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Clean up config and STT naming

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Thales Maciel 2026-02-24 11:15:48 -03:00
parent b74aaaa1c4
commit 8c68719041
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9 changed files with 42 additions and 98 deletions
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8
AGENTS.md
View file

@ -2,9 +2,9 @@
## Project Structure & Module Organization
- `src/leld.py` is the primary entrypoint (X11 transcription daemon).
- `src/leld.py` is the primary entrypoint (X11 STT daemon).
- `src/recorder.py` handles audio capture using PortAudio via `sounddevice`.
- `src/transcription.py` wraps faster-whisper for transcription.
- `src/stt.py` wraps faster-whisper for STT.
## Build, Test, and Development Commands
@ -29,5 +29,5 @@
## Configuration Tips
- Audio input is controlled via `WHISPER_FFMPEG_IN` (device index or name).
- Model, language, device, and extra args can be set with `WHISPER_MODEL`, `WHISPER_LANG`, `WHISPER_DEVICE`, and `WHISPER_EXTRA_ARGS`.
- Audio input is configured via the `recording.input` field in `config.json`.
- STT model and device are configured via the `stt` section in `config.json`.

15
README.md
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@ -1,6 +1,6 @@
# lel
Python X11 transcription daemon that records audio, runs Whisper, logs the transcript, and can optionally run AI post-processing before injecting text.
Python X11 STT daemon that records audio, runs Whisper, logs the transcript, and can optionally run AI post-processing before injecting text.
## Requirements
@ -35,11 +35,10 @@ Create `~/.config/lel/config.json`:
{
"daemon": { "hotkey": "Cmd+m" },
"recording": { "input": "0" },
"transcription": { "model": "base", "device": "cpu" },
"stt": { "model": "base", "device": "cpu" },
"injection": { "backend": "clipboard" },
"ai_cleanup": {
"enabled": true,
"model": "llama3.2:3b",
"temperature": 0.0,
"base_url": "http://localhost:11434",
@ -48,14 +47,6 @@ Create `~/.config/lel/config.json`:
}
```
Env overrides:
- `WHISPER_MODEL`, `WHISPER_DEVICE`
- `WHISPER_FFMPEG_IN` (device index or name)
- `LEL_HOTKEY`, `LEL_INJECTION_BACKEND`
- `LEL_AI_CLEANUP_ENABLED`, `LEL_AI_CLEANUP_MODEL`, `LEL_AI_CLEANUP_TEMPERATURE`
- `LEL_AI_CLEANUP_BASE_URL`, `LEL_AI_CLEANUP_API_KEY`
Recording input can be a device index (preferred) or a substring of the device
name.
@ -72,7 +63,7 @@ systemctl --user enable --now lel
## Usage
- Press the hotkey once to start recording.
- Press it again to stop and transcribe.
- Press it again to stop and run STT.
- The transcript is logged to stderr.
Injection backends:

2
pyproject.toml
View file

@ -1,7 +1,7 @@
[project]
name = "lel"
version = "0.0.0"
description = "X11 transcription daemon with faster-whisper and optional AI cleanup"
description = "X11 STT daemon with faster-whisper and optional AI cleanup"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [

3
src/aiprocess.py
View file

@ -149,9 +149,6 @@ def main() -> int:
json.dumps(redacted_dict(cfg), indent=2),
)
if not cfg.ai_cleanup.get("enabled", False):
logging.warning("ai_enabled is false; proceeding anyway")
prompt = load_system_prompt("")
logging.info("system prompt:\n%s", prompt)

58
src/config.py
View file

@ -1,22 +1,16 @@
import json
import os
from dataclasses import dataclass, field
from pathlib import Path
def _parse_bool(val: str) -> bool:
return val.strip().lower() in {"1", "true", "yes", "on"}
@dataclass
class Config:
daemon: dict = field(default_factory=lambda: {"hotkey": "Cmd+m"})
recording: dict = field(default_factory=lambda: {"input": ""})
transcription: dict = field(default_factory=lambda: {"model": "base", "device": "cpu"})
stt: dict = field(default_factory=lambda: {"model": "base", "device": "cpu"})
injection: dict = field(default_factory=lambda: {"backend": "clipboard"})
ai_cleanup: dict = field(
default_factory=lambda: {
"enabled": False,
"model": "llama3.2:3b",
"temperature": 0.0,
"base_url": "http://localhost:11434",
@ -36,19 +30,16 @@ def load(path: str | None) -> Config:
