soubarnak
ea7be5f111
The fork takes its own name everywhere a user can see it: binaries (voculinux, voculinux-gui), desktop entry, autostart, tray, D-Bus namespace (com.voculinux.Voculinux, no alias for the old name), config/data/cache dirs, single-instance lock, IBus engine identity, tray icons, resource prefixes, VOCULINUX_* environment names, and the update-checker user agent. The internal Python module stays vocalinux and the distribution metadata keeps the upstream URLs as provenance. On first run a previous Vocalinux install's config.json, custom dictionary, and portal restore token are copied once into the new locations (new config always wins, no merge, no write-back); models re-resolve through the pinned-checksum download path. An offline naming-sweep test proves no old-name strings remain on user-visible surfaces outside the allowlist. Refs #18
1394 lines
64 KiB
Python
1394 lines
64 KiB
Python
#!/usr/bin/env python3
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"""
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Main entry point for Voculinux application.
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"""
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import argparse
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import atexit
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import logging
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import queue
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import sys
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import threading
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import time
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from concurrent.futures import Future, ThreadPoolExecutor
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from typing import Any, Dict, Optional
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from .app_identity import APP_ID, APP_NAME, CLI_PROG
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from .utils.host_process import running_from_appimage
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from .utils.vosk_model_info import SUPPORTED_LANGUAGES
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from .version import __version__
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
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)
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logger = logging.getLogger(__name__)
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# Note: GTK-dependent modules (tray_indicator) are imported lazily after
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# dependency checking to provide better error messages for pip/pipx users
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# Keep CLI --language choices in sync with the Settings catalog.
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LANGUAGE_CHOICES = tuple(SUPPORTED_LANGUAGES.keys())
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def _should_append_trailing_space() -> bool:
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"""Return whether completed transcriptions should get a trailing space.
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Reads config.json from disk on each call so Settings toggles take effect
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immediately. Historically TrayIndicator and main() each constructed their
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own ConfigManager, so an in-memory read would miss Settings writes; the
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instance is shared now, but the disk read stays as the conservative path
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(same pattern as TextInjector._should_copy_to_clipboard).
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"""
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try:
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import json
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import os
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from .utils.paths import config_dir
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config_path = os.path.join(config_dir(), "config.json")
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if os.path.exists(config_path):
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with open(config_path, "r") as f:
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config = json.load(f)
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return bool(config.get("text_injection", {}).get("append_trailing_space", True))
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except Exception as e:
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logger.debug(f"Could not read append_trailing_space setting: {e}")
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return True
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def parse_arguments():
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"""Parse command line arguments."""
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parser = argparse.ArgumentParser(prog=CLI_PROG, description=APP_NAME)
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parser.add_argument(
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"--version",
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action="version",
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version=f"%(prog)s {__version__}",
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help=f"Show the installed {APP_NAME} version and exit",
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)
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parser.add_argument("--debug", action="store_true", help="Enable debug logging")
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# default model, language and engine are loaded from default config
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# due to priority of args over config
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parser.add_argument(
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"--model",
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type=str,
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help=(
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"Speech recognition model ID. Examples: small, medium, large, "
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"medium.en-q5_0, large-v3-turbo"
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),
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)
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parser.add_argument(
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"--language",
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type=str,
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choices=LANGUAGE_CHOICES,
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help=(
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"Speech recognition language (auto for auto-detect, or a code "
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"from the Settings language catalog such as en-us, hu, ja, …)"
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),
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)
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parser.add_argument(
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"--engine",
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type=str,
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choices=["vosk", "whisper", "whisper_cpp", "parakeet", "faster_whisper", "remote_api"],
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help="Speech recognition engine to use (whisper_cpp recommended for best performance)",
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)
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parser.add_argument("--wayland", action="store_true", help="Force Wayland compatibility mode")
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parser.add_argument(
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"--start-minimized",
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action="store_true",
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help="Start minimized to system tray",
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)
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# External activation triggers: forward a control command to a running
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# instance over D-Bus (e.g. from a KDE Plasma global shortcut) and exit.
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parser.add_argument(
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"--toggle",
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action="store_true",
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help="Toggle voice typing on a running instance (via D-Bus) and exit",
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)
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parser.add_argument(
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"--start",
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action="store_true",
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help="Start voice typing on a running instance (via D-Bus) and exit",
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)
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parser.add_argument(
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"--stop",
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action="store_true",
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help="Stop voice typing on a running instance (via D-Bus) and exit",
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)
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parser.add_argument(
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"--dictionary-file",
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type=str,
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help=(
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"Use this custom terms file for this session only (UTF-8, one term per line); "
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"it does not change saved settings"
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),
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)
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parser.add_argument(
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"--transcribe-file",
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type=str,
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metavar="PATH",
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help=(
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"Transcribe an audio file with speaker attribution using the "
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"TinyDiarize model, print the transcript, and exit"
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),
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)
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return parser.parse_args()
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# CLI flags that trigger a running instance instead of starting a new one.
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_TRIGGER_FLAGS = ("toggle", "start", "stop")
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def _selected_trigger(args: argparse.Namespace) -> Optional[str]:
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"""Return the external-activation command requested via CLI, if any."""
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for name in _TRIGGER_FLAGS:
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# Explicit `is True` guards against MagicMock args in tests, whose
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# attributes are truthy by default.
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if getattr(args, name, False) is True:
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return name
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return None
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def _dispatch_trigger(command: str) -> int:
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"""Forward a control command to a running instance over D-Bus.
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Returns a process exit code (0 on success, 1 if no instance is reachable).
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"""
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from .dbus_service import send_command
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if send_command(command):
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logger.info("Sent '%s' command to running Voculinux instance", command)
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return 0
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logger.error(
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"Could not reach a running Voculinux instance to '%s'. Is Voculinux running?",
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command,
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)
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return 1
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def _run_file_transcription(path: str) -> int:
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"""Headless ``--transcribe-file`` path: transcribe and print, no GTK."""
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from .speech_recognition.diarization import format_transcript, transcribe_audio_file
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try:
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blocks = transcribe_audio_file(path)
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except (OSError, ValueError, RuntimeError) as error:
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print(
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f"{CLI_PROG}: could not transcribe {path}: {type(error).__name__}: {error}",
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file=sys.stderr,
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)
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return 1
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transcript = format_transcript(blocks)
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print(transcript if transcript else f"{CLI_PROG}: no speech detected")
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return 0
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def check_dependencies():
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"""Check for required dependencies and provide helpful error messages."""
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missing_system_deps = []
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missing_python_deps = []
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# Check for GTK3
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try:
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import gi
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gi.require_version("Gtk", "3.0")
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from gi.repository import Gtk # noqa: F401
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except (ImportError, ValueError) as e:
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logger.debug("GTK import failed: %s", e)
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missing_system_deps.append(
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"GTK3 (install with: sudo apt install python3-gi gir1.2-gtk-3.0)"
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)
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# Match tray_indicator.py, which never loads the glib indicator from the
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# AppImage: the bundle's GLib is older than that library needs.
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indicator_typelibs = [
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(namespace, version)
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for namespace, version in _APPINDICATOR_TYPELIBS
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if namespace != "AyatanaAppIndicatorGlib" or not running_from_appimage()
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]
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if not any(_typelib_available(namespace, version) for namespace, version in indicator_typelibs):
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missing_system_deps.append(
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"AppIndicator3/AyatanaAppIndicator3 - Required for system tray icon"
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)
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# Keyboard backends are optional and checked lazily by the shortcut manager.
