V1.0: 2026-02-04

.gitignore

@@ -21,6 +21,7 @@ parts/
 sdist/
 var/
 wheels/
+media/
 *.egg-info/
 .installed.cfg
 *.egg
@@ -33,6 +34,7 @@ wheels/
 htmlcov/
 .tox/
 .nox/
+archives/
 
 # =============================================================================
 # IDE and Editors
@@ -87,6 +89,7 @@ Thumbs.db
 # Old/Archive Folders
 # =============================================================================
 0ld/
+dev/
 
 # =============================================================================
 # Keep Example Configs (negation patterns)

app.py

@@ -38,12 +38,11 @@ from core.assistant import Assistant
 
 # Configuration
 WAKEWORD = config['general']['wakeword']
-SLM_MODEL = "./data/models/Qwen3-4B-Instruct-2507-Q4_K_M.gguf"
-
+USE_AI = config['general']['use_ai']
 
 def main():
     """Main entry point for the voice assistant."""
-    assistant = Assistant(wakeword=WAKEWORD, slm_model_path=SLM_MODEL)
+    assistant = Assistant(wakeword=WAKEWORD, use_ai=USE_AI)
     assistant.run()
 
 

core/__init__.py

@@ -15,8 +15,8 @@ from .audio import (
     SAMPLE_RATE,
     SILENCE_THRESHOLD,
 )
-from .asr import load_moonshine, stream_generator
-from .tts import speak_stream, kpipeline
+from .asr import load_asr_model, stream_generator
+from .tts import speak_stream
 from .slm import load_slm, generate_slm
 from .assistant import Assistant
 
@@ -27,11 +27,10 @@ __all__ = [
     "SAMPLE_RATE",
     "SILENCE_THRESHOLD",
     # ASR
-    "load_moonshine",
+    "load_asr_model",
     "stream_generator",
     # TTS
     "speak_stream",
-    "kpipeline",
     # SLM
     "load_slm",
     "generate_slm",

core/asr-tiny.py

@@ -0,0 +1,64 @@
+"""
+Automatic Speech Recognition module using Moonshine ASR.
+
+Handles loading and running the Moonshine speech recognition model.
+"""
+
+import logging
+from typing import Generator
+
+import torch
+from transformers import AutoProcessor, MoonshineForConditionalGeneration, pipeline
+
+logger = logging.getLogger(__name__)
+
+# Model configuration
+ASR_MODEL_NAME = "UsefulSensors/moonshine-tiny"
+
+# Device configuration
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
+
+
+def load_asr_model():
+    """
+    Load the Moonshine ASR model and create a pipeline.
+
+    Returns:
+        A Hugging Face pipeline configured for automatic speech recognition
+    """
+    logger.info(f"Loading {ASR_MODEL_NAME} on {DEVICE}...")
+
+    processor = AutoProcessor.from_pretrained(ASR_MODEL_NAME)
+    asr_model = MoonshineForConditionalGeneration.from_pretrained(ASR_MODEL_NAME).to(
+        device=DEVICE,
+        dtype=DTYPE,
+    )
+
+    asr_pipe = pipeline(
+        task="automatic-speech-recognition",
+        model=asr_model,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor,
+        device=-1 if DEVICE == "cuda" else 0,
+        dtype=DTYPE,
+    )
+
+    return asr_pipe
+
+
+def stream_generator(queue) -> Generator:
+    """
+    Generator that yields audio from a queue for the ASR pipeline.
+
+    Args:
+        queue: Queue containing audio numpy arrays. None signals stop.
+
+    Yields:
+        Audio numpy arrays until None is received
+    """
+    while True:
+        audio = queue.get()
+        if audio is None:
+            break
+        yield audio

core/asr.py

@@ -1,61 +1,89 @@
-"""
-Automatic Speech Recognition module using Moonshine ASR.
-
-Handles loading and running the Moonshine speech recognition model.
-"""
-
 import logging
-from typing import Generator
-
 import torch
-from transformers import AutoProcessor, MoonshineForConditionalGeneration, pipeline
+import numpy as np
+from typing import Generator, Optional, Union
+from qwen_asr import Qwen3ASRModel
 
 logger = logging.getLogger(__name__)
 
 # Model configuration
-ASR_MODEL_NAME = "UsefulSensors/moonshine-tiny"
+ASR_MODEL_NAME = "Qwen/Qwen3-ASR-0.6B"
+SAMPLE_RATE = 16000 # Qwen3-ASR standard sample rate
 
