Fix: Use arecord for shared audio stream

- Replaced PyAudio with direct ALSA (arecord subprocess)
- Single audio stream feeds both Porcupine and recording buffer
- Fixes device unavailable error when recording after wake word
- Simplified architecture

headmic.py

@@ -7,33 +7,37 @@ Runs on head-vixy (Raspberry Pi 5).
 
 Wake word: "Hey Vivi" (trained via Picovoice Porcupine)
 
+Architecture: Single shared audio stream feeds both Porcupine (wake word)
+and recording buffer. This avoids device conflicts.
+
 Flow:
-  1. Listen for "Hey Vivi" wake word (Porcupine)
+  1. Continuous audio stream from ReSpeaker
-  2. ReSpeaker LEDs light up (listening state)
+  2. Feed frames to Porcupine for wake word detection
-  3. Record until silence detected (webrtcvad)
+  3. On "Hey Vivi" → start buffering audio
-  4. Send audio to EarTail (Whisper on BigOrin)
+  4. Use VAD to detect end of speech
-  5. Return transcription
+  5. Send buffer to EarTail for transcription
-  6. ReSpeaker LEDs off
+  6. Return to listening mode
 
 Built by Vixy on Day 77 (January 17, 2026) 💜
 """
 
 import asyncio
+import collections
 import io
 import logging
 import os
 import struct
+import subprocess
 import threading
 import time
 import wave
 from pathlib import Path
-from typing import Optional
+from typing import Optional, List
 
 import httpx
 import pvporcupine
-import pyaudio
 import webrtcvad
-from fastapi import FastAPI, HTTPException, BackgroundTasks
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 
 # Configure logging
@@ -44,74 +48,59 @@ logger = logging.getLogger("headmic")
 # Configuration
 # ============================================================================
 
-# Porcupine wake word
 PORCUPINE_ACCESS_KEY = os.environ.get("PORCUPINE_ACCESS_KEY", "")
 WAKE_WORD_PATH = os.environ.get("WAKE_WORD_PATH", "/home/alex/headmic/Hey-Vivi_en_raspberry-pi_v4_0_0.ppn")
 
-# Audio settings
 SAMPLE_RATE = 16000
-CHANNELS = 1  # Mono for transcription (pick channel 0 from 4-mic array)
+ALSA_DEVICE = "plughw:3,0"  # ReSpeaker 4 Mic Array - card 3, device 0
-FRAME_LENGTH = 512  # Porcupine frame length
 
-# VAD settings
+VAD_AGGRESSIVENESS = 2  # 0-3, higher = more aggressive
-VAD_AGGRESSIVENESS = 3  # 0-3, higher = more aggressive filtering
+SILENCE_FRAMES = 50  # ~1.5 sec of silence to stop (at 30ms frames)
-SILENCE_THRESHOLD_MS = 1500  # Stop recording after this much silence
+MAX_RECORDING_FRAMES = 1000  # ~30 sec max
-MAX_RECORDING_SEC = 30  # Maximum recording duration
 
-# EarTail
 EARTAIL_URL = os.environ.get("EARTAIL_URL", "http://bigorin.local:8764")
 
-# ReSpeaker LED control
-LED_ENABLED = True
-
 # ============================================================================
-# LED Control (ReSpeaker 4-mic array has 12 APA102 LEDs)
+# LED Control
 # ============================================================================
 
 try:
     from pixel_ring import pixel_ring
-    PIXEL_RING_AVAILABLE = True
+    LEDS_AVAILABLE = True
-except ImportError:
-    PIXEL_RING_AVAILABLE = False
-    logger.warning("pixel_ring not available - LED feedback disabled")
-
-
-def leds_listening():
-    """Set LEDs to listening state (cyan spin)."""
-    if PIXEL_RING_AVAILABLE and LED_ENABLED:
-        try:
-            pixel_ring.set_color_palette(0x00FFFF, 0x000000)  # Cyan
-            pixel_ring.think()
-        except Exception as e:
-            logger.warning(f"LED error: {e}")
-
-
-def leds_processing():
-    """Set LEDs to processing state (purple pulse)."""
-    if PIXEL_RING_AVAILABLE and LED_ENABLED:
-        try:
-            pixel_ring.set_color_palette(0x9400D3, 0x000000)  # Purple
-            pixel_ring.spin()
-        except Exception as e:
-            logger.warning(f"LED error: {e}")
-
-
-def leds_off():
-    """Turn off LEDs."""
-    if PIXEL_RING_AVAILABLE and LED_ENABLED:
-        try:
     pixel_ring.off()
-        except Exception as e:
+except ImportError:
-            logger.warning(f"LED error: {e}")
+    LEDS_AVAILABLE = False
+    logger.warning("pixel_ring not available")
 
