with speaker labels
-- **AI Integration** — Connect to AI providers to ask questions about the conversation and generate condensed notes/summaries
 
 ## Platform Support
 
-| Platform | Status |
+| Platform | Architecture | Status |
-|----------|--------|
+|----------|-------------|--------|
-| Linux    | Planned (initial target) |
+| Linux    | x86_64      | Supported |
-| Windows  | Planned (initial target) |
+| Windows  | x86_64      | Supported |
-| macOS    | Future (pending hardware) |
+| macOS    | ARM (Apple Silicon) | Supported |
 
-## Project Status
+## Tech Stack
 
-**Early planning phase** — Architecture and technology decisions in progress.
+- **Desktop shell:** Tauri v2 (Rust backend + Svelte 5 / TypeScript frontend)
+- **ML pipeline:** Python sidecar (faster-whisper, pyannote.audio) — frozen via PyInstaller for distribution
+- **Audio playback:** wavesurfer.js with Web Audio API backend
+- **AI providers:** OpenAI, Anthropic, OpenAI-compatible endpoints (local or remote)
+- **Local AI:** Bundled llama-server (llama.cpp)
+- **Caption export:** pysubs2
+
+## Development
+
+### Prerequisites
+
+- Node.js 20+
+- Rust (stable)
+- Python 3.11+ with ML dependencies
+- System: `libgtk-3-dev`, `libwebkit2gtk-4.1-dev` (Linux)
+
+### Getting Started
+
+```bash
+# Install frontend dependencies
+npm install
+
+# Install Python sidecar dependencies
+cd python && pip install -e . && cd ..
+
+# Run in dev mode (uses system Python for the sidecar)
+npm run tauri:dev
+```
+
+### Building for Distribution
+
+```bash
+# Build the frozen Python sidecar
+npm run sidecar:build
+
+# Build the Tauri app (requires sidecar in src-tauri/binaries/)
+npm run tauri build
+```
+
+### CI/CD
+
+Gitea Actions workflows are in `.gitea/workflows/`. The build pipeline:
+
+1. **Build sidecar** — PyInstaller-frozen Python binary per platform (CPU-only PyTorch)
+2. **Build Tauri app** — Bundles the sidecar via `externalBin`, produces .deb/.AppImage (Linux), .msi (Windows), .dmg (macOS)
+
+#### Required Secrets
+
+| Secret | Purpose | Required? |
+|--------|---------|-----------|
+| `TAURI_SIGNING_PRIVATE_KEY` | Signs Tauri update bundles | Optional (for auto-updates) |
+
+No other secrets are needed for building. AI provider API keys and HuggingFace tokens are configured by end users in the app's Settings.
+
+### Project Structure
+
+```
+src/                    # Svelte 5 frontend
+src-tauri/              # Rust backend (Tauri commands, sidecar manager, SQLite)
+python/                 # Python sidecar (transcription, diarization, AI)
+  voice_to_notes/       # Python package
+  build_sidecar.py      # PyInstaller build script
+  voice_to_notes.spec   # PyInstaller spec
+.gitea/workflows/       # Gitea Actions CI/CD
+```
 
 ## License
 

package.json

@@ -11,7 +11,9 @@
     "check:watch": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json --watch",
     "lint": "eslint .",
     "test": "vitest",
-    "tauri": "tauri"
+    "tauri": "tauri",
+    "tauri:dev": "VOICE_TO_NOTES_DEV=1 tauri dev",
+    "sidecar:build": "cd python && python3 build_sidecar.py"
   },
   "license": "MIT",
   "dependencies": {

python/build_sidecar.py

@@ -0,0 +1,241 @@
+#!/usr/bin/env python3
+"""Build the Voice to Notes sidecar as a standalone binary using PyInstaller.
+
+Usage:
+    python build_sidecar.py [--cpu-only]
+
+Produces a directory `dist/voice-to-notes-sidecar/` containing the frozen
+sidecar binary and all dependencies. The main binary is renamed to include
+the Tauri target triple for externalBin resolution.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import platform
+import shutil
+import stat
+import subprocess
+import sys
+import urllib.request
+import zipfile
+from pathlib import Path
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+DIST_DIR = SCRIPT_DIR / "dist"
+BUILD_DIR = SCRIPT_DIR / "build"
+SPEC_FILE = SCRIPT_DIR / "voice_to_notes.spec"
+
+# Static ffmpeg download URLs (GPL-licensed builds)
+FFMPEG_URLS: dict[str, str] = {
+    "linux-x86_64": "https://johnvansickle.com/ffmpeg/releases/ffmpeg-release-amd64-static.tar.xz",
+    "darwin-x86_64": "https://evermeet.cx/ffmpeg/getrelease/zip",
+    "darwin-arm64": "https://evermeet.cx/ffmpeg/getrelease/zip",
+    "win32-x86_64": "https://www.gyan.dev/ffmpeg/builds/ffmpeg-release-essentials.zip",
+}
+
+
+def get_target_triple() -> str:
+    """Determine the Tauri-compatible target triple for the current platform."""
+    machine = platform.machine().lower()
+    system = platform.system().lower()
+
+    arch_map = {
+        "x86_64": "x86_64",
+        "amd64": "x86_64",
+        "aarch64": "aarch64",
+        "arm64": "aarch64",
+    }
+    arch = arch_map.get(machine, machine)
+
+    if system == "linux":
+        return f"{arch}-unknown-linux-gnu"
+    elif system == "darwin":
+        return f"{arch}-apple-darwin"
+    elif system == "windows":
+        return f"{arch}-pc-windows-msvc"
+    else:
+        return f"{arch}-unknown-{system}"
+
+
+def _has_uv() -> bool:
+    """Check if uv is available."""
+    try:
+        subprocess.run(["uv", "--version"], capture_output=True, check=True)
+        return True
+    except (FileNotFoundError, subprocess.CalledProcessError):
+        return False
+
+
+def create_venv_and_install(cpu_only: bool) -> Path:
+    """Create a fresh venv and install dependencies.
+
+    Uses uv if available (much faster), falls back to standard venv + pip.
+    """
+    venv_dir = BUILD_DIR / "sidecar-venv"
+    if venv_dir.exists():
+        shutil.rmtree(venv_dir)
+
+    use_uv = _has_uv()
+
+    if use_uv:
+        print(f"[build] Creating venv with uv at {venv_dir}")
+        subprocess.run(
+            ["uv", "venv", "--python", f"{sys.version_info.major}.{sys.version_info.minor}",
+             str(venv_dir)],
+            check=True,
+        )
+    else:
+        print(f"[build] Creating venv at {venv_dir}")
+        subprocess.run([sys.executable, "-m", "venv", str(venv_dir)], check=True)
+
+    # Determine python path inside venv
+    if sys.platform == "win32":
+        python = str(venv_dir / "Scripts" / "python")
+    else:
+        python = str(venv_dir / "bin" / "python")
+
+    def pkg_install(*args: str) -> None:
+        if use_uv:
+            subprocess.run(["uv", "pip", "install", "--python", python, *args], check=True)
+        else:
+            subprocess.run([python, "-m", "pip", *args], check=True)
+
+    if not use_uv:
+        # Upgrade pip (uv doesn't need this)
+        pkg_install("install", "--upgrade", "pip", "setuptools", "wheel")
+
+    # Install torch (CPU-only to avoid bundling ~2GB of CUDA libs)
+    if cpu_only:
+        print("[build] Installing PyTorch (CPU-only)")
+        pkg_install(
+            "install", "torch", "torchaudio",
+            "--index-url", "https://download.pytorch.org/whl/cpu",
+        )
+    else:
+        print("[build] Installing PyTorch (default, may include CUDA)")
+        pkg_install("install", "torch", "torchaudio")
+
+    # Install project and dev deps (includes pyinstaller)
+    print("[build] Installing project dependencies")
+    pkg_install("install", "-e", f"{SCRIPT_DIR}[dev]")
+
+    return Path(python)
+
+
+def run_pyinstaller(python: Path) -> Path:
+    """Run PyInstaller using the spec file."""
+    print("[build] Running PyInstaller")
+    subprocess.run(
+        [str(python), "-m", "PyInstaller", "--clean", "--noconfirm", str(SPEC_FILE)],
+        cwd=str(SCRIPT_DIR),
+        check=True,
+    )
+    output_dir = DIST_DIR / "voice-to-notes-sidecar"
+    if not output_dir.exists():
+        raise RuntimeError(f"PyInstaller output not found at {output_dir}")
+    return output_dir
+
+
+def download_ffmpeg(output_dir: Path) -> None:
+    """Download a static ffmpeg/ffprobe binary for the current platform."""
+    system = sys.platform
+    machine = platform.machine().lower()
+    if machine in ("amd64", "x86_64"):
+        machine = "x86_64"
+    elif machine in ("aarch64", "arm64"):
+        machine = "arm64"
+
+    key = f"{system}-{machine}"
+    if system == "win32":
+        key = f"win32-{machine}"
+    elif system == "linux":
+        key = f"linux-{machine}"
+
+    url = FFMPEG_URLS.get(key)
+    if not url:
+        print(f"[build] Warning: No ffmpeg download URL for platform {key}, skipping")
+        return
+
+    print(f"[build] Downloading ffmpeg for {key}")
+    tmp_path = output_dir / "ffmpeg_download"
+    try:
+        urllib.request.urlretrieve(url, str(tmp_path))
+
+        if url.endswith(".tar.xz"):
+            # Linux static build
+            import tarfile
+            with tarfile.open(str(tmp_path), "r:xz") as tar:
+                for member in tar.getmembers():
+                    basename = os.path.basename(member.name)
+                    if basename in ("ffmpeg", "ffprobe"):
+                        member.name = basename
+                        tar.extract(member, path=str(output_dir))
+                        dest = output_dir / basename
+                        dest.chmod(dest.stat().st_mode | stat.S_IEXEC)
+        elif url.endswith(".zip"):
+            with zipfile.ZipFile(str(tmp_path), "r") as zf:
+                for name in zf.namelist():
+                    basename = os.path.basename(name)
+                    if basename in ("ffmpeg", "ffprobe", "ffmpeg.exe", "ffprobe.exe"):
+                        data = zf.read(name)
+                        dest = output_dir / basename
+                        dest.write_bytes(data)
+                        if sys.platform != "win32":
+                            dest.chmod(dest.stat().st_mode | stat.S_IEXEC)
+        print("[build] ffmpeg downloaded successfully")
+    except Exception as e:
+        print(f"[build] Warning: Failed to download ffmpeg: {e}")
+    finally:
+        if tmp_path.exists():
+            tmp_path.unlink()
+
+
+def rename_binary(output_dir: Path, target_triple: str) -> None:
+    """Rename the main binary to include the target triple for Tauri."""
+    if sys.platform == "win32":
+        src = output_dir / "voice-to-notes-sidecar.exe"
+        dst = output_dir / f"voice-to-notes-sidecar-{target_triple}.exe"
+    else:
+        src = output_dir / "voice-to-notes-sidecar"
+        dst = output_dir / f"voice-to-notes-sidecar-{target_triple}"
+
+    if src.exists():
+        print(f"[build] Renaming {src.name} -> {dst.name}")
+        src.rename(dst)
+    else:
+        print(f"[build] Warning: Expected binary not found at {src}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Build the Voice to Notes sidecar binary")
+    parser.add_argument(
+        "--cpu-only",
+        action="store_true",
+        default=True,
+        help="Install CPU-only PyTorch (default: True, avoids bundling CUDA)",
+    )
+    parser.add_argument(
+        "--with-cuda",
+        action="store_true",
+        help="Install PyTorch with CUDA support",
+    )
+    args = parser.parse_args()
+    cpu_only = not args.with_cuda
+
+    target_triple = get_target_triple()
+    print(f"[build] Target triple: {target_triple}")
+    print(f"[build] CPU-only: {cpu_only}")
+
+    python = create_venv_and_install(cpu_only)
+    output_dir = run_pyinstaller(python)
+    download_ffmpeg(output_dir)
+    rename_binary(output_dir, target_triple)
+
+    print(f"\n[build] Done! Sidecar built at: {output_dir}")
+    print(f"[build] Copy contents to src-tauri/binaries/ for Tauri bundling")
+
+
+if __name__ == "__main__":
+    main()

python/pyproject.toml

@@ -13,6 +13,8 @@ dependencies = [
     "faster-whisper>=1.1.0",
     "pyannote.audio>=3.1.0",
     "pysubs2>=1.7.0",
+    "openai>=1.0.0",
+    "anthropic>=0.20.0",
 ]
 
 [project.optional-dependencies]
@@ -20,6 +22,7 @@ dev = [
     "ruff>=0.8.0",
     "pytest>=8.0.0",
     "pytest-asyncio>=0.24.0",
+    "pyinstaller>=6.0",
 ]
 
 [tool.ruff]

python/tests/test_diarize.py

@@ -1,7 +1,13 @@
 """Tests for diarization service data structures and payload conversion."""
 
+import time
+from unittest.mock import MagicMock, patch
+
+import pytest
+
 from voice_to_notes.services.diarize import (
     DiarizationResult,
+    DiarizeService,
     SpeakerSegment,
     diarization_to_payload,
 )
@@ -31,3 +37,74 @@ def test_diarization_to_payload_empty():
     assert payload["num_speakers"] == 0
     assert payload["speaker_segments"] == []
     assert payload["speakers"] == []
+
+
+def test_diarize_threading_progress(monkeypatch):
+    """Test that diarization emits progress while running in background thread."""
+    # Track written messages
+    written_messages = []
+    def mock_write(msg):
+        written_messages.append(msg)
+
+    # Mock pipeline that takes ~5 seconds
+    def slow_pipeline(file_path, **kwargs):
+        time.sleep(5)
+        # Return a mock diarization result (use spec=object to prevent
+        # hasattr returning True for speaker_diarization)
+        mock_result = MagicMock(spec=[])
+        mock_track = MagicMock()
+        mock_track.start = 0.0
+        mock_track.end = 5.0
+        mock_result.itertracks = MagicMock(return_value=[(mock_track, None, "SPEAKER_00")])
+        return mock_result
+
+    mock_pipeline_obj = MagicMock()
+    mock_pipeline_obj.side_effect = slow_pipeline
+
+    service = DiarizeService()
+    service._pipeline = mock_pipeline_obj
+
+    with patch("voice_to_notes.services.diarize.write_message", mock_write):
+        result = service.diarize(
+            request_id="req-1",
+            file_path="/fake/audio.wav",
+            audio_duration_sec=60.0,
+        )
+
+    # Filter for diarizing progress messages (not loading_diarization or done)
+    diarizing_msgs = [
+        m for m in written_messages
+        if m.type == "progress" and m.payload.get("stage") == "diarizing"
+        and "elapsed" in m.payload.get("message", "")
+    ]
+
+    # Should have at least 1 progress message (5s sleep / 2s interval = ~2 messages)
+    assert len(diarizing_msgs) >= 1, (
+        f"Expected at least 1 diarizing progress message, got {len(diarizing_msgs)}"
+    )
+
+    # Progress percent should be between 20 and 85
+    for msg in diarizing_msgs:
+        pct = msg.payload["percent"]
+        assert 20 <= pct <= 85, f"Progress {pct} out of expected range 20-85"
+
+    # Result should be valid
+    assert result.num_speakers == 1
+    assert result.speakers == ["SPEAKER_00"]
+
+
+def test_diarize_threading_error_propagation(monkeypatch):
+    """Test that errors from the background thread are properly raised."""
+    mock_pipeline_obj = MagicMock()
+    mock_pipeline_obj.side_effect = RuntimeError("Pipeline crashed")
+
+    service = DiarizeService()
+    service._pipeline = mock_pipeline_obj
+
+    with patch("voice_to_notes.services.diarize.write_message", lambda m: None):
+        with pytest.raises(RuntimeError, match="Pipeline crashed"):
+            service.diarize(
+                request_id="req-1",
+                file_path="/fake/audio.wav",
+                audio_duration_sec=30.0,
+            )

python/tests/test_messages.py

@@ -3,8 +3,10 @@
 from voice_to_notes.ipc.messages import (
     IPCMessage,
     error_message,
+    partial_segment_message,
     progress_message,
     ready_message,
+    speaker_update_message,
 )
 
 
@@ -48,3 +50,16 @@ def test_ready_message():
     assert msg.type == "ready"
     assert msg.id == "system"
     assert "version" in msg.payload
+
+
+def test_partial_segment_message():
+    msg = partial_segment_message("req-1", {"index": 0, "text": "hello"})
+    assert msg.type == "pipeline.segment"
+    assert msg.payload["index"] == 0
+    assert msg.payload["text"] == "hello"
+
+
+def test_speaker_update_message():
+    msg = speaker_update_message("req-1", [{"index": 0, "speaker": "SPEAKER_00"}])
+    assert msg.type == "pipeline.speaker_update"
+    assert msg.payload["updates"][0]["speaker"] == "SPEAKER_00"

python/tests/test_pipeline.py

@@ -88,3 +88,18 @@ def test_merge_results_no_speaker_segments():
 
     result = service._merge_results(transcription, [])
     assert result.segments[0].speaker is None
+
+
+def test_speaker_update_generation():
+    """Test that speaker updates are generated after merge."""
+    result = PipelineResult(
+        segments=[
+            PipelineSegment(text="Hello", start_ms=0, end_ms=1000, speaker="SPEAKER_00"),
+            PipelineSegment(text="World", start_ms=1000, end_ms=2000, speaker="SPEAKER_01"),
+            PipelineSegment(text="Foo", start_ms=2000, end_ms=3000, speaker=None),
+        ],
+    )
+    updates = [{"index": i, "speaker": seg.speaker} for i, seg in enumerate(result.segments) if seg.speaker]
+    assert len(updates) == 2
+    assert updates[0] == {"index": 0, "speaker": "SPEAKER_00"}
+    assert updates[1] == {"index": 1, "speaker": "SPEAKER_01"}

python/tests/test_protocol.py

@@ -5,16 +5,23 @@ import json
 
 from voice_to_notes.ipc.messages import IPCMessage
 from voice_to_notes.ipc.protocol import read_message, write_message
+import voice_to_notes.ipc.protocol as protocol
 
 
-def test_write_message(capsys):
+def test_write_message():
-    msg = IPCMessage(id="req-1", type="pong", payload={"ok": True})
+    buf = io.StringIO()
-    write_message(msg)
+    # Temporarily replace the IPC output stream
-    captured = capsys.readouterr()
+    old_out = protocol._ipc_out
-    parsed = json.loads(captured.out.strip())
+    protocol._ipc_out = buf
-    assert parsed["id"] == "req-1"
+    try:
-    assert parsed["type"] == "pong"
+        msg = IPCMessage(id="req-1", type="pong", payload={"ok": True})
-    assert parsed["payload"]["ok"] is True
+        write_message(msg)
+        parsed = json.loads(buf.getvalue().strip())
+        assert parsed["id"] == "req-1"
+        assert parsed["type"] == "pong"
+        assert parsed["payload"]["ok"] is True
+    finally:
+        protocol._ipc_out = old_out
 
 
 def test_read_message(monkeypatch):

python/tests/test_transcribe.py

@@ -1,7 +1,10 @@
 """Tests for transcription service."""
 
