87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
import time
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import numpy as np
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from faster_whisper import WhisperModel
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from whisper_app import app, config, grammar, typer
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def load_model() -> None:
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app.log(f"Loading {config.config['model']} on {config.config['device']}...")
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model_dir = config.config.get("model_dir") or None
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app.model = WhisperModel(
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config.config["model"],
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device=config.config["device"],
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compute_type=config.config["compute_type"],
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download_root=model_dir,
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)
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app.log("Model ready.")
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if config.config.get("grammar_check"):
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grammar.init(config.config.get("language") or "de", log=app.log)
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def stop_and_transcribe() -> None:
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if app.state != app.AppState.RECORDING:
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return
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set_state(app.AppState.TRANSCRIBING)
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try:
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_do_transcribe()
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except Exception as e:
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app.log(f"Transcription error: {e}")
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finally:
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set_state(app.AppState.IDLE)
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def _do_transcribe() -> None:
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chunks = list(app.audio_chunks)
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if not chunks:
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return
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if app.model is None:
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app.log("Model not loaded yet — skipped.")
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return
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audio = np.concatenate(chunks, axis=0).flatten().astype(np.float32)
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duration = len(audio) / config.config["sample_rate"]
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rms = float(np.sqrt(np.mean(audio ** 2)))
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app.log(f"Audio: {duration:.1f}s RMS: {rms:.5f}")
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if duration < 0.3 or rms < 0.0001:
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app.log("Too short or silent — skipped.")
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return
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target_rms = 0.05
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if rms > 0:
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audio = audio * (target_rms / rms)
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audio = np.clip(audio, -1.0, 1.0)
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lang = config.config["language"] if config.config["language"] else None
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prompt = config.get_initial_prompt()
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segments, _ = app.model.transcribe(
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audio, language=lang, beam_size=5, vad_filter=True,
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initial_prompt=prompt if prompt else None,
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)
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text = " ".join(s.text for s in segments).strip()
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text = config.apply_vocab(text)
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if config.config.get("grammar_check"):
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text = grammar.correct(text)
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app.log(f"Result: {repr(text)}")
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if text:
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time.sleep(0.15)
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typer.type_text(text)
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def set_state(new_state: app.AppState) -> None: # semi-public, used by main.py
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app.state = new_state
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if app.tray_icon:
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from whisper_app import tray
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tray.update_icon(new_state)
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if new_state == app.AppState.RECORDING:
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from whisper_app import overlay
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overlay.show()
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else:
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from whisper_app import overlay
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overlay.hide()
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