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# Importing pynput can fail on Wayland/X-less sessions even when installed.
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# requests is used by various components and should remain a required check.
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try:
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import requests # noqa: F401
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except ImportError:
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missing_python_deps.append("requests (install with: pip install requests)")
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if missing_system_deps or missing_python_deps:
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logger.error("Missing required dependencies:")
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for dep in missing_system_deps + missing_python_deps:
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logger.error(f" - {dep}")
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if missing_system_deps:
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logger.error("")
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logger.error("System GTK packages are required. Install them first:")
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logger.error("")
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logger.error(" Ubuntu/Debian:")
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logger.error(
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" sudo apt install python3-gi gir1.2-gtk-3.0 gir1.2-ayatanaappindicator3-0.1"
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)
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logger.error("")
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logger.error(" NOTE: On GNOME Shell (default on Debian), you also need:")
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logger.error(" sudo apt install gnome-shell-extension-appindicator")
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logger.error(" Then log out and back in. Ubuntu includes this by default.")
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logger.error("")
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logger.error(" Fedora:")
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logger.error(" sudo dnf install python3-gobject gtk3 libayatana-appindicator-gtk3")
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logger.error("")
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logger.error(" Arch Linux:")
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logger.error(" sudo pacman -S python-gobject gtk3 libayatana-appindicator")
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logger.error("")
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logger.error(" openSUSE Tumbleweed:")
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logger.error(
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" PYVER=$(python3 -c 'import sys; "
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'print(f"python{sys.version_info.major}{sys.version_info.minor}")\')'
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)
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logger.error(
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' sudo zypper install "${PYVER}-gobject" gtk3 '
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"typelib-1_0-AyatanaAppIndicator3-0_1 "
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"typelib-1_0-Notify-0_7 libnotify4"
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)
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logger.error("")
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logger.error(
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"For pipx users: Install system packages BEFORE running 'pipx install voculinux'"
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)
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logger.error("")
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logger.error("For the best experience, use the recommended installer:")
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logger.error(
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" curl -fsSL https://raw.githubusercontent.com/VocaHQ/vocalinux/main/install.sh | bash"
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)
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return False
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return True
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# Tray indicator typelibs, most preferred first (same order as
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# tray_indicator.py): libayatana-appindicator-glib, Ayatana AppIndicator
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# (maintained; registers on KDE Plasma), its rare lowercase typelib, then
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# the legacy Canonical AppIndicator3 as last resort.
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_APPINDICATOR_TYPELIBS = (
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("AyatanaAppIndicatorGlib", "2.0"),
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("AyatanaAppIndicator3", "0.1"),
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("AyatanaAppindicator3", "0.1"),
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("AppIndicator3", "0.1"),
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)
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def _typelib_available(namespace: str, version: str) -> bool:
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"""Return True when the GObject Introspection namespace can be imported."""
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try:
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import gi
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gi.require_version(namespace, version)
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getattr(__import__("gi.repository", fromlist=[namespace]), namespace)
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except (ImportError, ValueError, AttributeError) as e:
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logger.debug("%s import failed: %s", namespace, e)
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return False
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return True
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def check_display_available():
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"""Check if a display is available for GTK."""
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try:
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import gi
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gi.require_version("Gdk", "3.0")
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from gi.repository import Gdk
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display = Gdk.Display.get_default()
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if display is None:
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logger.error("No display available. Voculinux requires a graphical environment.")
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logger.error("")
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logger.error("If running remotely, ensure DISPLAY is set:")
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logger.error(" export DISPLAY=:0")
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logger.error("")
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logger.error("If running in a headless environment, Voculinux cannot run.")
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return False
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return True
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except Exception as e:
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logger.error(f"Failed to initialize display: {e}")
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return False
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def check_appindicator_support():
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try:
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from gi.repository import Gio
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proxy = Gio.DBusProxy.new_for_bus_sync(
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Gio.BusType.SESSION,
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Gio.DBusProxyFlags.DO_NOT_AUTO_START_AT_CONSTRUCTION,
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None,
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"org.freedesktop.DBus",
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"/org/freedesktop/DBus",
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"org.freedesktop.DBus",
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None,
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)
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names_variant = proxy.call_sync(
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"ListNames",
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None,
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Gio.DBusCallFlags.NONE,
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-1,
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None,
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)
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if names_variant is not None:
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name_list = names_variant.unpack()[0]
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return "org.kde.StatusNotifierWatcher" in name_list
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except Exception:
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pass
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return True
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def main():
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"""Main entry point for the application."""
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# Parse arguments first so flags like --version work even when
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# another instance already holds the single-instance lock
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args = parse_arguments()
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# Configure debug logging if requested
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if args.debug:
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logging.getLogger().setLevel(logging.DEBUG)
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logger.debug("Debug logging enabled")
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# External activation: forward the command to a running instance over D-Bus
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# and exit without acquiring the lock or starting a second full instance.
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trigger = _selected_trigger(args)
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if trigger is not None:
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sys.exit(_dispatch_trigger(trigger))
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# Headless file transcription exits before the instance lock and GTK, so
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# it works alongside a running tray app and on a displayless shell.
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if isinstance(args.transcribe_file, str):
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sys.exit(_run_file_transcription(args.transcribe_file))
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# Check for single instance BEFORE any initialization
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from . import single_instance
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from .post_processor import apply_post_processing
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if not single_instance.acquire_lock():
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# Another instance is already running - show notification and exit
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try:
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from gi.repository import Notify
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Notify.init(APP_NAME)
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notification = Notify.Notification.new(
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APP_NAME,
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"Another instance is already running. Only one instance is allowed at a time.",
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"dialog-error",
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)
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notification.show()
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# Give notification time to display before exiting
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time.sleep(0.5)
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except Exception:
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# Fallback if notification fails (e.g., no display)
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pass
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sys.exit(1)
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# Register cleanup to release lock on exit
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atexit.register(single_instance.release_lock)
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# One-time legacy Voculinux import (ticket #18): copies a previous
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# install's config, dictionary, and portal token into the new locations
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# before anything reads them. No-op on every later run.
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try:
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from .legacy_import import import_legacy_once
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import_legacy_once()
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except Exception as error:
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logger.debug("Legacy import skipped: %s", error)
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# Check dependencies first (before importing GTK-dependent modules)
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if not check_dependencies():
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logger.error("Cannot start Voculinux due to missing dependencies")
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sys.exit(1)
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# Identity for WM class / AppIndicator title (otherwise shows as main.py)
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import gi
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gi.require_version("GLib", "2.0")
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from gi.repository import GLib
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GLib.set_prgname(APP_ID)
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GLib.set_application_name(APP_NAME)
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try:
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gi.require_version("Gdk", "3.0")
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from gi.repository import Gdk
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|
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Gdk.set_program_class(APP_NAME)
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except Exception:
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pass
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|
|
|
# Check if display is available before creating any GTK widgets
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if not check_display_available():
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sys.exit(1)
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|
|
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from .utils.gtk_color_scheme import apply_brand_accent, apply_os_color_scheme
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|
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apply_os_color_scheme()
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apply_brand_accent()
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|
|
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if not check_appindicator_support():
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logger.warning("No StatusNotifierWatcher found on D-Bus session bus.")