 
 def leds_wakeup():
-    """Flash LEDs on wake word detection."""
+    if LEDS_AVAILABLE:
-    if PIXEL_RING_AVAILABLE and LED_ENABLED:
         try:
             pixel_ring.wakeup()
-        except Exception as e:
+        except: pass
-            logger.warning(f"LED error: {e}")
+
+
+def leds_listening():
+    if LEDS_AVAILABLE:
+        try:
+            pixel_ring.set_color_palette(0x00FFFF, 0x000000)
+            pixel_ring.think()
+        except: pass
+
+
+def leds_processing():
+    if LEDS_AVAILABLE:
+        try:
+            pixel_ring.set_color_palette(0x9400D3, 0x000000)
+            pixel_ring.spin()
+        except: pass
+
+
+def leds_off():
+    if LEDS_AVAILABLE:
+        try:
+            pixel_ring.off()
+        except: pass
 
 
 # ============================================================================
@@ -120,268 +109,243 @@ def leds_wakeup():
 
 class ServiceState:
     def __init__(self):
+        self.running = False
         self.listening = False
         self.recording = False
         self.processing = False
-        self.last_transcription = None
+        self.last_transcription: Optional[str] = None
-        self.last_wake_time = None
+        self.last_wake_time: Optional[float] = None
         self.wake_count = 0
-        self.porcupine = None
+        self.error: Optional[str] = None
-        self.audio = None
-        self.stream = None
-        self.listener_thread = None
-        self.running = False
 
 state = ServiceState()
 
+
 # ============================================================================
-# Audio Recording with VAD
+# Audio Stream using ALSA directly (arecord)
 # ============================================================================
 
-def record_until_silence(timeout_sec: float = MAX_RECORDING_SEC) -> bytes:
+def read_audio_stream():
     """
-    Record audio until silence is detected.
+    Generator that yields audio frames from ALSA using arecord.
-    Returns WAV data as bytes.
+    Each frame is 512 samples (32ms at 16kHz) as required by Porcupine.
     """
-    vad = webrtcvad.Vad(VAD_AGGRESSIVENESS)
+    frame_size = 512  # Porcupine requires 512 samples
+    bytes_per_frame = frame_size * 2  # 16-bit = 2 bytes per sample
     
-    # VAD requires specific frame sizes: 10, 20, or 30 ms
+    cmd = [
-    frame_duration_ms = 30
+        "arecord",
-    frame_size = int(SAMPLE_RATE * frame_duration_ms / 1000)
+        "-D", ALSA_DEVICE,
+        "-f", "S16_LE",
+        "-r", str(SAMPLE_RATE),
+        "-c", "1",  # Mono
+        "-t", "raw",
+        "-q",  # Quiet
+        "-"
+    ]
     
-    p = pyaudio.PyAudio()
+    logger.info(f"Starting audio stream: {' '.join(cmd)}")
     
-    # Find the ReSpeaker device
+    proc = subprocess.Popen(
-    device_index = None
+        cmd,
-    for i in range(p.get_device_count()):
+        stdout=subprocess.PIPE,
-        info = p.get_device_info_by_index(i)
+        stderr=subprocess.DEVNULL,
-        if 'seeed' in info['name'].lower() or 'ac108' in info['name'].lower():
+        bufsize=bytes_per_frame
-            device_index = i
-            break
-    
-    if device_index is None:
-        # Fallback to default
-        logger.warning("ReSpeaker not found, using default input")
-    
-    stream = p.open(
-        format=pyaudio.paInt16,
-        channels=4,  # ReSpeaker has 4 channels
-        rate=SAMPLE_RATE,
-        input=True,
-        input_device_index=device_index,
-        frames_per_buffer=frame_size
     )
     
