> For the complete documentation index, see [llms.txt](https://docs.clore.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.clore.ai/guides/guides_v2-de/audio-and-sprache/demucs-separation.md).

# Demucs Trennung

Teile Musik mit Demucs in Stems auf (Gesang, Schlagzeug, Bass, sonstige).

{% hint style="success" %}
Alle Beispiele können auf GPU-Servern ausgeführt werden, die über [CLORE.AI Marketplace](https://clore.ai/marketplace).
{% endhint %}

## Mieten auf CLORE.AI

1. Besuchen Sie [CLORE.AI Marketplace](https://clore.ai/marketplace)
2. Nach GPU-Typ, VRAM und Preis filtern
3. Wählen **On-Demand** (Festpreis) oder **Spot** (Gebotspreis)
4. Konfigurieren Sie Ihre Bestellung:
   * Docker-Image auswählen
   * Ports festlegen (TCP für SSH, HTTP für Web-UIs)
   * Umgebungsvariablen bei Bedarf hinzufügen
   * Startbefehl eingeben
5. Zahlung auswählen: **CLORE**, **BTC**, oder **USDT/USDC**
6. Bestellung erstellen und auf Bereitstellung warten

### Zugriff auf Ihren Server

* Verbindungsdetails finden Sie in **Meine Bestellungen**
* Webschnittstellen: Verwenden Sie die HTTP-Port-URL
* SSH: `ssh -p <port> root@<proxy-address>`

## Was ist Demucs?

Demucs von Meta AI kann:

* Gesang von Musik trennen
* Schlagzeug, Bass und andere Instrumente extrahieren
* Jedes Audi Format verarbeiten
* Hochwertige Stem-Extraktion

## Schnelle Bereitstellung

**Docker-Image:**

```
pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime
```

**Ports:**

```
22/tcp
7860/http
```

**Befehl:**

```bash
pip install demucs gradio && \
python -c "
import gradio as gr
from demucs.pretrained import get_model
from demucs.apply import apply_model
import torch
import torchaudio
import tempfile
import os

model = get_model('htdemucs')
model.cuda()

def separate(audio_path, stem):
    wav, sr = torchaudio.load(audio_path)
    wav = wav.cuda()

    with torch.no_grad():
        sources = apply_model(model, wav.unsqueeze(0), split=True)[0]

    stems = {'drums': 0, 'bass': 1, 'other': 2, 'vocals': 3}
    output = sources[stems[stem]].cpu()

    with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as f:
        torchaudio.save(f.name, output, sr)
        return f.name

demo = gr.Interface(
    fn=separate,
    inputs=[gr.Audio(type='filepath'), gr.Dropdown(['vocals', 'drums', 'bass', 'other'])],
    outputs=gr.Audio(),
    title='Demucs Audio Separator'
)
demo.launch(server_name='0.0.0.0', server_port=7860)
"
```

## Zugriff auf Ihren Dienst

Nach der Bereitstellung finden Sie Ihre `http_pub` URL in **Meine Bestellungen**:

1. Gehen Sie zur **Meine Bestellungen** Seite
2. Klicken Sie auf Ihre Bestellung
3. Finden Sie die `http_pub` URL (z. B., `abc123.clorecloud.net`)

Verwenden Sie `https://IHRE_HTTP_PUB_URL` anstelle von `localhost` in den Beispielen unten.

## Installation

```bash
pip install demucs

# oder
pip install -e git+https://github.com/facebookresearch/demucs#egg=demucs
```

## Kommandozeilenverwendung

### Grundlegende Trennung

```bash

# In 4 Stems trennen
demucs song.mp3

# Ausgabe: separated/htdemucs/song/{drums,bass,other,vocals}.wav
```

### Optionen

```bash
demucs \
    --two-stems vocals \     # Nur Gesang + Instrumental
    -n htdemucs \            # Modellname
    -d cuda \                # GPU verwenden
    -o ./output \            # Ausgabeverzeichnis
    --mp3 \                  # Als MP3 ausgeben
    song.mp3
```