p = Path(path) if path else default_path()
if p.exists():
data = json.loads(p.read_text(encoding="utf-8"))
if any(k in data for k in ("daemon", "recording", "transcription", "transcribing", "injection", "ai_cleanup", "ai")):
if any(k in data for k in ("daemon", "recording", "stt", "injection", "ai_cleanup", "ai")):
for k, v in data.items():
if hasattr(cfg, k):
setattr(cfg, k, v)
if "transcribing" in data and "transcription" not in data:
cfg.transcription = data.get("transcribing", cfg.transcription)
else:
cfg.daemon["hotkey"] = data.get("hotkey", cfg.daemon["hotkey"])
cfg.recording["input"] = data.get("ffmpeg_input", cfg.recording["input"])
cfg.transcription["model"] = data.get("whisper_model", cfg.transcription["model"])
cfg.transcription["device"] = data.get("whisper_device", cfg.transcription["device"])
cfg.recording["input"] = data.get("input", cfg.recording["input"])
cfg.stt["model"] = data.get("whisper_model", cfg.stt["model"])
cfg.stt["device"] = data.get("whisper_device", cfg.stt["device"])
cfg.injection["backend"] = data.get("injection_backend", cfg.injection["backend"])
cfg.ai_cleanup["enabled"] = data.get("ai_enabled", cfg.ai_cleanup["enabled"])
cfg.ai_cleanup["model"] = data.get("ai_model", cfg.ai_cleanup["model"])
cfg.ai_cleanup["temperature"] = data.get("ai_temperature", cfg.ai_cleanup["temperature"])
cfg.ai_cleanup["base_url"] = data.get("ai_base_url", cfg.ai_cleanup["base_url"])
@ -58,13 +49,12 @@ def load(path: str | None) -> Config:
cfg.daemon = {"hotkey": "Cmd+m"}
if not isinstance(cfg.recording, dict):
cfg.recording = {"input": ""}
if not isinstance(cfg.transcription, dict):
cfg.transcription = {"model": "base", "device": "cpu"}
if not isinstance(cfg.stt, dict):
cfg.stt = {"model": "base", "device": "cpu"}
if not isinstance(cfg.injection, dict):
cfg.injection = {"backend": "clipboard"}
if not isinstance(cfg.ai_cleanup, dict):
cfg.ai_cleanup = {
"enabled": False,
"model": "llama3.2:3b",
"temperature": 0.0,
"base_url": "http://localhost:11434",
@ -80,40 +70,6 @@ def load(path: str | None) -> Config:
except Exception:
pass
# env overrides
if os.getenv("WHISPER_MODEL"):
cfg.transcription["model"] = os.environ["WHISPER_MODEL"]
if os.getenv("WHISPER_DEVICE"):
cfg.transcription["device"] = os.environ["WHISPER_DEVICE"]
if os.getenv("WHISPER_FFMPEG_IN"):
cfg.recording["input"] = os.environ["WHISPER_FFMPEG_IN"]
if os.getenv("LEL_HOTKEY"):
cfg.daemon["hotkey"] = os.environ["LEL_HOTKEY"]
if os.getenv("LEL_INJECTION_BACKEND"):
cfg.injection["backend"] = os.environ["LEL_INJECTION_BACKEND"]
if os.getenv("LEL_AI_CLEANUP_ENABLED"):
cfg.ai_cleanup["enabled"] = _parse_bool(os.environ["LEL_AI_CLEANUP_ENABLED"])
if os.getenv("LEL_AI_CLEANUP_MODEL"):
cfg.ai_cleanup["model"] = os.environ["LEL_AI_CLEANUP_MODEL"]
if os.getenv("LEL_AI_CLEANUP_TEMPERATURE"):
cfg.ai_cleanup["temperature"] = float(os.environ["LEL_AI_CLEANUP_TEMPERATURE"])
if os.getenv("LEL_AI_CLEANUP_BASE_URL"):
cfg.ai_cleanup["base_url"] = os.environ["LEL_AI_CLEANUP_BASE_URL"]
if os.getenv("LEL_AI_CLEANUP_API_KEY"):
cfg.ai_cleanup["api_key"] = os.environ["LEL_AI_CLEANUP_API_KEY"]
if os.getenv("LEL_AI_ENABLED"):
cfg.ai_cleanup["enabled"] = _parse_bool(os.environ["LEL_AI_ENABLED"])
if os.getenv("LEL_AI_MODEL"):
cfg.ai_cleanup["model"] = os.environ["LEL_AI_MODEL"]
if os.getenv("LEL_AI_TEMPERATURE"):
cfg.ai_cleanup["temperature"] = float(os.environ["LEL_AI_TEMPERATURE"])
if os.getenv("LEL_AI_BASE_URL"):
cfg.ai_cleanup["base_url"] = os.environ["LEL_AI_BASE_URL"]
if os.getenv("LEL_AI_API_KEY"):
cfg.ai_cleanup["api_key"] = os.environ["LEL_AI_API_KEY"]
validate(cfg)
return cfg

38
src/leld.py
View file

@ -11,7 +11,7 @@ from pathlib import Path
from config import Config, load, redacted_dict
from recorder import start_recording, stop_recording
from transcription import FasterWhisperTranscriber, TranscriptionConfig
from stt import FasterWhisperSTT, STTConfig
from aiprocess import AIConfig, build_processor
from inject import inject
from x11_hotkey import listen
@ -21,7 +21,7 @@ from tray import run_tray
class State:
