Add local TTS/STT wrapper servers (chatterbox, faster-whisper) as openai_compatible providers

Wraps the locally installed chatterbox-tts and faster-whisper packages in thin
FastAPI servers implementing OpenAI's audio API shape, since neither Ollama nor
OpenRouter support speech. Pinned to GPU 2 to avoid contending with Ollama's
resident models on GPU 1. Requires UFW rules for 8901/8902 (same pattern as
the existing 11434 rule) since UFW defaults to deny-incoming.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

services/stt_server.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+"""OpenAI-compatible STT wrapper around faster-whisper, for Open Notebook.
+
+Implements exactly the endpoint esperanto's OpenAICompatibleSpeechToTextModel calls:
+    POST /audio/transcriptions  (multipart: file, model, language?, prompt?) -> {"text": ...}
+
+Start:
+    python3 stt_server.py
+
+Env vars:
+    STT_HOST (default 0.0.0.0), STT_PORT (default 8902)
+    WHISPER_MODEL (default large-v3), WHISPER_COMPUTE_TYPE (default float16)
+    CUDA_VISIBLE_DEVICES should be set by the caller (e.g. "1,2") to keep GPU 0 free.
+"""
+from __future__ import annotations
+
+import os
+import tempfile
+from pathlib import Path
+
+from faster_whisper import WhisperModel
+from fastapi import FastAPI, File, Form, UploadFile
+
+app = FastAPI(title="faster-whisper STT (OpenAI-compatible)", version="1.0")
+
+_MODEL_SIZE = os.environ.get("WHISPER_MODEL", "large-v3")
+_COMPUTE_TYPE = os.environ.get("WHISPER_COMPUTE_TYPE", "float16")
+_model = WhisperModel(_MODEL_SIZE, device="cuda", compute_type=_COMPUTE_TYPE)
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok", "model": _MODEL_SIZE}
+
+
+@app.post("/audio/transcriptions")
+async def transcriptions(
+    file: UploadFile = File(...),
+    model: str = Form(default="whisper-1"),
+    language: str | None = Form(default=None),
+    prompt: str | None = Form(default=None),
+):
+    suffix = Path(file.filename or "audio").suffix or ".wav"
+    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
+        tmp.write(await file.read())
+        tmp_path = tmp.name
+
+    try:
+        segments, _info = _model.transcribe(
+            tmp_path,
+            language=language,
+            initial_prompt=prompt,
+        )
+        text = "".join(segment.text for segment in segments).strip()
+    finally:
+        os.unlink(tmp_path)
+
+    return {"text": text}
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host=os.environ.get("STT_HOST", "0.0.0.0"),
+                port=int(os.environ.get("STT_PORT", "8902")))