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
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"""GPU/CPU detection and VRAM estimation."""
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from __future__ import annotations
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2026-02-26 15:53:09 -08:00
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import os
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import sys
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from dataclasses import dataclass
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@dataclass
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class HardwareInfo:
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"""Detected hardware capabilities."""
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has_cuda: bool = False
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cuda_device_name: str = ""
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vram_mb: int = 0
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ram_mb: int = 0
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cpu_cores: int = 0
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recommended_model: str = "base"
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recommended_device: str = "cpu"
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recommended_compute_type: str = "int8"
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def detect_hardware() -> HardwareInfo:
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"""Detect available hardware and recommend model configuration."""
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info = HardwareInfo()
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# CPU info
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info.cpu_cores = os.cpu_count() or 1
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# RAM info
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try:
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with open("/proc/meminfo") as f:
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for line in f:
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if line.startswith("MemTotal:"):
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# Value is in kB
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info.ram_mb = int(line.split()[1]) // 1024
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break
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except (FileNotFoundError, ValueError):
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pass
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# CUDA detection
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try:
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import torch
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if torch.cuda.is_available():
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info.has_cuda = True
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info.cuda_device_name = torch.cuda.get_device_name(0)
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info.vram_mb = torch.cuda.get_device_properties(0).total_mem // (1024 * 1024)
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except ImportError:
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print("[sidecar] torch not available, GPU detection skipped", file=sys.stderr, flush=True)
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# Model recommendation based on hardware
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if info.has_cuda and info.vram_mb >= 8000:
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info.recommended_model = "large-v3-turbo"
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info.recommended_device = "cuda"
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info.recommended_compute_type = "int8"
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elif info.has_cuda and info.vram_mb >= 4000:
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info.recommended_model = "medium"
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info.recommended_device = "cuda"
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info.recommended_compute_type = "int8"
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elif info.ram_mb >= 16000:
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info.recommended_model = "medium"
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info.recommended_device = "cpu"
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info.recommended_compute_type = "int8"
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elif info.ram_mb >= 8000:
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info.recommended_model = "small"
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info.recommended_device = "cpu"
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info.recommended_compute_type = "int8"
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else:
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info.recommended_model = "base"
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info.recommended_device = "cpu"
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info.recommended_compute_type = "int8"
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return info
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