feat: add whisper.cpp ROCm backend support for AMD GPU acceleration

- transcription.py: new _transcribe_remote_whispercpp() using /inference endpoint
- transcription.py: backend param routes to openai or whispercpp remote path
- config.py: whisper.backend default 'openai', alt 'whispercpp'
- pipeline.py: passes backend from config to transcribe_file
- settings: backend dropdown (OpenAI-compat / whisper.cpp)
- SETUP.md: whisper.cpp ROCm build and systemd setup instructions

whisper-cpp-server running on beastix :8080 (ROCm0, gfx1030, RX 6800 XT)

api/pipeline.py

@@ -103,6 +103,7 @@ async def _run_meeting_pipeline(cfg, wav_path, output_dir, instructions, diar_cf
             model_name=cfg["whisper"]["model"],
             device=cfg["whisper"]["device"],
             base_url=cfg["whisper"].get("base_url", ""),
+            backend=cfg["whisper"].get("backend", "openai"),
             with_segments=True,
         )
     )

config.py

@@ -13,6 +13,7 @@ DEFAULTS = {
         "language": "de",
         "device": "auto",  # "auto" = use GPU if ROCm available, else CPU
         "base_url": "",
+        "backend": "openai",  # "openai" = OpenAI-compatible API, "whispercpp" = whisper.cpp /inference
     },
     "audio": {
         "device": "",

docs/SETUP.md

@@ -20,34 +20,41 @@ Einstellungsseite.
 
 ## Beastix (Server-Setup, einmalig)
 
-### 1. faster-whisper-server installieren
+### 1. whisper.cpp mit ROCm/GPU kompilieren
+
+Voraussetzung: ROCm installiert (Arch: `sudo pacman -S rocm-hip-sdk`).
 
 ```bash
-sudo pacman -S python-pipx   # Arch Linux
+mkdir -p ~/src && cd ~/src
-pipx install faster-whisper-server
+git clone https://github.com/ggml-org/whisper.cpp.git --depth=1
-pipx ensurepath
+cd whisper.cpp
+
+# Für AMD RX 6800 XT (gfx1030) — gfx-Target ggf. anpassen
+cmake -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release -DWHISPER_BUILD_SERVER=ON
+cmake --build build -j$(nproc)
+
+# Modell large-v3 herunterladen (~2.9 GB)
+bash models/download-ggml-model.sh large-v3
 ```
 
-**Bekannter Bug in Version 0.0.2** — fehlende `pyproject.toml` im pipx-venv:
+`gfx1030` = RX 6800 XT. Andere AMD GPUs: `rocminfo | grep gfx`
-
-```bash
-cat > ~/.local/share/pipx/venvs/faster-whisper-server/lib/python*/site-packages/pyproject.toml << 'EOF'
-[project]
-name = "faster-whisper-server"
-version = "0.0.2"
-EOF
-```
 
 ### 2. Als systemd-User-Service einrichten
 
 ```bash
-cat > ~/.config/systemd/user/faster-whisper-server.service << 'EOF'
+cat > ~/.config/systemd/user/whisper-cpp-server.service << 'EOF'
 [Unit]
-Description=faster-whisper-server (OpenAI-compatible Whisper API)
+Description=whisper.cpp Server (ROCm/GPU)
 After=network.target
 
 [Service]
-ExecStart=%h/.local/bin/faster-whisper-server --host 0.0.0.0 --port 8000 --model large-v3
+ExecStart=%h/src/whisper.cpp/build/bin/whisper-server \
+    --host 0.0.0.0 \
+    --port 8080 \
+    --model %h/src/whisper.cpp/models/ggml-large-v3.bin \
+    --language de \
+    --threads 4 \
+    --convert
 Restart=on-failure
 RestartSec=5
 
@@ -56,9 +63,12 @@ WantedBy=default.target
 EOF
 
 systemctl --user daemon-reload
-systemctl --user enable --now faster-whisper-server.service
+systemctl --user enable --now whisper-cpp-server.service
 ```
 
