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v1.5.0
blitztext-app-linux/linux/blitztext/config.py
mARTin-B78 d55cdafcc1 on-screen dictation overlay at the cursor; Release 1.5.0 
Show a translucent, click-through bubble the moment recording starts (by
hotkey or wakeword): a pulsing microphone, a live waveform of the mic
level, and the recognised text (word-by-word when streaming, otherwise a
brief final-result confirmation). The tail points at the text caret via
AT-SPI accessibility, falling back to the mouse pointer, then a screen
corner. Also gives hands-free wakeword sessions visible feedback, whose
notifications are suppressed by design. X11 only.

- overlay.py: GTK override-redirect HUD (mic + waveform + bubble), drawn
  with Cairo; thread-safe, marshalled onto the GTK loop.
- caret.py: best-effort anchor (AT-SPI caret -> pointer -> window/corner).
- daemon: optional level_cb/text_cb hooks; reuses the VAD level meter for
  non-streaming, a dedicated meter for streaming. Stays UI-agnostic.
- gtkui: instantiate the overlay, drive show/update/hide from status.
- General settings: "Visual overlay" toggle; config overlay_enabled /
  overlay_anchor (default on, "caret").
- Docs: CHANGELOG 1.5.0, version bump, README + MANUAL.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-07 13:13:41 +02:00

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"""Configuration loading and the default config template."""
from __future__ import annotations
import os
import tomllib
from dataclasses import dataclass, field
from pathlib import Path
from .llm import LLMEngine
from .stt import STTEngine
CONFIG_DIR = Path(os.environ.get("XDG_CONFIG_HOME", Path.home() / ".config")) / "blitztext"
CONFIG_PATH = CONFIG_DIR / "config.toml"
@dataclass
class Workflow:
name: str
hotkey: str
mode: str # "transcribe" | "rewrite" | "stream"
prompt: str = ""
# Spoken trigger phrases for voice routing (matched at start/end of speech).
keywords: list[str] = field(default_factory=list)
# Optional per-workflow overrides of the [rewrite] defaults.
model: str | None = None
temperature: float | None = None
# Cosmetic, used by the GUI.
description: str = ""
icon: str = "⚡"
@dataclass
class Config:
# general
recorder: str = "auto"
mic: str = "" # pactl/pipewire source name; "" = default device
output: str = "type" # type | paste
type_delay_ms: int = 12
notify: bool = True
notify_routing: bool = True # announce which preset/keyword a voice command matched
language: str = "de" # whisper hint; "" = autodetect
# on-screen overlay (mic + live waveform + recognised-text bubble)
overlay_enabled: bool = True
overlay_anchor: str = "caret" # caret (best-effort AT-SPI) | pointer | corner
# input scheme
input_mode: str = "modifiers" # "modifiers" (Ctrl+Win/Ctrl/Alt/Esc) | "hotkeys" (combos)
push_to_talk: bool = False
key_start: str = "<ctrl>+<cmd>" # start recording (Ctrl+Win)
key_stop: str = "<ctrl>" # stop -> paste
key_send: str = "<alt>" # stop -> paste -> Enter
key_cancel: str = "<esc>" # discard
# quality gate
min_speech_seconds: float = 0.4
silence_rms: float = 150.0
reject_hallucinations: bool = True
strip_trailing_punctuation: bool = False
# audio cues (paths to WAV files; "" = built-in system sound)
sounds_enabled: bool = True # master switch for all start/stop/wakeword cues
sound_before: str = ""
