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/.vscode/settings.json

@@ -1,5 +1,11 @@
 {
     "python-envs.defaultEnvManager": "ms-python.python:conda",
     "python-envs.defaultPackageManager": "ms-python.python:conda",
-    "python-envs.pythonProjects": []
+    "python-envs.pythonProjects": [],
+    "python.testing.pytestEnabled": true,
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestArgs": [
+        "tests",
+        "-v"
+    ]
 }

python/tool-speechtotext/CLAUDE.md

@@ -12,11 +12,20 @@ Speech-to-text command line utilities leveraging local models (faster-whisper, O
 ## Tools
 - `assistant.py` / `talk.sh` — transcribe speech, copy to clipboard, optionally send to Ollama
 - `voice_to_terminal.py` / `terminal.sh` — voice-controlled terminal via Ollama tool calling
-- `voice_to_xdotool.py` / `dotool.sh` — hands-free voice typing into any focused window (VAD + xdotool)
+- `voice_to_xdotool.py` / `xdotool.sh` — hands-free voice typing into any focused window (VAD + xdotool)
+
+## Shared Library
+- `sttlib/` — shared package used by all scripts and importable by other projects
+  - `whisper_loader.py` — model loading with GPU→CPU fallback
+  - `audio.py` — press-enter recording, PCM conversion
+  - `transcription.py` — Whisper transcribe wrapper, hallucination filter
+  - `vad.py` — VADProcessor, audio callback, constants
+- Other projects import via: `sys.path.insert(0, "/path/to/tool-speechtotext")`
 
 ## Testing
-- To test scripts: `mamba run -n whisper-ollama python <script.py> --model-size base`
+- Run tests: `mamba run -n whisper-ollama python -m pytest tests/`
 - Use `--model-size base` for faster iteration during development
+- Tests mock hardware (Whisper model, VAD, mic) — no GPU/mic needed to run them
 - Audio device is available — live mic testing is possible
 - Test xdotool output by focusing a text editor window
 
@@ -24,12 +33,13 @@ Speech-to-text command line utilities leveraging local models (faster-whisper, O
 - Conda: faster-whisper, sounddevice, numpy, pyperclip, requests, ollama
 - Pip (in conda env): webrtcvad
 - System: libportaudio2, xdotool
+- Dev: pytest
 
 ## Conventions
 - Shell wrappers go in .sh files using `mamba run -n whisper-ollama`
-- All scripts set `CT2_CUDA_ALLOW_FP16=1`
+- Shared code lives in `sttlib/` — scripts are thin entry points that import from it
 - Whisper model loading always has GPU (cuda/float16) -> CPU (cpu/int8) fallback
-- Keep scripts self-contained (no shared module)
+- `CT2_CUDA_ALLOW_FP16=1` is set by `sttlib.whisper_loader` at import time
 - Don't print output for non-actionable events
 
 ## Preferences

python/tool-speechtotext/assistant.py

@@ -1,57 +1,17 @@
-import sounddevice as sd
+import argparse
-import numpy as np
 import pyperclip
 import requests
-import sys
+from sttlib import load_whisper_model, record_until_enter, transcribe
-import argparse
-from faster_whisper import WhisperModel
-
-import os
-os.environ["CT2_CUDA_ALLOW_FP16"] = "1"
 
 # --- Configuration ---
-MODEL_SIZE = "medium"  # Options: "base", "small", "medium", "large-v3"
+OLLAMA_URL = "http://localhost:11434/api/generate"
-OLLAMA_URL = "http://localhost:11434/api/generate"  # Default is 11434
 DEFAULT_OLLAMA_MODEL = "qwen3:latest"
 