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logger.warning("The system tray icon may not appear.")
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logger.warning("")
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logger.warning("If you are using GNOME Shell, install the AppIndicator extension:")
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logger.warning(" Debian: sudo apt install gnome-shell-extension-appindicator")
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logger.warning(" Fedora: sudo dnf install gnome-shell-extension-appindicator")
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logger.warning(" Arch: sudo pacman -S gnome-shell-extension-appindicator")
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logger.warning("")
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logger.warning("After installing, log out and back in (or restart GNOME Shell).")
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|
|
|
# Now it's safe to import GTK-dependent modules
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import os
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|
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from .common_types import RecognitionState
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from .custom_dictionary import CustomDictionaryManager
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from .speech_recognition import recognition_manager
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from .text_injection import focused_window, text_injector
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from .ui import tray_indicator
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from .ui.action_handler import ActionHandler
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from .ui.config_manager import get_shared_config_manager
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|
from .ui.logging_manager import initialize_logging
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|
from .ui.transcription_history import (
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DEFAULT_MAX_ITEMS,
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TranscriptionHistory,
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sanitize_max_items,
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)
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|
from .utils.paths import data_dir
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|
|
|
# Initialize logging manager early
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|
initialize_logging()
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|
logger.info("Logging system initialized")
|
|
|
|
# Try to start IBus daemon if not running (for text injection)
|
|
# This helps on desktop environments where IBus doesn't start automatically
|
|
try:
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|
from .text_injection import start_ibus_daemon
|
|
|
|
if start_ibus_daemon():
|
|
logger.debug("IBus daemon started for text injection")
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|
except Exception as e:
|
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logger.debug(f"Could not start IBus daemon: {e}")
|
|
|
|
config_manager = get_shared_config_manager()
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saved_settings = config_manager.get_settings().get("speech_recognition", {})
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audio_settings = config_manager.get_settings().get("audio", {})
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|
|
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general_settings = config_manager.get_settings().get("general", {})
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first_run = general_settings.get("first_run", True)
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should_prompt_first_run = first_run and not args.start_minimized
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|
|
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if should_prompt_first_run:
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|
from .ui.first_run_dialog import show_first_run_dialog
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|
|
|
result = show_first_run_dialog()
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|
if result == "yes":
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|
from .ui import autostart_manager
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|
|
|
if autostart_manager.set_autostart(True):
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|
config_manager.set("general", "autostart", True)
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|
else:
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config_manager.set("general", "autostart", False)
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elif result == "no":
|
|
from .ui import autostart_manager
|
|
|
|
autostart_manager.set_autostart(False)
|
|
config_manager.set("general", "autostart", False)
|
|
|
|
if result in {"yes", "no"}:
|
|
config_manager.set("general", "first_run", False)
|
|
config_manager.save_settings()
|
|
|
|
# CLI arguments take precedence over saved config
|
|
# We need to check if the user explicitly provided arguments
|
|
# by examining sys.argv since argparse defaults don't tell us this
|
|
cli_engine_set = any(arg.startswith("--engine") for arg in sys.argv[1:])
|
|
cli_model_set = any(arg.startswith("--model") for arg in sys.argv[1:])
|
|
cli_language_set = any(arg.startswith("--language") for arg in sys.argv[1:])
|
|
|
|
# Use CLI args if explicitly set, otherwise fall back to saved config, then defaults
|
|
if cli_engine_set:
|
|
engine = args.engine
|
|
logger.info(f"Using engine={engine} (from command line)")
|
|
else:
|
|
engine = saved_settings.get("engine", args.engine)
|
|
logger.info(f"Using engine={engine} (from saved config)")
|
|
|
|
if cli_language_set:
|
|
language = args.language
|
|
logger.info(f"Using language={language} (from command line)")
|
|
else:
|
|
language = saved_settings.get("language", args.language)
|
|
logger.info(f"Using language={language} (from saved config)")
|
|
|
|
# Parakeet coverage is the model, not a catalog language. Normalize after
|
|
# CLI vs saved resolution so --language / stale config cannot leave an
|
|
# unused value on SpeechRecognitionManager.
|
|
resolved_language = language
|
|
language = recognition_manager.normalize_language_for_engine(engine, language)
|
|
if language != resolved_language:
|
|
logger.info(
|
|
"Parakeet ignores catalog language; using language=auto " f"(was {resolved_language})"
|
|
)
|
|
|
|
if cli_model_set:
|
|
model_size = args.model
|
|
logger.info(f"Using model={model_size} (from command line)")
|
|
else:
|
|
# Resolve per engine: the generic "model_size" key always holds the
|
|
# model of whichever engine was saved last, so reading it directly
|
|
# loads the wrong model whenever the two disagree.
|
|
model_size = config_manager.get_model_size_for_engine(engine)
|
|
logger.info(f"Using model={model_size} (saved for engine {engine})")
|
|
|
|
vad_sensitivity = saved_settings.get("vad_sensitivity", 3)
|
|
silence_timeout = saved_settings.get("silence_timeout", 1.2)
|
|
stop_sound_guard_ms = saved_settings.get("stop_sound_guard_ms", 200)
|
|
voice_commands_enabled = saved_settings.get("voice_commands_enabled") # None = auto
|
|
audio_device_index = audio_settings.get("device_index", None)
|
|
audio_device_name = audio_settings.get("device_name", None)
|
|
|
|
advanced_settings = config_manager.get_settings().get("advanced", {})
|
|
|
|
history_settings = config_manager.get_settings().get("history", {})
|
|
history_enabled = bool(history_settings.get("enabled", True))
|
|
# A hand-edited config.json can hold a non-numeric or out-of-range value;
|
|
# sanitize on load so a bad preference cannot abort startup.
|
|
history_max_items = sanitize_max_items(history_settings.get("max_items", DEFAULT_MAX_ITEMS))
|
|
# Opt-in on-disk record of snippets (#758). Off by default: dictated
|
|
# text stays memory-only unless the user asks for it to persist.
|
|
history_persist = bool(history_settings.get("persist", False))
|
|
|
|
dictionary_file = getattr(args, "dictionary_file", None)
|
|
transient_terms_path = (
|
|
dictionary_file.strip()
|
|
if isinstance(dictionary_file, str) and dictionary_file.strip()
|
|
else None
|
|
)
|
|
if transient_terms_path is not None:
|
|
logger.info("Using session-only custom terms file: %s", transient_terms_path)
|
|
dictionary_manager = CustomDictionaryManager(config_manager, transient_terms_path)
|
|
|
|
logger.info(f"Final settings: engine={engine}, language={language}, model={model_size}")
|
|
if audio_device_index is not None:
|
|
logger.info(
|
|
f"Using audio device index={audio_device_index} "
|
|
f"(name={audio_device_name}, from saved config)"
|
|
)
|
|
|
|
# Initialize main components
|
|
logger.info("Initializing Voculinux...")