sound_after: str = ""
# whisper
model: str = "small"
device: str = "auto" # auto | cuda | cpu
compute_type: str = "auto" # auto | int8 | float16 | int8_float16
beam_size: int = 5
# rewrite (OpenAI-compatible)
base_url: str = "https://api.openai.com/v1"
api_key_env: str = "OPENAI_API_KEY"
rewrite_model: str = "gpt-4o-mini"
temperature: float = 0.3
timeout: int = 45
# voice-keyword routing
routing_enabled: bool = True
routing_hotkey: str = "<ctrl>+<alt>+<space>"
routing_default: str = "" # preset name used when no keyword matches; "" = first
routing_threshold: float = 0.82
# speech-to-text engines (presets)
stt_engines: list[STTEngine] = field(default_factory=list)
stt_active: str = ""
# llm engines (presets) for the rewrite step
llm_engines: list[LLMEngine] = field(default_factory=list)
llm_active: str = ""
# wakeword
wakeword_enabled: bool = False
wakeword_uri: str = "tcp://127.0.0.1:10400"
wakeword_model: str = "okay_computer"
wakeword_sound_detected: str = "" # WAV played when the wakeword fires (speak now)
wakeword_sound_done: str = "" # WAV played when the command is captured
wakeword_silence_seconds: float = 2.0 # auto-stop after this much trailing silence
# workflows
workflows: list[Workflow] = field(default_factory=list)
@property
def active_stt(self) -> STTEngine:
e = next((x for x in self.stt_engines if x.name == self.stt_active), None)
if e:
return e
return self.stt_engines[0] if self.stt_engines else STTEngine("Local", "local", model=self.model)
@property
def active_llm(self) -> LLMEngine:
e = next((x for x in self.llm_engines if x.name == self.llm_active), None)
if e:
return e
if self.llm_engines:
return self.llm_engines[0]
return LLMEngine("Default", self.base_url, self.rewrite_model, self.api_key_env, self.temperature)
def preset_by_name(self, name: str | None) -> "Workflow | None":
if not name:
return None
return next((w for w in self.workflows if w.name == name), None)
@property
def default_preset(self) -> "Workflow | None":
named = self.preset_by_name(self.routing_default)
if named:
return named
if not self.workflows:
return None
# No explicit default configured: prefer a plain transcribe preset over
# whatever happens to be first, so the no-keyword fallback never silently
# routes to an LLM rewrite.
return next((w for w in self.workflows if w.mode == "transcribe"), self.workflows[0])
@property
def all_keywords(self) -> list[str]:
out: list[str] = []
for w in self.workflows:
out.extend(w.keywords)
if w.name and w.name not in out:
out.append(w.name) # names are implicit triggers — bias STT for them too
return out
@property
def api_key(self) -> str | None:
return os.environ.get(self.api_key_env) or None
def load(path: Path = CONFIG_PATH) -> Config:
"""Load config from TOML, creating a default file on first run."""
if not path.exists():
ensure_default(path)
with path.open("rb") as fh:
data = tomllib.load(fh)
g = data.get("general", {})
w = data.get("whisper", {})
r = data.get("rewrite", {})
rt = data.get("routing", {})
inp = data.get("input", {})
q = data.get("quality", {})
snd = data.get("sounds", {})
ww = data.get("wakeword", {})
cfg = Config(
recorder=g.get("recorder", "auto"),
mic=g.get("mic", ""),
output=g.get("output", "type"),
type_delay_ms=int(g.get("type_delay_ms", 4)),
notify=bool(g.get("notify", True)),
