Cross-platform distribution, UI improvements, and performance optimizations

- PyInstaller frozen sidecar: spec file, build script, and ffmpeg path resolver
  for self-contained distribution without Python prerequisites
- Dual-mode sidecar launcher: frozen binary (production) with dev mode fallback
- Parallel transcription + diarization pipeline (~30-40% faster)
- GPU auto-detection for diarization (CUDA when available)
- Async run_pipeline command for real-time progress event delivery
- Web Audio API backend for instant playback and seeking
- OpenAI-compatible provider replacing LiteLLM client-side routing
- Cross-platform RAM detection (Linux/macOS/Windows)
- Settings: speaker count hint, token reveal toggles, dark dropdown styling
- Loading splash screen, flexbox layout fix for viewport overflow
- Gitea Actions CI/CD pipeline (Linux, Windows, macOS ARM)
- Updated README and CLAUDE.md documentation

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

python/voice_to_notes/services/diarize.py

@@ -16,6 +16,7 @@ from typing import Any
 # 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
 
@@ -40,7 +41,7 @@ def _ensure_wav(file_path: str) -> tuple[str, str | None]:
     try:
         subprocess.run(
             [
-                "ffmpeg", "-y", "-i", file_path,
+                get_ffmpeg_path(), "-y", "-i", file_path,
                 "-ar", "16000", "-ac", "1", "-c:a", "pcm_s16le",
                 tmp.name,
             ],
@@ -118,6 +119,14 @@ class DiarizeService:
 
                 self._pipeline = Pipeline.from_pretrained(model_name, token=hf_token)
                 print(f"[sidecar] Loaded diarization model: {model_name}", file=sys.stderr, flush=True)
+                # 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