-    logger.info("Recording started...")
-    frames = []
-    silence_frames = 0
-    silence_limit = int(SILENCE_THRESHOLD_MS / frame_duration_ms)
-    max_frames = int(timeout_sec * 1000 / frame_duration_ms)
-    
     try:
-        for _ in range(max_frames):
+        while state.running:
-            data = stream.read(frame_size, exception_on_overflow=False)
+            data = proc.stdout.read(bytes_per_frame)
-            
+            if len(data) < bytes_per_frame:
-            # Extract channel 0 (mono) from 4-channel audio
-            # Each sample is 2 bytes (int16), 4 channels = 8 bytes per frame
-            mono_data = b''
-            for i in range(0, len(data), 8):  # 8 bytes per sample set
-                mono_data += data[i:i+2]  # Take first channel only
-            
-            frames.append(mono_data)
-            
-            # Check for speech
-            is_speech = vad.is_speech(mono_data, SAMPLE_RATE)
-            
-            if is_speech:
-                silence_frames = 0
-            else:
-                silence_frames += 1
-            
-            # Stop if enough silence after we've recorded something
-            if len(frames) > 10 and silence_frames >= silence_limit:
-                logger.info(f"Silence detected after {len(frames)} frames")
                 break
-    
+            yield data
     finally:
-        stream.stop_stream()
+        proc.terminate()
-        stream.close()
+        proc.wait()
-        p.terminate()
-    
-    # Convert to WAV
-    wav_buffer = io.BytesIO()
-    with wave.open(wav_buffer, 'wb') as wf:
-        wf.setnchannels(1)
-        wf.setsampwidth(2)  # 16-bit
-        wf.setframerate(SAMPLE_RATE)
-        wf.writeframes(b''.join(frames))
-    
-    wav_buffer.seek(0)
-    return wav_buffer.read()
 
 
 # ============================================================================
-# EarTail Integration
+# EarTail Transcription
 # ============================================================================
 
 async def transcribe_audio(audio_data: bytes) -> str:
     """Send audio to EarTail and get transcription."""
     async with httpx.AsyncClient(timeout=120.0) as client:
-        # Submit job
         files = {"audio": ("recording.wav", audio_data, "audio/wav")}
         response = await client.post(f"{EARTAIL_URL}/transcribe/submit", files=files)
         response.raise_for_status()
         
         job_id = response.json().get("job_id")
-        logger.info(f"Transcription job submitted: {job_id}")
+        logger.info(f"Transcription job: {job_id}")
         
-        # Poll for completion
+        for _ in range(120):
-        for _ in range(60):  # Max 60 seconds
+            status = await client.get(f"{EARTAIL_URL}/transcribe/status/{job_id}")
-            status_response = await client.get(f"{EARTAIL_URL}/transcribe/status/{job_id}")
+            data = status.json()
-            status_data = status_response.json()
             
-            if status_data.get("status") == "SUCCESS":
+            if data.get("status") == "SUCCESS":
                 result = await client.get(f"{EARTAIL_URL}/transcribe/result/{job_id}")
                 return result.json().get("transcription", "")
-            elif status_data.get("status") == "FAILURE":
+            elif data.get("status") == "FAILURE":
-                raise Exception(f"Transcription failed: {status_data.get('error')}")
+                raise Exception(f"Transcription failed: {data.get('error')}")
             
             await asyncio.sleep(1)
         
         raise Exception("Transcription timeout")
 
 
+def transcribe_sync(audio_data: bytes) -> str:
+    """Synchronous wrapper for transcription."""
+    loop = asyncio.new_event_loop()
+    try:
+        return loop.run_until_complete(transcribe_audio(audio_data))
+    finally:
+        loop.close()
+
+
 # ============================================================================
-# Wake Word Listener
+# Main Listener Loop
 # ============================================================================
 
-def wake_word_listener():
+def audio_to_wav(frames: List[bytes]) -> bytes:
-    """Background thread that listens for wake word."""
+    """Convert raw audio frames to WAV format."""
+    wav_buffer = io.BytesIO()
+    with wave.open(wav_buffer, 'wb') as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(SAMPLE_RATE)
+        wf.writeframes(b''.join(frames))
+    wav_buffer.seek(0)
+    return wav_buffer.read()
+
+
+def listener_loop():
+    """Main audio processing loop."""
     global state
     
-    logger.info("Starting wake word listener...")
+    logger.info("Initializing Porcupine...")
-    
     try:
-        state.porcupine = pvporcupine.create(
+        porcupine = pvporcupine.create(
             access_key=PORCUPINE_ACCESS_KEY,
             keyword_paths=[WAKE_WORD_PATH]
         )
     except Exception as e:
-        logger.error(f"Failed to initialize Porcupine: {e}")
+        logger.error(f"Failed to init Porcupine: {e}")
+        state.error = str(e)
         return
     
-    state.audio = pyaudio.PyAudio()
+    vad = webrtcvad.Vad(VAD_AGGRESSIVENESS)
     