notify_routing=bool(g.get("notify_routing", True)),
language=g.get("language", "de"),
overlay_enabled=bool(g.get("overlay_enabled", True)),
overlay_anchor=g.get("overlay_anchor", "caret"),
model=w.get("model", "small"),
device=w.get("device", "auto"),
compute_type=w.get("compute_type", "auto"),
beam_size=int(w.get("beam_size", 5)),
base_url=r.get("base_url", "https://api.openai.com/v1").rstrip("/"),
api_key_env=r.get("api_key_env", "OPENAI_API_KEY"),
rewrite_model=r.get("model", "gpt-4o-mini"),
temperature=float(r.get("temperature", 0.3)),
timeout=int(r.get("timeout", 45)),
routing_enabled=bool(rt.get("enabled", True)),
routing_hotkey=rt.get("hotkey", "<ctrl>+<alt>+<space>"),
routing_default=rt.get("default", ""),
routing_threshold=float(rt.get("threshold", 0.82)),
input_mode=inp.get("mode", "modifiers"),
push_to_talk=bool(inp.get("push_to_talk", False)),
key_start=inp.get("start", "<ctrl>+<cmd>"),
key_stop=inp.get("stop", "<ctrl>"),
key_send=inp.get("send", "<alt>"),
key_cancel=inp.get("cancel", "<esc>"),
min_speech_seconds=float(q.get("min_speech_seconds", 0.4)),
silence_rms=float(q.get("silence_rms", 150.0)),
reject_hallucinations=bool(q.get("reject_hallucinations", True)),
strip_trailing_punctuation=bool(q.get("strip_trailing_punctuation", False)),
sounds_enabled=bool(snd.get("enabled", True)),
sound_before=snd.get("before", ""),
sound_after=snd.get("after", ""),
wakeword_enabled=bool(ww.get("enabled", False)),
wakeword_uri=ww.get("uri", "tcp://127.0.0.1:10400"),
wakeword_model=ww.get("model", "okay_computer"),
wakeword_sound_detected=ww.get("sound_detected", ""),
wakeword_sound_done=ww.get("sound_done", ""),
wakeword_silence_seconds=float(ww.get("silence_seconds", 2.0)),
)
for entry in data.get("workflow", []):
cfg.workflows.append(
Workflow(
name=entry["name"],
hotkey=entry.get("hotkey", ""),
mode=entry.get("mode", "transcribe"),
prompt=entry.get("prompt", ""),
keywords=list(entry.get("keywords", [])),
model=entry.get("model"),
temperature=entry.get("temperature"),
description=entry.get("description", ""),
icon=entry.get("icon", "⚡"),
)
)
if not cfg.workflows:
raise ValueError(f"No [[workflow]] entries defined in {path}")
# STT engines (default: a single local faster-whisper engine).
cfg.stt_engines = [
STTEngine(
name=e["name"],
type=e.get("type", "local"),
url=e.get("url", "").rstrip("/"),
model=e.get("model", ""),
api_key_env=e.get("api_key_env", ""),
)
for e in data.get("stt_engine", [])
] or [STTEngine("Local faster-whisper", "local", model=cfg.model)]
cfg.stt_active = data.get("stt", {}).get("active", cfg.stt_engines[0].name)
# LLM engines (default: synthesized from the legacy [rewrite] block).
cfg.llm_engines = [
LLMEngine(
name=e["name"],
url=e.get("url", "https://api.openai.com/v1").rstrip("/"),
model=e.get("model", "gpt-4o-mini"),
api_key_env=e.get("api_key_env", ""),
temperature=float(e.get("temperature", cfg.temperature)),
type=e.get("type", "cloud"),
)
for e in data.get("llm_engine", [])
] or [LLMEngine("Default", cfg.base_url, cfg.rewrite_model, cfg.api_key_env, cfg.temperature)]
cfg.llm_active = data.get("llm", {}).get("active", cfg.llm_engines[0].name)
return cfg
def save(cfg: Config, path: Path = CONFIG_PATH) -> None:
"""Write the config back to TOML (used by the settings UI).
Note: inline comments from the template are not preserved on save.