 # Device configuration
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
+DTYPE = torch.bfloat16 if DEVICE == "cuda" else torch.float32
 
 
-def load_moonshine():
+class QwenASRPipelineWrapper:
     """
-    Load the Moonshine ASR model and create a pipeline.
-
-    Returns:
-        A Hugging Face pipeline configured for automatic speech recognition
+    Wrapper for Qwen3-ASR to mimic basic HF pipeline behavior for streaming.
     """
+    def __init__(self, model):
+        self.model = model
+
+    def __call__(self, audio_input: Union[np.ndarray, Generator], batch_size: int = 1, generate_kwargs: Optional[dict] = None, **kwargs):
+        logger.info(f"[Batch size {batch_size}] and {generate_kwargs} are not being used")
+
+        if isinstance(audio_input, Generator):
+            for chunk in audio_input:
+                if chunk is not None:
+                    if isinstance(chunk, np.ndarray):
+                        audio_tuple = (chunk, SAMPLE_RATE)
+                    elif isinstance(chunk, torch.Tensor):
+                        audio_tuple = (chunk.cpu().numpy(), SAMPLE_RATE)
+                    else:
+                        audio_tuple = (np.array(chunk), SAMPLE_RATE)
+
+                    try:
+                        results = self.model.transcribe(
+                            audio=[audio_tuple], 
+                            return_time_stamps=False
+                        )
+                    except TypeError as e:
+                        logger.warning(f"Transcribe argument error: {e}. Retrying.")
+                        results = self.model.transcribe(
+                            audio=[audio_tuple], 
+                            return_time_stamps=False
+                        )
+                        
+                    if isinstance(results, list):
+                        for res in results:
+                            yield {"text": getattr(res, 'text', str(res))}
+                    else:
+                        yield {"text": getattr(results, 'text', str(results))}
+
+        else:
+            if isinstance(audio_input, np.ndarray):
+                audio_tuple = (audio_input, SAMPLE_RATE)
+            else:
+                 audio_tuple = (np.array(audio_input), SAMPLE_RATE)
+
+            results = self.model.transcribe(audio=[audio_tuple])
+            
+            out_text = []
+            if isinstance(results, list):
+                out_text = [{"text": getattr(r, 'text', str(r))} for r in results]
+            else:
+                out_text = [{"text": getattr(results, 'text', str(results))}]
+            return out_text
+
+
+def load_asr_model():
     logger.info(f"Loading {ASR_MODEL_NAME} on {DEVICE}...")
 
-    processor = AutoProcessor.from_pretrained(ASR_MODEL_NAME)
-    asr_model = MoonshineForConditionalGeneration.from_pretrained(ASR_MODEL_NAME).to(
-        device=DEVICE,
+    model = Qwen3ASRModel.from_pretrained(
+        ASR_MODEL_NAME,
+        device_map=DEVICE,
         dtype=DTYPE,
     )
 
-    asr_pipe = pipeline(
-        task="automatic-speech-recognition",
-        model=asr_model,
-        tokenizer=processor.tokenizer,
-        feature_extractor=processor.feature_extractor,
-        device=-1 if DEVICE == "cuda" else 0,
-        dtype=DTYPE,
-    )
-
-    return asr_pipe
+    return QwenASRPipelineWrapper(model)
 
 
 def stream_generator(queue) -> Generator:
     """
-    Generator that yields audio from a queue for the ASR pipeline.
-
-    Args:
-        queue: Queue containing audio numpy arrays. None signals stop.
-
-    Yields:
-        Audio numpy arrays until None is received
+    Generator that yields audio from a queue.
     """
     while True:
         audio = queue.get()

core/assistant.py

@@ -11,9 +11,9 @@ import threading
 from typing import Optional
 
 from .audio import AudioCapture
-from .asr import load_moonshine, stream_generator
+from .asr import load_asr_model, stream_generator
 from .tts import speak_stream, remove_emoji
-from .slm import load_slm, generate_slm, SLM_MODEL
+from .slm import load_slm, generate_slm
 
 from utils.system_prompts import getIntentSystemPrompt, getChatSystemPrompt
 from utils.intent_catch import catchAll
@@ -34,7 +34,7 @@ class Assistant:
         grammar: JSON grammar for structured output
     """
 
-    def __init__(self, wakeword: str, slm_model_path: str = SLM_MODEL):
+    def __init__(self, wakeword: str, use_ai: bool):
         """
         Initialize the assistant.
 