-# Load Whisper on GPU
-# float16 is faster and uses less VRAM on NVIDIA cards
-print("Loading Whisper model...")
-
-
-try:
-    model = WhisperModel(MODEL_SIZE, device="cuda", compute_type="float16")
-except Exception as e:
-    print(f"Error loading GPU: {e}")
-    print("Falling back to CPU (Check your CUDA/cuDNN installation)")
-    model = WhisperModel(MODEL_SIZE, device="cuda", compute_type="int16")
-
-
-def record_audio():
-    fs = 16000
-    print("\n[READY] Press Enter to START recording...")
-    input()
-    print("[RECORDING] Press Enter to STOP...")
-
-    recording = []
-
-    def callback(indata, frames, time, status):
-        if status:
-            print(status, file=sys.stderr)
-        recording.append(indata.copy())
-
-    with sd.InputStream(samplerate=fs, channels=1, callback=callback):
-        input()
-
-    return np.concatenate(recording, axis=0)
-
 
 def main():
-    # 1. Setup Parser
     print(f"System active. Model: {DEFAULT_OLLAMA_MODEL}")
     parser = argparse.ArgumentParser(description="Whisper + Ollama CLI")
 
-    # Known Arguments (Hardcoded logic)
     parser.add_argument("--nollm", "-n", action='store_true',
                         help="turn off llm")
     parser.add_argument("--system", "-s", default=None,
@@ -65,30 +25,27 @@ def main():
     parser.add_argument(
         "--temp", default='0.7', help="temperature")
 
-    # 2. Capture "Unknown" arguments
-    # args = known values, unknown = a list like ['--num_ctx', '4096', '--temp', '0.7']
     args, unknown = parser.parse_known_args()
 
     # Convert unknown list to a dictionary for the Ollama 'options' field
-    # This logic pairs ['--key', 'value'] into {key: value}
     extra_options = {}
     for i in range(0, len(unknown), 2):
-        key = unknown[i].lstrip('-')  # remove the '--'
+        key = unknown[i].lstrip('-')
         val = unknown[i+1]
-        # Try to convert numbers to actual ints/floats
         try:
             val = float(val) if '.' in val else int(val)
         except ValueError:
             pass
         extra_options[key] = val
 
+    model = load_whisper_model(args.model_size)
+
     while True:
         try:
-            audio_data = record_audio()
+            audio_data = record_until_enter()
 
             print("[TRANSCRIBING]...")
-            segments, _ = model.transcribe(audio_data.flatten(), beam_size=5)
+            text = transcribe(model, audio_data.flatten())
-            text = "".join([segment.text for segment in segments]).strip()
 
             if not text:
                 print("No speech detected. Try again.")
@@ -97,8 +54,7 @@ def main():
             print(f"You said: {text}")
             pyperclip.copy(text)
 
-            if (args.nollm == False):
+            if not args.nollm:
-                # Send to Ollama
                 print(f"[OLLAMA] Thinking...")
                 payload = {
                     "model": args.ollama_model,
@@ -108,9 +64,9 @@ def main():
                 }
 
                 if args.system:
-                    payload["system"] = args
+                    payload["system"] = args.system
-                response = requests.post(OLLAMA_URL, json=payload)
 
+                response = requests.post(OLLAMA_URL, json=payload)
                 result = response.json().get("response", "")
                 print(f"\nLLM Response:\n{result}\n")
             else:

python/tool-speechtotext/sttlib/__init__.py

@@ -0,0 +1,7 @@
+from sttlib.whisper_loader import load_whisper_model
+from sttlib.audio import record_until_enter, pcm_bytes_to_float32
+from sttlib.transcription import transcribe, is_hallucination, HALLUCINATION_PATTERNS
+from sttlib.vad import (
+    VADProcessor, audio_callback, audio_queue,
+    SAMPLE_RATE, CHANNELS, FRAME_DURATION_MS, FRAME_SIZE, MIN_UTTERANCE_FRAMES,
+)