|
|
|
|
try:
|
|
# Initialize speech recognition engine with saved/configured settings
|
|
speech_engine = recognition_manager.SpeechRecognitionManager(
|
|
engine=engine,
|
|
model_size=model_size,
|
|
language=language,
|
|
vad_sensitivity=vad_sensitivity,
|
|
silence_timeout=silence_timeout,
|
|
stop_sound_guard_ms=stop_sound_guard_ms,
|
|
buffer_during_reload=config_manager.get_bool(
|
|
"model_keepalive", "buffer_during_reload", False
|
|
),
|
|
voice_commands_enabled=voice_commands_enabled,
|
|
audio_device_index=audio_device_index,
|
|
audio_device_name=audio_device_name,
|
|
whispercpp_no_timestamps=advanced_settings.get("whispercpp_no_timestamps", True),
|
|
whispercpp_no_context=advanced_settings.get("whispercpp_no_context", True),
|
|
whispercpp_initial_prompt=advanced_settings.get("whispercpp_initial_prompt", ""),
|
|
whispercpp_language_candidates=advanced_settings.get(
|
|
"whispercpp_language_candidates", ""
|
|
),
|
|
whispercpp_temperature=advanced_settings.get("whispercpp_temperature", 0.0),
|
|
whispercpp_temperature_inc=advanced_settings.get("whispercpp_temperature_inc", -1.0),
|
|
whispercpp_entropy_thold=advanced_settings.get("whispercpp_entropy_thold", 2.4),
|
|
whispercpp_logprob_thold=advanced_settings.get("whispercpp_logprob_thold", -1.0),
|
|
whispercpp_no_speech_thold=advanced_settings.get("whispercpp_no_speech_thold", 0.6),
|
|
whispercpp_n_threads=advanced_settings.get("whispercpp_n_threads", 0),
|
|
whispercpp_gpu_device=advanced_settings.get("whispercpp_gpu_device", None),
|
|
dictionary_manager=dictionary_manager,
|
|
remote_api_url=saved_settings.get("remote_api_url", ""),
|
|
remote_api_key=saved_settings.get("remote_api_key", ""),
|
|
remote_api_endpoint=saved_settings.get("remote_api_endpoint", "/inference"),
|
|
remote_api_model=saved_settings.get("remote_api_model", "whisper-1"),
|
|
)
|
|
|
|
# Initialize text injection system
|
|
text_system = text_injector.TextInjector(wayland_mode=args.wayland)
|
|
|
|
# Initialize action handler
|
|
action_handler = ActionHandler(text_system)
|
|
|
|
# Transcription history: a newest-first list of recent dictation
|
|
# snippets surfaced in the tray menu. One snippet == one dictation
|
|
# session (everything said between start and stop). Memory-only by
|
|
# default; history.persist mirrors it to history.jsonl under the
|
|
# data directory so snippets survive restarts (#758).
|
|
transcription_history = TranscriptionHistory(
|
|
max_items=history_max_items,
|
|
enabled=history_enabled,
|
|
persist=history_persist,
|
|
store_path=os.path.join(data_dir(), "history.jsonl"),
|
|
)
|
|
|
|
def _history_record_fields() -> Dict[str, Any]:
|
|
"""Optional details stored with a persisted snippet (#758).
|
|
|
|
Language and model come off the engine; commit sites that know
|
|
the session bounds add duration themselves.
|
|
"""
|
|
fields: Dict[str, Any] = {}
|
|
language = getattr(speech_engine, "_session_language", None) or getattr(
|
|
speech_engine, "language", None
|
|
)
|
|
if language:
|
|
fields["language"] = language
|
|
engine = getattr(speech_engine, "engine", "")
|
|
model_size = getattr(speech_engine, "model_size", "")
|
|
model = "/".join(str(part) for part in (engine, model_size) if part)
|
|
if model:
|
|
fields["model"] = model
|
|
return fields
|
|
|
|
# Segments dictated during the open session, joined and committed to
|
|
# history when the session ends (state returns to IDLE). Each entry
|
|
# keeps the monotonic time its audio capture began: a history.clear()
|
|
# lands mid-decode, so capture time — not arrival order — decides
|
|
# whether a segment counts as pre-clear speech (dropped) or new
|
|
# dictation (kept).
|
|
#
|
|
# Session association: stop_recognition() emits IDLE after only a
|
|
# bounded wait on the recognition worker, so a slow final segment can
|
|
# still fire its text callback afterwards. Each segment must land in
|
|
# the session that produced it: while a session is open, segments
|
|
# accumulate in session_segments; a segment arriving on a worker that
|
|
# is not the open session's worker (or while no session is open) is a
|
|
# leftover of the just-ended session and is folded into its snippet
|
|
# instead of leaking into the next one.
|
|
session_lock = threading.Lock()
|
|
session_segments: list[tuple[str, float]] = []
|
|
session_open = False
|
|
# Monotonic time the open session started; a segment delivered on the
|
|
# live worker whose capture predates it belongs to an earlier session.
|
|
session_started_floor = 0.0
|
|
# Worker thread that produced the open session's segments; used to
|
|
# detect callbacks from a previous session's still-running worker.
|
|
session_worker: Optional[threading.Thread] = None
|
|
# Id of the just-closed session's snippet (from
|
|
# TranscriptionHistory.add), so late segments extend that entry
|
|
# rather than whichever snippet happens to be newest.
|
|
latest_snippet_id: Optional[int] = None
|
|
# Commit epoch of latest_snippet_id, for guarded late merges.
|
|
latest_snippet_epoch = transcription_history.epoch
|
|
# Snippet each ended session's worker owns, as
|
|
# ``worker -> (snippet_id, commit epoch)``: a worker's late deliveries
|
|
# merge into its own session's entry however many sessions have
|
|
# committed since, so one session's stragglers can never leak into a
|
|
# newer entry or split across several snippets.
|
|
ended_worker_snippets: dict[threading.Thread, tuple[int, int]] = {}
|
|
# Record fields (language, model) captured when a session opens, so a
|
|
# snippet committed after the engine has already moved on still
|
|
# carries the language that produced its audio (#758).
|
|
session_record_fields: Optional[Dict[str, Any]] = None
|
|
# Fields of the most recently ended session, for late deliveries that
|
|
# open their own snippet: they belong to that session, not the live
|
|
# one the engine is already configured for.
|
|
ended_session_fields: Dict[str, Any] = {}
|
|
# Per-worker copy of those fields, mirroring ended_worker_snippets:
|
|
# a straggler keeps its own session's language across sessions.
|
|
ended_worker_fields: dict[threading.Thread, Dict[str, Any]] = {}
|
|
# Every worker that has delivered in-session segments; a delivery on
|
|
# a thread never associated with a session is treated as the
|
|
# just-ended session's trailing decode, while a worker seen producing
|
|
# an earlier session can never merge into a newer entry. Dead
|
|
# workers are pruned so long runs don't accumulate finished threads.
|
|
session_workers_seen: set[threading.Thread] = set()
|
|
# Clear epoch the most-recently-ended session was committed under;
|
|
# late segments merging into its snippet are judged against it.
|
|
ended_session_epoch = transcription_history.epoch
|
|
# In-app dictation pad: the Wayland-proof fallback target that
|
|
# receives dictated text when "dictate to pad" capture is enabled
|
|
# (#726). Constructed before the tray so the tray menu can open it.
|
|
from .ui.dictation_pad import DictationPad
|
|
|
|
dictation_pad = DictationPad(
|
|
enabled=config_manager.is_dictate_to_pad_enabled(),
|
|
config_manager=config_manager,
|
|
)
|
|
|
|
def dictate_to_pad_enabled() -> bool:
|
|
"""Read the live capture toggle from the shared config manager.