+import inspect
+
 from voice_to_notes.services.transcribe import (
     SegmentResult,
+    TranscribeService,
     TranscriptionResult,
     WordResult,
     result_to_payload,
@@ -49,3 +52,149 @@ def test_result_to_payload_empty():
     assert payload["segments"] == []
     assert payload["language"] == ""
     assert payload["duration_ms"] == 0
+
+
+def test_on_segment_callback():
+    """Test that on_segment callback is invoked with correct SegmentResult and index."""
+    callback_args = []
+
+    def mock_callback(seg: SegmentResult, index: int):
+        callback_args.append((seg.text, index))
+
+    # Test that passing on_segment doesn't break the function signature
+    # (Full integration test would require mocking WhisperModel)
+    service = TranscribeService()
+    # Verify the parameter exists by checking the signature
+    sig = inspect.signature(service.transcribe)
+    assert "on_segment" in sig.parameters
+
+
+def test_progress_every_segment(monkeypatch):
+    """Verify a progress message is sent for every segment, not just every 5th."""
+    from unittest.mock import MagicMock, patch
+    from voice_to_notes.services.transcribe import TranscribeService
+
+    # Mock WhisperModel
+    mock_model = MagicMock()
+
+    # Create mock segments (8 of them to test > 5)
+    mock_segments = []
+    for i in range(8):
+        seg = MagicMock()
+        seg.start = i * 1.0
+        seg.end = (i + 1) * 1.0
+        seg.text = f"Segment {i}"
+        seg.words = []
+        mock_segments.append(seg)
+
+    # Mock info object
+    mock_info = MagicMock()
+    mock_info.language = "en"
+    mock_info.language_probability = 0.99
+    mock_info.duration = 8.0
+
+    mock_model.transcribe.return_value = (iter(mock_segments), mock_info)
+
+    # Track write_message calls
+    written_messages = []
+
+    def mock_write(msg):
+        written_messages.append(msg)
+
+    service = TranscribeService()
+    service._model = mock_model
+    service._current_model_name = "base"
+    service._current_device = "cpu"
+    service._current_compute_type = "int8"
+
+    with patch("voice_to_notes.services.transcribe.write_message", mock_write):
+        service.transcribe("req-1", "/fake/audio.wav")
+
+    # Filter for "transcribing" stage progress messages
+    transcribing_msgs = [
+        m for m in written_messages
+        if m.type == "progress" and m.payload.get("stage") == "transcribing"
+    ]
+
+    # Should have one per segment (8) + the initial "Starting transcription..." message
+    # The initial "Starting transcription..." is also stage "transcribing" — so 8 + 1 = 9
+    assert len(transcribing_msgs) >= 8, (
+        f"Expected at least 8 transcribing progress messages (one per segment), got {len(transcribing_msgs)}"
+    )
+
+
+def test_chunk_report_size_progress():
+    """Test CHUNK_REPORT_SIZE progress emission."""
+    from voice_to_notes.services.transcribe import CHUNK_REPORT_SIZE
+    assert CHUNK_REPORT_SIZE == 10
+
+
+def test_transcribe_chunked_with_mocked_ffmpeg(monkeypatch):
+    """Test transcribe_chunked with mocked ffmpeg/ffprobe and mocked WhisperModel."""
+    from unittest.mock import MagicMock, patch
+    from voice_to_notes.services.transcribe import TranscribeService, SegmentResult, WordResult
+
+    # Mock subprocess.run for ffprobe (returns duration of 700s = ~2 chunks at 300s each)
+    original_run = __import__("subprocess").run
+
+    def mock_subprocess_run(cmd, **kwargs):
+        if "ffprobe" in cmd:
+            result = MagicMock()
+            result.stdout = "700.0\n"
+            result.returncode = 0
+            return result
+        elif "ffmpeg" in cmd:
+            # Create an empty temp file (simulate chunk extraction)
+            # The output file is the last argument
+            import pathlib
+            output_file = cmd[-1]
+            pathlib.Path(output_file).touch()
+            result = MagicMock()
+            result.returncode = 0
+            return result
+        return original_run(cmd, **kwargs)
+
+    # Mock WhisperModel
+    mock_model = MagicMock()
+    def mock_transcribe_call(file_path, **kwargs):
+        mock_segments = []
+        for i in range(3):
+            seg = MagicMock()
+            seg.start = i * 1.0
+            seg.end = (i + 1) * 1.0
+            seg.text = f"Segment {i}"
+            seg.words = []
+            mock_segments.append(seg)
+        mock_info = MagicMock()
+        mock_info.language = "en"
+        mock_info.language_probability = 0.99
+        mock_info.duration = 300.0
+        return iter(mock_segments), mock_info
+
+    mock_model.transcribe = mock_transcribe_call
+
+    service = TranscribeService()
+    service._model = mock_model
+    service._current_model_name = "base"
+    service._current_device = "cpu"
+    service._current_compute_type = "int8"
+
+    written_messages = []
+    def mock_write(msg):
+        written_messages.append(msg)
+
+    with patch("subprocess.run", mock_subprocess_run), \
+         patch("voice_to_notes.services.transcribe.write_message", mock_write):
+        result = service.transcribe_chunked("req-1", "/fake/long_audio.wav")
+
+    # Should have segments from multiple chunks
+    assert len(result.segments) > 0
+
+    # Verify timestamp offsets — segments from chunk 1 should start at 0,
+    # segments from chunk 2 should be offset by 300000ms
+    if len(result.segments) > 3:
+        # Chunk 2 segments should have offset timestamps
+        assert result.segments[3].start_ms >= 300000
+
+    assert result.duration_ms == 700000
+    assert result.language == "en"

python/voice_to_notes.spec

@@ -0,0 +1,67 @@
+# -*- mode: python ; coding: utf-8 -*-
+"""PyInstaller spec for the Voice to Notes sidecar binary."""
+
+from PyInstaller.utils.hooks import collect_all
+
+block_cipher = None
+
+# Collect all files for packages that have shared libraries / data files
+# PyInstaller often misses these for ML packages
+ctranslate2_datas, ctranslate2_binaries, ctranslate2_hiddenimports = collect_all("ctranslate2")
+faster_whisper_datas, faster_whisper_binaries, faster_whisper_hiddenimports = collect_all(
+    "faster_whisper"
+)
+pyannote_datas, pyannote_binaries, pyannote_hiddenimports = collect_all("pyannote")
+
+a = Analysis(
+    ["voice_to_notes/main.py"],
+    pathex=[],
+    binaries=ctranslate2_binaries + faster_whisper_binaries + pyannote_binaries,
+    datas=ctranslate2_datas + faster_whisper_datas + pyannote_datas,
+    hiddenimports=[
+        "torch",
+        "torchaudio",
+        "huggingface_hub",
+        "pysubs2",
+        "openai",
+        "anthropic",
+        "litellm",
+    ]
+    + ctranslate2_hiddenimports
+    + faster_whisper_hiddenimports
+    + pyannote_hiddenimports,
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=["tkinter", "test", "unittest", "pip", "setuptools"],
+    win_no_prefer_redirects=False,
+    win_private_assemblies=False,
+    cipher=block_cipher,
+    noarchive=False,
+)
+
+pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    [],
+    exclude_binaries=True,
+    name="voice-to-notes-sidecar",
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    console=True,
+)
+
+coll = COLLECT(
+    exe,
+    a.binaries,
+    a.zipfiles,
+    a.datas,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    name="voice-to-notes-sidecar",
+)

python/voice_to_notes/hardware/detect.py

@@ -2,7 +2,10 @@
 
 from __future__ import annotations
 
+import ctypes
 import os
+import platform
+import subprocess
 import sys
 from dataclasses import dataclass
 
@@ -21,6 +24,77 @@ class HardwareInfo:
     recommended_compute_type: str = "int8"
 
 
+def _detect_ram_mb() -> int:
+    """Detect total system RAM in MB (cross-platform).
+
+    Tries platform-specific methods in order:
+    1. Linux: read /proc/meminfo
+    2. macOS: sysctl hw.memsize
+    3. Windows: GlobalMemoryStatusEx via ctypes
+    4. Fallback: os.sysconf (most Unix systems)
+
+    Returns 0 if all methods fail.
+    """
+    # Linux: read /proc/meminfo
+    if sys.platform == "linux":
+        try:
+            with open("/proc/meminfo") as f:
+                for line in f:
+                    if line.startswith("MemTotal:"):
+                        # Value is in kB
+                        return int(line.split()[1]) // 1024
+        except (FileNotFoundError, ValueError, OSError):
+            pass
+
+    # macOS: sysctl hw.memsize (returns bytes)
+    if sys.platform == "darwin":
+        try:
+            result = subprocess.run(
+                ["sysctl", "-n", "hw.memsize"],
+                capture_output=True,
+                text=True,
+                check=True,
+            )
+            return int(result.stdout.strip()) // (1024 * 1024)
+        except (subprocess.SubprocessError, ValueError, OSError):
+            pass
+
+    # Windows: GlobalMemoryStatusEx via ctypes
+    if sys.platform == "win32":
+        try:
+
+            class MEMORYSTATUSEX(ctypes.Structure):
+                _fields_ = [
+                    ("dwLength", ctypes.c_ulong),
+                    ("dwMemoryLoad", ctypes.c_ulong),
+                    ("ullTotalPhys", ctypes.c_ulonglong),
+                    ("ullAvailPhys", ctypes.c_ulonglong),
+                    ("ullTotalPageFile", ctypes.c_ulonglong),
+                    ("ullAvailPageFile", ctypes.c_ulonglong),
+                    ("ullTotalVirtual", ctypes.c_ulonglong),
+                    ("ullAvailVirtual", ctypes.c_ulonglong),
+                    ("ullAvailExtendedVirtual", ctypes.c_ulonglong),
+                ]
+
+            mem_status = MEMORYSTATUSEX()
+            mem_status.dwLength = ctypes.sizeof(MEMORYSTATUSEX)
+            if ctypes.windll.kernel32.GlobalMemoryStatusEx(ctypes.byref(mem_status)):
+                return int(mem_status.ullTotalPhys) // (1024 * 1024)
+        except (AttributeError, OSError):
+            pass
+
+    # Fallback: os.sysconf (works on most Unix systems)
+    try:
+        page_size = os.sysconf("SC_PAGE_SIZE")
+        phys_pages = os.sysconf("SC_PHYS_PAGES")
+        if page_size > 0 and phys_pages > 0:
+            return (page_size * phys_pages) // (1024 * 1024)
+    except (ValueError, OSError, AttributeError):
+        pass
+
+    return 0
+
+
 def detect_hardware() -> HardwareInfo:
     """Detect available hardware and recommend model configuration."""
     info = HardwareInfo()
@@ -28,16 +102,8 @@ def detect_hardware() -> HardwareInfo:
     # CPU info
     info.cpu_cores = os.cpu_count() or 1
 
-    # RAM info
+    # RAM info (cross-platform)
-    try:
+    info.ram_mb = _detect_ram_mb()
-        with open("/proc/meminfo") as f:
-            for line in f:
-                if line.startswith("MemTotal:"):
-                    # Value is in kB
-                    info.ram_mb = int(line.split()[1]) // 1024
-                    break
-    except (FileNotFoundError, ValueError):
-        pass
 
     # CUDA detection
     try:

python/voice_to_notes/ipc/handlers.py

@@ -88,6 +88,79 @@ def make_diarize_handler() -> HandlerFunc:
     return handler
 
 
+def make_diarize_download_handler() -> HandlerFunc:
+    """Create a handler that downloads/validates the diarization model."""
+    import os
+
+    def handler(msg: IPCMessage) -> IPCMessage:
+        payload = msg.payload
+        hf_token = payload.get("hf_token")
+
+        try:
+            import huggingface_hub
+
+            # Disable pyannote telemetry (has a bug in v4.0.4)
+            os.environ.setdefault("PYANNOTE_METRICS_ENABLED", "false")
+            from pyannote.audio import Pipeline
+
+            # Persist token globally so ALL huggingface_hub downloads use auth.
+            # Setting env var alone isn't enough — pyannote's internal sub-downloads
+            # (e.g. PLDA.from_pretrained) don't forward the token= parameter.
+            # login() writes the token to ~/.cache/huggingface/token which
+            # huggingface_hub reads automatically for all downloads.
+            if hf_token:
+                os.environ["HF_TOKEN"] = hf_token
+                huggingface_hub.login(token=hf_token, add_to_git_credential=False)
+
+            # Pre-download sub-models that pyannote loads internally.
+            # This ensures they're cached before Pipeline.from_pretrained
+            # tries to load them (where token forwarding can fail).
+            sub_models = [
+                "pyannote/segmentation-3.0",
+                "pyannote/speaker-diarization-community-1",
+            ]
+            for model_id in sub_models:
+                print(f"[sidecar] Pre-downloading {model_id}...", file=sys.stderr, flush=True)
+                huggingface_hub.snapshot_download(model_id, token=hf_token)
+
+            print("[sidecar] Downloading diarization pipeline...", file=sys.stderr, flush=True)
+            pipeline = Pipeline.from_pretrained(
+                "pyannote/speaker-diarization-3.1",
+                token=hf_token,
+            )
+            print("[sidecar] Diarization model downloaded successfully", file=sys.stderr, flush=True)
+            return IPCMessage(
+                id=msg.id,
+                type="diarize.download.result",
+                payload={"ok": True},
+            )
+        except Exception as e:
+            error_msg = str(e)
+            print(f"[sidecar] Model download error: {error_msg}", file=sys.stderr, flush=True)
+            # Make common errors more user-friendly
+            if "403" in error_msg or "gated" in error_msg.lower():
+                # Try to extract the specific model name from the error
+                import re
+                model_match = re.search(r"pyannote/[\w-]+", error_msg)
+                if model_match:
+                    model_name = model_match.group(0)
+                    error_msg = (
+                        f"Access denied for {model_name}. "
+                        f"Please visit huggingface.co/{model_name} "
+                        f"and accept the license agreement, then try again."
+                    )
+                else:
+                    error_msg = (
+                        "Access denied. Please accept the license agreements for all "
+                        "required pyannote models on HuggingFace."
+                    )
+            elif "401" in error_msg:
+                error_msg = "Invalid token. Please check your HuggingFace token."
+            return error_message(msg.id, "download_error", error_msg)
+
+    return handler
+
+
 def make_pipeline_handler() -> HandlerFunc:
     """Create a full pipeline handler (transcribe + diarize + merge)."""
     from voice_to_notes.services.pipeline import PipelineService, pipeline_result_to_payload
@@ -107,6 +180,7 @@ def make_pipeline_handler() -> HandlerFunc:
             min_speakers=payload.get("min_speakers"),
             max_speakers=payload.get("max_speakers"),
             skip_diarization=payload.get("skip_diarization", False),
+            hf_token=payload.get("hf_token"),
         )
         return IPCMessage(
             id=msg.id,
@@ -186,10 +260,12 @@ def make_ai_chat_handler() -> HandlerFunc:
                     model=config.get("model", "claude-sonnet-4-6"),
                 ))
             elif provider_name == "litellm":
-                from voice_to_notes.providers.litellm_provider import LiteLLMProvider
+                from voice_to_notes.providers.litellm_provider import OpenAICompatibleProvider
 
-                service.register_provider("litellm", LiteLLMProvider(
+                service.register_provider("litellm", OpenAICompatibleProvider(
                     model=config.get("model", "gpt-4o-mini"),
+                    api_key=config.get("api_key"),
+                    api_base=config.get("api_base"),
                 ))
             return IPCMessage(
                 id=msg.id,

python/voice_to_notes/ipc/messages.py

@@ -34,6 +34,14 @@ def progress_message(request_id: str, percent: int, stage: str, message: str) ->
     )
 
 
+def partial_segment_message(request_id: str, segment_data: dict) -> IPCMessage:
+    return IPCMessage(id=request_id, type="pipeline.segment", payload=segment_data)
+
+
+def speaker_update_message(request_id: str, updates: list[dict]) -> IPCMessage:
+    return IPCMessage(id=request_id, type="pipeline.speaker_update", payload={"updates": updates})
+
+
 def error_message(request_id: str, code: str, message: str) -> IPCMessage:
     return IPCMessage(
         id=request_id,

python/voice_to_notes/ipc/protocol.py

@@ -1,13 +1,53 @@
-"""JSON-line protocol reader/writer over stdin/stdout."""
+"""JSON-line protocol reader/writer over stdin/stdout.
+
+IMPORTANT: stdout is reserved exclusively for IPC messages.
+At init time we save the real stdout, then redirect sys.stdout → stderr
+so that any rogue print() calls from libraries don't corrupt the IPC stream.
+"""
 
 from __future__ import annotations
 
+import io
 import json
+import os
 import sys
 from typing import Any
 
 from voice_to_notes.ipc.messages import IPCMessage
 
+# Save the real stdout fd for IPC before any library can pollute it.
+# Then redirect sys.stdout to stderr so library prints go to stderr.
+_ipc_out: io.TextIOWrapper | None = None
+
+
+def init_ipc() -> None:
+    """Capture real stdout for IPC and redirect sys.stdout to stderr.
+
+    Must be called once at sidecar startup, before importing any ML libraries.
+    """
+    global _ipc_out
+    if _ipc_out is not None:
+        return  # already initialised
+
+    # Duplicate the real stdout fd so we keep it even after redirect
+    real_stdout_fd = os.dup(sys.stdout.fileno())
+    _ipc_out = io.TextIOWrapper(
+        io.BufferedWriter(io.FileIO(real_stdout_fd, "w")),
+        encoding="utf-8",
+        line_buffering=True,
+    )
+
+    # Redirect sys.stdout → stderr so print() from libraries goes to stderr
+    sys.stdout = sys.stderr
+
+
+def _get_ipc_out() -> io.TextIOWrapper:
+    """Return the IPC output stream, falling back to sys.__stdout__."""
+    if _ipc_out is not None:
+        return _ipc_out
+    # Fallback if init_ipc() was never called (e.g. in tests)
+    return sys.__stdout__
+
 
 def read_message() -> IPCMessage | None:
     """Read a single JSON-line message from stdin. Returns None on EOF."""
@@ -29,17 +69,19 @@ def read_message() -> IPCMessage | None:
 
 
 def write_message(msg: IPCMessage) -> None:
-    """Write a JSON-line message to stdout."""
+    """Write a JSON-line message to the IPC channel (real stdout)."""
+    out = _get_ipc_out()
     line = json.dumps(msg.to_dict(), separators=(",", ":"))
-    sys.stdout.write(line + "\n")
+    out.write(line + "\n")
-    sys.stdout.flush()
+    out.flush()
 
 
 def write_dict(data: dict[str, Any]) -> None:
-    """Write a raw dict as a JSON-line message to stdout."""
+    """Write a raw dict as a JSON-line message to the IPC channel."""
+    out = _get_ipc_out()
     line = json.dumps(data, separators=(",", ":"))
-    sys.stdout.write(line + "\n")
+    out.write(line + "\n")
-    sys.stdout.flush()
+    out.flush()
 
 
 def _log(message: str) -> None:

python/voice_to_notes/main.py

@@ -5,18 +5,25 @@ from __future__ import annotations
 import signal
 import sys
 
-from voice_to_notes.ipc.handlers import (
+# CRITICAL: Capture real stdout for IPC *before* importing any ML libraries
+# that might print to stdout and corrupt the JSON-line protocol.
+from voice_to_notes.ipc.protocol import init_ipc
+
+init_ipc()
+
+from voice_to_notes.ipc.handlers import (  # noqa: E402
     HandlerRegistry,
     hardware_detect_handler,
     make_ai_chat_handler,
+    make_diarize_download_handler,
     make_diarize_handler,
     make_export_handler,
     make_pipeline_handler,
     make_transcribe_handler,
     ping_handler,
 )
-from voice_to_notes.ipc.messages import ready_message
+from voice_to_notes.ipc.messages import ready_message  # noqa: E402
-from voice_to_notes.ipc.protocol import read_message, write_message
+from voice_to_notes.ipc.protocol import read_message, write_message  # noqa: E402
 
 
 def create_registry() -> HandlerRegistry:
@@ -26,6 +33,7 @@ def create_registry() -> HandlerRegistry:
     registry.register("transcribe.start", make_transcribe_handler())
     registry.register("hardware.detect", hardware_detect_handler)
     registry.register("diarize.start", make_diarize_handler())
+    registry.register("diarize.download", make_diarize_download_handler())
     registry.register("pipeline.start", make_pipeline_handler())
     registry.register("export.start", make_export_handler())
     registry.register("ai.chat", make_ai_chat_handler())

python/voice_to_notes/providers/litellm_provider.py

@@ -1,4 +1,4 @@
-"""LiteLLM provider — multi-provider gateway."""
+"""OpenAI-compatible provider — works with any OpenAI-compatible API endpoint."""
 
 from __future__ import annotations
 
@@ -7,36 +7,44 @@ from typing import Any
 from voice_to_notes.providers.base import AIProvider
 
 
-class LiteLLMProvider(AIProvider):
+class OpenAICompatibleProvider(AIProvider):
-    """Routes through LiteLLM for access to 100+ LLM providers."""
+    """Connects to any OpenAI-compatible API (LiteLLM proxy, Ollama, vLLM, etc.)."""
 