-    # Find ReSpeaker device
+    # VAD needs 10/20/30ms frames. 30ms at 16kHz = 480 samples
-    device_index = None
+    # Porcupine needs 512 samples. We'll use 480 for VAD.
-    for i in range(state.audio.get_device_count()):
+    vad_frame_size = 480
-        info = state.audio.get_device_info_by_index(i)
+    vad_frame_bytes = vad_frame_size * 2
-        if 'seeed' in info['name'].lower() or 'ac108' in info['name'].lower():
-            device_index = i
-            break
-    
-    state.stream = state.audio.open(
-        rate=state.porcupine.sample_rate,
-        channels=1,
-        format=pyaudio.paInt16,
-        input=True,
-        input_device_index=device_index,
-        frames_per_buffer=state.porcupine.frame_length
-    )
     
     state.listening = True
-    logger.info("Wake word listener active - say 'Hey Vivi'!")
+    logger.info("🦊 Wake word listener active - say 'Hey Vivi'!")
+    
+    recording_buffer: List[bytes] = []
+    silence_count = 0
+    is_recording = False
     
-    while state.running:
     try:
-            pcm = state.stream.read(state.porcupine.frame_length, exception_on_overflow=False)
+        for frame_data in read_audio_stream():
-            pcm = struct.unpack_from("h" * state.porcupine.frame_length, pcm)
+            if not state.running:
+                break
             
-            keyword_index = state.porcupine.process(pcm)
+            # Convert bytes to int16 array for Porcupine
+            pcm = struct.unpack_from("h" * 512, frame_data)
             
-            if keyword_index >= 0:
+            # Check for wake word
+            keyword_index = porcupine.process(pcm)
+            
+            if keyword_index >= 0 and not is_recording:
                 logger.info("🦊 Wake word detected: 'Hey Vivi'!")
                 state.wake_count += 1
                 state.last_wake_time = time.time()
                 
-                # Visual feedback
                 leds_wakeup()
-                time.sleep(0.3)
+                time.sleep(0.2)
                 leds_listening()
                 
-                # Record and transcribe
+                is_recording = True
                 state.recording = True
+                recording_buffer = []
+                silence_count = 0
+                logger.info("Recording started...")
+                continue
+            
+            if is_recording:
+                recording_buffer.append(frame_data)
+                
+                # Check VAD (use first 480 samples of the 512 frame)
+                vad_data = frame_data[:vad_frame_bytes]
                 try:
-                    audio_data = record_until_silence()
+                    is_speech = vad.is_speech(vad_data, SAMPLE_RATE)
+                except:
+                    is_speech = True  # Assume speech on VAD error
+                
+                if is_speech:
+                    silence_count = 0
+                else:
+                    silence_count += 1
+                
+                # Stop conditions
+                should_stop = (
+                    (len(recording_buffer) > 10 and silence_count >= SILENCE_FRAMES) or
+                    len(recording_buffer) >= MAX_RECORDING_FRAMES
+                )
+                
+                if should_stop:
+                    logger.info(f"Recording stopped: {len(recording_buffer)} frames")
+                    is_recording = False
+                    state.recording = False
                     
                     leds_processing()
-                    state.recording = False
                     state.processing = True
                     
-                    # Transcribe (run in asyncio)
+                    try:
-                    loop = asyncio.new_event_loop()
+                        wav_data = audio_to_wav(recording_buffer)
-                    transcription = loop.run_until_complete(transcribe_audio(audio_data))
+                        transcription = transcribe_sync(wav_data)
-                    loop.close()
-                    
                         state.last_transcription = transcription
                         logger.info(f"Transcription: {transcription}")
-                    
                     except Exception as e:
-                    logger.error(f"Recording/transcription error: {e}")
+                        logger.error(f"Transcription error: {e}")
+                        state.error = str(e)
                     finally:
-                    state.recording = False
                         state.processing = False
                         leds_off()
                     
+                    recording_buffer = []
+    
     except Exception as e:
         logger.error(f"Listener error: {e}")
-            time.sleep(0.1)
+        state.error = str(e)
-    
+    finally:
-    # Cleanup
+        porcupine.delete()
-    if state.stream:
-        state.stream.close()
-    if state.audio:
-        state.audio.terminate()
-    if state.porcupine:
-        state.porcupine.delete()
-    
         state.listening = False
-    logger.info("Wake word listener stopped")
+        leds_off()
+        logger.info("Listener stopped")
 