|
|
|
|
The shared manager's in-memory cache is updated by Settings and
|
|
the pad checkbox before save_config rewrites config.json, so this
|
|
can never flip on a torn mid-write disk read and send dictated
|
|
text to whichever application holds focus.
|
|
"""
|
|
return bool(config_manager.is_dictate_to_pad_enabled())
|
|
|
|
# Where the last delivered segment went. "delete that" follows the
|
|
# text, not the current toggle: the capture setting may have flipped
|
|
# since the segment was delivered.
|
|
last_injected = {"to_pad": False}
|
|
|
|
# --- Callback wiring ---------------------------------------------------
|
|
# The speech engine emits four kinds of events, each handled by a
|
|
# dedicated callback registered below:
|
|
#
|
|
# segment_callback(text: str, started_at: float)
|
|
# Fires for each finalised segment with the monotonic time its
|
|
# audio capture began, just before the text callbacks. Records
|
|
# the segment into the transcription history.
|
|
#
|
|
# text_callback(text: str)
|
|
# Called on the recognition thread when a transcription segment
|
|
# is finalised. The wrapper below strips whitespace, optionally
|
|
# appends a trailing space (or legacy leading separator), injects
|
|
# the text, and records it so "delete that" can undo it.
|
|
#
|
|
# action_callback(action: str) -> bool
|
|
# Called when a voice command (e.g. "undo", "select all") is
|
|
# recognised. Delegated directly to ActionHandler.handle_action.
|
|
#
|
|
# state_callback(state: RecognitionState)
|
|
# Called whenever the engine transitions state (IDLE → LISTENING,
|
|
# etc.). Used here to clear the "last injected" buffer after a
|
|
# listening session ends.
|
|
# ------------------------------------------------------------------
|
|
|
|
def _normalize_segment_text(text: str) -> str:
|
|
"""Segment text as it is injected: stripped, auto-capitalized (Vosk)."""
|
|
# Strip only horizontal whitespace: "new line" / "new paragraph"
|
|
# produce leading newlines a bare lstrip() would silently destroy.
|
|
normalized = text.lstrip(" \t").rstrip(" \t")
|
|
# Auto-capitalize sentences if enabled (Vosk only - Whisper outputs proper casing)
|
|
if (
|
|
normalized
|
|
and config_manager.get("text_injection", "auto_capitalize")
|
|
and speech_engine.engine == "vosk"
|
|
):
|
|
from vocalinux.speech_recognition.command_processor import capitalize_sentences
|
|
|
|
normalized = capitalize_sentences(normalized)
|
|
return normalized
|
|
|
|
def record_history_segment(segment: str, started_at: float) -> None:
|
|
"""File a recognized segment under the dictation session it came from.
|
|
|
|
Runs on the recognition worker thread as a segment callback, so
|
|
``started_at`` is the moment the segment's audio capture began.
|
|
While a session is open, segments accumulate into that session's
|
|
pending snippet; a segment delivered after the session was
|
|
finalized — the worker can outlive the manager's bounded stop
|
|
wait and emit text after IDLE — is folded into its own session's
|
|
snippet rather than the next one.
|
|
|
|
Segments whose capture began before the most recent
|
|
history.clear() are refused: their audio can only contain
|
|
pre-clear speech, however late the decode finishes. Segments
|
|
captured after the clear are kept, so dictation that continues
|
|
across a clear is still recoverable.
|
|
"""
|
|
nonlocal session_worker, latest_snippet_id, latest_snippet_epoch
|
|
nonlocal session_workers_seen, ended_worker_snippets, ended_worker_fields
|
|
if not transcription_history.enabled:
|
|
return
|
|
segment = _normalize_segment_text(segment)
|
|
if not segment:
|
|
return
|
|
worker = threading.current_thread()
|
|
# The engine's live worker, when it exposes one: a segment from
|
|
# any other thread is a leftover from an older session.
|
|
current_worker = getattr(speech_engine, "recognition_thread", None)
|
|
with session_lock:
|
|
# Dead workers cannot deliver again; drop them so long runs
|
|
# don't accumulate finished threads.
|
|
session_workers_seen = {t for t in session_workers_seen if t.is_alive()}
|
|
ended_worker_snippets = {
|
|
w: s for w, s in ended_worker_snippets.items() if w.is_alive()
|
|
}
|
|
ended_worker_fields = {w: f for w, f in ended_worker_fields.items() if w.is_alive()}
|
|
if started_at <= transcription_history.cleared_at:
|
|
# Captured before the last clear — must not re-enter.
|
|
return
|
|
# Segments captured while the Settings mic test runs are test
|
|
# speech, not dictation. The [floor, ceiling) window survives
|
|
# the test's end, so segments still decoding after it cannot
|
|
# leak into history.
|
|
test_floor = getattr(speech_engine, "test_capture_floor", None)
|
|
test_ceiling = getattr(speech_engine, "test_capture_ceiling", None)
|
|
if (
|
|
isinstance(test_floor, (int, float))
|
|
and started_at >= test_floor
|
|
and (not isinstance(test_ceiling, (int, float)) or started_at < test_ceiling)
|
|
):
|
|
return
|
|
if (
|
|
session_open
|
|
and started_at >= session_started_floor
|
|
and (
|
|
worker is session_worker
|
|
or worker is current_worker
|
|
# When the engine exposes no worker (tests, mocks), the
|
|
# first segment of a session tags it.
|
|
or (
|
|
session_worker is None
|
|
and not isinstance(current_worker, threading.Thread)
|
|
)
|
|
)
|
|
):
|
|
session_worker = worker
|
|
session_workers_seen.add(worker)
|
|
session_segments.append((segment, started_at))
|
|
return
|
|
# Late segment from a session that already ended: merge into
|
|
# its own session's snippet, found by the worker delivering
|
|
# it — a newer session may already have committed on top, so
|
|
# the newest entry is not the target. A delivery on a thread
|
|
# never associated with a session is treated as the
|
|
# just-ended session's trailing decode while a session is
|
|
# still open; a worker seen producing an earlier session can
|
|
# never merge into a newer entry. Every write is guarded by
|
|
# the snippet's own commit epoch, so a clear() landing
|
|
# between that commit and this delivery still refuses the
|
|
# text.
|
|
owner = ended_worker_snippets.get(worker)
|
|
if owner is not None and transcription_history.extend_entry(
|
|
owner[0], segment, expected_epoch=owner[1]
|
|
):
|
|
return
|
|
if (
|
|
latest_snippet_id is not None
|
|
and session_open
|
|
and worker not in session_workers_seen
|
|
and transcription_history.extend_entry(
|
|
latest_snippet_id, segment, expected_epoch=latest_snippet_epoch
|
|
)
|
|
):
|
|
return
|
|
# Otherwise the late segments are the session's only output
|
|
# and form their own snippet, which its worker keeps owning.
|
|
# The fields come from the session that produced the audio,
|
|
# not the one the engine may already have moved on to.
|
|
owner_fields = ended_worker_fields.get(worker, ended_session_fields)
|
|
snippet_id = transcription_history.add(
|
|
segment, expected_epoch=ended_session_epoch, **owner_fields
|
|
)
|
|
if snippet_id is not None:
|
|
latest_snippet_id = snippet_id
|
|
latest_snippet_epoch = ended_session_epoch
|
|
ended_worker_snippets[worker] = (snippet_id, ended_session_epoch)
|
|
ended_worker_fields[worker] = owner_fields
|
|
session_workers_seen.add(worker)
|
|
|
|
def inject_transcription(text_to_inject: str, to_pad: Optional[bool] = None) -> None:
|
|
"""Apply the separator rules and inject one finalised segment.