"""
import tomli_w
data: dict = {
"general": {
"recorder": cfg.recorder,
"mic": cfg.mic,
"output": cfg.output,
"type_delay_ms": cfg.type_delay_ms,
"notify": cfg.notify,
"notify_routing": cfg.notify_routing,
"language": cfg.language,
"overlay_enabled": cfg.overlay_enabled,
"overlay_anchor": cfg.overlay_anchor,
},
"whisper": {
"model": cfg.model,
"device": cfg.device,
"compute_type": cfg.compute_type,
"beam_size": cfg.beam_size,
},
"rewrite": {
"base_url": cfg.base_url,
"api_key_env": cfg.api_key_env,
"model": cfg.rewrite_model,
"temperature": cfg.temperature,
"timeout": cfg.timeout,
},
"input": {
"mode": cfg.input_mode,
"push_to_talk": cfg.push_to_talk,
"start": cfg.key_start,
"stop": cfg.key_stop,
"send": cfg.key_send,
"cancel": cfg.key_cancel,
},
"routing": {
"enabled": cfg.routing_enabled,
"hotkey": cfg.routing_hotkey,
"default": cfg.routing_default,
"threshold": cfg.routing_threshold,
},
"quality": {
"min_speech_seconds": cfg.min_speech_seconds,
"silence_rms": cfg.silence_rms,
"reject_hallucinations": cfg.reject_hallucinations,
"strip_trailing_punctuation": cfg.strip_trailing_punctuation,
},
"sounds": {
"enabled": cfg.sounds_enabled,
"before": cfg.sound_before,
"after": cfg.sound_after,
},
"wakeword": {
"enabled": cfg.wakeword_enabled,
"uri": cfg.wakeword_uri,
"model": cfg.wakeword_model,
"sound_detected": cfg.wakeword_sound_detected,
"sound_done": cfg.wakeword_sound_done,
"silence_seconds": cfg.wakeword_silence_seconds,
},
"stt": {"active": cfg.stt_active},
"stt_engine": [
{k: v for k, v in {
"name": e.name, "type": e.type, "url": e.url,
"model": e.model, "api_key_env": e.api_key_env,
}.items() if v or k in ("name", "type")}
for e in cfg.stt_engines
],
"llm": {"active": cfg.llm_active},
"llm_engine": [
{"name": e.name, "type": e.type, "url": e.url, "model": e.model,
"api_key_env": e.api_key_env, "temperature": e.temperature}
for e in cfg.llm_engines
],
"workflow": [],
}
for wf in cfg.workflows:
entry: dict = {"name": wf.name, "hotkey": wf.hotkey, "mode": wf.mode}
if wf.keywords:
entry["keywords"] = wf.keywords
if wf.prompt:
entry["prompt"] = wf.prompt
if wf.model:
entry["model"] = wf.model
if wf.temperature is not None:
entry["temperature"] = wf.temperature
if wf.description:
entry["description"] = wf.description
if wf.icon and wf.icon != "⚡":
entry["icon"] = wf.icon
data["workflow"].append(entry)
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("wb") as fh:
tomli_w.dump(data, fh)
def ensure_default(path: Path = CONFIG_PATH) -> Path:
path.parent.mkdir(parents=True, exist_ok=True)
if not path.exists():
path.write_text(DEFAULT_CONFIG, encoding="utf-8")
return path
# Hotkey syntax is pynput's GlobalHotKeys format, e.g. "<ctrl>+<alt>+space".
DEFAULT_CONFIG = """\
# Blitztext for Linux — configuration
# Hotkey format follows pynput: <ctrl> <alt> <shift> <cmd> + a letter/keyname.
# Each hotkey TOGGLES recording: press to start speaking, press again to finish.
[general]
recorder = "auto" # auto | pw-record | parecord | arecord
output = "type" # "type" = xdotool types it; "paste" = clipboard + Ctrl+V
type_delay_ms = 12 # per-keystroke delay for xdotool type (raise if chars drop)
notify = true # desktop notifications for each phase
notify_routing = true # announce which preset/keyword a voice command matched (shown even hands-free)
language = "de" # Whisper language hint; "" = autodetect
overlay_enabled = true # on-screen mic + live waveform + recognised-text bubble at the cursor
overlay_anchor = "caret" # caret (best-effort, follows the text cursor) | pointer | corner
[input]
# How you start/stop dictation.
# mode = "modifiers" -> Ctrl+Win start | Ctrl stop+paste | Alt stop+paste+Enter | Esc cancel
# mode = "hotkeys" -> the per-preset combos + the [routing] hotkey below
# Voice-keyword routing still applies to what you say in either mode.
mode = "modifiers"
push_to_talk = false # modifiers mode: hold Start to record, release to stop+paste
start = "<ctrl>+<cmd>" # <cmd> = the Super/Windows key
stop = "<ctrl>"
send = "<alt>"
cancel = "<esc>"
[quality]
# Reject silence/too-short clips and the stock phrases Whisper invents on
# silence (e.g. "Thank you.", "Untertitel ...") so you don't paste garbage.
min_speech_seconds = 0.4 # discard clips shorter than this
silence_rms = 150.0 # discard clips quieter than this RMS (0..32767)
reject_hallucinations = true
strip_trailing_punctuation = false
[sounds]