python/tool-speechtotext/sttlib/audio.py

@@ -0,0 +1,28 @@
+import sys
+import numpy as np
+import sounddevice as sd
+
+
+def record_until_enter(sample_rate=16000):
+    """Record audio until user presses Enter. Returns float32 numpy array."""
+    print("\n[READY] Press Enter to START recording...")
+    input()
+    print("[RECORDING] Press Enter to STOP...")
+
+    recording = []
+
+    def callback(indata, frames, time, status):
+        if status:
+            print(status, file=sys.stderr)
+        recording.append(indata.copy())
+
+    with sd.InputStream(samplerate=sample_rate, channels=1, callback=callback):
+        input()
+
+    return np.concatenate(recording, axis=0)
+
+
+def pcm_bytes_to_float32(pcm_bytes):
+    """Convert raw 16-bit PCM bytes to float32 array normalized to [-1, 1]."""
+    audio_int16 = np.frombuffer(pcm_bytes, dtype=np.int16)
+    return audio_int16.astype(np.float32) / 32768.0

python/tool-speechtotext/sttlib/transcription.py

@@ -0,0 +1,19 @@
+HALLUCINATION_PATTERNS = [
+    "thank you", "thanks for watching", "subscribe",
+    "bye", "the end", "thank you for watching",
+    "please subscribe", "like and subscribe",
+]
+
+
+def transcribe(model, audio_float32):
+    """Transcribe audio using Whisper. Returns stripped text."""
+    segments, _ = model.transcribe(audio_float32, beam_size=5)
+    return "".join(segment.text for segment in segments).strip()
+
+
+def is_hallucination(text):
+    """Return True if text looks like a Whisper hallucination."""
+    lowered = text.lower().strip()
+    if len(lowered) < 3:
+        return True
+    return any(p in lowered for p in HALLUCINATION_PATTERNS)

python/tool-speechtotext/sttlib/vad.py

@@ -0,0 +1,58 @@
+import sys
+import queue
+import collections
+import webrtcvad
+
+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
+
+audio_queue = queue.Queue()
+
+
+def audio_callback(indata, frames, time_info, status):
+    """sounddevice callback that pushes raw bytes to the audio queue."""
+    if status:
+        print(status, file=sys.stderr)
+    audio_queue.put(bytes(indata))
+
+
+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)
+        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

python/tool-speechtotext/sttlib/whisper_loader.py

@@ -0,0 +1,15 @@
+import os
+from faster_whisper import WhisperModel
+
+os.environ["CT2_CUDA_ALLOW_FP16"] = "1"
+
+
+def load_whisper_model(model_size):
+    """Load Whisper with GPU (cuda/float16) -> CPU (cpu/int8) fallback."""
+    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")

python/tool-speechtotext/tests/test_audio.py

@@ -0,0 +1,38 @@
+import struct
+import numpy as np
+from sttlib.audio import pcm_bytes_to_float32
+
+
+def test_known_value():
+    # 16384 in int16 -> 0.5 in float32
+    pcm = struct.pack("<h", 16384)
+    result = pcm_bytes_to_float32(pcm)
+    assert abs(result[0] - 0.5) < 1e-5
+
+
+def test_silence():
+    pcm = b"\x00\x00" * 10
+    result = pcm_bytes_to_float32(pcm)
+    assert np.all(result == 0.0)
+
+
+def test_full_scale():
+    # max int16 = 32767 -> ~1.0
+    pcm = struct.pack("<h", 32767)
+    result = pcm_bytes_to_float32(pcm)
+    assert abs(result[0] - (32767 / 32768.0)) < 1e-5
+
+
+def test_negative():
+    # min int16 = -32768 -> -1.0
+    pcm = struct.pack("<h", -32768)
+    result = pcm_bytes_to_float32(pcm)
+    assert result[0] == -1.0
+
+
+def test_round_trip_shape():
+    # 100 samples worth of bytes
+    pcm = b"\x00\x00" * 100
+    result = pcm_bytes_to_float32(pcm)
+    assert result.shape == (100,)
+    assert result.dtype == np.float32