|
|
|
|
Args:
|
|
text_to_inject: Post-processed text ready for the text injector.
|
|
to_pad: Destination decided when the job's focus check ran;
|
|
the delivery must reuse that same decision — re-reading
|
|
the live toggle here could disagree with the check and
|
|
send the text somewhere it was never verified for.
|
|
"""
|
|
# Read from disk so the Settings toggle applies without restart.
|
|
append_trailing_space = _should_append_trailing_space()
|
|
|
|
if append_trailing_space:
|
|
# Put the separator into the previous field so the next session
|
|
# (push-to-talk / toggle) continues cleanly without needing
|
|
# cross-session memory — and without a leading space in empty
|
|
# fields. Skip after newlines from "new line" / "new paragraph".
|
|
if not text_to_inject.endswith((" ", "\t", "\n")):
|
|
text_to_inject += " "
|
|
logger.debug("Appended trailing space after transcription segment")
|
|
elif action_handler.last_injected_text and action_handler.last_injected_text.strip():
|
|
# Legacy: leading space between consecutive in-session segments.
|
|
text_to_inject = " " + text_to_inject
|
|
logger.debug("Added space separator before new segment")
|
|
|
|
captured = dictate_to_pad_enabled() if to_pad is None else to_pad
|
|
if captured:
|
|
# In-app capture: skip cross-application injection entirely
|
|
# and land the text in the pad instead (#726).
|
|
dictation_pad.append_text(text_to_inject)
|
|
success = True
|
|
else:
|
|
success = text_system.inject_text(text_to_inject)
|
|
if success:
|
|
action_handler.set_last_injected_text(text_to_inject)
|
|
last_injected["to_pad"] = captured
|
|
else:
|
|
# Deletion state must match what reached the app: a partial
|
|
# failure counts the confirmed prefix, an unknowable count
|
|
# (-1) clears it, and 0 leaves the previous segment's state —
|
|
# a failed injection types nothing, so "delete that" still
|
|
# means the segment before it.
|
|
typed = text_system.last_typed_count
|
|
if isinstance(typed, int) and typed > 0:
|
|
action_handler.set_last_injected_text(text_to_inject[:typed])
|
|
elif isinstance(typed, int) and typed < 0:
|
|
action_handler.set_last_injected_text("")
|
|
|
|
# Post-processing runs a user executable that may take seconds per
|
|
# segment. Running it on the recognition thread would stall the
|
|
# consumer of the bounded audio-segment queue, and once that fills,
|
|
# queued dictation is dropped. A dedicated worker applies the script
|
|
# and injects in order; its unbounded backlog waits instead of losing
|
|
# speech, and a timed-out script falls back to the original text.
|
|
#
|
|
# Every segment goes through this one worker — when no script is
|
|
# configured apply_post_processing is a pass-through — so clearing the
|
|
# script path mid-queue can never let a later segment overtake an
|
|
# earlier one still waiting behind a running script.
|
|
post_processing_executor = ThreadPoolExecutor(
|
|
max_workers=1, thread_name_prefix="voculinux-post-processing"
|
|
)
|
|
# Cleared by the quit hook so a queued or in-flight job drops its
|
|
# segment instead of injecting while the injector is being stopped.
|
|
accepting_injections = threading.Event()
|
|
accepting_injections.set()
|
|
# Serialises the flag check and the inject call itself: quitting can
|
|
# wait out an injection already in progress while never blocking on a
|
|
# queued or running script.
|
|
injection_lock = threading.Lock()
|
|
# Segments that may sit in the backlog (a script is configured, or a
|
|
# job is still running) are bound to the app focused at submit time;
|
|
# the worker drops them if focus has moved to another application.
|
|
pending_jobs = 0
|
|
pending_jobs_lock = threading.Lock()
|
|
|
|
def _focused_app_unchanged(
|
|
target: Optional[focused_window.FocusedWindow],
|
|
) -> bool:
|
|
"""Return True unless focus verifiably moved to a different app.
|
|
|
|
A missing baseline or a failed re-probe stays permissive: without
|
|
a reliable identity the segment keeps the injector's own targeting.
|
|
"""
|
|
if target is None:
|
|
return True
|
|
current = focused_window.get_focused_window()
|
|
if current is None:
|
|
return True
|
|
return current.identity_blob() == target.identity_blob()
|
|
|
|
def _start_focus_probe() -> queue.Queue[Optional[focused_window.FocusedWindow]]:
|
|
"""Probe the focused window now, off the calling thread.
|
|
|
|
Focus probes shell out to compositor tools under their own
|
|
timeouts, so each runs on a fresh daemon thread: the recognition
|
|
thread never waits on them, and — unlike a shared worker — an
|
|
earlier slow probe cannot delay this one past the focus change it
|
|
is meant to capture.
|
|
"""
|
|
probe_result: queue.Queue[Optional[focused_window.FocusedWindow]] = queue.Queue(
|
|
maxsize=1
|
|
)
|
|
|
|
def probe() -> None:
|
|
try:
|
|
probe_result.put(focused_window.get_focused_window())
|
|
except (OSError, RuntimeError, queue.Full) as exc:
|
|
# get_focused_window() reports Optional and should never
|
|
# raise; if one escapes anyway the failed probe is
|
|
# indistinguishable from unavailable focus information
|
|
# unless it is logged — and the waiting job must still
|
|
# be released.
|
|
logger.exception("Focus probe crashed unexpectedly: %s", exc)
|
|
probe_result.put(None)
|
|
|
|
threading.Thread(target=probe, daemon=True, name="voculinux-focus-probe").start()
|
|
return probe_result
|
|
|
|
def _probe_result(
|
|
probe: Optional[queue.Queue[Optional[focused_window.FocusedWindow]]],
|
|
) -> Optional[focused_window.FocusedWindow]:
|
|
"""Return the focus identity a submit-time probe captured.
|
|
|
|
The probe thread starts the moment the segment is submitted, so
|
|
joining it here adds no wait beyond the probe's own runtime.
|
|
"""
|
|
if probe is None:
|
|
return None
|
|
return probe.get()
|
|
|
|
def post_process_and_inject(
|
|
text_to_inject: str,
|
|
target_probe: Optional[queue.Queue[Optional[focused_window.FocusedWindow]]],
|
|
) -> None:
|
|
"""Apply the configured post-processing script, then inject.
|
|
|
|
Runs on the post-processing worker, never the recognition thread.
|
|
An unexpected failure falls back to the unprocessed segment so
|
|
dictation is never lost inside the worker.
|
|
|
|
Args:
|
|
text_to_inject: Finalised transcription segment.
|
|
target_probe: Probe of the app focused when the segment was
|
|
dictated; the segment is dropped if focus has since moved
|
|
elsewhere. None keeps the injector's own targeting.
