feat: Jarvis v2 - CPU optimized always-listening for iMac Ubuntu

- PipeWire/pw-record backend (no PortAudio needed) - tested mic live - VAD: Energy (immediate) + Silero (torch CPU) switchable - Wakeword: openWakeWord TFLite hey_jarvis 0.4 - STT: faster-whisper base int8 CPU - model loads OK on i3 - SpeakerID: ECAPA embeddings + fallback stats, enrollment store - Vision: OpenCV Haar + face_recognition + webcam cam0 640x480 verified - LLM: ollama/qwen2.5:3b, gemini_cli, hermes_api, echo strategies - TTS: Piper + espeak + echo fallbacks - Core pipeline: IDLE -> WAKE -> RECORDING -> STT -> SpeakerID/FaceID fusion -> LLM -> TTS -> followup window - Config via jarvis.yaml, soul.md persona - CLI tools: run, devices, enroll-speaker, enroll-face, test-vad, test-mic - Systemd user service ready - Tested on iMac 2010 i3 550 15GB Ubuntu 26.04 CPU-only
2026-07-10 12:16:03 -04:00
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+"""Test STT with mic"""
+from jarvis.audio.capture import AudioCapture
+from jarvis.stt.faster_whisper_engine import FasterWhisperSTT
+from jarvis.audio.vad import create_vad
+from jarvis.config import load_config
+import numpy as np, time, collections
+
+cfg = load_config(None)
+vad = create_vad(cfg)
+stt = FasterWhisperSTT(model=cfg.get('stt',{}).get('model','base'), device='cpu', compute_type='int8')
+cap = AudioCapture()
+cap.start()
+
+print("Speak, with 1.5s silence to trigger transcription. Ctrl+C to exit")
+buffer = []
+silence_start = None
+recording = False
+
+try:
+    while True:
+        chunk = cap.read(timeout=0.1)
+        if chunk is None:
+            continue
+        is_speech = vad.is_speech(chunk)
+        if is_speech:
+            if not recording:
+                print("Speech start")
+                recording = True
+            buffer.append(chunk)
+            silence_start = None
+        else:
+            if recording:
+                if silence_start is None:
+                    silence_start = time.time()
+                buffer.append(chunk)
+                if time.time() - silence_start > 1.2:
+                    # transcribe
+                    audio = np.concatenate(buffer)
+                    print(f"Transcribing {len(audio)/16000:.2f}s...")
+                    res = stt.transcribe(audio)
+                    print(f" => {res.get('text')}")
+                    buffer = []
+                    recording = False
+                    silence_start = None
+                    print("Listening again...")
+            # else silence, continue
+except KeyboardInterrupt:
+    pass
+finally:
+    cap.stop()