Refactor tool-speechtotext: extract sttlib shared library and add tests

Extract duplicated code (Whisper loading, audio recording, transcription,
VAD processing) into reusable sttlib/ package. Rewrite all 3 scripts as
thin wrappers. Add 24 unit tests with mocked hardware. Fix GPU fallback
bug in assistant.py and args.system assignment bug.

python/tool-speechtotext/voice_to_xdotool.py

@@ -1,91 +1,14 @@
-import sounddevice as sd
-import numpy as np
-import webrtcvad
-import subprocess
-import sys
-import os
 import argparse
+import subprocess
 import threading
 import queue
-import collections
 import time
-from faster_whisper import WhisperModel
-
-os.environ["CT2_CUDA_ALLOW_FP16"] = "1"
-
-# --- Constants ---
-SAMPLE_RATE = 16000
-CHANNELS = 1
-FRAME_DURATION_MS = 30
-FRAME_SIZE = int(SAMPLE_RATE * FRAME_DURATION_MS / 1000)  # 480 samples
-MIN_UTTERANCE_FRAMES = 10  # ~300ms minimum to filter coughs/clicks
-
-HALLUCINATION_PATTERNS = [
-    "thank you", "thanks for watching", "subscribe",
-    "bye", "the end", "thank you for watching",
-    "please subscribe", "like and subscribe",
-]
-
-# --- Thread-safe audio queue ---
-audio_queue = queue.Queue()
-
-
-def audio_callback(indata, frames, time_info, status):
-    if status:
-        print(status, file=sys.stderr)
-    audio_queue.put(bytes(indata))
-
-
-# --- Whisper model loading (reused pattern from assistant.py) ---
-def load_whisper_model(model_size):
-    print(f"Loading Whisper model ({model_size})...")
-    try:
-        return WhisperModel(model_size, device="cuda", compute_type="float16")
-    except Exception as e:
-        print(f"GPU loading failed: {e}")
-        print("Falling back to CPU (int8)")
-        return WhisperModel(model_size, device="cpu", compute_type="int8")
-
-
-# --- VAD State Machine ---
-class VADProcessor:
-    def __init__(self, aggressiveness, silence_threshold):
-        self.vad = webrtcvad.Vad(aggressiveness)
-        self.silence_threshold = silence_threshold
-        self.reset()
-
-    def reset(self):
-        self.triggered = False
-        self.utterance_frames = []
-        self.silence_duration = 0.0
-        self.pre_buffer = collections.deque(maxlen=10)  # ~300ms pre-roll
-
-    def process_frame(self, frame_bytes):
-        """Process one 30ms frame. Returns utterance bytes when complete, else None."""
-        is_speech = self.vad.is_speech(frame_bytes, SAMPLE_RATE)
-
-        if not self.triggered:
-            self.pre_buffer.append(frame_bytes)
-            if is_speech:
-                self.triggered = True
-                self.silence_duration = 0.0
-                self.utterance_frames = list(self.pre_buffer)
-                self.utterance_frames.append(frame_bytes)
-                pass  # silent until transcription confirms speech
-        else:
-            self.utterance_frames.append(frame_bytes)
-            if is_speech:
-                self.silence_duration = 0.0
-            else:
-                self.silence_duration += FRAME_DURATION_MS / 1000.0
-                if self.silence_duration >= self.silence_threshold:
-                    if len(self.utterance_frames) < MIN_UTTERANCE_FRAMES:
-                        self.reset()
-                        return None
-                    result = b"".join(self.utterance_frames)
-                    self.reset()
-                    return result
-        return None
+import sounddevice as sd
+from sttlib import (
+    load_whisper_model, transcribe, is_hallucination, pcm_bytes_to_float32,
+    VADProcessor, audio_callback, audio_queue,
+    SAMPLE_RATE, CHANNELS, FRAME_SIZE,
+)
 
 
 # --- Typer Interface (xdotool) ---
@@ -99,6 +22,7 @@ class Typer:
         except FileNotFoundError:
             print("ERROR: xdotool not found. Install it:")
             print("  sudo apt-get install xdotool")
+            import sys
             sys.exit(1)
 
     def type_text(self, text, submit_now=False):
@@ -120,24 +44,6 @@ class Typer:
         pass
 
 
-# --- Helpers ---
-def pcm_bytes_to_float32(pcm_bytes):
-    audio_int16 = np.frombuffer(pcm_bytes, dtype=np.int16)
-    return audio_int16.astype(np.float32) / 32768.0
-
-
-def transcribe(model, audio_float32):
-    segments, _ = model.transcribe(audio_float32, beam_size=5)
-    return "".join(segment.text for segment in segments).strip()
-
-
-def is_hallucination(text):
-    lowered = text.lower().strip()
-    if len(lowered) < 3:
-        return True
-    return any(p in lowered for p in HALLUCINATION_PATTERNS)
-
-
 # --- CLI ---
 def parse_args():
     parser = argparse.ArgumentParser(