python/tool-speechtotext/tests/test_transcription.py

@@ -0,0 +1,78 @@
+from unittest.mock import MagicMock
+from sttlib.transcription import transcribe, is_hallucination
+
+
+# --- is_hallucination tests ---
+
+def test_known_hallucinations():
+    assert is_hallucination("Thank you")
+    assert is_hallucination("thanks for watching")
+    assert is_hallucination("Subscribe")
+    assert is_hallucination("the end")
+
+
+def test_short_text():
+    assert is_hallucination("hi")
+    assert is_hallucination("")
+    assert is_hallucination("a")
+
+
+def test_normal_text():
+    assert not is_hallucination("Hello how are you")
+    assert not is_hallucination("Please open the terminal")
+
+
+def test_case_insensitivity():
+    assert is_hallucination("THANK YOU")
+    assert is_hallucination("Thank You For Watching")
+
+
+def test_substring_match():
+    assert is_hallucination("I want to subscribe to your channel")
+
+
+def test_exactly_three_chars():
+    assert not is_hallucination("hey")
+
+
+# --- transcribe tests ---
+
+def _make_segment(text):
+    seg = MagicMock()
+    seg.text = text
+    return seg
+
+
+def test_transcribe_joins_segments():
+    model = MagicMock()
+    model.transcribe.return_value = (
+        [_make_segment("Hello "), _make_segment("world")],
+        None,
+    )
+    result = transcribe(model, MagicMock())
+    assert result == "Hello world"
+
+
+def test_transcribe_empty():
+    model = MagicMock()
+    model.transcribe.return_value = ([], None)
+    result = transcribe(model, MagicMock())
+    assert result == ""
+
+
+def test_transcribe_strips_whitespace():
+    model = MagicMock()
+    model.transcribe.return_value = (
+        [_make_segment("  hello  ")],
+        None,
+    )
+    result = transcribe(model, MagicMock())
+    assert result == "hello"
+
+
+def test_transcribe_passes_beam_size():
+    model = MagicMock()
+    model.transcribe.return_value = ([], None)
+    audio = MagicMock()
+    transcribe(model, audio)
+    model.transcribe.assert_called_once_with(audio, beam_size=5)

