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Phase 1 foundation: Tauri shell, Python sidecar, SQLite database Tauri v2 + Svelte + TypeScript frontend: - App shell with workspace layout (waveform, transcript, speakers, AI chat) - Placeholder components for all major UI areas - Typed stores (project, transcript, playback, AI) - TypeScript interfaces matching the database schema - Tauri bridge service with typed invoke wrappers - svelte-check passes with 0 errors Rust backend: - Tauri v2 app entry point with command registration - SQLite database layer (rusqlite with bundled SQLite) - Full schema: projects, media_files, speakers, segments, words, ai_outputs, annotations (with indexes) - Model structs with serde serialization - CRUD queries for projects, speakers, segments, words - Segment text editing preserves original text - Schema versioning for future migrations - 6 tests passing - Command stubs for project, transcribe, export, AI, settings, system - App state management Python sidecar: - JSON-line IPC protocol (stdin/stdout) - Message types: IPCMessage, progress, error, ready - Handler registry with routing and error handling - Ping/pong handler for connectivity testing - Service stubs: transcribe, diarize, pipeline, AI, export - Provider stubs: local (llama-server), OpenAI, Anthropic, LiteLLM - Hardware detection stubs - 14 tests passing, ruff clean Also adds: - Testing strategy document (docs/TESTING.md) - Validation script (scripts/validate.sh) - Updated .gitignore for Svelte, Rust, Python artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-26 15:16:06 -08:00
"""GPU/CPU detection and VRAM estimation."""
from __future__ import annotations
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