@@ -43,7 +43,7 @@ class Assistant:
             slm_model_path: Path to SLM model (empty to disable AI)
         """
         self.wakeword = wakeword.lower()
-        self.slm_model_path = slm_model_path
+        self.use_ai = use_ai
         self.audio_capture = AudioCapture()
 
         # Models loaded lazily in transcriber thread
@@ -55,10 +55,10 @@ class Assistant:
 
     def _load_models(self):
         """Load ASR and optionally SLM models."""
-        self.asr_pipe = load_moonshine()
+        self.asr_pipe = load_asr_model()
 
-        if self.slm_model_path:
-            self.grammar, self.slm_model = load_slm(self.slm_model_path)
+        if self.use_ai:
+            self.grammar, self.slm_model = load_slm()
             self.intent_prompt = getIntentSystemPrompt()
             self.chat_prompt = getChatSystemPrompt()
 

core/slm.py

@@ -14,7 +14,7 @@ from llama_cpp import Llama, LlamaGrammar
 logger = logging.getLogger(__name__)
 
 # Model configuration
-SLM_MODEL = "./data/models/Qwen3-4B-Instruct-2507-Q4_K_M.gguf"
+MODEL_PATH = "./data/models/Qwen3-4B-Instruct-2507-Q4_K_M.gguf"#"./data/models/Qwen3-0.6B-Q4_K_M.gguf"
 GRAMMAR_FILE = "./data/models/grammars/json.gbnf"
 
 N_CONTEXT = 8192
@@ -26,7 +26,7 @@ DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
 
 def load_slm(
-    model_path: str = SLM_MODEL,
+    model_path: str = MODEL_PATH,
     grammar_path: str = GRAMMAR_FILE,
     n_ctx: int = N_CONTEXT,
     n_threads: int = N_THREADS,

core/tts.py

@@ -32,6 +32,9 @@ EMOJI_PATTERN = re.compile(
     flags=re.UNICODE,
 )
 
+# Thinking removal pattern
+THINK_PATTERN = r"<think>.*?</think>"
+
 # Create global pipeline (loads model once)
 logger.info(f"Loading {TTS_MODEL_NAME} on {DEVICE}...")
 kpipeline = KPipeline(
@@ -41,8 +44,12 @@ kpipeline = KPipeline(
 )
 
 
-def remove_emoji(text: str) -> str:
-    """Remove emoji characters from text."""
+def remove_emoji(text: str, rem_think: bool = True) -> str:
+    """Remove emoji characters and thinking from text."""
+    if rem_think:
+        text = re.sub(THINK_PATTERN, "", text, flags=re.DOTALL)
+        text = text.strip()
+
     return EMOJI_PATTERN.sub("", text)
 
 

data/config.example.yml

@@ -9,6 +9,10 @@ general:
   # Wakeword to activate the assistant (case-insensitive)
   # Other examples: "alexa", "jeeves", "mycroft"
   wakeword: "computer" 
+  # Use a small language model for extra capabilities
+  use_ai: True
+  # Use the Moonshine Tiny ASR if running on very small edge devices
+  use_tiny_asr: False
 
 # =============================================================================
 # Spotify Integration

launch.sh

@@ -11,8 +11,9 @@ QWEN_REPO="unsloth/Qwen3-4B-Instruct-2507-GGUF"
 QWEN_FILE="Qwen3-4B-Instruct-2507-Q4_K_M.gguf"
 
 # Define the cache folders
-KOKORO_DIR="$HUB_DIR/models--hexgrad--Kokoro-82M"
-MOONSHINE_DIR="$HUB_DIR/models--UsefulSensors--moonshine-tiny"
+ASR_TINY_DIR="$HUB_DIR/models--UsefulSensors--moonshine-tiny"
+ASR_DIR="$HUB_DIR/models--Qwen--Qwen3-ASR-0.6B"
+TTS_DIR="$HUB_DIR/models--hexgrad--Kokoro-82M"
 