python/tool-speechtotext/tests/test_vad.py

@@ -0,0 +1,151 @@
+from unittest.mock import patch, MagicMock
+from sttlib.vad import VADProcessor, FRAME_DURATION_MS, MIN_UTTERANCE_FRAMES
+
+
+def _make_vad_processor(aggressiveness=3, silence_threshold=0.8):
+    """Create VADProcessor with a mocked webrtcvad.Vad."""
+    with patch("sttlib.vad.webrtcvad.Vad") as mock_vad_cls:
+        mock_vad = MagicMock()
+        mock_vad_cls.return_value = mock_vad
+        proc = VADProcessor(aggressiveness, silence_threshold)
+    return proc, mock_vad
+
+
+def _frame(label="x"):
+    """Return a fake 30ms frame (just needs to be distinct bytes)."""
+    return label.encode() * 960  # 480 samples * 2 bytes
+
+
+def test_no_speech_returns_none():
+    proc, mock_vad = _make_vad_processor()
+    mock_vad.is_speech.return_value = False
+
+    for _ in range(100):
+        assert proc.process_frame(_frame()) is None
+
+
+def test_speech_then_silence_triggers_utterance():
+    proc, mock_vad = _make_vad_processor(silence_threshold=0.3)
+
+    # Feed enough speech frames
+    speech_count = MIN_UTTERANCE_FRAMES + 5
+    mock_vad.is_speech.return_value = True
+    for _ in range(speech_count):
+        result = proc.process_frame(_frame("s"))
+        assert result is None  # not done yet
+
+    # Feed silence frames until threshold (0.3s = 10 frames at 30ms)
+    mock_vad.is_speech.return_value = False
+    result = None
+    for _ in range(20):
+        result = proc.process_frame(_frame("q"))
+        if result is not None:
+            break
+
+    assert result is not None
+    assert len(result) > 0
+
+
+def test_short_utterance_filtered():
+    # Use very short silence threshold so silence frames don't push total
+    # past MIN_UTTERANCE_FRAMES. With threshold=0.09s (3 frames of silence):
+    # 0 pre-buffer + 1 speech + 3 silence = 4 total < MIN_UTTERANCE_FRAMES (10)
+    proc, mock_vad = _make_vad_processor(silence_threshold=0.09)
+
+    # Single speech frame triggers VAD
+    mock_vad.is_speech.return_value = True
+    proc.process_frame(_frame("s"))
+
+    # Immediately go silent — threshold reached in 3 frames
+    mock_vad.is_speech.return_value = False
+    result = None
+    for _ in range(20):
+        result = proc.process_frame(_frame("q"))
+        if result is not None:
+            break
+
+    # Should be filtered (too short — only 4 total frames)
+    assert result is None
+
+
+def test_pre_buffer_included():
+    proc, mock_vad = _make_vad_processor(silence_threshold=0.3)
+
+    # Fill pre-buffer with non-speech frames
+    mock_vad.is_speech.return_value = False
+    pre_frame = _frame("p")
+    for _ in range(10):
+        proc.process_frame(pre_frame)
+
+    # Speech starts
+    mock_vad.is_speech.return_value = True
+    speech_frame = _frame("s")
+    for _ in range(MIN_UTTERANCE_FRAMES):
+        proc.process_frame(speech_frame)
+
+    # Silence to trigger
+    mock_vad.is_speech.return_value = False
+    result = None
+    for _ in range(20):
+        result = proc.process_frame(_frame("q"))
+        if result is not None:
+            break
+
+    assert result is not None
+    # Result should contain pre-buffer frames
+    assert pre_frame in result
+
+
+def test_reset_after_utterance():
+    proc, mock_vad = _make_vad_processor(silence_threshold=0.3)
+
+    # First utterance
+    mock_vad.is_speech.return_value = True
+    for _ in range(MIN_UTTERANCE_FRAMES + 5):
+        proc.process_frame(_frame("s"))
+
+    mock_vad.is_speech.return_value = False
+    for _ in range(20):
+        result = proc.process_frame(_frame("q"))
+        if result is not None:
+            break
+    assert result is not None
+
+    # After reset, should be able to collect a second utterance
+    assert not proc.triggered
+    assert proc.utterance_frames == []
+
+    mock_vad.is_speech.return_value = True
+    for _ in range(MIN_UTTERANCE_FRAMES + 5):
+        proc.process_frame(_frame("s"))
+
+    mock_vad.is_speech.return_value = False
+    result2 = None
+    for _ in range(20):
+        result2 = proc.process_frame(_frame("q"))
+        if result2 is not None:
+            break
+    assert result2 is not None
+
+
+def test_silence_threshold_boundary():
+    # Use 0.3s threshold: 0.3 / 0.03 = exactly 10 frames needed
+    threshold = 0.3
+    proc, mock_vad = _make_vad_processor(silence_threshold=threshold)
+
+    # Start with speech
+    mock_vad.is_speech.return_value = True
+    for _ in range(MIN_UTTERANCE_FRAMES + 5):
+        proc.process_frame(_frame("s"))
+
+    frames_needed = 10  # 0.3s / 0.03s per frame
+    mock_vad.is_speech.return_value = False
+
+    # Feed one less than needed — should NOT trigger
+    for i in range(frames_needed - 1):
+        result = proc.process_frame(_frame("q"))
+        assert result is None, f"Triggered too early at frame {i}"
+
+    # The 10th frame should trigger (silence_duration = 0.3 >= 0.3)
+    result = proc.process_frame(_frame("q"))
+    assert result is not None

python/tool-speechtotext/tests/test_whisper_loader.py

@@ -0,0 +1,37 @@
+from unittest.mock import patch, MagicMock
+from sttlib.whisper_loader import load_whisper_model
+
+
+@patch("sttlib.whisper_loader.WhisperModel")
+def test_gpu_success(mock_cls):
+    mock_model = MagicMock()
+    mock_cls.return_value = mock_model
+
+    result = load_whisper_model("base")
+
+    assert result is mock_model
+    mock_cls.assert_called_once_with("base", device="cuda", compute_type="float16")
+
+
+@patch("sttlib.whisper_loader.WhisperModel")
+def test_gpu_fails_cpu_fallback(mock_cls):
+    mock_model = MagicMock()
+    mock_cls.side_effect = [RuntimeError("no CUDA"), mock_model]
+
+    result = load_whisper_model("base")
+
+    assert result is mock_model
+    assert mock_cls.call_count == 2
+    _, kwargs = mock_cls.call_args
+    assert kwargs == {"device": "cpu", "compute_type": "int8"}
+
+
+@patch("sttlib.whisper_loader.WhisperModel")
+def test_both_fail_propagates(mock_cls):
+    mock_cls.side_effect = RuntimeError("no device")
+
+    try:
+        load_whisper_model("base")
+        assert False, "Should have raised"
+    except RuntimeError:
+        pass