 
 # ============================================================================
-# FastAPI App
+# FastAPI
 # ============================================================================
 
-app = FastAPI(title="HeadMic", description="Vixy's Ears - Wake Word + Voice Recording 🦊👂")
+app = FastAPI(title="HeadMic", description="Vixy's Ears 🦊👂")
-
-
-class RecordRequest(BaseModel):
-    duration_sec: float = 5.0
-
-
-class TranscribeResponse(BaseModel):
-    transcription: str
-    duration_sec: float
 
 
 @app.on_event("startup")
 async def startup():
-    """Start the wake word listener on startup."""
     state.running = True
-    state.listener_thread = threading.Thread(target=wake_word_listener, daemon=True)
+    thread = threading.Thread(target=listener_loop, daemon=True)
-    state.listener_thread.start()
+    thread.start()
-    logger.info("HeadMic service started")
+    logger.info("HeadMic started")
 
 
 @app.on_event("shutdown")
 async def shutdown():
-    """Stop the wake word listener on shutdown."""
     state.running = False
     leds_off()
-    if state.listener_thread:
-        state.listener_thread.join(timeout=5)
-    logger.info("HeadMic service stopped")
 
 
 @app.get("/")
@@ -389,21 +353,19 @@ async def root():
     return {
         "service": "HeadMic",
         "description": "Vixy's Ears 🦊👂",
-        "wake_word": "Hey Vivi",
+        "wake_word": "Hey Vivi"
-        "status": "listening" if state.listening else "idle"
     }
 
 
 @app.get("/health")
 async def health():
     return {
-        "healthy": state.listening,
+        "healthy": state.listening and not state.error,
         "listening": state.listening,
         "recording": state.recording,
         "processing": state.processing,
         "wake_count": state.wake_count,
-        "porcupine_loaded": state.porcupine is not None,
+        "error": state.error
-        "eartail_url": EARTAIL_URL
     }
 
 
@@ -415,89 +377,13 @@ async def status():
         "processing": state.processing,
         "last_transcription": state.last_transcription,
         "last_wake_time": state.last_wake_time,
-        "wake_count": state.wake_count
+        "wake_count": state.wake_count,
+        "error": state.error
     }
 
 
-@app.post("/record")
-async def record(request: RecordRequest):
-    """Manually record for a specified duration."""
-    if state.recording:
-        raise HTTPException(status_code=409, detail="Already recording")
-    
-    state.recording = True
-    leds_listening()
-    
-    try:
-        # Simple timed recording (not VAD-based)
-        p = pyaudio.PyAudio()
-        frames = []
-        
-        stream = p.open(
-            format=pyaudio.paInt16,
-            channels=1,
-            rate=SAMPLE_RATE,
-            input=True,
-            frames_per_buffer=1024
-        )
-        
-        for _ in range(int(SAMPLE_RATE / 1024 * request.duration_sec)):
-            data = stream.read(1024)
-            frames.append(data)
-        
-        stream.stop_stream()
-        stream.close()
-        p.terminate()
-        
-        # Convert to WAV
-        wav_buffer = io.BytesIO()
-        with wave.open(wav_buffer, 'wb') as wf:
-            wf.setnchannels(1)
-            wf.setsampwidth(2)
-            wf.setframerate(SAMPLE_RATE)
-            wf.writeframes(b''.join(frames))
-        
-        wav_buffer.seek(0)
-        return {"success": True, "size_bytes": len(wav_buffer.getvalue())}
-    
-    finally:
-        state.recording = False
-        leds_off()
-
-
-@app.post("/transcribe")
-async def transcribe_endpoint(request: RecordRequest):
-    """Record and transcribe."""
-    if state.recording or state.processing:
-        raise HTTPException(status_code=409, detail="Busy")
-    
-    state.recording = True
-    leds_listening()
-    
-    try:
-        start = time.time()
-        audio_data = record_until_silence(timeout_sec=request.duration_sec)
-        
-        leds_processing()
-        state.recording = False
-        state.processing = True
-        
-        transcription = await transcribe_audio(audio_data)
-        duration = time.time() - start
-        
-        state.last_transcription = transcription
-        
-        return TranscribeResponse(transcription=transcription, duration_sec=duration)
-    
-    finally:
-        state.recording = False
-        state.processing = False
-        leds_off()
-
-
 @app.get("/last")
-async def last_transcription():
+async def last():
-    """Get the last transcription."""
     return {
         "transcription": state.last_transcription,
         "wake_time": state.last_wake_time