# Audio cues for MANUAL (keyboard/hotkey) dictation. The hands-free wakeword
# cues are separate and independent — see [wakeword] sound_detected/sound_done.
# enabled = on/off for these manual cues. "before"/"after" are optional WAV
# files; leave empty for the built-in system sound. "before" plays when
# recording starts; "after" on any stop (paste, paste+Enter, or auto-stop).
enabled = true
before = ""
after = ""
[whisper]
model = "small" # tiny | base | small | medium | large-v3, or a local path
device = "auto" # auto | cuda | cpu (auto tries cuda, falls back to cpu)
compute_type = "auto" # auto | int8 | float16 | int8_float16
beam_size = 5
[rewrite]
# OpenAI-compatible chat endpoint. Define your own provider/API/model here.
# Point base_url at OpenAI, OR any local server that speaks the OpenAI chat API
# (vLLM, llama-swap, Ollama's /v1, LM Studio, ...). Examples:
# base_url = "https://api.openai.com/v1" (OpenAI)
# base_url = "http://localhost:8000/v1" (local vLLM / llama-swap)
# api_key_env names the ENV VAR holding the key (local servers often ignore it).
base_url = "https://api.openai.com/v1"
api_key_env = "OPENAI_API_KEY"
model = "gpt-4o-mini" # default model for rewrite workflows
temperature = 0.3
timeout = 45
[routing]
# Voice-keyword routing: ONE hotkey to dictate. Say a preset's keyword at the
# START or END of your speech and that preset is applied; otherwise the default
# preset is used. (Per-preset hotkeys below still work as direct shortcuts.)
enabled = true
hotkey = "<ctrl>+<alt>+<space>"
default = "Transcribe" # preset used when no keyword is recognised
threshold = 0.82 # 0..1 fuzzy-match strictness (higher = stricter)
[wakeword]
# Hands-free dictation using an external wyoming-openwakeword server.
# Respects the /tmp/wake_muted file to disable listening.
enabled = false
uri = "tcp://127.0.0.1:10400"
model = "okay_computer"
# Audio cues for hands-free sessions, independent of [sounds] above: play the
# given WAV when the wakeword fires (speak now) and when the command is captured.
# Leave empty for NO sound — these are a hands-free session's only feedback,
# since its desktop notifications are suppressed.
sound_detected = ""
sound_done = ""
# Auto-stop the recording this many seconds after you stop speaking (silence).
silence_seconds = 2.0
# ----------------------------------------------------------------------------
# Speech-to-text engines (presets). The active one is used for transcription.
# type = "local" -> in-process faster-whisper (uses [whisper] above)
# type = "openai" -> remote OpenAI-compatible /audio/transcriptions server
# (faster-whisper-server, Groq, WhisperX, batch ASR NIMs, ...)
# type = "riva_realtime" -> Riva/NIM realtime WebSocket STT, for mode="stream"
# ----------------------------------------------------------------------------
[stt]
active = "Local faster-whisper"
[[stt_engine]]
name = "Local faster-whisper"
type = "local"