IDLE = "idle"
RECORDING = "recording"
TRANSCRIBING = "transcribing"
STT = "stt"
PROCESSING = "processing"
OUTPUTTING = "outputting"
@ -34,11 +34,11 @@ class Daemon:
self.proc = None
self.record = None
self.timer = None
self.transcriber = FasterWhisperTranscriber(
TranscriptionConfig(
model=cfg.transcription.get("model", "base"),
self.stt = FasterWhisperSTT(
STTConfig(
model=cfg.stt.get("model", "base"),
language=None,
device=cfg.transcription.get("device", "cpu"),
device=cfg.stt.get("device", "cpu"),
vad_filter=True,
)
)
@ -61,7 +61,7 @@ class Daemon:
self._start_recording_locked()
return
if self.state == State.RECORDING:
self.state = State.TRANSCRIBING
self.state = State.STT
threading.Thread(target=self._stop_and_process, daemon=True).start()
return
logging.info("busy (%s), trigger ignored", self.state)
@ -86,7 +86,7 @@ class Daemon:
with self.lock:
if self.state != State.RECORDING:
return
self.state = State.TRANSCRIBING
self.state = State.STT
threading.Thread(target=self._stop_and_process, daemon=True).start()
def _stop_and_process(self):
@ -116,11 +116,11 @@ class Daemon:
return
try:
self.set_state(State.TRANSCRIBING)
logging.info("transcription started")
text = self.transcriber.transcribe(record.wav_path, language="en")
self.set_state(State.STT)
logging.info("stt started")
text = self.stt.transcribe(record.wav_path, language="en")
except Exception as exc:
logging.error("transcription failed: %s", exc)
logging.error("stt failed: %s", exc)
self.set_state(State.IDLE)
return
@ -129,21 +129,21 @@ class Daemon:
self.set_state(State.IDLE)
return
logging.info("transcription: %s", text)
logging.info("stt: %s", text)
ai_enabled = self.cfg.ai_cleanup.get("enabled", False)
ai_prompt_file = ""
if ai_enabled:
ai_model = (self.cfg.ai_cleanup.get("model") or "").strip()
ai_base_url = (self.cfg.ai_cleanup.get("base_url") or "").strip()
if ai_model and ai_base_url:
self.set_state(State.PROCESSING)
logging.info("ai processing started")
try:
processor = build_processor(
AIConfig(
model=self.cfg.ai_cleanup.get("model", ""),
model=ai_model,
temperature=self.cfg.ai_cleanup.get("temperature", 0.0),
system_prompt_file=ai_prompt_file,
base_url=self.cfg.ai_cleanup.get("base_url", ""),
base_url=ai_base_url,
api_key=self.cfg.ai_cleanup.get("api_key", ""),
timeout_sec=25,
language_hint="en",
@ -171,7 +171,7 @@ class Daemon:
with self.lock:
if self.state != State.RECORDING:
return
self.state = State.TRANSCRIBING
self.state = State.STT
threading.Thread(target=self._stop_and_process, daemon=True).start()

8
src/transcription.py → src/stt.py
View file

@ -6,7 +6,7 @@ from faster_whisper import WhisperModel
@dataclass
class TranscriptionConfig:
class STTConfig:
model: str
language: str | None
device: str
@ -20,8 +20,8 @@ def _compute_type(device: str) -> str:
return "int8"
class FasterWhisperTranscriber:
def __init__(self, cfg: TranscriptionConfig):
class FasterWhisperSTT:
def __init__(self, cfg: STTConfig):
self.cfg = cfg
self._model: WhisperModel | None = None
@ -35,7 +35,7 @@ class FasterWhisperTranscriber:
def transcribe(self, wav_path: str, language: str | None = None) -> str:
self._load()
segments, _info = self._model.transcribe(
segments, _info = self._model.transcribe( # type: ignore[union-attr]
wav_path,
language=language or self.cfg.language,
vad_filter=self.cfg.vad_filter,

6
src/tray.py
View file

@ -28,7 +28,7 @@ class Tray:
def _icon_path(self, state: str) -> str:
if state == "recording":
return str(self.base / "recording.png")
if state == "transcribing":
if state == "stt":
return str(self.base / "transcribing.png")
if state == "processing":
return str(self.base / "processing.png")
@ -37,8 +37,8 @@ class Tray:
def _title(self, state: str) -> str:
if state == "recording":
return "Recording"
if state == "transcribing":
return "Transcribing"
if state == "stt":
return "STT"
if state == "processing":
return "AI Processing"
return "Idle"

2
systemd/lel.service
View file

@ -1,5 +1,5 @@
[Unit]
Description=lel X11 transcription daemon
Description=lel X11 STT daemon
After=default.target
[Service]