python/tool-speechtotext/voice_to_terminal.py

@@ -1,31 +1,16 @@
-import sounddevice as sd
-import numpy as np
-import pyperclip
-import sys
 import argparse
-import os
 import subprocess
-import ollama
 import json
-from faster_whisper import WhisperModel
+import ollama
+from sttlib import load_whisper_model, record_until_enter, transcribe
 
 # --- Configuration ---
-os.environ["CT2_CUDA_ALLOW_FP16"] = "1"
-MODEL_SIZE = "medium"
 OLLAMA_MODEL = "qwen2.5-coder:7b"
 CONFIRM_COMMANDS = True  # Set to False to run commands instantly
 
-# Load Whisper on GPU
-print("Loading Whisper model...")
-try:
-    model = WhisperModel(MODEL_SIZE, device="cuda", compute_type="float16")
-except Exception as e:
-    print(f"Error loading GPU: {e}, falling back to CPU")
-    model = WhisperModel(MODEL_SIZE, device="cpu", compute_type="int8")
 
 # --- Terminal Tool ---
 
-
 def run_terminal_command(command: str):
     """
     Executes a bash command in the Linux terminal.
@@ -33,8 +18,7 @@ def run_terminal_command(command: str):
     """
     if CONFIRM_COMMANDS:
         print(f"\n{'='*40}")
-        print(f"⚠️  AI SUGGESTED: \033[1;32m{command}\033[0m")
+        print(f"\u26a0\ufe0f  AI SUGGESTED: \033[1;32m{command}\033[0m")
-        # Allow user to provide feedback if they say 'n'
         choice = input("   Confirm? [Y/n] or provide feedback: ").strip()
 
         if choice.lower() == 'n':
@@ -57,22 +41,15 @@ def run_terminal_command(command: str):
         return f"Execution Error: {str(e)}"
 
 
-def record_audio():
-    fs, recording = 16000, []
-    print("\n[READY] Press Enter to START...")
-    input()
-    print("[RECORDING] Press Enter to STOP...")
-    def cb(indata, f, t, s): recording.append(indata.copy())
-    with sd.InputStream(samplerate=fs, channels=1, callback=cb):
-        input()
-    return np.concatenate(recording, axis=0)
-
-
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--model", default=OLLAMA_MODEL)
+    parser.add_argument("--model-size", default="medium",
+                        help="Whisper model size")
     args, _ = parser.parse_known_args()
 
+    whisper_model = load_whisper_model(args.model_size)
+
     # Initial System Prompt
     messages = [{
         'role': 'system',
@@ -88,9 +65,8 @@ def main():
     while True:
         try:
             # 1. Voice Capture
-            audio_data = record_audio()
+            audio_data = record_until_enter()
-            segments, _ = model.transcribe(audio_data.flatten(), beam_size=5)
+            user_text = transcribe(whisper_model, audio_data.flatten())
-            user_text = "".join([s.text for s in segments]).strip()
             if not user_text:
                 continue
 

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
+import sounddevice as sd
-
+from sttlib import (
-os.environ["CT2_CUDA_ALLOW_FP16"] = "1"
+    load_whisper_model, transcribe, is_hallucination, pcm_bytes_to_float32,
-
+    VADProcessor, audio_callback, audio_queue,
-# --- Constants ---
+    SAMPLE_RATE, CHANNELS, FRAME_SIZE,
-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
 
 
 # --- 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(