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

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- 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)
This commit is contained in:
Thomas Kopp
2026-04-02 01:33:32 +02:00
parent 56d41b8620
commit c7cad4bb2a
6 changed files with 75 additions and 19 deletions
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api/pipeline.py
+1
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@@ -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
+1
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@@ -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
+29 -18
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@@ -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
pipx install faster-whisper-server
pipx ensurepath
mkdir -p ~/src && cd ~/src
git clone https://github.com/ggml-org/whisper.cpp.git --depth=1
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:
```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
```
`gfx1030` = RX 6800 XT. Andere AMD GPUs: `rocminfo | grep gfx`
### 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
+8 -1
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@@ -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
+2
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@@ -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
+34
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@@ -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,