# [[stt_engine]]
# name = "faster-whisper-server"
# type = "openai"
# url = "http://localhost:8010/v1"
# model = "Systran/faster-whisper-base"
# api_key_env = "" # e.g. GROQ_API_KEY for a cloud endpoint
# [[stt_engine]]
# name = "Nemotron ASR Streaming"
# type = "riva_realtime"
# url = "http://localhost:8006/v1"
# model = "" # blank = use the realtime server default
# api_key_env = ""
# ----------------------------------------------------------------------------
# LLM engines (presets) for the rewrite step. Any OpenAI-compatible chat API
# (OpenAI, vLLM, llama-swap, Ollama /v1, LM Studio, Groq, OpenRouter, ...).
# ----------------------------------------------------------------------------
[llm]
active = "Default"
[[llm_engine]]
name = "Default"
type = "cloud" # "local" | "cloud"
url = "https://api.openai.com/v1"
model = "gpt-4o-mini"
api_key_env = "OPENAI_API_KEY"
temperature = 0.3
# [[llm_engine]]
# name = "Local llama-swap"
# type = "local"
# url = "http://localhost:28080/v1"
# model = "Qwen3.5-4B"
# api_key_env = ""
# temperature = 0.3
# ----------------------------------------------------------------------------
# Workflows / presets. mode = "transcribe" types the raw transcript. mode =
# "rewrite" sends it through the LLM with `prompt` as the system prompt.
# "stream" writes live words via a riva_realtime STT engine.
# keywords = spoken trigger phrases for voice routing (start or end of speech)
# hotkey = optional direct global hotkey ("" = none; voice routing is primary)
# A workflow may override the [rewrite] defaults with its own model/temperature.
# ----------------------------------------------------------------------------
[[workflow]]
name = "Transcribe"
icon = "⚡"
description = "Speak, get plain text."
hotkey = ""
mode = "transcribe"
# [[workflow]]
# name = "STT Streaming"
# icon = "⚡"
# description = "Live words while you speak."
# hotkey = "<ctrl>+<alt>+s"
# mode = "stream"
[[workflow]]
name = "Nicer email"
icon = "✉"
description = "Rough notes → polished email."
keywords = ["nicer email", "bessere email", "schöne mail"]
hotkey = "<ctrl>+<alt>+e"
mode = "rewrite"
prompt = '''Du bist ein Schreibassistent fuer E-Mails. Du erhaeltst ein gesprochenes Transkript.
Schreibe daraus eine freundliche, gut formulierte und etwas ausfuehrlichere E-Mail:
- Korrigiere Rechtschreibung und Grammatik
- Formuliere hoeflich, klar und professionell
- Ergaenze sinnvolle Hoeflichkeitsfloskeln (Anrede/Gruss), wenn passend
- Behalte die urspruengliche Aussage und Absicht bei, erfinde keine Fakten
- Antworte in der Sprache des Transkripts
- Gib NUR den E-Mail-Text zurueck, keine Erklaerungen'''
[[workflow]]
name = "Improve text"
icon = "✨"
description = "Speak → cleaner writing."
keywords = ["improve text", "verbessere text", "bessere schreibweise"]
hotkey = "<ctrl>+<alt>+i"
mode = "rewrite"
prompt = '''Du bist ein Lektor und Schreibassistent. Verbessere den folgenden gesprochenen Text:
- Korrigiere Rechtschreibung und Grammatik
- Verbessere Formulierung und Lesefluss, behalte die Bedeutung bei
- Antworte in der Sprache des Transkripts
- Gib NUR den verbesserten Text zurueck, keine Erklaerungen'''
[[workflow]]
name = "Calm down"
icon = "☺"
description = "Frustrated in → calm out."
keywords = ["calm down", "beruhige das", "entspannte nachricht"]
hotkey = "<ctrl>+<alt>+c"
mode = "rewrite"
prompt = '''Du erhaeltst ein gesprochenes, frustriertes oder veraergertes Transkript.
Formuliere es in eine ruhige, sachliche und hoefliche Nachricht um, die dasselbe
Anliegen professionell vermittelt. Antworte in der Sprache des Transkripts.
Gib NUR die umformulierte Nachricht zurueck, keine Erklaerungen.'''
[[workflow]]
name = "Add emojis"
icon = "✿"
description = "Text in → emojis out."
keywords = ["add emojis", "mit emojis", "emojis dazu"]
hotkey = "<ctrl>+<alt>+j"
mode = "rewrite"
prompt = '''Du erhaeltst ein gesprochenes Transkript. Gib den Text moeglichst originalgetreu
zurueck, fuege aber regelmaessig passende Emojis ein (etwa alle 1-2 Saetze).
Korrigiere offensichtliche Fehler, behalte Stil und Bedeutung bei.
Gib NUR den Text mit Emojis zurueck, keine Erklaerungen.'''
"""
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