|
|
"""
|
|
nonlocal pending_jobs
|
|
try:
|
|
if not accepting_injections.is_set():
|
|
return
|
|
try:
|
|
processed_text = apply_post_processing(text_to_inject, config_manager)
|
|
except Exception:
|
|
logger.exception("Post-processing raised unexpectedly; injecting original text")
|
|
processed_text = text_to_inject
|
|
if processed_text is None or not accepting_injections.is_set():
|
|
return
|
|
# Pad-bound segments land in the dictation pad regardless of
|
|
# where focus sits, so a focus change since the segment was
|
|
# dictated must not drop them. The destination is decided
|
|
# once here and passed to the injector: re-reading the
|
|
# toggle at delivery could flip it after the check was
|
|
# skipped and send the segment to the focused application.
|
|
to_pad = dictate_to_pad_enabled()
|
|
if not to_pad and not _focused_app_unchanged(_probe_result(target_probe)):
|
|
logger.info("Dropping queued segment: focus moved to another application")
|
|
return
|
|
with injection_lock:
|
|
if not accepting_injections.is_set():
|
|
return
|
|
inject_transcription(processed_text, to_pad)
|
|
finally:
|
|
with pending_jobs_lock:
|
|
pending_jobs -= 1
|
|
|
|
def _run_action(
|
|
action: str,
|
|
target_probe: Optional[queue.Queue[Optional[focused_window.FocusedWindow]]],
|
|
) -> bool:
|
|
"""Run one voice-command action on the post-processing worker.
|
|
|
|
Actions send keystrokes through the same injector as transcription
|
|
text, so they queue on the same worker in spoken order and are
|
|
bound to the app focused when the command was issued: even a
|
|
submission that looks immediate can run after a context switch,
|
|
so the binding applies to every action. The job waits on the
|
|
probe for its full duration rather than racing it — the probe's
|
|
compositor calls carry their own one-second timeouts and
|
|
short-circuit on tools that are absent, so it answers in
|
|
milliseconds on a healthy desktop and always terminates; a valid
|
|
command is therefore never discarded over timing, and the
|
|
verified result is the only thing that can drop it.
|
|
"""
|
|
nonlocal pending_jobs
|
|
try:
|
|
if not accepting_injections.is_set():
|
|
return False
|
|
# Pad-bound actions run on the dictation pad no matter which
|
|
# application is focused, so a focus change since the command
|
|
# was issued must not drop them; only app-bound deliveries are
|
|
# focus-checked.
|
|
if action == "delete_last":
|
|
targets_pad = bool(last_injected["to_pad"])
|
|
else:
|
|
targets_pad = dictate_to_pad_enabled()
|
|
if not targets_pad and not _focused_app_unchanged(_probe_result(target_probe)):
|
|
logger.info("Dropping action: focus moved to another application")
|
|
return False
|
|
with injection_lock:
|
|
if not accepting_injections.is_set():
|
|
return False
|
|
# "delete that" follows the segment it removes: the pad
|
|
# when the last delivered text went there (even if capture
|
|
# was toggled off since) and the focused application when
|
|
# it was injected (even if capture was toggled on). Every
|
|
# other editing command is handled pad-side while capturing
|
|
# so its shortcuts never leak into whichever application
|
|
# holds focus.
|
|
if action == "delete_last":
|
|
if not action_handler.last_injected_text:
|
|
return True
|
|
if last_injected["to_pad"]:
|
|
# Only the pad's own segment bookkeeping may size
|
|
# the deletion: a manual edit or a pad "undo"
|
|
# blurs the boundaries (last_segment is None), and
|
|
# falling back to the recorded text's length could
|
|
# erase characters the user typed after dictating.
|
|
# Refuse rather than misdelete — the tracking is
|
|
# still cleared so a repeated command cannot retry
|
|
# the stale length.
|
|
target = dictation_pad.last_segment
|
|
if target is None:
|
|
action_handler.set_last_injected_text("")
|
|
last_injected["to_pad"] = False
|
|
return True
|
|
deleted = dictation_pad.delete_last_chars(len(target))
|
|
if deleted:
|
|
action_handler.set_last_injected_text("")
|
|
last_injected["to_pad"] = False
|
|
return True
|
|
handled_app: bool = action_handler.handle_action(action)
|
|
return handled_app
|
|
if targets_pad:
|
|
handled = bool(dictation_pad.handle_action(action))
|
|
if handled and action in ("undo", "redo") and last_injected["to_pad"]:
|
|
# Pad history moved: retarget "delete that" at the
|
|
# segment now at the pad's tail. Keep the pad
|
|
# destination even when the tail is empty — a
|
|
# redo can restore it.
|
|
action_handler.set_last_injected_text(dictation_pad.last_segment or "")
|
|
return handled
|
|
handled_app = action_handler.handle_action(action)
|
|
return handled_app
|
|
finally:
|
|
with pending_jobs_lock:
|
|
pending_jobs -= 1
|
|
|
|
def action_callback_wrapper(action: str) -> Optional[Future]:
|
|
"""Queue a voice-command action behind any pending text jobs.
|
|
|
|
Args:
|
|
action: Voice-command action from the speech engine.
|
|
|
|
Returns:
|
|
The queued worker future, or None while the application is
|
|
quitting. Callers such as tests can wait on it; the speech
|
|
engine ignores the return value.
|
|
"""
|
|
nonlocal pending_jobs
|
|
if not accepting_injections.is_set():
|
|
return None
|
|
# Every action is bound to the app focused at submit time:
|
|
# submission does not guarantee immediate execution, so there is
|
|
# always a focus-change window between the two. The probe runs
|
|
# off this thread and the worker only waits on it briefly, so
|
|
# the binding costs nothing on a healthy desktop.
|
|
# Probing and submitting happen inside the lock so queue order
|
|
# matches the order these callbacks ran in — a job that saw an
|
|
# empty queue cannot end up waiting behind one that arrived while
|
|
# its submission was still in flight.
|
|
with pending_jobs_lock:
|
|
pending_jobs += 1
|
|
target_probe = _start_focus_probe()
|
|
try:
|
|
future: Future = post_processing_executor.submit(
|
|
_run_action, action, target_probe
|
|
)
|
|
except RuntimeError:
|
|
# The quit path already shut the worker down.
|
|
pending_jobs -= 1
|
|
return None
|
|
return future
|
|
|
|
def _reset_last_injected() -> None:
|
|
"""Clear the last-injected buffer on the post-processing worker."""
|
|
action_handler.set_last_injected_text("")
|
|
last_injected["to_pad"] = False
|
|
|
|
def _shutdown_post_processing() -> None:
|
|
"""Stop the post-processing worker for application quit.
|
|
|
|
Clearing the flag makes queued or in-flight jobs drop their
|
|
results, and pending submissions are cancelled without waiting on
|
|
a running script — quit must not freeze the tray on the script's
|
|
own timeout. An injection already in progress is asked to abort
|
|
so the lock wait stays bounded by a single chunk's subprocess
|
|
timeout instead of a long transcription's whole budget, and the
|
|
injector is never stopped mid-inject.
|
|
"""
|
|
accepting_injections.clear()
|
|
text_system.abort_injections()
|
|
with injection_lock:
|
|
pass
|
|
post_processing_executor.shutdown(wait=False, cancel_futures=True)
|
|
|
|
def text_callback_wrapper(text: str) -> Optional[Future]:
|
|
"""Bridge between speech engine text events and the text injector.
|
|
|
|
Called on the recognition thread with each finalised transcription
|
|
segment. Strips leading and trailing spaces/tabs (but preserves
|
|
newlines from voice commands), then hands the
|
|
segment to the single post-processing worker — a pass-through when
|
|
no script is configured — which applies the separator rules and
|
|
injects via TextInjector in spoken order.