-    def __init__(self, model: str = "gpt-4o-mini", **kwargs: Any) -> None:
+    def __init__(
+        self,
+        api_key: str | None = None,
+        api_base: str | None = None,
+        model: str = "gpt-4o-mini",
+        **kwargs: Any,
+    ) -> None:
+        self._api_key = api_key or "sk-no-key"
+        self._api_base = api_base
         self._model = model
         self._extra_kwargs = kwargs
 
     def chat(self, messages: list[dict[str, str]], **kwargs: Any) -> str:
-        try:
+        from openai import OpenAI
-            import litellm
-        except ImportError:
-            raise RuntimeError("litellm package is required. Install with: pip install litellm")
 
-        merged_kwargs = {**self._extra_kwargs, **kwargs}
+        client_kwargs: dict[str, Any] = {"api_key": self._api_key}
-        response = litellm.completion(
+        if self._api_base:
-            model=merged_kwargs.get("model", self._model),
+            client_kwargs["base_url"] = self._api_base
+
+        client = OpenAI(**client_kwargs)
+        response = client.chat.completions.create(
+            model=kwargs.get("model", self._model),
             messages=messages,
-            temperature=merged_kwargs.get("temperature", 0.7),
+            temperature=kwargs.get("temperature", 0.7),
-            max_tokens=merged_kwargs.get("max_tokens", 2048),
+            max_tokens=kwargs.get("max_tokens", 2048),
         )
         return response.choices[0].message.content or ""
 
     def is_available(self) -> bool:
         try:
-            import litellm  # noqa: F401
+            import openai  # noqa: F401
-
+            return bool(self._api_key and self._api_base)
-            return True
         except ImportError:
             return False
 
     @property
     def name(self) -> str:
-        return "LiteLLM"
+        return "OpenAI Compatible"

python/voice_to_notes/services/ai_provider.py

@@ -92,7 +92,7 @@ class AIProviderService:
 def create_default_service() -> AIProviderService:
     """Create an AIProviderService with all supported providers registered."""
     from voice_to_notes.providers.anthropic_provider import AnthropicProvider
-    from voice_to_notes.providers.litellm_provider import LiteLLMProvider
+    from voice_to_notes.providers.litellm_provider import OpenAICompatibleProvider
     from voice_to_notes.providers.local_provider import LocalProvider
     from voice_to_notes.providers.openai_provider import OpenAIProvider
 
@@ -100,5 +100,5 @@ def create_default_service() -> AIProviderService:
     service.register_provider("local", LocalProvider())
     service.register_provider("openai", OpenAIProvider())
     service.register_provider("anthropic", AnthropicProvider())
-    service.register_provider("litellm", LiteLLMProvider())
+    service.register_provider("litellm", OpenAICompatibleProvider())
     return service

python/voice_to_notes/services/diarize.py

@@ -2,15 +2,69 @@
 
 from __future__ import annotations
 
+import os
+import subprocess
 import sys
+import tempfile
+import threading
 import time
 from dataclasses import dataclass, field
+from pathlib import Path
 from typing import Any
 
+# Disable pyannote telemetry — it has a bug in v4.0.4 where
+# np.isfinite(None) crashes when max_speakers is not set.
+os.environ.setdefault("PYANNOTE_METRICS_ENABLED", "false")
+
+from voice_to_notes.utils.ffmpeg import get_ffmpeg_path
 from voice_to_notes.ipc.messages import progress_message
 from voice_to_notes.ipc.protocol import write_message
 
 
+def _ensure_wav(file_path: str) -> tuple[str, str | None]:
+    """Convert audio to 16kHz mono WAV if needed.
+
+    pyannote.audio v4.0.4 has a bug where its AudioDecoder returns
+    duration=None for some formats (FLAC, etc.), causing crashes.
+    Converting to WAV ensures the duration header is always present.
+
+    Returns:
+        (path_to_use, temp_path_or_None)
+        If conversion was needed, temp_path is the WAV file to clean up.
+    """
+    ext = Path(file_path).suffix.lower()
+    if ext == ".wav":
+        return file_path, None
+
+    tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+    tmp.close()
+    try:
+        subprocess.run(
+            [
+                get_ffmpeg_path(), "-y", "-i", file_path,
+                "-ar", "16000", "-ac", "1", "-c:a", "pcm_s16le",
+                tmp.name,
+            ],
+            check=True,
+            capture_output=True,
+        )
+        print(
+            f"[sidecar] Converted {ext} to WAV for diarization",
+            file=sys.stderr,
+            flush=True,
+        )
+        return tmp.name, tmp.name
+    except (subprocess.CalledProcessError, FileNotFoundError) as e:
+        # ffmpeg not available or failed — try original file and hope for the best
+        print(
+            f"[sidecar] WAV conversion failed ({e}), using original file",
+            file=sys.stderr,
+            flush=True,
+        )
+        os.unlink(tmp.name)
+        return file_path, None
+
+
 @dataclass
 class SpeakerSegment:
     """A time span assigned to a speaker."""
@@ -35,45 +89,59 @@ class DiarizeService:
     def __init__(self) -> None:
         self._pipeline: Any = None
 
-    def _ensure_pipeline(self) -> Any:
+    def _ensure_pipeline(self, hf_token: str | None = None) -> Any:
         """Load the pyannote diarization pipeline (lazy)."""
         if self._pipeline is not None:
             return self._pipeline
 
         print("[sidecar] Loading pyannote diarization pipeline...", file=sys.stderr, flush=True)
 
-        try:
+        # Use token from argument, fall back to environment variable
-            from pyannote.audio import Pipeline
+        if not hf_token:
+            hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN") or None
 
-            self._pipeline = Pipeline.from_pretrained(
+        # Persist token globally so ALL huggingface_hub sub-downloads use auth.
-                "pyannote/speaker-diarization-3.1",
+        # Pyannote has internal dependencies that don't forward the token= param.
-                use_auth_token=False,
+        if hf_token:
-            )
+            os.environ["HF_TOKEN"] = hf_token
-        except Exception:
+            import huggingface_hub
-            # Fall back to a simpler approach if the model isn't available
+            huggingface_hub.login(token=hf_token, add_to_git_credential=False)
-            # pyannote requires HuggingFace token for some models
+
-            # Try the community model first
+        models = [
+            "pyannote/speaker-diarization-3.1",
+            "pyannote/speaker-diarization",
+        ]
+
+        last_error: Exception | None = None
+        for model_name in models:
             try:
                 from pyannote.audio import Pipeline
 
-                self._pipeline = Pipeline.from_pretrained(
+                self._pipeline = Pipeline.from_pretrained(model_name, token=hf_token)
-                    "pyannote/speaker-diarization",
+                print(f"[sidecar] Loaded diarization model: {model_name}", file=sys.stderr, flush=True)
-                    use_auth_token=False,
+                # Move pipeline to GPU if available
-                )
+                try:
+                    import torch
+                    if torch.cuda.is_available():
+                        self._pipeline = self._pipeline.to(torch.device("cuda"))
+                        print(f"[sidecar] Diarization pipeline moved to GPU", file=sys.stderr, flush=True)
+                except Exception as e:
+                    print(f"[sidecar] GPU not available for diarization: {e}", file=sys.stderr, flush=True)
+                return self._pipeline
             except Exception as e:
+                last_error = e
                 print(
-                    f"[sidecar] Warning: Could not load pyannote pipeline: {e}",
+                    f"[sidecar] Warning: Could not load {model_name}: {e}",
                     file=sys.stderr,
                     flush=True,
                 )
-                raise RuntimeError(
-                    "pyannote.audio pipeline not available. "
-                    "You may need to accept the model license at "
-                    "https://huggingface.co/pyannote/speaker-diarization-3.1 "
-                    "and set a HF_TOKEN environment variable."
-                ) from e
 
-        return self._pipeline
+        raise RuntimeError(
+            "pyannote.audio pipeline not available. "
+            "You may need to accept the model license at "
+            "https://huggingface.co/pyannote/speaker-diarization-3.1 "
+            "and set a HF_TOKEN environment variable."
+        ) from last_error
 
     def diarize(
         self,
@@ -82,6 +150,8 @@ class DiarizeService:
         num_speakers: int | None = None,
         min_speakers: int | None = None,
         max_speakers: int | None = None,
+        hf_token: str | None = None,
+        audio_duration_sec: float | None = None,
     ) -> DiarizationResult:
         """Run speaker diarization on an audio file.
 
@@ -99,7 +169,7 @@ class DiarizeService:
             progress_message(request_id, 0, "loading_diarization", "Loading diarization model...")
         )
 
-        pipeline = self._ensure_pipeline()
+        pipeline = self._ensure_pipeline(hf_token=hf_token)
 
         write_message(
             progress_message(request_id, 20, "diarizing", "Running speaker diarization...")
@@ -116,8 +186,55 @@ class DiarizeService:
         if max_speakers is not None:
             kwargs["max_speakers"] = max_speakers
 
-        # Run diarization
+        # Convert to WAV to work around pyannote v4.0.4 duration bug
-        diarization = pipeline(file_path, **kwargs)
+        audio_path, temp_wav = _ensure_wav(file_path)
+
+        print(
+            f"[sidecar] Running diarization on {audio_path} with kwargs: {kwargs}",
+            file=sys.stderr,
+            flush=True,
+        )
+
+        # Run diarization in background thread for progress reporting
+        result_holder: list = [None]
+        error_holder: list[Exception | None] = [None]
+        done_event = threading.Event()
+
+        def _run():
+            try:
+                result_holder[0] = pipeline(audio_path, **kwargs)
+            except Exception as e:
+                error_holder[0] = e
+            finally:
+                done_event.set()
+
+        thread = threading.Thread(target=_run, daemon=True)
+        thread.start()
+
+        elapsed = 0.0
+        estimated_total = max(audio_duration_sec * 0.5, 30.0) if audio_duration_sec else 120.0
+        while not done_event.wait(timeout=2.0):
+            elapsed += 2.0
+            pct = min(20 + int((elapsed / estimated_total) * 65), 85)
+            write_message(progress_message(
+                request_id, pct, "diarizing",
+                f"Analyzing speakers ({int(elapsed)}s elapsed)..."))
+
+        thread.join()
+
+        # Clean up temp file
+        if temp_wav:
+            os.unlink(temp_wav)
+
+        if error_holder[0] is not None:
+            raise error_holder[0]
+        raw_result = result_holder[0]
+
+        # pyannote 4.0+ returns DiarizeOutput; older versions return Annotation directly
+        if hasattr(raw_result, "speaker_diarization"):
+            diarization = raw_result.speaker_diarization
+        else:
+            diarization = raw_result
 
         # Convert pyannote output to our format
         result = DiarizationResult()

python/voice_to_notes/services/pipeline.py

@@ -2,13 +2,19 @@
 
 from __future__ import annotations
 
+import concurrent.futures
 import sys
 import time
 from dataclasses import dataclass, field
 from typing import Any
 
-from voice_to_notes.ipc.messages import progress_message
+from voice_to_notes.ipc.messages import (
+    partial_segment_message,
+    progress_message,
+    speaker_update_message,
+)
 from voice_to_notes.ipc.protocol import write_message
+from voice_to_notes.utils.ffmpeg import get_ffprobe_path
 from voice_to_notes.services.diarize import DiarizeService, SpeakerSegment
 from voice_to_notes.services.transcribe import (
     SegmentResult,
@@ -60,6 +66,7 @@ class PipelineService:
         min_speakers: int | None = None,
         max_speakers: int | None = None,
         skip_diarization: bool = False,
+        hf_token: str | None = None,
     ) -> PipelineResult:
         """Run the full transcription + diarization pipeline.
 
@@ -77,22 +84,59 @@ class PipelineService:
         """
         start_time = time.time()
 
-        # Step 1: Transcribe
+        # Step 0: Probe audio duration for conditional chunked transcription
         write_message(
             progress_message(request_id, 0, "pipeline", "Starting transcription pipeline...")
         )
 
-        transcription = self._transcribe_service.transcribe(
+        def _emit_segment(seg: SegmentResult, index: int) -> None:
-            request_id=request_id,
+            write_message(partial_segment_message(request_id, {
-            file_path=file_path,
+                "index": index,
-            model_name=model_name,
+                "text": seg.text,
-            device=device,
+                "start_ms": seg.start_ms,
-            compute_type=compute_type,
+                "end_ms": seg.end_ms,
-            language=language,
+                "words": [{"word": w.word, "start_ms": w.start_ms, "end_ms": w.end_ms, "confidence": w.confidence} for w in seg.words],
-        )
+            }))
+
+        audio_duration_sec = None
+        try:
+            import subprocess
+            probe_result = subprocess.run(
+                [get_ffprobe_path(), "-v", "quiet", "-show_entries", "format=duration",
+                 "-of", "default=noprint_wrappers=1:nokey=1", file_path],
+                capture_output=True, text=True, check=True,
+            )
+            audio_duration_sec = float(probe_result.stdout.strip())
+        except (subprocess.CalledProcessError, FileNotFoundError, ValueError):
+            pass
+
+        def _run_transcription() -> TranscriptionResult:
+            """Run transcription (chunked or standard based on duration)."""
+            from voice_to_notes.services.transcribe import LARGE_FILE_THRESHOLD_SEC
+            if audio_duration_sec and audio_duration_sec > LARGE_FILE_THRESHOLD_SEC:
+                return self._transcribe_service.transcribe_chunked(
+                    request_id=request_id,
+                    file_path=file_path,
+                    model_name=model_name,
+                    device=device,
+                    compute_type=compute_type,
+                    language=language,
+                    on_segment=_emit_segment,
+                )
+            else:
+                return self._transcribe_service.transcribe(
+                    request_id=request_id,
+                    file_path=file_path,
+                    model_name=model_name,
+                    device=device,
+                    compute_type=compute_type,
+                    language=language,
+                    on_segment=_emit_segment,
+                )
 
         if skip_diarization:
-            # Convert transcription directly without speaker labels
+            # Sequential: transcribe only, no diarization needed
+            transcription = _run_transcription()
             result = PipelineResult(
                 language=transcription.language,
                 language_probability=transcription.language_probability,
@@ -110,37 +154,63 @@ class PipelineService:
                 )
             return result
 
-        # Step 2: Diarize (with graceful fallback)
+        # Parallel execution: run transcription (0-45%) and diarization (45-90%)
+        # concurrently, then merge (90-100%).
         write_message(
-            progress_message(request_id, 50, "pipeline", "Starting speaker diarization...")
+            progress_message(
+                request_id, 0, "pipeline",
+                "Starting transcription and diarization in parallel..."
+            )
         )
 
         diarization = None
-        try:
+        diarization_error = None
-            diarization = self._diarize_service.diarize(
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
+            transcription_future = executor.submit(_run_transcription)
+
+            # Use probed audio_duration_sec for diarization progress estimation
+            # (transcription hasn't finished yet, so we can't use transcription.duration_ms)
+            diarization_future = executor.submit(
+                self._diarize_service.diarize,
                 request_id=request_id,
                 file_path=file_path,
                 num_speakers=num_speakers,
                 min_speakers=min_speakers,
                 max_speakers=max_speakers,
+                hf_token=hf_token,
+                audio_duration_sec=audio_duration_sec,
             )
-        except Exception as e:
+
-            print(
+            # Wait for both futures. We need the transcription result regardless,
-                f"[sidecar] Diarization failed, falling back to transcription-only: {e}",
+            # but diarization may fail gracefully.
-                file=sys.stderr,
+            transcription = transcription_future.result()
-                flush=True,
-            )
             write_message(
-                progress_message(
+                progress_message(request_id, 45, "pipeline", "Transcription complete")
-                    request_id, 80, "pipeline",
-                    "Diarization unavailable, using transcription only..."
-                )
             )
 
+            try:
+                diarization = diarization_future.result()
+            except Exception as e:
+                import traceback
+                diarization_error = e
+                print(
+                    f"[sidecar] Diarization failed, falling back to transcription-only: {e}",
+                    file=sys.stderr,
+                    flush=True,
+                )
+                traceback.print_exc(file=sys.stderr)
+                write_message(
+                    progress_message(
+                        request_id, 80, "pipeline",
+                        f"Diarization failed ({e}), using transcription only..."
+                    )
+                )
+
         # Step 3: Merge (or skip if diarization failed)
         if diarization is not None:
             write_message(
-                progress_message(request_id, 90, "pipeline", "Merging transcript with speakers...")
+                progress_message(request_id, 90, "merging", "Merging transcript with speakers...")
             )
             result = self._merge_results(transcription, diarization.speaker_segments)
             result.speakers = diarization.speakers
@@ -170,6 +240,10 @@ class PipelineService:
             flush=True,
         )
 
+        updates = [{"index": i, "speaker": seg.speaker} for i, seg in enumerate(result.segments) if seg.speaker]
+        if updates:
+            write_message(speaker_update_message(request_id, updates))
+
         write_message(
             progress_message(request_id, 100, "done", "Pipeline complete")
         )

python/voice_to_notes/services/transcribe.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 
 import sys
 import time
+from collections.abc import Callable
 from dataclasses import dataclass, field
 from typing import Any
 
@@ -11,6 +12,10 @@ from faster_whisper import WhisperModel
 
 from voice_to_notes.ipc.messages import progress_message
 from voice_to_notes.ipc.protocol import write_message
+from voice_to_notes.utils.ffmpeg import get_ffmpeg_path, get_ffprobe_path
+
+CHUNK_REPORT_SIZE = 10
+LARGE_FILE_THRESHOLD_SEC = 3600  # 1 hour
 
 
 @dataclass
@@ -90,6 +95,7 @@ class TranscribeService:
         device: str = "cpu",
         compute_type: str = "int8",
         language: str | None = None,
+        on_segment: Callable[[SegmentResult, int], None] | None = None,
     ) -> TranscriptionResult:
         """Transcribe an audio file with word-level timestamps.
 