+Logs prüfen: `journalctl --user -u whisper-cpp-server -f`
+GPU-Nutzung bestätigt wenn in den Logs steht: `using ROCm0 backend`
+
 ### 3. Ollama installieren (falls noch nicht vorhanden)
 
 ```bash
@@ -105,7 +115,8 @@ Als Admin einloggen → Zahnrad-Icon im Header → Einstellungen:
 
 | Feld | Wert (Beispiel) |
 |------|-----------------|
-| Whisper Server URL | `http://beastix:8000` |
+| Whisper Backend | `whisper.cpp Server` |
+| Whisper Server URL | `http://beastix:8080` |
 | Whisper Modell | `large-v3` |
 | Ollama Server URL | `http://beastix:11434` |
 | Ollama Modell | `gemma3:12b` (aus Dropdown wählen) |

frontend/settings.html

@@ -74,9 +74,16 @@
 
     <section>
       <h2>Verarbeitung</h2>
+      <div class="field">
+        <label>Whisper Backend</label>
+        <select id="whisper-backend">
+          <option value="openai">OpenAI-kompatibel (faster-whisper-server)</option>
+          <option value="whispercpp">whisper.cpp Server</option>
+        </select>
+      </div>
       <div class="field">
         <label>Whisper Server URL (leer = lokal)</label>
-        <input type="text" id="whisper-url" placeholder="http://beastix:8000">
+        <input type="text" id="whisper-url" placeholder="http://beastix:8080">
       </div>
       <div class="field">
         <label>Whisper Modell</label>

frontend/settings.js

@@ -53,6 +53,7 @@ async function loadConfig() {
   if (!r.ok) return;
   const cfg = await r.json();
   document.getElementById('audio-device').value = (cfg.audio && cfg.audio.device) || '';
+  document.getElementById('whisper-backend').value = (cfg.whisper && cfg.whisper.backend) || 'openai';
   document.getElementById('whisper-url').value = (cfg.whisper && cfg.whisper.base_url) || '';
   document.getElementById('whisper-model').value = (cfg.whisper && cfg.whisper.model) || 'large-v3';
   const ollamaUrl = (cfg.ollama && cfg.ollama.base_url) || 'http://localhost:11434';
@@ -96,6 +97,7 @@ document.getElementById('save-btn').addEventListener('click', async function() {
     whisper: {
       base_url: document.getElementById('whisper-url').value,
       model: document.getElementById('whisper-model').value,
+      backend: document.getElementById('whisper-backend').value,
     },
     ollama: {
       base_url: document.getElementById('ollama-url').value,

transcription.py

@@ -27,8 +27,13 @@ class TranscriptionEngine:
         device: str = "auto",
         base_url: str = "",
         with_segments: bool = False,
+        backend: str = "openai",
     ) -> Union[str, list[dict]]:
         if base_url:
+            if backend == "whispercpp":
+                return await self._transcribe_remote_whispercpp(
+                    audio_path, language, base_url, with_segments
+                )
             return await self._transcribe_remote(
                 audio_path, language, model_name, base_url, with_segments
             )
@@ -67,6 +72,35 @@ class TranscriptionEngine:
             ]
         return [{"start": 0.0, "end": 9999.0, "text": body["text"].strip()}]
 
+    async def _transcribe_remote_whispercpp(
+        self,
+        audio_path: str,
+        language: str,
+        base_url: str,
+        with_segments: bool,
+    ) -> Union[str, list[dict]]:
+        async with httpx.AsyncClient(timeout=300) as client:
+            with open(audio_path, "rb") as f:
+                data = {"language": language}
+                if with_segments:
+                    data["response_format"] = "verbose_json"
+                r = await client.post(
+                    f"{base_url}/inference",
+                    files={"file": ("audio.wav", f, "audio/wav")},
+                    data=data,
+                )
+            r.raise_for_status()
+            body = r.json()
+        if not with_segments:
+            return body.get("text", "").strip()
+        raw_segs = body.get("segments") or []
+        if raw_segs:
+            return [
+                {"start": s["start"], "end": s["end"], "text": s["text"].strip()}
+                for s in raw_segs
+            ]
+        return [{"start": 0.0, "end": 9999.0, "text": body.get("text", "").strip()}]
+
     async def _transcribe_local(
         self,
         audio_path: str,