Hitonabi
037c4b11df
- main.py: CustomTkinter GUI (400x520) mit Status-Indikator, Conversation-Log, Screenshot-Toggle und Settings-Dialog. Thread-sicherer UI-Queue für Hotkey-Callbacks. - hotkey_listener.py: pynput globaler Hotkey (press/release) + KeyCapturer für interaktive Hotkey-Konfiguration im Settings-Dialog. - config_manager.py: JSON-Settings in ~/.hermes-voice/settings.json mit DEFAULTS, load() merged gespeicherte mit Default-Werten. - audio_input.py: Vereinfacht auf start_recording/stop_recording/transcribe (kein Wake-Word-Loop mehr, direkt hotkey-gesteuert). - ai_client.py: Settings-dict statt config.py-Imports, MC2-URL/Modelle konfigurierbar. - requirements.txt: customtkinter>=5.2.0 + pynput>=1.7.6 hinzugefügt, pystray entfernt. - build.bat: PyInstaller --onefile --windowed → dist/HermesVoice.exe. - setup.bat: Veraltete Wake-Word-Hinweise entfernt, GUI-Check angepasst. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
92 lines
2.3 KiB
Python
92 lines
2.3 KiB
Python
"""
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Audio-Input: Aufnahme + Whisper STT.
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Kein Wake-Word-Loop — Aufnahme startet direkt auf Hotkey-Signal.
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"""
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import os
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import tempfile
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import threading
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import numpy as np
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import sounddevice as sd
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import scipy.io.wavfile as wav
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from faster_whisper import WhisperModel
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from pathlib import Path
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SAMPLE_RATE = 16000
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CHUNK = 1024
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ENERGY_THRESHOLD = 0.008
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_model: WhisperModel | None = None
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_model_size: str = ""
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_recording = False
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_frames: list[np.ndarray] = []
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_stream: sd.InputStream | None = None
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_lock = threading.Lock()
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def load_model(model_size: str):
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global _model, _model_size
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if _model is None or _model_size != model_size:
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model_dir = Path.home() / ".hermes-voice" / "whisper-models"
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model_dir.mkdir(parents=True, exist_ok=True)
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_model = WhisperModel(
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model_size,
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device="cpu",
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compute_type="int8",
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download_root=str(model_dir),
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)
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_model_size = model_size
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def start_recording():
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global _recording, _frames, _stream
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with _lock:
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_recording = True
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_frames = []
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def callback(indata, frames, time, status):
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if _recording:
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_frames.append(indata[:, 0].copy())
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_stream = sd.InputStream(
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samplerate=SAMPLE_RATE,
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channels=1,
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dtype="float32",
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blocksize=CHUNK,
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callback=callback,
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)
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_stream.start()
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def stop_recording() -> np.ndarray:
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global _recording, _stream
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with _lock:
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_recording = False
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if _stream:
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_stream.stop()
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_stream.close()
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_stream = None
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if not _frames:
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return np.array([], dtype=np.float32)
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return np.concatenate(_frames)
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def transcribe(audio: np.ndarray, model_size: str, language: str) -> str:
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if len(audio) < SAMPLE_RATE * 0.3:
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return ""
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load_model(model_size)
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
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tmp = f.name
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try:
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wav.write(tmp, SAMPLE_RATE, (audio * 32767).astype(np.int16))
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lang = language if language != "auto" else None
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segments, _ = _model.transcribe(
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tmp,
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language=lang,
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beam_size=3,
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vad_filter=True,
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)
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return " ".join(s.text for s in segments).strip()
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finally:
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os.unlink(tmp)
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