@@ -145,16 +151,23 @@ class TranscribeService:
                 )
             )
 
-            # Send progress every few segments
+            if on_segment:
-            if segment_count % 5 == 0:
+                on_segment(result.segments[-1], segment_count - 1)
-                write_message(
+
-                    progress_message(
+            write_message(
-                        request_id,
+                progress_message(
-                        progress_pct,
+                    request_id,
-                        "transcribing",
+                    progress_pct,
-                        f"Processed {segment_count} segments...",
+                    "transcribing",
-                    )
+                    f"Transcribing segment {segment_count} ({progress_pct}% of audio)...",
                 )
+            )
+
+            if segment_count % CHUNK_REPORT_SIZE == 0:
+                write_message(progress_message(
+                    request_id, progress_pct, "transcribing",
+                    f"Completed chunk of {CHUNK_REPORT_SIZE} segments "
+                    f"({segment_count} total, {progress_pct}% of audio)..."))
 
         elapsed = time.time() - start_time
         print(
@@ -166,6 +179,113 @@ class TranscribeService:
         write_message(progress_message(request_id, 100, "done", "Transcription complete"))
         return result
 
+    def transcribe_chunked(
+        self,
+        request_id: str,
+        file_path: str,
+        model_name: str = "base",
+        device: str = "cpu",
+        compute_type: str = "int8",
+        language: str | None = None,
+        on_segment: Callable[[SegmentResult, int], None] | None = None,
+        chunk_duration_sec: int = 300,
+    ) -> TranscriptionResult:
+        """Transcribe a large audio file by splitting into chunks.
+
+        Uses ffmpeg to split the file into chunks, transcribes each chunk,
+        then merges the results with corrected timestamps.
+
+        Falls back to standard transcribe() if ffmpeg is not available.
+        """
+        import subprocess
+        import tempfile
+
+        # Get total duration via ffprobe
+        try:
+            probe_result = subprocess.run(
+                [get_ffprobe_path(), "-v", "quiet", "-show_entries", "format=duration",
+                 "-of", "default=noprint_wrappers=1:nokey=1", file_path],
+                capture_output=True, text=True, check=True,
+            )
+            total_duration = float(probe_result.stdout.strip())
+        except (subprocess.CalledProcessError, FileNotFoundError, ValueError):
+            # ffprobe not available or failed — fall back to standard transcription
+            write_message(progress_message(
+                request_id, 5, "transcribing",
+                "ffmpeg not available, using standard transcription..."))
+            return self.transcribe(request_id, file_path, model_name, device,
+                                   compute_type, language, on_segment=on_segment)
+
+        num_chunks = max(1, int(total_duration / chunk_duration_sec) + 1)
+        write_message(progress_message(
+            request_id, 5, "transcribing",
+            f"Splitting {total_duration:.0f}s file into {num_chunks} chunks..."))
+
+        merged_result = TranscriptionResult()
+        global_segment_index = 0
+
+        for chunk_idx in range(num_chunks):
+            chunk_start = chunk_idx * chunk_duration_sec
+            if chunk_start >= total_duration:
+                break
+
+            chunk_start_ms = int(chunk_start * 1000)
+
+            # Extract chunk to temp file
+            tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+            tmp.close()
+            try:
+                subprocess.run(
+                    [get_ffmpeg_path(), "-y", "-ss", str(chunk_start),
+                     "-t", str(chunk_duration_sec),
+                     "-i", file_path,
+                     "-ar", "16000", "-ac", "1", "-c:a", "pcm_s16le",
+                     tmp.name],
+                    capture_output=True, check=True,
+                )
+
+                # Wrap on_segment to offset the index
+                chunk_on_segment = None
+                if on_segment:
+                    base_index = global_segment_index
+                    def chunk_on_segment(seg: SegmentResult, idx: int, _base=base_index) -> None:
+                        on_segment(seg, _base + idx)
+
+                chunk_result = self.transcribe(
+                    request_id, tmp.name, model_name, device,
+                    compute_type, language, on_segment=chunk_on_segment,
+                )
+
+                # Offset timestamps and merge
+                for seg in chunk_result.segments:
+                    seg.start_ms += chunk_start_ms
+                    seg.end_ms += chunk_start_ms
+                    for word in seg.words:
+                        word.start_ms += chunk_start_ms
+                        word.end_ms += chunk_start_ms
+                    merged_result.segments.append(seg)
+
+                global_segment_index += len(chunk_result.segments)
+
+                # Take language from first chunk
+                if chunk_idx == 0:
+                    merged_result.language = chunk_result.language
+                    merged_result.language_probability = chunk_result.language_probability
+
+            finally:
+                import os
+                os.unlink(tmp.name)
+
+            # Chunk progress
+            chunk_pct = min(10 + int(((chunk_idx + 1) / num_chunks) * 80), 90)
+            write_message(progress_message(
+                request_id, chunk_pct, "transcribing",
+                f"Completed chunk {chunk_idx + 1}/{num_chunks}..."))
+
+        merged_result.duration_ms = int(total_duration * 1000)
+        write_message(progress_message(request_id, 100, "done", "Transcription complete"))
+        return merged_result
+
 
 def result_to_payload(result: TranscriptionResult) -> dict[str, Any]:
     """Convert TranscriptionResult to IPC payload dict."""

python/voice_to_notes/utils/ffmpeg.py

@@ -0,0 +1,43 @@
+"""Resolve ffmpeg/ffprobe paths for both frozen and development builds."""
+
+from __future__ import annotations
+
+import os
+import sys
+
+
+def get_ffmpeg_path() -> str:
+    """Return the path to the ffmpeg binary.
+
+    When running as a frozen PyInstaller bundle, looks next to sys.executable.
+    Otherwise falls back to the system PATH.
+    """
+    if getattr(sys, "frozen", False):
+        # Frozen PyInstaller bundle — ffmpeg is next to the sidecar binary
+        bundle_dir = os.path.dirname(sys.executable)
+        candidates = [
+            os.path.join(bundle_dir, "ffmpeg.exe" if sys.platform == "win32" else "ffmpeg"),
+            os.path.join(bundle_dir, "ffmpeg"),
+        ]
+        for path in candidates:
+            if os.path.isfile(path):
+                return path
+    return "ffmpeg"
+
+
+def get_ffprobe_path() -> str:
+    """Return the path to the ffprobe binary.
+
+    When running as a frozen PyInstaller bundle, looks next to sys.executable.
+    Otherwise falls back to the system PATH.
+    """
+    if getattr(sys, "frozen", False):
+        bundle_dir = os.path.dirname(sys.executable)
+        candidates = [
+            os.path.join(bundle_dir, "ffprobe.exe" if sys.platform == "win32" else "ffprobe"),
+            os.path.join(bundle_dir, "ffprobe"),
+        ]
+        for path in candidates:
+            if os.path.isfile(path):
+                return path
+    return "ffprobe"

src-tauri/src/commands/ai.rs

@@ -39,7 +39,11 @@ pub fn ai_chat(
     if response.msg_type == "error" {
         return Err(format!(
             "AI error: {}",
-            response.payload.get("message").and_then(|v| v.as_str()).unwrap_or("unknown")
+            response
+                .payload
+                .get("message")
+                .and_then(|v| v.as_str())
+                .unwrap_or("unknown")
         ));
     }
 

src-tauri/src/commands/export.rs

@@ -33,7 +33,11 @@ pub fn export_transcript(
     if response.msg_type == "error" {
         return Err(format!(
             "Export error: {}",
-            response.payload.get("message").and_then(|v| v.as_str()).unwrap_or("unknown")
+            response
+                .payload
+                .get("message")
+                .and_then(|v| v.as_str())
+                .unwrap_or("unknown")
         ));
     }
 

src-tauri/src/commands/project.rs

@@ -1,9 +1,110 @@
+use serde::{Deserialize, Serialize};
+use std::fs;
 use tauri::State;
 
 use crate::db::models::Project;
 use crate::db::queries;
 use crate::state::AppState;
 
+// ── File-based project types ────────────────────────────────────
+
+#[derive(Serialize, Deserialize)]
+pub struct ProjectFile {
+    pub version: u32,
+    pub name: String,
+    pub audio_file: String,
+    pub created_at: String,
+    pub segments: Vec<ProjectFileSegment>,
+    pub speakers: Vec<ProjectFileSpeaker>,
+}
+
+#[derive(Serialize, Deserialize)]
+pub struct ProjectFileSegment {
+    pub text: String,
+    pub start_ms: i64,
+    pub end_ms: i64,
+    pub speaker: Option<String>,
+    pub is_edited: bool,
+    pub words: Vec<ProjectFileWord>,
+}
+
+#[derive(Serialize, Deserialize)]
+pub struct ProjectFileWord {
+    pub word: String,
+    pub start_ms: i64,
+    pub end_ms: i64,
+    pub confidence: f64,
+}
+
+#[derive(Serialize, Deserialize)]
+pub struct ProjectFileSpeaker {
+    pub label: String,
+    pub display_name: Option<String>,
+    pub color: String,
+}
+
+// ── Input types for save_project_transcript ──────────────────────
+
+#[derive(Deserialize)]
+pub struct WordInput {
+    pub word: String,
+    pub start_ms: i64,
+    pub end_ms: i64,
+    pub confidence: f64,
+}
+
+#[derive(Deserialize)]
+pub struct SegmentInput {
+    pub text: String,
+    pub start_ms: i64,
+    pub end_ms: i64,
+    pub speaker: Option<String>, // speaker label, not id
+    pub words: Vec<WordInput>,
+}
+
+#[derive(Deserialize)]
+pub struct SpeakerInput {
+    pub label: String,
+    pub color: String,
+}
+
+// ── Output types for load_project_transcript ─────────────────────
+
+#[derive(Serialize)]
+pub struct WordOutput {
+    pub word: String,
+    pub start_ms: i64,
+    pub end_ms: i64,
+    pub confidence: Option<f64>,
+}
+
+#[derive(Serialize)]
+pub struct SegmentOutput {
+    pub id: String,
+    pub text: String,
+    pub start_ms: i64,
+    pub end_ms: i64,
+    pub speaker: Option<String>, // speaker label
+    pub words: Vec<WordOutput>,
+}
+
+#[derive(Serialize)]
+pub struct SpeakerOutput {
+    pub id: String,
+    pub label: String,
+    pub display_name: Option<String>,
+    pub color: Option<String>,
+}
+
+#[derive(Serialize)]
+pub struct ProjectTranscript {
+    pub file_path: String,
+    pub segments: Vec<SegmentOutput>,
+    pub speakers: Vec<SpeakerOutput>,
+}
+
+// ── Commands ─────────────────────────────────────────────────────
+
 #[tauri::command]
 pub fn create_project(name: String, state: State<AppState>) -> Result<Project, String> {
     let conn = state.db.lock().map_err(|e| e.to_string())?;
@@ -21,3 +122,176 @@ pub fn list_projects(state: State<AppState>) -> Result<Vec<Project>, String> {
     let conn = state.db.lock().map_err(|e| e.to_string())?;
     queries::list_projects(&conn).map_err(|e| e.to_string())
 }
+
+#[tauri::command]
+pub fn delete_project(id: String, state: State<AppState>) -> Result<(), String> {
+    let conn = state.db.lock().map_err(|e| e.to_string())?;
+    queries::delete_project(&conn, &id).map_err(|e| e.to_string())
+}
+
+#[tauri::command]
+pub fn update_segment(
+    segment_id: String,
+    new_text: String,
+    state: State<AppState>,
+) -> Result<(), String> {
+    let conn = state.db.lock().map_err(|e| e.to_string())?;
+    queries::update_segment_text(&conn, &segment_id, &new_text).map_err(|e| e.to_string())
+}
+
+#[tauri::command]
+pub fn save_project_transcript(
+    project_id: String,
+    file_path: String,
+    segments: Vec<SegmentInput>,
+    speakers: Vec<SpeakerInput>,
+    state: State<AppState>,
+) -> Result<Project, String> {
+    let conn = state.db.lock().map_err(|e| e.to_string())?;
+
+    // 1. Create media file entry
+    let media_file =
+        queries::create_media_file(&conn, &project_id, &file_path).map_err(|e| e.to_string())?;
+
+    // 2. Create speaker entries and build label -> id map
+    let mut speaker_map = std::collections::HashMap::new();
+    for speaker_input in &speakers {
+        let speaker = queries::create_speaker(
+            &conn,
+            &project_id,
+            &speaker_input.label,
+            Some(&speaker_input.color),
+        )
+        .map_err(|e| e.to_string())?;
+        speaker_map.insert(speaker_input.label.clone(), speaker.id);
+    }
+
+    // 3. Create segments with words
+    for (index, seg_input) in segments.iter().enumerate() {
+        let speaker_id = seg_input
+            .speaker
+            .as_ref()
+            .and_then(|label| speaker_map.get(label));
+
+        let segment = queries::create_segment(
+            &conn,
+            &project_id,
+            &media_file.id,
+            speaker_id.map(|s| s.as_str()),
+            seg_input.start_ms,
+            seg_input.end_ms,
+            &seg_input.text,
+            index as i32,
+        )
+        .map_err(|e| e.to_string())?;
+
+        // Create words for this segment
+        for (word_index, word_input) in seg_input.words.iter().enumerate() {
+            queries::create_word(
+                &conn,
+                &segment.id,
+                &word_input.word,
+                word_input.start_ms,
+                word_input.end_ms,
+                Some(word_input.confidence),
+                word_index as i32,
+            )
+            .map_err(|e| e.to_string())?;
+        }
+    }
+
+    // 4. Return updated project info
+    queries::get_project(&conn, &project_id)
+        .map_err(|e| e.to_string())?
+        .ok_or_else(|| "Project not found".to_string())
+}
+
+#[tauri::command]
+pub fn load_project_transcript(
+    project_id: String,
+    state: State<AppState>,
+) -> Result<Option<ProjectTranscript>, String> {
+    let conn = state.db.lock().map_err(|e| e.to_string())?;
+
+    // 1. Get media files for the project
+    let media_files =
+        queries::get_media_files_for_project(&conn, &project_id).map_err(|e| e.to_string())?;
+
+    let media_file = match media_files.first() {
+        Some(mf) => mf,
+        None => return Ok(None),
+    };
+
+    // 2. Get speakers for the project and build id -> label map
+    let speakers =
+        queries::get_speakers_for_project(&conn, &project_id).map_err(|e| e.to_string())?;
+    let speaker_label_map: std::collections::HashMap<String, String> = speakers
+        .iter()
+        .map(|s| (s.id.clone(), s.label.clone()))
+        .collect();
+
+    // 3. Get segments for the media file
+    let db_segments =
+        queries::get_segments_for_media(&conn, &media_file.id).map_err(|e| e.to_string())?;
+
+    // 4. Build output segments with nested words
+    let mut segment_outputs = Vec::with_capacity(db_segments.len());
+    for seg in &db_segments {
+        let words = queries::get_words_for_segment(&conn, &seg.id).map_err(|e| e.to_string())?;
+        let word_outputs: Vec<WordOutput> = words
+            .into_iter()
+            .map(|w| WordOutput {
+                word: w.word,
+                start_ms: w.start_ms,
+                end_ms: w.end_ms,
+                confidence: w.confidence,
+            })
+            .collect();
+
+        let speaker_label = seg
+            .speaker_id
+            .as_ref()
+            .and_then(|sid| speaker_label_map.get(sid))
+            .cloned();
+
+        segment_outputs.push(SegmentOutput {
+            id: seg.id.clone(),
+            text: seg.text.clone(),
+            start_ms: seg.start_ms,
+            end_ms: seg.end_ms,
+            speaker: speaker_label,
+            words: word_outputs,
+        });
+    }
+
+    // 5. Build speaker outputs
+    let speaker_outputs: Vec<SpeakerOutput> = speakers
+        .into_iter()
+        .map(|s| SpeakerOutput {
+            id: s.id,
+            label: s.label,
+            display_name: s.display_name,
+            color: s.color,
+        })
+        .collect();
+
+    Ok(Some(ProjectTranscript {
+        file_path: media_file.file_path.clone(),
+        segments: segment_outputs,
+        speakers: speaker_outputs,
+    }))
+}
+
+// ── File-based project commands ─────────────────────────────────
+
+#[tauri::command]
+pub fn save_project_file(path: String, project: ProjectFile) -> Result<(), String> {
+    let json = serde_json::to_string_pretty(&project).map_err(|e| e.to_string())?;
+    fs::write(&path, json).map_err(|e| format!("Failed to save project: {e}"))
+}
+
+#[tauri::command]
+pub fn load_project_file(path: String) -> Result<ProjectFile, String> {
+    let json = fs::read_to_string(&path).map_err(|e| format!("Failed to read project: {e}"))?;
+    serde_json::from_str(&json).map_err(|e| format!("Failed to parse project: {e}"))
+}

src-tauri/src/commands/system.rs

@@ -22,9 +22,7 @@ pub fn llama_start(
     threads: Option<u32>,
 ) -> Result<LlamaStatus, String> {
     let config = LlamaConfig {
-        binary_path: PathBuf::from(
+        binary_path: PathBuf::from(binary_path.unwrap_or_else(|| "llama-server".to_string())),
-            binary_path.unwrap_or_else(|| "llama-server".to_string()),
-        ),
         model_path: PathBuf::from(model_path),
         port: port.unwrap_or(0),
         n_gpu_layers: n_gpu_layers.unwrap_or(0),

src-tauri/src/commands/transcribe.rs

@@ -33,16 +33,47 @@ pub fn transcribe_file(
     if response.msg_type == "error" {
         return Err(format!(
             "Transcription error: {}",
-            response.payload.get("message").and_then(|v| v.as_str()).unwrap_or("unknown")
+            response
+                .payload
+                .get("message")
+                .and_then(|v| v.as_str())
+                .unwrap_or("unknown")
         ));
     }
 