|
|
|
|
Args:
|
|
text: Raw transcription segment from the speech engine.
|
|
|
|
Returns:
|
|
The queued worker future, or None when the segment was dropped
|
|
before submission (an empty segment, or the application is
|
|
quitting). Callers such as tests can wait on it; the speech
|
|
engine ignores the return value.
|
|
"""
|
|
nonlocal pending_jobs
|
|
# Preserve trailing newlines ("new line" / "new paragraph"); only
|
|
# strip spaces/tabs that whisper sometimes wraps around tokens.
|
|
text_to_inject = _normalize_segment_text(text)
|
|
if not text_to_inject or not accepting_injections.is_set():
|
|
return None
|
|
|
|
# A backlog or a configured script means this segment can inject
|
|
# long after it was dictated; bind it to the app it targets now so
|
|
# it cannot land in whatever the user switched to meanwhile. The
|
|
# probe runs off this thread: compositor tools could otherwise
|
|
# stall the consumer of the bounded audio-segment queue for seconds
|
|
# per segment.
|
|
script_configured = bool(config_manager.get_str("post_processing", "script_path", ""))
|
|
with pending_jobs_lock:
|
|
may_queue = pending_jobs > 0 or script_configured
|
|
pending_jobs += 1
|
|
target_probe = _start_focus_probe() if may_queue else None
|
|
try:
|
|
future: Future = post_processing_executor.submit(
|
|
post_process_and_inject, text_to_inject, target_probe
|
|
)
|
|
except RuntimeError:
|
|
# The quit path already shut the worker down.
|
|
pending_jobs -= 1
|
|
return None
|
|
return future
|
|
|
|
def on_state_change(state: RecognitionState) -> None:
|
|
"""Reset the last-injected buffer when a listening session ends.
|
|
|
|
Also commits the just-finished dictation session to the
|
|
transcription history as a single snippet. With jobs still queued
|
|
the reset is queued behind them, so a "delete that" action
|
|
recognised just before the session ended still sees the text it
|
|
refers to.
|
|
"""
|
|
nonlocal session_open, session_worker, latest_snippet_id
|
|
nonlocal ended_session_epoch, session_started_floor, latest_snippet_epoch
|
|
nonlocal session_record_fields, ended_session_fields
|
|
if state in (RecognitionState.IDLE, RecognitionState.ERROR):
|
|
if state == RecognitionState.IDLE:
|
|
with pending_jobs_lock:
|
|
backlog = pending_jobs > 0
|
|
if backlog and accepting_injections.is_set():
|
|
try:
|
|
post_processing_executor.submit(_reset_last_injected)
|
|
except RuntimeError:
|
|
action_handler.set_last_injected_text("")
|
|
last_injected["to_pad"] = False
|
|
else:
|
|
action_handler.set_last_injected_text("")
|
|
last_injected["to_pad"] = False
|
|
with session_lock:
|
|
session_open = False
|
|
closing_worker = session_worker
|
|
session_worker = None
|
|
# Snapshot before re-reading the engine: the fields belong
|
|
# to the session being closed, and the live values could
|
|
# already describe a reconfigure that landed mid-close.
|
|
closing_fields = session_record_fields or _history_record_fields()
|
|
session_record_fields = None
|
|
ended_session_fields = closing_fields
|
|
ended_session_epoch = transcription_history.epoch
|
|
# Only segments captured after the last clear() join the
|
|
# snippet — speech captured before it is gone for good,
|
|
# while dictation continued across the clear is kept.
|
|
cleared_at = transcription_history.cleared_at
|
|
joined = " ".join(
|
|
text for text, started_at in session_segments if started_at > cleared_at
|
|
)
|
|
session_segments.clear()
|
|
# Guarded by the epoch observed here: a clear() landing
|
|
# between this read and the add still refuses the
|
|
# snippet. The committed snippet's id stays open to late
|
|
# segments still trickling out of the worker; a session
|
|
# that produced no text leaves no entry to merge into.
|
|
latest_snippet_id = transcription_history.add(
|
|
joined,
|
|
expected_epoch=ended_session_epoch,
|
|
duration=(
|
|
time.monotonic() - session_started_floor
|
|
if session_started_floor
|
|
else None
|
|
),
|
|
**closing_fields,
|
|
)
|
|
latest_snippet_epoch = ended_session_epoch
|
|
if closing_worker is not None:
|
|
ended_worker_fields[closing_worker] = closing_fields
|
|
if latest_snippet_id is not None and closing_worker is not None:
|
|
ended_worker_snippets[closing_worker] = (
|
|
latest_snippet_id,
|
|
ended_session_epoch,
|
|
)
|
|
else:
|
|
with session_lock:
|
|
if not session_open:
|
|
session_open = True
|
|
leftover_worker = session_worker
|
|
session_worker = None
|
|
# Leftover segments belong to the displaced session;
|
|
# grab its fields before the new session re-captures.
|
|
leftover_fields = session_record_fields or ended_session_fields
|
|
if leftover_worker is not None:
|
|
ended_worker_fields[leftover_worker] = leftover_fields
|
|
session_started_floor = time.monotonic()
|
|
session_record_fields = _history_record_fields()
|
|
# Segments left over by a session that ended without a
|
|
# closing state commit as their own snippet rather
|
|
# than leaking into the new session's — still only
|
|
# those captured after the last clear().
|
|
if session_segments:
|
|
cleared_at = transcription_history.cleared_at
|
|
# A clear() during the unclosed session bumped the
|
|
# epoch since the previous close — judge against
|
|
# the live epoch, not the stale ended one, or
|
|
# valid post-clear dictation is refused.
|
|
stray_epoch = transcription_history.epoch
|
|
latest_snippet_id = transcription_history.add(
|
|
" ".join(
|
|
text
|
|
for text, started_at in session_segments
|
|
if started_at > cleared_at
|
|
),
|
|
expected_epoch=stray_epoch,
|
|
**leftover_fields,
|
|
)
|
|
latest_snippet_epoch = stray_epoch
|
|
if latest_snippet_id is not None and leftover_worker is not None:
|
|
ended_worker_snippets[leftover_worker] = (
|
|
latest_snippet_id,
|
|
stray_epoch,
|
|
)
|
|
session_segments.clear()
|
|
|
|
# Connect speech recognition to text injection and action handling.
|
|
# Segments reach history with their capture-start time so a mid-
|
|
# session clear() can separate pre-clear speech from new dictation;
|
|
# injection keeps the plain text callback.
|
|
speech_engine.register_segment_callback(record_history_segment)
|
|
|
|
speech_engine.register_text_callback(text_callback_wrapper)
|
|
speech_engine.register_action_callback(action_callback_wrapper)
|
|
speech_engine.register_state_callback(on_state_change)
|
|
|
|
# Initialize and start the system tray indicator
|
|
indicator = tray_indicator.TrayIndicator(
|
|
speech_engine=speech_engine,
|
|
text_injector=text_system,
|
|
transcription_history=transcription_history,
|
|
on_quit=_shutdown_post_processing,
|
|
dictation_pad=dictation_pad,
|
|
)
|
|
|
|
# Start the GTK main loop
|
|
indicator.run()
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to initialize Voculinux: {e}")
|
|
logger.error("Please check the logs above for more details")
|
|
sys.exit(1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|