 # 1. Ensure Dependencies are installed
 if ! command -v huggingface-cli &> /dev/null; then
@@ -22,16 +23,15 @@ fi
 
 # 2. Create Directory Structure
 echo "📂 Checking directory structure..."
-mkdir -p "$GRAMMAR_DIR"
 mkdir -p "$HUB_DIR"
 
 # 3. Check and Download json.gbnf
 if [ ! -f "$GRAMMAR_DIR/json.gbnf" ]; then
-    echo "⬇️ Downloading json.gbnf..."
-    wget -q --show-progress -O "$GRAMMAR_DIR/json.gbnf" \
-        "https://raw.githubusercontent.com/ggml-org/llama.cpp/master/grammars/json.gbnf"
+echo "⬇️ Downloading json.gbnf..."
+wget -q --show-progress -O "$GRAMMAR_DIR/json.gbnf" \
+"https://raw.githubusercontent.com/ggml-org/llama.cpp/master/grammars/json.gbnf"
 else
-    echo "✅ json.gbnf exists."
+echo "✅ json.gbnf exists."
 fi
 
 # 4. Check and Download Qwen3 GGUF
@@ -45,21 +45,30 @@ else
 fi
 
 # 5. Check and Download Kokoro-82M (TTS)
-if [ ! -d "$KOKORO_DIR" ]; then
-    echo "⬇️ Downloading Kokoro-82M..."
-    huggingface-cli download hexgrad/Kokoro-82M \
-        --cache-dir "$HUB_DIR"
+if [ ! -d "$TTS_DIR" ]; then
+echo "⬇️ Downloading Kokoro-82M..."
+huggingface-cli download hexgrad/Kokoro-82M \
+--cache-dir "$HUB_DIR"
 else
-    echo "✅ Kokoro-82M exists."
+echo "✅ Kokoro-82M exists."
 fi
 
-# 6. Check and Download Moonshine Tiny (STT)
-if [ ! -d "$MOONSHINE_DIR" ]; then
-    echo "⬇️ Downloading Moonshine Tiny..."
-    huggingface-cli download UsefulSensors/moonshine-tiny \
+# 6. Check and Download Qwen3 ASR model
+if [ ! -d "$ASR_DIR" ]; then
+    echo "⬇️ Downloading Qwen3 ASR..."
+    huggingface-cli download Qwen/Qwen3-ASR-0.6B \
         --cache-dir "$HUB_DIR"
 else
-    echo "✅ Moonshine-tiny exists."
+    echo "✅ ASR model exists."
+fi
+
+# 6a. Check and Download Moonshine Tiny ASR model
+if [ ! -d "$ASR_TINY_DIR" ]; then
+echo "⬇️ Downloading Moonshine Tiny..."
+huggingface-cli download UsefulSensors/moonshine-tiny \
+--cache-dir "$HUB_DIR"
+else
+echo "✅ Moonshine-tiny exists."
 fi
 
 # 7. Prompt the user

pyproject.toml

@@ -100,10 +100,10 @@ all = [
 fulloch = "app:main"
 
 [project.urls]
-Homepage = "https://github.com/liampetti/fulloch"
-Documentation = "https://github.com/liampetti/fulloch#readme"
-Repository = "https://github.com/liampetti/fulloch"
-Issues = "https://github.com/liampetti/fulloch/issues"
+Homepage = "https://github.com/yourusername/fulloch"
+Documentation = "https://github.com/yourusername/fulloch#readme"
+Repository = "https://github.com/yourusername/fulloch"
+Issues = "https://github.com/yourusername/fulloch/issues"
 
 [tool.setuptools.packages.find]
 where = ["."]

readme.md

@@ -68,7 +68,7 @@ A privacy-focused voice assistant that runs speech recognition, text-to-speech,
 ### 1. Clone and Install
 
 ```bash
-git clone https://github.com/liampetti/fulloch.git
+git clone https://github.com/yourusername/fulloch.git
 cd fulloch
 pip install -r requirements.txt
 ```

requirements.txt

@@ -12,10 +12,12 @@ torchaudio==2.8.0
 
 # AI and ML
 torch==2.8.0
-transformers==4.57.1
+transformers==4.57.6
 kokoro==0.9.4
 accelerate==1.12.0
 llama_cpp_python==0.3.16
+qwen-tts==0.0.5
+qwen-asr==0.0.6
 
 # Smart home integration
 phue==1.1.0

searxng_data/settings.yml

@@ -95,7 +95,7 @@ server:
   # If your instance owns a /etc/searxng/settings.yml file, then set the following
   # values there.
 
-  secret_key: "changeme"  # Is overwritten by ${SEARXNG_SECRET}
+  secret_key: "JFHpQvFGOR9gpXzAuR0FBUNBOvCjaNi7"  # Is overwritten by ${SEARXNG_SECRET}
   # Proxy image results through SearXNG. Is overwritten by ${SEARXNG_IMAGE_PROXY}
   image_proxy: false
   # 1.0 and 1.1 are supported