     Ok(response.payload)
 }
 
+/// Download and validate the diarization model via the Python sidecar.
+#[tauri::command]
+pub fn download_diarize_model(hf_token: String) -> Result<Value, String> {
+    let manager = sidecar();
+    manager.ensure_running()?;
+
+    let request_id = uuid::Uuid::new_v4().to_string();
+    let msg = IPCMessage::new(
+        &request_id,
+        "diarize.download",
+        json!({
+            "hf_token": hf_token,
+        }),
+    );
+
+    let response = manager.send_and_receive(&msg)?;
+
+    if response.msg_type == "error" {
+        return Ok(json!({
+            "ok": false,
+            "error": response.payload.get("message").and_then(|v| v.as_str()).unwrap_or("unknown"),
+        }));
+    }
+
+    Ok(json!({ "ok": true }))
+}
+
 /// Run the full transcription + diarization pipeline via the Python sidecar.
 #[tauri::command]
-pub fn run_pipeline(
+pub async fn run_pipeline(
     app: AppHandle,
     file_path: String,
     model: Option<String>,
@@ -52,6 +83,7 @@ pub fn run_pipeline(
     min_speakers: Option<u32>,
     max_speakers: Option<u32>,
     skip_diarization: Option<bool>,
+    hf_token: Option<String>,
 ) -> Result<Value, String> {
     let manager = sidecar();
     manager.ensure_running()?;
@@ -70,19 +102,38 @@ pub fn run_pipeline(
             "min_speakers": min_speakers,
             "max_speakers": max_speakers,
             "skip_diarization": skip_diarization.unwrap_or(false),
+            "hf_token": hf_token,
         }),
     );
 
-    let response = manager.send_and_receive_with_progress(&msg, |progress| {
+    // Run the blocking sidecar I/O on a separate thread so the async runtime
-        let _ = app.emit("pipeline-progress", &progress.payload);
+    // can deliver emitted events to the webview while processing is ongoing.
-    })?;
+    let app_handle = app.clone();
+    tauri::async_runtime::spawn_blocking(move || {
+        let response = manager.send_and_receive_with_progress(&msg, |msg| {
+            let event_name = match msg.msg_type.as_str() {
+                "pipeline.segment" => "pipeline-segment",
+                "pipeline.speaker_update" => "pipeline-speaker-update",
+                _ => "pipeline-progress",
+            };
+            if let Err(e) = app_handle.emit(event_name, &msg.payload) {
+                eprintln!("[sidecar-rs] Failed to emit {event_name}: {e}");
+            }
+        })?;
 
-    if response.msg_type == "error" {
+        if response.msg_type == "error" {
-        return Err(format!(
+            return Err(format!(
-            "Pipeline error: {}",
+                "Pipeline error: {}",
-            response.payload.get("message").and_then(|v| v.as_str()).unwrap_or("unknown")
+                response
-        ));
+                    .payload
-    }
+                    .get("message")
+                    .and_then(|v| v.as_str())
+                    .unwrap_or("unknown")
+            ));
+        }
 
-    Ok(response.payload)
+        Ok(response.payload)
+    })
+    .await
+    .map_err(|e| format!("Pipeline task failed: {e}"))?
 }

src-tauri/src/db/queries.rs

@@ -85,6 +85,57 @@ pub fn delete_project(conn: &Connection, id: &str) -> Result<(), DatabaseError>
     Ok(())
 }
 
+// ── Media Files ──────────────────────────────────────────────────
+
+pub fn create_media_file(
+    conn: &Connection,
+    project_id: &str,
+    file_path: &str,
+) -> Result<MediaFile, DatabaseError> {
+    let id = Uuid::new_v4().to_string();
+    let now = Utc::now().to_rfc3339();
+    conn.execute(
+        "INSERT INTO media_files (id, project_id, file_path, created_at) VALUES (?1, ?2, ?3, ?4)",
+        params![id, project_id, file_path, now],
+    )?;
+    Ok(MediaFile {
+        id,
+        project_id: project_id.to_string(),
+        file_path: file_path.to_string(),
+        file_hash: None,
+        duration_ms: None,
+        sample_rate: None,
+        channels: None,
+        format: None,
+        file_size: None,
+        created_at: now,
+    })
+}
+
+pub fn get_media_files_for_project(
+    conn: &Connection,
+    project_id: &str,
+) -> Result<Vec<MediaFile>, DatabaseError> {
+    let mut stmt = conn.prepare(
+        "SELECT id, project_id, file_path, file_hash, duration_ms, sample_rate, channels, format, file_size, created_at FROM media_files WHERE project_id = ?1 ORDER BY created_at",
+    )?;
+    let rows = stmt.query_map(params![project_id], |row| {
+        Ok(MediaFile {
+            id: row.get(0)?,
+            project_id: row.get(1)?,
+            file_path: row.get(2)?,
+            file_hash: row.get(3)?,
+            duration_ms: row.get(4)?,
+            sample_rate: row.get(5)?,
+            channels: row.get(6)?,
+            format: row.get(7)?,
+            file_size: row.get(8)?,
+            created_at: row.get(9)?,
+        })
+    })?;
+    Ok(rows.collect::<Result<Vec<_>, _>>()?)
+}
+
 // ── Speakers ──────────────────────────────────────────────────────
 
 pub fn create_speaker(
@@ -194,6 +245,39 @@ pub fn reassign_speaker(
     Ok(())
 }
 
+// ── Segments (create) ────────────────────────────────────────────
+
+pub fn create_segment(
+    conn: &Connection,
+    project_id: &str,
+    media_file_id: &str,
+    speaker_id: Option<&str>,
+    start_ms: i64,
+    end_ms: i64,
+    text: &str,
+    segment_index: i32,
+) -> Result<Segment, DatabaseError> {
+    let id = Uuid::new_v4().to_string();
+    conn.execute(
+        "INSERT INTO segments (id, project_id, media_file_id, speaker_id, start_ms, end_ms, text, is_edited, segment_index) VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, 0, ?8)",
+        params![id, project_id, media_file_id, speaker_id, start_ms, end_ms, text, segment_index],
+    )?;
+    Ok(Segment {
+        id,
+        project_id: project_id.to_string(),
+        media_file_id: media_file_id.to_string(),
+        speaker_id: speaker_id.map(String::from),
+        start_ms,
+        end_ms,
+        text: text.to_string(),
+        original_text: None,
+        confidence: None,
+        is_edited: false,
+        edited_at: None,
+        segment_index,
+    })
+}
+
 // ── Words ─────────────────────────────────────────────────────────
 
 pub fn get_words_for_segment(
@@ -217,6 +301,31 @@ pub fn get_words_for_segment(
     Ok(rows.collect::<Result<Vec<_>, _>>()?)
 }
 
+pub fn create_word(
+    conn: &Connection,
+    segment_id: &str,
+    word: &str,
+    start_ms: i64,
+    end_ms: i64,
+    confidence: Option<f64>,
+    word_index: i32,
+) -> Result<Word, DatabaseError> {
+    let id = Uuid::new_v4().to_string();
+    conn.execute(
+        "INSERT INTO words (id, segment_id, word, start_ms, end_ms, confidence, word_index) VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7)",
+        params![id, segment_id, word, start_ms, end_ms, confidence, word_index],
+    )?;
+    Ok(Word {
+        id,
+        segment_id: segment_id.to_string(),
+        word: word.to_string(),
+        start_ms,
+        end_ms,
+        confidence,
+        word_index,
+    })
+}
+
 #[cfg(test)]
 mod tests {
     use super::*;

src-tauri/src/db/schema.rs

@@ -96,11 +96,7 @@ pub fn create_tables(conn: &Connection) -> Result<(), DatabaseError> {
     )?;
 
     // Initialize schema version if empty
-    let count: i32 = conn.query_row(
+    let count: i32 = conn.query_row("SELECT COUNT(*) FROM schema_version", [], |row| row.get(0))?;
-        "SELECT COUNT(*) FROM schema_version",
-        [],
-        |row| row.get(0),
-    )?;
     if count == 0 {
         conn.execute(
             "INSERT INTO schema_version (version) VALUES (?1)",

src-tauri/src/lib.rs

@@ -4,12 +4,18 @@ pub mod llama;
 pub mod sidecar;
 pub mod state;
 
+use tauri::window::Color;
+use tauri::Manager;
+
 use commands::ai::{ai_chat, ai_configure, ai_list_providers};
 use commands::export::export_transcript;
-use commands::project::{create_project, get_project, list_projects};
+use commands::project::{
+    create_project, delete_project, get_project, list_projects, load_project_file,
+    load_project_transcript, save_project_file, save_project_transcript, update_segment,
+};
 use commands::settings::{load_settings, save_settings};
 use commands::system::{get_data_dir, llama_list_models, llama_start, llama_status, llama_stop};
-use commands::transcribe::{run_pipeline, transcribe_file};
+use commands::transcribe::{download_diarize_model, run_pipeline, transcribe_file};
 use state::AppState;
 
 #[cfg_attr(mobile, tauri::mobile_entry_point)]
@@ -20,12 +26,26 @@ pub fn run() {
         .plugin(tauri_plugin_opener::init())
         .plugin(tauri_plugin_dialog::init())
         .manage(app_state)
+        .setup(|app| {
+            // Set the webview background to match the app's dark theme
+            if let Some(window) = app.get_webview_window("main") {
+                let _ = window.set_background_color(Some(Color(10, 10, 35, 255)));
+            }
+            Ok(())
+        })
         .invoke_handler(tauri::generate_handler![
             create_project,
             get_project,
             list_projects,
+            delete_project,
+            save_project_transcript,
+            load_project_transcript,
+            update_segment,
+            save_project_file,
+            load_project_file,
             transcribe_file,
             run_pipeline,
+            download_diarize_model,
             export_transcript,
             ai_chat,
             ai_list_providers,

src-tauri/src/llama/mod.rs

@@ -237,11 +237,7 @@ impl LlamaManager {
 
     /// Get the current status.
     pub fn status(&self) -> LlamaStatus {
-        let running = self
+        let running = self.process.lock().ok().map_or(false, |p| p.is_some());
-            .process
-            .lock()
-            .ok()
-            .map_or(false, |p| p.is_some());
         let port = self.port.lock().ok().map_or(0, |p| *p);
         let model = self
             .model_path

src-tauri/src/sidecar/mod.rs

@@ -13,8 +13,13 @@ pub fn sidecar() -> &'static SidecarManager {
     INSTANCE.get_or_init(SidecarManager::new)
 }
 
-/// Manages the Python sidecar process lifecycle.
+/// Manages the sidecar process lifecycle.
-/// Uses separated stdin/stdout ownership to avoid BufReader conflicts.
+///
+/// Supports two modes:
+/// - **Production**: spawns a frozen PyInstaller binary (no Python required)
+/// - **Dev mode**: spawns system Python with `-m voice_to_notes.main`
+///
+/// Dev mode is active when compiled in debug mode or when `VOICE_TO_NOTES_DEV=1`.
 pub struct SidecarManager {
     process: Mutex<Option<Child>>,
     stdin: Mutex<Option<ChildStdin>>,
@@ -30,38 +35,141 @@ impl SidecarManager {
         }
     }
 
+    /// Check if we should use dev mode (system Python).
+    fn is_dev_mode() -> bool {
+        cfg!(debug_assertions) || std::env::var("VOICE_TO_NOTES_DEV").is_ok()
+    }
+
+    /// Resolve the frozen sidecar binary path (production mode).
+    fn resolve_sidecar_path() -> Result<std::path::PathBuf, String> {
+        let exe = std::env::current_exe().map_err(|e| format!("Cannot get current exe: {e}"))?;
+        let exe_dir = exe
+            .parent()
+            .ok_or_else(|| "Cannot get exe parent directory".to_string())?;
+
+        let binary_name = if cfg!(target_os = "windows") {
+            "voice-to-notes-sidecar.exe"
+        } else {
+            "voice-to-notes-sidecar"
+        };
+
+        // Tauri places externalBin next to the app binary
+        let path = exe_dir.join(binary_name);
+        if path.exists() {
+            return Ok(path);
+        }
+
+        // Also check inside a subdirectory (onedir PyInstaller output)
+        let subdir_path = exe_dir.join("voice-to-notes-sidecar").join(binary_name);
+        if subdir_path.exists() {
+            return Ok(subdir_path);
+        }
+
+        Err(format!(
+            "Sidecar binary not found. Looked for:\n  {}\n  {}",
+            path.display(),
+            subdir_path.display(),
+        ))
+    }
+
+    /// Find a working Python command for the current platform.
+    fn find_python_command() -> &'static str {
+        if cfg!(target_os = "windows") {
+            "python"
+        } else {
+            "python3"
+        }
+    }
+
+    /// Resolve the Python sidecar directory for dev mode.
+    fn resolve_python_dir() -> Result<std::path::PathBuf, String> {
+        let manifest_dir = env!("CARGO_MANIFEST_DIR");
+        let python_dir = std::path::Path::new(manifest_dir)
+            .join("../python")
+            .canonicalize()
+            .map_err(|e| format!("Cannot find python directory: {e}"))?;
+
+        if python_dir.exists() {
+            return Ok(python_dir);
+        }
+
+        // Fallback: relative to current exe
+        let exe = std::env::current_exe().map_err(|e| e.to_string())?;
+        let alt = exe
+            .parent()
+            .ok_or_else(|| "No parent dir".to_string())?
+            .join("../python")
+            .canonicalize()
+            .map_err(|e| format!("Cannot find python directory: {e}"))?;
+
+        Ok(alt)
+    }
+
     /// Ensure the sidecar is running, starting it if needed.
     pub fn ensure_running(&self) -> Result<(), String> {
         if self.is_running() {
             return Ok(());
         }
 
-        let python_path = std::env::current_dir()
+        if Self::is_dev_mode() {
-            .map_err(|e| e.to_string())?
+            self.start_python_dev()
-            .join("../python")
+        } else {
-            .canonicalize()
+            match Self::resolve_sidecar_path() {
-            .map_err(|e| format!("Cannot find python directory: {e}"))?;
+                Ok(path) => self.start_binary(&path),
-
+                Err(e) => {
-        self.start(&python_path.to_string_lossy())
+                    eprintln!(
+                        "[sidecar-rs] Frozen binary not found ({e}), falling back to dev mode"
+                    );
+                    self.start_python_dev()
+                }
+            }
+        }
     }
 
-    /// Spawn the Python sidecar process.
+    /// Spawn the frozen sidecar binary (production mode).
-    pub fn start(&self, python_path: &str) -> Result<(), String> {
+    fn start_binary(&self, path: &std::path::Path) -> Result<(), String> {
-        // Stop existing process if any
         self.stop().ok();
+        eprintln!("[sidecar-rs] Starting frozen sidecar: {}", path.display());
 
-        let mut child = Command::new("python3")
+        let child = Command::new(path)
-            .arg("-m")
-            .arg("voice_to_notes.main")
-            .current_dir(python_path)
-            .env("PYTHONPATH", python_path)
             .stdin(Stdio::piped())
             .stdout(Stdio::piped())
             .stderr(Stdio::inherit())
             .spawn()
-            .map_err(|e| format!("Failed to start sidecar: {e}"))?;
+            .map_err(|e| format!("Failed to start sidecar binary: {e}"))?;
 
-        // Take ownership of stdin and stdout separately
+        self.attach(child)?;
+        self.wait_for_ready()
+    }
+
+    /// Spawn the Python sidecar in dev mode (system Python).
+    fn start_python_dev(&self) -> Result<(), String> {
+        self.stop().ok();
+        let python_dir = Self::resolve_python_dir()?;
+        let python_cmd = Self::find_python_command();
+        eprintln!(
+            "[sidecar-rs] Starting dev sidecar: {} -m voice_to_notes.main ({})",
+            python_cmd,
+            python_dir.display()
+        );
+
+        let child = Command::new(python_cmd)
+            .arg("-m")
+            .arg("voice_to_notes.main")
+            .current_dir(&python_dir)
+            .env("PYTHONPATH", &python_dir)
+            .stdin(Stdio::piped())
+            .stdout(Stdio::piped())
+            .stderr(Stdio::inherit())
+            .spawn()
+            .map_err(|e| format!("Failed to start Python sidecar: {e}"))?;
+
+        self.attach(child)?;
+        self.wait_for_ready()
+    }
+
+    /// Take ownership of a spawned child's stdin/stdout and store the process handle.
+    fn attach(&self, mut child: Child) -> Result<(), String> {
         let stdin = child.stdin.take().ok_or("Failed to get sidecar stdin")?;
         let stdout = child.stdout.take().ok_or("Failed to get sidecar stdout")?;
         let buf_reader = BufReader::new(stdout);
@@ -78,10 +186,6 @@ impl SidecarManager {
             let mut r = self.reader.lock().map_err(|e| e.to_string())?;
             *r = Some(buf_reader);
         }
-
-        // Wait for the "ready" message
-        self.wait_for_ready()?;
-
         Ok(())
     }
 
@@ -107,8 +211,12 @@ impl SidecarManager {
                         return Ok(());
                     }
                 }
-                // Non-ready message: something is wrong
+                // Non-JSON or non-ready line — skip and keep waiting
-                break;
+                eprintln!(
+                    "[sidecar-rs] Skipping pre-ready line: {}",
+                    &trimmed[..trimmed.len().min(200)]
+                );
+                continue;
             }
         }
         Err("Sidecar did not send ready message".to_string())
@@ -120,12 +228,16 @@ impl SidecarManager {
         self.send_and_receive_with_progress(msg, |_| {})
     }
 
-    /// Send a message and read the response, calling on_progress for each progress message.
+    /// Send a message and receive the response, calling a callback for intermediate messages.
-    pub fn send_and_receive_with_progress(
+    /// Intermediate messages include progress, pipeline.segment, and pipeline.speaker_update.
+    pub fn send_and_receive_with_progress<F>(
         &self,
         msg: &IPCMessage,
-        on_progress: impl Fn(&IPCMessage),
+        on_intermediate: F,
-    ) -> Result<IPCMessage, String> {
+    ) -> Result<IPCMessage, String>
+    where
+        F: Fn(&IPCMessage),
+    {
         // Write to stdin
         {
             let mut stdin_guard = self.stdin.lock().map_err(|e| e.to_string())?;
@@ -160,14 +272,19 @@ impl SidecarManager {
                     if trimmed.is_empty() {
                         continue;
                     }
-                    let response: IPCMessage = serde_json::from_str(trimmed)
+                    let response: IPCMessage =
-                        .map_err(|e| format!("Parse error: {e}"))?;
+                        serde_json::from_str(trimmed).map_err(|e| format!("Parse error: {e}"))?;
 
-                    if response.msg_type == "progress" {
+                    // Forward intermediate messages via callback, return the final result/error
-                        on_progress(&response);
+                    let is_intermediate = matches!(
-                        continue;
+                        response.msg_type.as_str(),
+                        "progress" | "pipeline.segment" | "pipeline.speaker_update"
+                    );
+                    if is_intermediate {
+                        on_intermediate(&response);
+                    } else {
+                        return Ok(response);
                     }
-                    return Ok(response);
                 }
             } else {
                 Err("Sidecar stdout not available".to_string())

src-tauri/src/state.rs

@@ -15,12 +15,10 @@ pub struct AppState {
 impl AppState {
     pub fn new() -> Result<Self, String> {
         let data_dir = LlamaManager::data_dir();
-        std::fs::create_dir_all(&data_dir)
+        std::fs::create_dir_all(&data_dir).map_err(|e| format!("Cannot create data dir: {e}"))?;
-            .map_err(|e| format!("Cannot create data dir: {e}"))?;
 
         let db_path = data_dir.join("voice_to_notes.db");
-        let conn = db::open_database(&db_path)
+        let conn = db::open_database(&db_path).map_err(|e| format!("Cannot open database: {e}"))?;
-            .map_err(|e| format!("Cannot open database: {e}"))?;
 
