Phase 2: Core transcription pipeline and audio playback

- Implement faster-whisper TranscribeService with word-level timestamps,
  progress reporting, and hardware auto-detection
- Wire up Rust SidecarManager for Python process lifecycle (spawn, IPC, shutdown)
- Add transcribe_file Tauri command bridging frontend to Python sidecar
- Integrate wavesurfer.js WaveformPlayer with play/pause, skip, seek controls
- Build TranscriptEditor with word-level click-to-seek and active highlighting
- Connect file import flow: prompt → asset load → transcribe → display
- Add typed tauri-bridge service with TranscriptionResult interface
- Add Python tests for hardware detection and transcription result formatting

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

python/voice_to_notes/hardware/detect.py

@@ -2,8 +2,74 @@
 
 from __future__ import annotations
 
-# TODO: Implement hardware detection
-# - Check torch.cuda.is_available()
-# - Detect VRAM size
-# - Detect CPU cores and available RAM
-# - Return recommended model configuration
+import os
+import sys
+from dataclasses import dataclass
+
+
+@dataclass
+class HardwareInfo:
+    """Detected hardware capabilities."""
+
+    has_cuda: bool = False
+    cuda_device_name: str = ""
+    vram_mb: int = 0
+    ram_mb: int = 0
+    cpu_cores: int = 0
+    recommended_model: str = "base"
+    recommended_device: str = "cpu"
+    recommended_compute_type: str = "int8"
+
+
+def detect_hardware() -> HardwareInfo:
+    """Detect available hardware and recommend model configuration."""
+    info = HardwareInfo()
+
+    # CPU info
+    info.cpu_cores = os.cpu_count() or 1
+
+    # RAM info
+    try:
+        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:
+        import torch
+
+        if torch.cuda.is_available():
+            info.has_cuda = True
+            info.cuda_device_name = torch.cuda.get_device_name(0)
+            info.vram_mb = torch.cuda.get_device_properties(0).total_mem // (1024 * 1024)
+    except ImportError:
+        print("[sidecar] torch not available, GPU detection skipped", file=sys.stderr, flush=True)
+
+    # Model recommendation based on hardware
+    if info.has_cuda and info.vram_mb >= 8000:
+        info.recommended_model = "large-v3-turbo"
+        info.recommended_device = "cuda"
+        info.recommended_compute_type = "int8"
+    elif info.has_cuda and info.vram_mb >= 4000:
+        info.recommended_model = "medium"
+        info.recommended_device = "cuda"
+        info.recommended_compute_type = "int8"
+    elif info.ram_mb >= 16000:
+        info.recommended_model = "medium"
+        info.recommended_device = "cpu"
+        info.recommended_compute_type = "int8"
+    elif info.ram_mb >= 8000:
+        info.recommended_model = "small"
+        info.recommended_device = "cpu"
+        info.recommended_compute_type = "int8"
+    else:
+        info.recommended_model = "base"
+        info.recommended_device = "cpu"
+        info.recommended_compute_type = "int8"
+
+    return info