feat: FRIDAY Voice stack on the GPU machine; VoiceCoach history

FRIDAY Voice (speaches GPU STT/TTS + hermes agent + push-to-talk console) on
.150 Docker Desktop, fronted by coach.hobbs.farm. Also captures the VoiceCoach
compose/agent trees and deploy notes it replaced, which had never been committed.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

compose/hf-pdocker-01/voicecoach/speechbrain-service/main.py

@@ -0,0 +1,66 @@
+"""
+FastAPI service wrapping VocalAnalyzer.
+Internal-only -- not exposed via Traefik.
+"""
+import logging
+import os
+from contextlib import asynccontextmanager
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import JSONResponse
+from dataclasses import asdict
+
+from analyzer import VocalAnalyzer
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+MODEL_DIR = os.getenv("MODEL_DIR", "/app/models")
+analyzer: VocalAnalyzer = None
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global analyzer
+    logger.info("Starting VocalAnalyzer...")
+    analyzer = VocalAnalyzer(model_dir=MODEL_DIR)
+    logger.info("VocalAnalyzer ready.")
+    yield
+    logger.info("Shutting down.")
+
+
+app = FastAPI(title="SpeechBrain Vocal Analysis Service", lifespan=lifespan)
+
+
+@app.get("/health")
+async def health():
+    if analyzer is None:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+    return {"status": "ok", "model": "emotion-recognition-wav2vec2-IEMOCAP"}
+
+
+@app.post("/analyze")
+async def analyze(audio: UploadFile = File(...)):
+    if analyzer is None:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+
+    content = await audio.read()
+    if len(content) == 0:
+        raise HTTPException(status_code=400, detail="Empty audio file")
+
+    result = analyzer.analyze(content, content_type=audio.content_type or "audio/wav")
+    return JSONResponse(content=asdict(result))
+
+
+@app.post("/calibrate")
+async def calibrate(audio: UploadFile = File(...)):
+    if analyzer is None:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+
+    content = await audio.read()
+    if len(content) == 0:
+        raise HTTPException(status_code=400, detail="Empty audio file")
+
+    result = analyzer.analyze(content, content_type=audio.content_type or "audio/wav")
+    result_dict = asdict(result)
+    result_dict["is_baseline"] = True
+    return JSONResponse(content=result_dict)