         Ok(Self {
             db: Mutex::new(conn),

src-tauri/tauri.conf.json

@@ -16,7 +16,9 @@
         "width": 1200,
         "height": 800,
         "minWidth": 800,
-        "minHeight": 600
+        "minHeight": 600,
+        "decorations": true,
+        "transparent": false
       }
     ],
     "security": {
@@ -44,7 +46,7 @@
     "license": "MIT",
     "linux": {
       "deb": {
-        "depends": ["python3", "python3-pip"]
+        "depends": []
       },
       "appimage": {
         "bundleMediaFramework": true

src/app.html

@@ -1,5 +1,5 @@
 <!doctype html>
-<html lang="en">
+<html lang="en" style="margin:0;padding:0;background:#0a0a23;height:100%;">
   <head>
     <meta charset="utf-8" />
     <link rel="icon" href="%sveltekit.assets%/favicon.png" />
@@ -7,7 +7,7 @@
     <title>Voice to Notes</title>
     %sveltekit.head%
   </head>
-  <body data-sveltekit-preload-data="hover">
+  <body data-sveltekit-preload-data="hover" style="margin:0;padding:0;background:#0a0a23;overflow:hidden;">
     <div style="display: contents">%sveltekit.body%</div>
   </body>
 </html>

src/lib/components/AIChatPanel.svelte

@@ -1,6 +1,7 @@
 <script lang="ts">
   import { invoke } from '@tauri-apps/api/core';
   import { segments, speakers } from '$lib/stores/transcript';
+  import { settings } from '$lib/stores/settings';
 
   interface ChatMessage {
     role: 'user' | 'assistant';
@@ -43,9 +44,23 @@
         content: m.content,
       }));
 
+      // Ensure the provider is configured with current credentials before chatting
+      const s = $settings;
+      const configMap: Record<string, Record<string, string>> = {
+        openai: { api_key: s.openai_api_key, model: s.openai_model },
+        anthropic: { api_key: s.anthropic_api_key, model: s.anthropic_model },
+        litellm: { api_key: s.litellm_api_key, api_base: s.litellm_api_base, model: s.litellm_model },
+        local: { model: s.local_model_path, base_url: 'http://localhost:8080' },
+      };
+      const config = configMap[s.ai_provider];
+      if (config) {
+        await invoke('ai_configure', { provider: s.ai_provider, config });
+      }
+
       const result = await invoke<{ response: string }>('ai_chat', {
         messages: chatMessages,
         transcriptContext: getTranscriptContext(),
+        provider: s.ai_provider,
       });
 
       messages = [...messages, { role: 'assistant', content: result.response }];
@@ -73,6 +88,88 @@
     messages = [];
   }
 
+  function formatMarkdown(text: string): string {
+    // Split into lines for block-level processing
+    const lines = text.split('\n');
+    const result: string[] = [];
+    let inList = false;
+
+    for (let i = 0; i < lines.length; i++) {
+      let line = lines[i];
+
+      // Headers
+      if (line.startsWith('### ')) {
+        if (inList) { result.push('</ul>'); inList = false; }
+        const content = applyInlineFormatting(line.slice(4));
+        result.push(`<h4>${content}</h4>`);
+        continue;
+      }
+      if (line.startsWith('## ')) {
+        if (inList) { result.push('</ul>'); inList = false; }
+        const content = applyInlineFormatting(line.slice(3));
+        result.push(`<h3>${content}</h3>`);
+        continue;
+      }
+      if (line.startsWith('# ')) {
+        if (inList) { result.push('</ul>'); inList = false; }
+        const content = applyInlineFormatting(line.slice(2));
+        result.push(`<h2>${content}</h2>`);
+        continue;
+      }
+
+      // List items (- or *)
+      if (/^[\-\*] /.test(line)) {
+        if (!inList) { result.push('<ul>'); inList = true; }
+        const content = applyInlineFormatting(line.slice(2));
+        result.push(`<li>${content}</li>`);
+        continue;
+      }
+
+      // Numbered list items
+      if (/^\d+\.\s/.test(line)) {
+        if (!inList) { result.push('<ol>'); inList = true; }
+        const content = applyInlineFormatting(line.replace(/^\d+\.\s/, ''));
+        result.push(`<li>${content}</li>`);
+        continue;
+      }
+
+      // Non-list line: close any open list
+      if (inList) {
+        // Check if previous list was ordered or unordered
+        const lastOpen = result.findLast(r => r === '<ul>' || r === '<ol>');
+        result.push(lastOpen === '<ol>' ? '</ol>' : '</ul>');
+        inList = false;
+      }
+
+      // Empty line = paragraph break
+      if (line.trim() === '') {
+        result.push('<br>');
+        continue;
+      }
+
+      // Regular text line
+      result.push(applyInlineFormatting(line));
+    }
+
+    // Close any trailing open list
+    if (inList) {
+      const lastOpen = result.findLast(r => r === '<ul>' || r === '<ol>');
+      result.push(lastOpen === '<ol>' ? '</ol>' : '</ul>');
+    }
+
+    return result.join('\n');
+  }
+
+  function applyInlineFormatting(text: string): string {
+    // Code blocks (backtick) — process first to avoid conflicts
+    text = text.replace(/`([^`]+)`/g, '<code>$1</code>');
+    // Bold (**text**)
+    text = text.replace(/\*\*([^*]+)\*\*/g, '<strong>$1</strong>');
+    // Italic (*text*) — only single asterisks not already consumed by bold
+    text = text.replace(/\*([^*]+)\*/g, '<em>$1</em>');
+    return text;
+  }
+
   // Quick action buttons
   async function summarize() {
     inputText = 'Please summarize this transcript in bullet points.';
@@ -107,7 +204,11 @@
     {:else}
       {#each messages as msg}
         <div class="message {msg.role}">
-          <div class="message-content">{msg.content}</div>
+          {#if msg.role === 'assistant'}
+            <div class="message-content">{@html formatMarkdown(msg.content)}</div>
+          {:else}
+            <div class="message-content">{msg.content}</div>
+          {/if}
         </div>
       {/each}
       {#if isLoading}
@@ -177,47 +278,101 @@
   }
   .empty-state {
     text-align: center;
-    color: #666;
+    color: #888;
-    font-size: 0.8rem;
+    font-size: 0.85rem;
-    padding: 1rem 0;
+    padding: 2rem 1rem;
+  }
+  .empty-state p {
+    margin-bottom: 1rem;
   }
   .quick-actions {
     display: flex;
-    gap: 0.5rem;
+    gap: 0.75rem;
     justify-content: center;
-    margin-top: 0.5rem;
+    margin-top: 1rem;
   }
   .quick-btn {
     background: rgba(233, 69, 96, 0.15);
     border: 1px solid rgba(233, 69, 96, 0.3);
     color: #e94560;
-    padding: 0.3rem 0.6rem;
+    padding: 0.45rem 0.85rem;
-    border-radius: 4px;
+    border-radius: 6px;
     cursor: pointer;
-    font-size: 0.75rem;
+    font-size: 0.8rem;
+    transition: background 0.15s;
   }
   .quick-btn:hover {
     background: rgba(233, 69, 96, 0.25);
   }
   .message {
-    margin-bottom: 0.5rem;
+    margin-bottom: 0.75rem;
-    padding: 0.5rem 0.75rem;
+    padding: 0.75rem 1rem;
-    border-radius: 6px;
+    border-radius: 8px;
     font-size: 0.8rem;
-    line-height: 1.4;
+    line-height: 1.55;
   }
   .message.user {
     background: rgba(233, 69, 96, 0.15);
-    margin-left: 1rem;
+    border-left: 3px solid rgba(233, 69, 96, 0.4);
   }
   .message.assistant {
     background: rgba(255, 255, 255, 0.05);
-    margin-right: 1rem;
+    border-left: 3px solid rgba(255, 255, 255, 0.1);
   }
   .message.loading {
     opacity: 0.6;
     font-style: italic;
   }
+
+  /* Markdown styles inside assistant messages */
+  .message.assistant :global(h2) {
+    font-size: 1rem;
+    font-weight: 600;
+    margin: 0.6rem 0 0.3rem;
+    color: #f0f0f0;
+  }
+  .message.assistant :global(h3) {
+    font-size: 0.9rem;
+    font-weight: 600;
+    margin: 0.5rem 0 0.25rem;
+    color: #e8e8e8;
+  }
+  .message.assistant :global(h4) {
+    font-size: 0.85rem;
+    font-weight: 600;
+    margin: 0.4rem 0 0.2rem;
+    color: #e0e0e0;
+  }
+  .message.assistant :global(strong) {
+    color: #f0f0f0;
+    font-weight: 600;
+  }
+  .message.assistant :global(em) {
+    color: #ccc;
+    font-style: italic;
+  }
+  .message.assistant :global(code) {
+    background: rgba(0, 0, 0, 0.3);
+    color: #e94560;
+    padding: 0.1rem 0.35rem;
+    border-radius: 3px;
+    font-size: 0.75rem;
+    font-family: 'Fira Code', 'Cascadia Code', 'Consolas', monospace;
+  }
+  .message.assistant :global(ul),
+  .message.assistant :global(ol) {
+    margin: 0.35rem 0;
+    padding-left: 1.3rem;
+  }
+  .message.assistant :global(li) {
+    margin-bottom: 0.25rem;
+    line-height: 1.5;
+  }
+  .message.assistant :global(br) {
+    display: block;
+    content: '';
+    margin-top: 0.35rem;
+  }
   .chat-input {
     display: flex;
     gap: 0.5rem;

src/lib/components/ProgressOverlay.svelte

@@ -7,16 +7,88 @@
   }
 
   let { visible = false, percent = 0, stage = '', message = '' }: Props = $props();
+
+  // Pipeline steps in order
+  const pipelineSteps = [
+    { key: 'loading_model', label: 'Load transcription model' },
+    { key: 'transcribing', label: 'Transcribe audio' },
+    { key: 'loading_diarization', label: 'Load speaker detection model' },
+    { key: 'diarizing', label: 'Identify speakers' },
+    { key: 'merging', label: 'Merge results' },
+  ];
+
+  const stepOrder = pipelineSteps.map(s => s.key);
+
+  // Track the highest step index we've reached (never goes backward)
+  let highestStepIdx = $state(-1);
+
+  // Map non-step stages to step indices for progress tracking
+  function stageToStepIdx(s: string): number {
+    const direct = stepOrder.indexOf(s);
+    if (direct >= 0) return direct;
+    // 'pipeline' stage appears before known steps — don't change highwater mark
+    return -1;
+  }
+
+  $effect(() => {
+    if (!visible) {
+      highestStepIdx = -1;
+      return;
+    }
+    const idx = stageToStepIdx(stage);
+    if (idx > highestStepIdx) {
+      highestStepIdx = idx;
+    }
+  });
+
+  function getStepStatus(stepIdx: number): 'pending' | 'active' | 'done' {
+    if (stepIdx < highestStepIdx) return 'done';
+    if (stepIdx === highestStepIdx) return 'active';
+    return 'pending';
+  }
+
+  // User-friendly display of current stage
+  const stageLabels: Record<string, string> = {
+    'pipeline': 'Initializing...',
+    'loading_model': 'Loading Model',
+    'transcribing': 'Transcribing',
+    'loading_diarization': 'Loading Diarization',
+    'diarizing': 'Speaker Detection',
+    'merging': 'Merging Results',
+    'done': 'Complete',
+  };
+
+  let displayStage = $derived(stageLabels[stage] || stage || 'Processing...');
 </script>
 
 {#if visible}
   <div class="overlay">
     <div class="progress-card">
-      <h3>{stage}</h3>
+      <div class="spinner-row">
-      <div class="bar-track">
+        <div class="spinner"></div>
-        <div class="bar-fill" style="width: {percent}%"></div>
+        <h3>{displayStage}</h3>
       </div>
-      <p>{percent}% — {message}</p>
+
+      <div class="steps">
+        {#each pipelineSteps as step, idx}
+          {@const status = getStepStatus(idx)}
+          <div class="step" class:step-done={status === 'done'} class:step-active={status === 'active'}>
+            <span class="step-icon">
+              {#if status === 'done'}
+                ✓
+              {:else if status === 'active'}
+                ⟳
+              {:else}
+                ·
+              {/if}
+            </span>
+            <span class="step-label">{step.label}</span>
+          </div>
+        {/each}
+      </div>
+
+      <p class="status-text">{message || 'Please wait...'}</p>
+      <p class="hint-text">This may take several minutes for large files</p>
     </div>
   </div>
 {/if}
@@ -25,34 +97,81 @@
   .overlay {
     position: fixed;
     inset: 0;
-    background: rgba(0, 0, 0, 0.7);
+    background: rgba(0, 0, 0, 0.8);
     display: flex;
     align-items: center;
     justify-content: center;
-    z-index: 1000;
+    z-index: 9999;
   }
   .progress-card {
     background: #16213e;
-    padding: 2rem;
+    padding: 2rem 2.5rem;
     border-radius: 12px;
-    min-width: 400px;
+    min-width: 380px;
+    max-width: 440px;
     color: #e0e0e0;
+    border: 1px solid #2a3a5e;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.5);
   }
-  h3 { margin: 0 0 1rem; text-transform: capitalize; }
+  .spinner-row {
-  .bar-track {
+    display: flex;
-    height: 8px;
+    align-items: center;
-    background: #0f3460;
+    gap: 0.75rem;
-    border-radius: 4px;
+    margin-bottom: 1.25rem;
-    overflow: hidden;
   }
-  .bar-fill {
+  .spinner {
-    height: 100%;
+    width: 20px;
-    background: #e94560;
+    height: 20px;
-    transition: width 0.3s;
+    border: 3px solid #2a3a5e;
+    border-top-color: #e94560;
+    border-radius: 50%;
+    animation: spin 0.8s linear infinite;
+    flex-shrink: 0;
   }
-  p {
+  @keyframes spin {
+    to { transform: rotate(360deg); }
+  }
+  h3 {
+    margin: 0;
+    font-size: 1.1rem;
+  }
+  .steps {
+    display: flex;
+    flex-direction: column;
+    gap: 0.4rem;
+    margin-bottom: 1rem;
+  }
+  .step {
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+    font-size: 0.85rem;
+    color: #555;
+  }
+  .step-done {
+    color: #4ecdc4;
+  }
+  .step-active {
+    color: #e0e0e0;
+    font-weight: 500;
+  }
+  .step-icon {
+    width: 1.2rem;
+    text-align: center;
+    flex-shrink: 0;
+  }
+  .step-active .step-icon {
+    animation: spin 1.5s linear infinite;
+    display: inline-block;
+  }
+  .status-text {
+    margin: 0.75rem 0 0;
+    font-size: 0.85rem;
+    color: #b0b0b0;
+  }
+  .hint-text {
     margin: 0.5rem 0 0;
-    font-size: 0.875rem;
+    font-size: 0.75rem;
-    color: #999;
+    color: #555;
   }
 </style>

src/lib/components/SettingsModal.svelte

@@ -1,4 +1,6 @@
 <script lang="ts">
+  import { invoke } from '@tauri-apps/api/core';
+  import { openUrl } from '@tauri-apps/plugin-opener';
   import { settings, saveSettings, type AppSettings } from '$lib/stores/settings';
 
   interface Props {
@@ -9,7 +11,34 @@
   let { visible, onClose }: Props = $props();
 
   let localSettings = $state<AppSettings>({ ...$settings });
-  let activeTab = $state<'transcription' | 'ai' | 'local'>('transcription');
+  let activeTab = $state<'transcription' | 'speakers' | 'ai' | 'local'>('transcription');
+  let modelStatus = $state<'idle' | 'downloading' | 'success' | 'error'>('idle');
+  let modelError = $state('');
+  let revealedFields = $state<Set<string>>(new Set());
+
+  async function testAndDownloadModel() {
+    if (!localSettings.hf_token) {
+      modelStatus = 'error';
+      modelError = 'Please enter a HuggingFace token first.';
+      return;
+    }
+    modelStatus = 'downloading';
+    modelError = '';
+    try {
+      const result = await invoke<{ ok: boolean; error?: string }>('download_diarize_model', {
+        hfToken: localSettings.hf_token,
+      });
+      if (result.ok) {
+        modelStatus = 'success';
+      } else {
+        modelStatus = 'error';
+        modelError = result.error || 'Unknown error';
+      }
+    } catch (err) {
+      modelStatus = 'error';
+      modelError = String(err);
+    }
+  }
 
   // Sync when settings store changes
   $effect(() => {
@@ -46,6 +75,9 @@
         <button class="tab" class:active={activeTab === 'transcription'} onclick={() => activeTab = 'transcription'}>
           Transcription
         </button>
+        <button class="tab" class:active={activeTab === 'speakers'} onclick={() => activeTab = 'speakers'}>
+          Speakers
+        </button>
         <button class="tab" class:active={activeTab === 'ai'} onclick={() => activeTab = 'ai'}>
           AI Provider
         </button>
@@ -77,10 +109,72 @@
             <label for="stt-lang">Language (blank = auto-detect)</label>
             <input id="stt-lang" type="text" bind:value={localSettings.transcription_language} placeholder="e.g., en, es, fr" />
           </div>
-          <div class="field checkbox">
+        {:else if activeTab === 'speakers'}
+          <div class="field">
+            <label for="hf-token">HuggingFace Token</label>
+            <div class="input-reveal">
+              <input id="hf-token" type={revealedFields.has('hf-token') ? 'text' : 'password'} bind:value={localSettings.hf_token} placeholder="hf_..." />
+              <button type="button" class="reveal-btn" onclick={() => { const s = new Set(revealedFields); s.has('hf-token') ? s.delete('hf-token') : s.add('hf-token'); revealedFields = s; }}>{revealedFields.has('hf-token') ? 'Hide' : 'Show'}</button>
+            </div>
+          </div>
+          <div class="info-box">
+            <p class="info-title">Setup (one-time)</p>
+            <p>Speaker detection uses <strong>pyannote.audio</strong> models hosted on HuggingFace. You must accept the license for each model:</p>
+            <ol>
+              <li>Create a free account at <!-- svelte-ignore a11y_no_static_element_interactions --><a class="ext-link" onclick={() => openUrl('https://huggingface.co/join')}>huggingface.co</a></li>
+              <li>Accept the license on <strong>all three</strong> of these pages:
+                <ul>
+                  <!-- svelte-ignore a11y_no_static_element_interactions -->
+                  <li><a class="ext-link" onclick={() => openUrl('https://huggingface.co/pyannote/speaker-diarization-3.1')}>pyannote/speaker-diarization-3.1</a></li>
+                  <!-- svelte-ignore a11y_no_static_element_interactions -->
+                  <li><a class="ext-link" onclick={() => openUrl('https://huggingface.co/pyannote/segmentation-3.0')}>pyannote/segmentation-3.0</a></li>
+                  <!-- svelte-ignore a11y_no_static_element_interactions -->
+                  <li><a class="ext-link" onclick={() => openUrl('https://huggingface.co/pyannote/speaker-diarization-community-1')}>pyannote/speaker-diarization-community-1</a></li>
+                </ul>
+              </li>
+              <!-- svelte-ignore a11y_no_static_element_interactions -->
+              <li>Create a token at <a class="ext-link" onclick={() => openUrl('https://huggingface.co/settings/tokens')}>huggingface.co/settings/tokens</a> (read access)</li>
+              <li>Paste the token above and click <strong>Test & Download</strong></li>
+            </ol>
+          </div>
+          <button
+            class="btn-download"
+            onclick={testAndDownloadModel}
+            disabled={modelStatus === 'downloading'}
+          >
+            {#if modelStatus === 'downloading'}
+              Downloading model...
+            {:else}
+              Test & Download Model
+            {/if}
+          </button>
+          {#if modelStatus === 'success'}
+            <p class="status-success">Model downloaded successfully. Speaker detection is ready.</p>
+          {/if}
+          {#if modelStatus === 'error'}
+            <p class="status-error">{modelError}</p>
+          {/if}
+          <div class="field" style="margin-top: 1rem;">
+            <label for="num-speakers">Number of speakers</label>
+            <select
+              id="num-speakers"
+              value={localSettings.num_speakers === null || localSettings.num_speakers === 0 ? '0' : String(localSettings.num_speakers)}
+              onchange={(e) => {
+                const v = parseInt((e.target as HTMLSelectElement).value, 10);
+                localSettings.num_speakers = v === 0 ? null : v;
+              }}
+            >
+              <option value="0">Auto-detect</option>
+              {#each Array.from({ length: 20 }, (_, i) => i + 1) as n}
+                <option value={String(n)}>{n}</option>
+              {/each}
+            </select>
+            <p class="hint">Hint the expected number of speakers to speed up diarization clustering.</p>
+          </div>
+          <div class="field checkbox" style="margin-top: 1rem;">
             <label>
               <input type="checkbox" bind:checked={localSettings.skip_diarization} />
-              Skip speaker diarization (faster, no speaker labels)
+              Skip speaker detection (faster, no speaker labels)
             </label>
           </div>
         {:else if activeTab === 'ai'}
@@ -90,14 +184,17 @@
               <option value="local">Local (llama-server)</option>
               <option value="openai">OpenAI</option>
               <option value="anthropic">Anthropic</option>
-              <option value="litellm">LiteLLM</option>
+              <option value="litellm">OpenAI Compatible</option>
             </select>
           </div>
 
           {#if localSettings.ai_provider === 'openai'}
             <div class="field">
               <label for="openai-key">OpenAI API Key</label>
-              <input id="openai-key" type="password" bind:value={localSettings.openai_api_key} placeholder="sk-..." />
+              <div class="input-reveal">
+                <input id="openai-key" type={revealedFields.has('openai-key') ? 'text' : 'password'} bind:value={localSettings.openai_api_key} placeholder="sk-..." />
+                <button type="button" class="reveal-btn" onclick={() => { const s = new Set(revealedFields); s.has('openai-key') ? s.delete('openai-key') : s.add('openai-key'); revealedFields = s; }}>{revealedFields.has('openai-key') ? 'Hide' : 'Show'}</button>
+              </div>
             </div>
             <div class="field">
               <label for="openai-model">Model</label>
@@ -106,13 +203,27 @@
           {:else if localSettings.ai_provider === 'anthropic'}
             <div class="field">
               <label for="anthropic-key">Anthropic API Key</label>
-              <input id="anthropic-key" type="password" bind:value={localSettings.anthropic_api_key} placeholder="sk-ant-..." />
+              <div class="input-reveal">
+                <input id="anthropic-key" type={revealedFields.has('anthropic-key') ? 'text' : 'password'} bind:value={localSettings.anthropic_api_key} placeholder="sk-ant-..." />
+                <button type="button" class="reveal-btn" onclick={() => { const s = new Set(revealedFields); s.has('anthropic-key') ? s.delete('anthropic-key') : s.add('anthropic-key'); revealedFields = s; }}>{revealedFields.has('anthropic-key') ? 'Hide' : 'Show'}</button>
+              </div>
             </div>
             <div class="field">
               <label for="anthropic-model">Model</label>
               <input id="anthropic-model" type="text" bind:value={localSettings.anthropic_model} />
             </div>
           {:else if localSettings.ai_provider === 'litellm'}
+            <div class="field">
+              <label for="litellm-base">API Base URL</label>
+              <input id="litellm-base" type="text" bind:value={localSettings.litellm_api_base} placeholder="https://your-litellm-proxy.example.com" />
+            </div>
+            <div class="field">
+              <label for="litellm-key">API Key</label>
+              <div class="input-reveal">
+                <input id="litellm-key" type={revealedFields.has('litellm-key') ? 'text' : 'password'} bind:value={localSettings.litellm_api_key} placeholder="sk-..." />
+                <button type="button" class="reveal-btn" onclick={() => { const s = new Set(revealedFields); s.has('litellm-key') ? s.delete('litellm-key') : s.add('litellm-key'); revealedFields = s; }}>{revealedFields.has('litellm-key') ? 'Hide' : 'Show'}</button>
+              </div>
+            </div>
             <div class="field">
               <label for="litellm-model">Model</label>
               <input id="litellm-model" type="text" bind:value={localSettings.litellm_model} placeholder="provider/model-name" />
@@ -220,11 +331,36 @@
     color: #aaa;
     margin-bottom: 0.3rem;
   }
+  .input-reveal {
+    display: flex;
+    gap: 0;
+  }
+  .input-reveal input {
+    flex: 1;
+    border-top-right-radius: 0;
+    border-bottom-right-radius: 0;
+  }
+  .reveal-btn {
+    background: #0f3460;
+    border: 1px solid #4a5568;
+    border-left: none;
+    color: #aaa;
+    padding: 0.5rem 0.6rem;
+    border-radius: 0 4px 4px 0;
+    cursor: pointer;
+    font-size: 0.75rem;
+    white-space: nowrap;
+  }
+  .reveal-btn:hover {
+    color: #e0e0e0;
+    background: #1a4a7a;
+  }
   .field input,
   .field select {
     width: 100%;
     background: #1a1a2e;
     color: #e0e0e0;
+    color-scheme: dark;
     border: 1px solid #4a5568;
     border-radius: 4px;
     padding: 0.5rem;
@@ -252,6 +388,79 @@
     color: #666;
     line-height: 1.4;
   }
+  .info-box {
+    background: rgba(233, 69, 96, 0.05);
+    border: 1px solid #2a3a5e;
+    border-radius: 6px;
+    padding: 0.75rem 1rem;
+    margin-bottom: 1rem;
+    font-size: 0.8rem;
+    color: #b0b0b0;
+    line-height: 1.5;
+  }
+  .info-box p {
+    margin: 0 0 0.5rem;
+  }
+  .info-box p:last-child {
+    margin-bottom: 0;
+  }
+  .info-box .info-title {
+    color: #e0e0e0;
+    font-weight: 600;
+    font-size: 0.8rem;
+  }
+  .info-box ol {
+    margin: 0.25rem 0 0.5rem;
+    padding-left: 1.25rem;
+  }
+  .info-box li {
+    margin-bottom: 0.25rem;
+  }
+  .info-box strong {
+    color: #e0e0e0;
+  }
+  .ext-link {
+    color: #e94560;
+    cursor: pointer;
+    text-decoration: underline;
+  }
+  .ext-link:hover {
+    color: #ff6b81;
+  }
+  .info-box ul {
+    margin: 0.25rem 0;
+    padding-left: 1.25rem;
+  }
+  .btn-download {
+    background: #0f3460;
+    border: 1px solid #4a5568;
+    color: #e0e0e0;
+    padding: 0.5rem 1rem;
+    border-radius: 6px;
+    cursor: pointer;
+    font-size: 0.85rem;
+    width: 100%;
+    margin-bottom: 0.5rem;
+  }
+  .btn-download:hover:not(:disabled) {
+    background: #1a4a7a;
+    border-color: #e94560;
+  }
+  .btn-download:disabled {
+    opacity: 0.6;
+    cursor: not-allowed;
+  }
+  .status-success {
+    color: #4ecdc4;
+    font-size: 0.8rem;
+    margin: 0.25rem 0;
+  }
+  .status-error {
+    color: #e94560;
+    font-size: 0.8rem;
+    margin: 0.25rem 0;
+    word-break: break-word;
+  }
   .modal-footer {
     display: flex;
     justify-content: flex-end;

src/lib/components/SpeakerManager.svelte

@@ -1,5 +1,6 @@
 <script lang="ts">
   import { speakers } from '$lib/stores/transcript';
+  import { settings } from '$lib/stores/settings';
   import type { Speaker } from '$lib/types/transcript';
 
   let editingSpeakerId = $state<string | null>(null);
@@ -34,7 +35,14 @@
 <div class="speaker-manager">
   <h3>Speakers</h3>
   {#if $speakers.length === 0}
-    <p class="empty-hint">No speakers detected yet</p>
+    <p class="empty-hint">No speakers detected</p>
+    {#if $settings.skip_diarization}
+      <p class="setup-hint">Speaker detection is disabled. Enable it in Settings &gt; Speakers.</p>
+    {:else if !$settings.hf_token}
+      <p class="setup-hint">Speaker detection requires a HuggingFace token. Configure it in Settings &gt; Speakers.</p>
+    {:else}
+      <p class="setup-hint">Speaker detection ran but found no distinct speakers, or the model may need to be downloaded. Check Settings &gt; Speakers.</p>
+    {/if}
   {:else}
     <ul class="speaker-list">
       {#each $speakers as speaker (speaker.id)}
@@ -78,6 +86,19 @@
   .empty-hint {
     color: #666;
     font-size: 0.875rem;
+    margin-bottom: 0.25rem;
+  }
+  .setup-hint {
+    color: #555;
+    font-size: 0.75rem;
+    line-height: 1.4;
+  }
+  .setup-hint code {
+    background: rgba(233, 69, 96, 0.15);
+    color: #e94560;
+    padding: 0.1rem 0.3rem;
+    border-radius: 3px;
+    font-size: 0.7rem;
   }
   .speaker-list {
     list-style: none;

src/lib/components/TranscriptEditor.svelte

@@ -60,12 +60,14 @@
   function finishEditing(segmentId: string) {
     const trimmed = editText.trim();
     if (trimmed) {
-      // Update the segment text in the store
       segments.update(segs => segs.map(s => {
         if (s.id !== segmentId) return s;
+        const newWordTexts = trimmed.split(/\s+/);
+        const newWords = redistributeWords(s, newWordTexts);
         return {
           ...s,
           text: trimmed,
+          words: newWords,
           original_text: s.original_text ?? s.text,
           is_edited: true,
           edited_at: new Date().toISOString(),
@@ -76,6 +78,106 @@
     editingSegmentId = null;
   }
 
+  /**
+   * Redistribute word timing after an edit.
+   *
+   * Uses a diff-like alignment between old and new word lists:
+   * - Unchanged words keep their original timing
+   * - Spelling fixes (same position, same count) keep timing
+   * - Split words (1 old → N new) divide the original time range proportionally
+   * - Inserted words with no match get interpolated timing
+   */
+  function redistributeWords(segment: Segment, newWordTexts: string[]): Word[] {
+    const oldWords = segment.words;
+
+    // Same word count — preserve per-word timing (spelling fixes)
+    if (newWordTexts.length === oldWords.length) {
+      return oldWords.map((w, i) => ({ ...w, word: newWordTexts[i] }));
+    }
+
+    // Align old words to new words using a simple greedy match.
+    // Build a mapping: for each old word, which new words does it cover?
+    const oldTexts = oldWords.map(w => w.word.toLowerCase());
+    const newTexts = newWordTexts.map(w => w.toLowerCase());
+
+    // Walk both lists, greedily matching old words to new words
+    const result: Word[] = [];
+    let oldIdx = 0;
+    let newIdx = 0;
+
+    while (newIdx < newTexts.length) {
+      if (oldIdx < oldTexts.length && oldTexts[oldIdx] === newTexts[newIdx]) {
+        // Exact match — keep original timing
+        result.push({ ...oldWords[oldIdx], word: newWordTexts[newIdx], word_index: newIdx });
+        oldIdx++;
+        newIdx++;
+      } else if (oldIdx < oldTexts.length) {
+        // Check if old word was split into multiple new words.
+        // E.g., "gonna" → "going to": see if concatenating upcoming new words
+        // matches the old word (or close enough — just check if old word's chars
+        // are consumed by the next few new words).
+        let splitCount = 0;
+        let combined = '';
+        for (let k = newIdx; k < newTexts.length && k - newIdx < 5; k++) {
+          combined += (k > newIdx ? '' : '') + newTexts[k];
+          if (combined.length >= oldTexts[oldIdx].length) {
+            splitCount = k - newIdx + 1;
+            break;
+          }
+        }
+
+        if (splitCount > 1) {
+          // Split: distribute the old word's time range proportionally
+          const ow = oldWords[oldIdx];
+          const totalDuration = ow.end_ms - ow.start_ms;
+          for (let k = 0; k < splitCount; k++) {
+            const fraction = 1 / splitCount;
+            result.push({
+              id: `${segment.id}-word-${newIdx + k}`,
+              segment_id: segment.id,
+              word: newWordTexts[newIdx + k],
+              start_ms: Math.round(ow.start_ms + totalDuration * fraction * k),
+              end_ms: Math.round(ow.start_ms + totalDuration * fraction * (k + 1)),
+              confidence: ow.confidence,
+              word_index: newIdx + k,
+            });
+          }
+          oldIdx++;
+          newIdx += splitCount;
+        } else {
+          // No match found — interpolate timing from neighbors
+          const prevEnd = result.length > 0 ? result[result.length - 1].end_ms : segment.start_ms;
+          const nextStart = oldIdx < oldWords.length ? oldWords[oldIdx].start_ms : segment.end_ms;
+          result.push({
+            id: `${segment.id}-word-${newIdx}`,
+            segment_id: segment.id,
+            word: newWordTexts[newIdx],
+            start_ms: prevEnd,
+            end_ms: nextStart,
+            confidence: 1.0,
+            word_index: newIdx,
+          });
+          newIdx++;
+        }
+      } else {
+        // No more old words — use end of segment
+        const prevEnd = result.length > 0 ? result[result.length - 1].end_ms : segment.start_ms;
+        result.push({
+          id: `${segment.id}-word-${newIdx}`,
+          segment_id: segment.id,
+          word: newWordTexts[newIdx],
+          start_ms: prevEnd,
+          end_ms: segment.end_ms,
+          confidence: 1.0,
+          word_index: newIdx,
+        });
+        newIdx++;
+      }
+    }
+
+    return result;
+  }
+
   function handleEditKeydown(e: KeyboardEvent, segmentId: string) {
     if (e.key === 'Escape') {
       editingSegmentId = null;
@@ -217,6 +319,8 @@
   .segment-text {
     line-height: 1.6;
     padding-left: 0.75rem;
+    word-wrap: break-word;
+    overflow-wrap: break-word;
   }
   .word {
     cursor: pointer;

src/lib/components/WaveformPlayer.svelte

@@ -12,6 +12,8 @@
 
   let container: HTMLDivElement;
   let wavesurfer: WaveSurfer | null = $state(null);
+  let isReady = $state(false);
+  let isLoading = $state(false);
   let currentTime = $state('0:00');
   let totalTime = $state('0:00');
 
@@ -31,6 +33,7 @@
       barWidth: 2,
       barGap: 1,
       barRadius: 2,
+      backend: 'WebAudio',
     });
 
     wavesurfer.on('timeupdate', (time: number) => {
@@ -39,6 +42,8 @@
     });
 
     wavesurfer.on('ready', () => {
+      isReady = true;
+      isLoading = false;
       const dur = wavesurfer!.getDuration();
       durationMs.set(Math.round(dur * 1000));
       totalTime = formatTime(dur);
@@ -48,8 +53,12 @@
     wavesurfer.on('pause', () => isPlaying.set(false));
     wavesurfer.on('finish', () => isPlaying.set(false));
 
+    wavesurfer.on('loading', () => {
+      isReady = false;
+    });
+
     if (audioUrl) {
-      wavesurfer.load(audioUrl);
+      loadAudio(audioUrl);
     }
   });
 
@@ -57,20 +66,21 @@
     wavesurfer?.destroy();
   });
 
-  /** Toggle play/pause. Exposed for keyboard shortcuts. */
+  /** Toggle play/pause from current position. Exposed for keyboard shortcuts. */
   export function togglePlayPause() {
-    wavesurfer?.playPause();
+    if (!wavesurfer || !isReady) return;
+    wavesurfer.playPause();
   }
 
   function skipBack() {
-    if (wavesurfer) {
+    if (wavesurfer && isReady) {
       const time = Math.max(0, wavesurfer.getCurrentTime() - 5);
       wavesurfer.setTime(time);
     }
   }
 
   function skipForward() {
-    if (wavesurfer) {
+    if (wavesurfer && isReady) {
       const time = Math.min(wavesurfer.getDuration(), wavesurfer.getCurrentTime() + 5);
       wavesurfer.setTime(time);
     }
@@ -78,16 +88,17 @@
 
   /** Seek to a specific time in milliseconds. Called from transcript click-to-seek. */
   export function seekTo(timeMs: number) {
-    if (wavesurfer) {
+    if (!wavesurfer || !isReady) {
-      wavesurfer.setTime(timeMs / 1000);
+      console.warn('[voice-to-notes] seekTo ignored — audio not ready yet');
-      if (!wavesurfer.isPlaying()) {
+      return;
-        wavesurfer.play();
-      }
     }
+    wavesurfer.setTime(timeMs / 1000);
   }
 
   /** Load a new audio file. */
   export function loadAudio(url: string) {
+    isReady = false;
+    isLoading = true;
     wavesurfer?.load(url);
   }
 </script>
@@ -95,11 +106,17 @@
 <div class="waveform-player">
   <div class="waveform-container" bind:this={container}></div>
   <div class="controls">
-    <button class="control-btn" onclick={skipBack} title="Back 5s">⏪</button>
+    <button class="control-btn" onclick={skipBack} title="Back 5s" disabled={!isReady}>⏪</button>
-    <button class="control-btn play-btn" onclick={togglePlayPause} title="Play/Pause">
+    <button class="control-btn play-btn" onclick={togglePlayPause} title="Play/Pause" disabled={!isReady}>
-      {#if $isPlaying}⏸{:else}▶{/if}
+      {#if !isReady}
+        ⏳
+      {:else if $isPlaying}
+        ⏸
+      {:else}
+        ▶
+      {/if}
     </button>
-    <button class="control-btn" onclick={skipForward} title="Forward 5s">⏩</button>
+    <button class="control-btn" onclick={skipForward} title="Forward 5s" disabled={!isReady}>⏩</button>
     <span class="time">{currentTime} / {totalTime}</span>
   </div>
 </div>
@@ -129,9 +146,13 @@
     cursor: pointer;
     font-size: 1rem;
   }
-  .control-btn:hover {
+  .control-btn:hover:not(:disabled) {
     background: #1a4a7a;
   }
+  .control-btn:disabled {
+    opacity: 0.4;
+    cursor: not-allowed;
+  }
   .play-btn {
     padding: 0.4rem 1rem;
     font-size: 1.2rem;

src/lib/stores/settings.ts

@@ -8,12 +8,16 @@ export interface AppSettings {
   openai_model: string;
   anthropic_model: string;
   litellm_model: string;
+  litellm_api_key: string;
+  litellm_api_base: string;
   local_model_path: string;
   local_binary_path: string;
   transcription_model: string;
   transcription_device: string;
   transcription_language: string;
   skip_diarization: boolean;
+  hf_token: string;
+  num_speakers: number | null;
 }
 
 const defaults: AppSettings = {
@@ -23,12 +27,16 @@ const defaults: AppSettings = {
   openai_model: 'gpt-4o-mini',
   anthropic_model: 'claude-sonnet-4-6',
   litellm_model: 'gpt-4o-mini',
+  litellm_api_key: '',
+  litellm_api_base: '',
   local_model_path: '',
   local_binary_path: 'llama-server',
   transcription_model: 'base',
   transcription_device: 'cpu',
   transcription_language: '',
   skip_diarization: false,
+  hf_token: '',
+  num_speakers: null,
 };
 
 export const settings = writable<AppSettings>({ ...defaults });
@@ -45,4 +53,20 @@ export async function loadSettings(): Promise<void> {
 export async function saveSettings(s: AppSettings): Promise<void> {
   settings.set(s);
   await invoke('save_settings', { settings: s });
+
+  // Configure the AI provider in the Python sidecar
+  const configMap: Record<string, Record<string, string>> = {
+    openai: { api_key: s.openai_api_key, model: s.openai_model },
+    anthropic: { api_key: s.anthropic_api_key, model: s.anthropic_model },
+    litellm: { api_key: s.litellm_api_key, api_base: s.litellm_api_base, model: s.litellm_model },
+    local: { model: s.local_model_path, base_url: 'http://localhost:8080' },
+  };
+  const config = configMap[s.ai_provider];
+  if (config) {
+    try {
+      await invoke('ai_configure', { provider: s.ai_provider, config });
+    } catch {
+      // Sidecar may not be running yet — provider will be configured on first use
+    }
+  }
 }

src/routes/+page.svelte

@@ -11,12 +11,18 @@
   import { segments, speakers } from '$lib/stores/transcript';
   import { settings, loadSettings } from '$lib/stores/settings';
   import type { Segment, Speaker } from '$lib/types/transcript';
-  import { onMount } from 'svelte';
+  import { onMount, tick } from 'svelte';
 
+  let appReady = $state(false);
   let waveformPlayer: WaveformPlayer;
   let audioUrl = $state('');
   let showSettings = $state(false);
 
+  // Project management state
+  let currentProjectPath = $state<string | null>(null);
+  let currentProjectName = $state('');
+  let audioFilePath = $state('');
+
   onMount(() => {
     loadSettings();
 
@@ -43,8 +49,8 @@
 
     // Close export dropdown on outside click
     function handleClickOutside(e: MouseEvent) {
+      const target = e.target as HTMLElement;
       if (showExportMenu) {
-        const target = e.target as HTMLElement;
         if (!target.closest('.export-dropdown')) {
           showExportMenu = false;
         }
@@ -54,6 +60,8 @@
     document.addEventListener('keydown', handleKeyDown);
     document.addEventListener('click', handleClickOutside);
 
+    appReady = true;
+
     return () => {
       document.removeEventListener('keydown', handleKeyDown);
       document.removeEventListener('click', handleClickOutside);
@@ -67,10 +75,136 @@
   // Speaker color palette for auto-assignment
   const speakerColors = ['#e94560', '#4ecdc4', '#ffe66d', '#a8e6cf', '#ff8b94', '#c7ceea', '#ffd93d', '#6bcb77'];
 
+  async function saveProject() {
+    const defaultName = currentProjectName || 'Untitled';
+    const outputPath = await save({
+      defaultPath: `${defaultName}.vtn`,
+      filters: [{ name: 'Voice to Notes Project', extensions: ['vtn'] }],
+    });
+    if (!outputPath) return;
+
+    const projectData = {
+      version: 1,
+      name: outputPath.split(/[\\/]/).pop()?.replace('.vtn', '') || defaultName,
+      audio_file: audioFilePath,
+      created_at: new Date().toISOString(),
+      segments: $segments.map(seg => {
+        const speaker = $speakers.find(s => s.id === seg.speaker_id);
+        return {
+          text: seg.text,
+          start_ms: seg.start_ms,
+          end_ms: seg.end_ms,
+          speaker: speaker?.label ?? null,
+          is_edited: seg.is_edited,
+          words: seg.words.map(w => ({
+            word: w.word,
+            start_ms: w.start_ms,
+            end_ms: w.end_ms,
+            confidence: w.confidence ?? 0,
+          })),
+        };
+      }),
+      speakers: $speakers.map(s => ({
+        label: s.label,
+        display_name: s.display_name,
+        color: s.color || '#e94560',
+      })),
+    };
+
+    try {
+      await invoke('save_project_file', { path: outputPath, project: projectData });
+      currentProjectPath = outputPath;
+      currentProjectName = projectData.name;
+    } catch (err) {
+      console.error('Failed to save project:', err);
+      alert(`Failed to save: ${err}`);
+    }
+  }
+
+  async function openProject() {
+    const filePath = await open({
+      filters: [{ name: 'Voice to Notes Project', extensions: ['vtn'] }],
+      multiple: false,
+    });
+    if (!filePath) return;
+
+    try {
+      const project = await invoke<{
+        version: number;
+        name: string;
+        audio_file: string;
+        segments: Array<{
+          text: string;
+          start_ms: number;
+          end_ms: number;
+          speaker: string | null;
+          is_edited: boolean;
+          words: Array<{ word: string; start_ms: number; end_ms: number; confidence: number }>;
+        }>;
+        speakers: Array<{ label: string; display_name: string | null; color: string }>;
+      }>('load_project_file', { path: filePath });
+
+      // Rebuild speakers
+      const newSpeakers: Speaker[] = project.speakers.map((s, idx) => ({
+        id: `speaker-${idx}`,
+        project_id: '',
+        label: s.label,
+        display_name: s.display_name,
+        color: s.color,
+      }));
+      speakers.set(newSpeakers);
+
+      const speakerLookup = new Map(newSpeakers.map(s => [s.label, s.id]));
+
+      // Rebuild segments
+      const newSegments: Segment[] = project.segments.map((seg, idx) => ({
+        id: `seg-${idx}`,
+        project_id: '',
+        media_file_id: '',
+        speaker_id: seg.speaker ? (speakerLookup.get(seg.speaker) ?? null) : null,
+        start_ms: seg.start_ms,
+        end_ms: seg.end_ms,
+        text: seg.text,
+        original_text: null,
+        confidence: null,
+        is_edited: seg.is_edited,
+        edited_at: null,
+        segment_index: idx,
+        words: seg.words.map((w, widx) => ({
+          id: `word-${idx}-${widx}`,
+          segment_id: `seg-${idx}`,
+          word: w.word,
+          start_ms: w.start_ms,
+          end_ms: w.end_ms,
+          confidence: w.confidence,
+          word_index: widx,
+        })),
+      }));
+      segments.set(newSegments);
+
+      // Load audio
+      audioFilePath = project.audio_file;
+      audioUrl = convertFileSrc(project.audio_file);
+      waveformPlayer?.loadAudio(audioUrl);
+
+      currentProjectPath = filePath as string;
+      currentProjectName = project.name;
+    } catch (err) {
+      console.error('Failed to load project:', err);
+      alert(`Failed to load project: ${err}`);
+    }
+  }
+
   function handleWordClick(timeMs: number) {
+    console.log('[voice-to-notes] Word clicked, seeking to', timeMs, 'ms');
     waveformPlayer?.seekTo(timeMs);
   }
 
+  function handleTextEdit(segmentId: string, newText: string) {
+    // In-memory store is already updated by TranscriptEditor.
+    // Changes persist when user saves the project file.
+  }
+
   async function handleFileImport() {
     const filePath = await open({
       multiple: false,
@@ -82,28 +216,99 @@
     });
     if (!filePath) return;
 
-    // Convert file path to asset URL for wavesurfer
+    // Track the original file path and convert to asset URL for wavesurfer
+    audioFilePath = filePath;
     audioUrl = convertFileSrc(filePath);
     waveformPlayer?.loadAudio(audioUrl);
 
+    // Clear previous results
+    segments.set([]);
+    speakers.set([]);
+
     // Start pipeline (transcription + diarization)
     isTranscribing = true;
     transcriptionProgress = 0;
     transcriptionStage = 'Starting...';
     transcriptionMessage = 'Initializing pipeline...';
 
+    // Flush DOM so the progress overlay renders before the blocking invoke
+    await tick();
+
     // Listen for progress events from the sidecar
     const unlisten = await listen<{
       percent: number;
       stage: string;
       message: string;
     }>('pipeline-progress', (event) => {
+      console.log('[voice-to-notes] Progress event:', event.payload);
       const { percent, stage, message } = event.payload;
       if (typeof percent === 'number') transcriptionProgress = percent;
       if (typeof stage === 'string') transcriptionStage = stage;
       if (typeof message === 'string') transcriptionMessage = message;
     });
 
+    const unlistenSegment = await listen<{
+      index: number;
+      text: string;
+      start_ms: number;
+      end_ms: number;
+      words: Array<{ word: string; start_ms: number; end_ms: number; confidence: number }>;
+    }>('pipeline-segment', (event) => {
+      const seg = event.payload;
+      const newSeg: Segment = {
+        id: `seg-${seg.index}`,
+        project_id: '',
+        media_file_id: '',
+        speaker_id: null,
+        start_ms: seg.start_ms,
+        end_ms: seg.end_ms,
+        text: seg.text,
+        original_text: null,
+        confidence: null,
+        is_edited: false,
+        edited_at: null,
+        segment_index: seg.index,
+        words: seg.words.map((w, widx) => ({
+          id: `word-${seg.index}-${widx}`,
+          segment_id: `seg-${seg.index}`,
+          word: w.word,
+          start_ms: w.start_ms,
+          end_ms: w.end_ms,
+          confidence: w.confidence,
+          word_index: widx,
+        })),
+      };
+      segments.update(segs => [...segs, newSeg]);
+    });
+
+    const unlistenSpeaker = await listen<{
+      updates: Array<{ index: number; speaker: string }>;
+    }>('pipeline-speaker-update', (event) => {
+      const { updates } = event.payload;
+      // Build speakers from unique labels
+      const uniqueLabels = [...new Set(updates.map(u => u.speaker))].sort();
+      const newSpeakers: Speaker[] = uniqueLabels.map((label, idx) => ({
+        id: `speaker-${idx}`,
+        project_id: '',
+        label,
+        display_name: null,
+        color: speakerColors[idx % speakerColors.length],
+      }));
+      speakers.set(newSpeakers);
+
+      // Update existing segments with speaker assignments
+      const speakerLookup = new Map(newSpeakers.map(s => [s.label, s.id]));
+      segments.update(segs =>
+        segs.map((seg, i) => {
+          const update = updates.find(u => u.index === i);
+          if (update) {
+            return { ...seg, speaker_id: speakerLookup.get(update.speaker) ?? null };
+          }
+          return seg;
+        })
+      );
+    });
+
     try {
       const result = await invoke<{
         segments: Array<{
@@ -128,6 +333,8 @@
         device: $settings.transcription_device || undefined,
         language: $settings.transcription_language || undefined,
         skipDiarization: $settings.skip_diarization || undefined,
+        hfToken: $settings.hf_token || undefined,
+        numSpeakers: $settings.num_speakers && $settings.num_speakers > 0 ? $settings.num_speakers : undefined,
       });
 
       // Create speaker entries from pipeline result
@@ -169,11 +376,18 @@
       }));
 
       segments.set(newSegments);
+
+      // Set project name from audio file name (user can save explicitly)
+      const fileName = filePath.split(/[\\/]/).pop() || 'Untitled';
+      currentProjectName = fileName.replace(/\.[^.]+$/, '');
+      currentProjectPath = null;
     } catch (err) {
       console.error('Pipeline failed:', err);
       alert(`Pipeline failed: ${err}`);
     } finally {
       unlisten();
+      unlistenSegment();
+      unlistenSpeaker();
       isTranscribing = false;
     }
   }
@@ -229,56 +443,77 @@
   }
 </script>
 
-<div class="app-header">
+{#if !appReady}
-  <h1>Voice to Notes</h1>
+  <div class="splash-screen">
-  <div class="header-actions">
+    <h1 class="splash-title">Voice to Notes</h1>
-    <button class="import-btn" onclick={handleFileImport}>
+    <p class="splash-subtitle">Loading...</p>
-      Import Audio/Video
+    <div class="splash-spinner"></div>
-    </button>
+  </div>
-    <button class="settings-btn" onclick={() => showSettings = true} title="Settings">
+{:else}
-      Settings
+  <div class="app-shell">
-    </button>
+  <div class="app-header">
-    {#if $segments.length > 0}
+    <div class="header-actions">
-      <div class="export-dropdown">
+      <button class="settings-btn" onclick={openProject} disabled={isTranscribing}>
-        <button class="export-btn" onclick={() => showExportMenu = !showExportMenu}>
+        Open Project
-          Export
+      </button>
+      {#if $segments.length > 0}
+        <button class="settings-btn" onclick={saveProject}>
+          Save Project
         </button>
-        {#if showExportMenu}
+      {/if}
-          <div class="export-menu">
+      <button class="import-btn" onclick={handleFileImport} disabled={isTranscribing}>
-            {#each exportFormats as fmt}
+        {#if isTranscribing}
-              <button class="export-option" onclick={() => handleExport(fmt.format, fmt.ext, fmt.name)}>
+          Processing...
-                {fmt.name} (.{fmt.ext})
+        {:else}
-              </button>
+          Import Audio/Video
-            {/each}
-          </div>
         {/if}
-      </div>
+      </button>
-    {/if}
+      <button class="settings-btn" onclick={() => showSettings = true} title="Settings">
+        Settings
+      </button>
+      {#if $segments.length > 0}
+        <div class="export-dropdown">
+          <button class="export-btn" onclick={() => showExportMenu = !showExportMenu}>
+            Export
+          </button>
+          {#if showExportMenu}
+            <div class="export-menu">
+              {#each exportFormats as fmt}
+                <button class="export-option" onclick={() => handleExport(fmt.format, fmt.ext, fmt.name)}>
+                  {fmt.name} (.{fmt.ext})
+                </button>
+              {/each}
+            </div>
+          {/if}
+        </div>
+      {/if}
+    </div>
   </div>
-</div>
 
-<div class="workspace">
+  <div class="workspace">
-  <div class="main-content">
+    <div class="main-content">
-    <WaveformPlayer bind:this={waveformPlayer} {audioUrl} />
+      <WaveformPlayer bind:this={waveformPlayer} {audioUrl} />
-    <TranscriptEditor onWordClick={handleWordClick} />
+      <TranscriptEditor onWordClick={handleWordClick} onTextEdit={handleTextEdit} />
+    </div>
+    <div class="sidebar-right">
+      <SpeakerManager />
+      <AIChatPanel />
+    </div>
   </div>
-  <div class="sidebar-right">
-    <SpeakerManager />
-    <AIChatPanel />
   </div>
-</div>
 
-<ProgressOverlay
+  <ProgressOverlay
-  visible={isTranscribing}
+    visible={isTranscribing}
-  percent={transcriptionProgress}
+    percent={transcriptionProgress}
-  stage={transcriptionStage}
+    stage={transcriptionStage}
-  message={transcriptionMessage}
+    message={transcriptionMessage}
-/>
+  />
 
-<SettingsModal
+  <SettingsModal
-  visible={showSettings}
+    visible={showSettings}
-  onClose={() => showSettings = false}
+    onClose={() => showSettings = false}
-/>
+  />
+{/if}
 
 <style>
   .app-header {
@@ -289,10 +524,6 @@
     background: #0f3460;
     color: #e0e0e0;
   }
-  h1 {
-    font-size: 1.25rem;
-    margin: 0;
-  }
   .import-btn {
     background: #e94560;
     border: none;
@@ -303,9 +534,18 @@
     font-size: 0.875rem;
     font-weight: 500;
   }
-  .import-btn:hover {
+  .import-btn:hover:not(:disabled) {
     background: #d63851;
   }
+  .import-btn:disabled {
+    opacity: 0.7;
+    cursor: not-allowed;
+    animation: pulse 1.5s ease-in-out infinite;
+  }
+  @keyframes pulse {
+    0%, 100% { opacity: 0.7; }
+    50% { opacity: 1; }
+  }
   .header-actions {
     display: flex;
     gap: 0.5rem;
@@ -320,10 +560,14 @@
     cursor: pointer;
     font-size: 0.875rem;
   }
-  .settings-btn:hover {
+  .settings-btn:hover:not(:disabled) {
     background: rgba(255,255,255,0.05);
     border-color: #e94560;
   }
+  .settings-btn:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+  }
   .export-dropdown {
     position: relative;
   }
@@ -366,11 +610,19 @@
   .export-option:hover {
     background: rgba(233, 69, 96, 0.2);
   }
+  .app-shell {
+    display: flex;
+    flex-direction: column;
+    height: 100vh;
+    overflow: hidden;
+  }
   .workspace {
     display: flex;
     gap: 1rem;
     padding: 1rem;
-    height: calc(100vh - 3.5rem);
+    flex: 1;
+    min-height: 0;
+    overflow: hidden;
     background: #0a0a23;
   }
   .main-content {
@@ -379,6 +631,8 @@
     flex-direction: column;
     gap: 1rem;
     min-width: 0;
+    min-height: 0;
+    overflow-y: auto;
   }
   .sidebar-right {
     width: 300px;
@@ -386,5 +640,38 @@
     flex-direction: column;
     gap: 1rem;
     flex-shrink: 0;
+    min-height: 0;
+    overflow-y: auto;
+  }
+  .splash-screen {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    height: 100vh;
+    background: #0a0a23;
+    color: #e0e0e0;
+    gap: 1rem;
+  }
+  .splash-title {
+    font-size: 2rem;
+    margin: 0;
+    color: #e94560;
+  }
+  .splash-subtitle {
+    font-size: 1rem;
+    color: #888;
+    margin: 0;
+  }
+  .splash-spinner {
+    width: 32px;
+    height: 32px;
+    border: 3px solid #2a3a5e;
+    border-top-color: #e94560;
+    border-radius: 50%;
+    animation: spin 0.8s linear infinite;
+  }
+  @keyframes spin {
+    to { transform: rotate(360deg); }
   }
 </style>