### Ordner verarbeiten

```bash
demucs --two-stems vocals -d cuda ./songs/*.mp3
```

## Python-API

### Grundlegende Trennung

```python
from demucs.pretrained import get_model
from demucs.apply import apply_model
import torchaudio
import torch

# Modell laden
model = get_model('htdemucs')
model.cuda()
model.eval()

# Audio laden
wav, sr = torchaudio.load("song.mp3")
wav = wav.cuda()

# Trennen
with torch.no_grad():
    sources = apply_model(model, wav.unsqueeze(0), split=True)[0]

# sources Form: [4, Kanäle, Samples]

# 0: drums, 1: bass, 2: other, 3: vocals

# Stems speichern
stems = ['drums', 'bass', 'other', 'vocals']
for i, stem in enumerate(stems):
    torchaudio.save(f"{stem}.wav", sources[i].cpu(), sr)
```

### Nur Gesang erhalten

```python
def extract_vocals(audio_path):
    wav, sr = torchaudio.load(audio_path)
    wav = wav.cuda()

    with torch.no_grad():
        sources = apply_model(model, wav.unsqueeze(0), split=True)[0]

    vocals = sources[3].cpu()  # Index 3 = Gesang
    return vocals, sr

vocals, sr = extract_vocals("song.mp3")
torchaudio.save("vocals.wav", vocals, sr)
```

### Instrumental erhalten (ohne Gesang)

```python
def extract_instrumental(audio_path):
    wav, sr = torchaudio.load(audio_path)
    wav = wav.cuda()

    with torch.no_grad():
        sources = apply_model(model, wav.unsqueeze(0), split=True)[0]

    # Schlagzeug + Bass + sonstiges summieren
    instrumental = sources[0] + sources[1] + sources[2]
    return instrumental.cpu(), sr

instrumental, sr = extract_instrumental("song.mp3")
torchaudio.save("instrumental.wav", instrumental, sr)
```

## Modellvarianten

| Modell       | Stems | Qualität  | Geschwindigkeit |
| ------------ | ----- | --------- | --------------- |
| htdemucs     | 4     | Am besten | Mittel          |
| htdemucs\_ft | 4     | Best+     | Langsam         |
| htdemucs\_6s | 6     | Großartig | Mittel          |
| mdx\_extra   | 4     | Großartig | Schnell         |

### 6-Stem-Modell

```python
model = get_model('htdemucs_6s')

# Stems: drums, bass, other, vocals, guitar, piano
```

### Feinabgestimmtes Modell

```python
model = get_model('htdemucs_ft')

# Höhere Qualität, aber langsamer
```

## Batch-Verarbeitung

```python
import os
from demucs.pretrained import get_model
from demucs.apply import apply_model
import torchaudio
import torch

model = get_model('htdemucs')
model.cuda()
model.eval()

input_dir = "./songs"
output_dir = "./separated"

for filename in os.listdir(input_dir):
    if filename.endswith(('.mp3', '.wav', '.flac')):
        input_path = os.path.join(input_dir, filename)
        song_output_dir = os.path.join(output_dir, filename.rsplit('.', 1)[0])
        os.makedirs(song_output_dir, exist_ok=True)

        print(f"Verarbeite: {filename}")

        wav, sr = torchaudio.load(input_path)
        wav = wav.cuda()

        with torch.no_grad():
            sources = apply_model(model, wav.unsqueeze(0), split=True)[0]

        stems = ['drums', 'bass', 'other', 'vocals']
        for i, stem in enumerate(stems):
            torchaudio.save(
                os.path.join(song_output_dir, f"{stem}.wav"),
                sources[i].cpu(),
                sr
            )

        print(f"Gespeichert: {song_output_dir}")
```

## API-Server

```python
from fastapi import FastAPI, UploadFile
from fastapi.responses import FileResponse
from demucs.pretrained import get_model
from demucs.apply import apply_model
import torchaudio
import torch
import tempfile
import os

app = FastAPI()

model = get_model('htdemucs')
model.cuda()
model.eval()

@app.post("/separate")
async def separate(file: UploadFile, stem: str = "vocals"):
    # Hochgeladene Datei speichern
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
        content = await file.read()
        tmp.write(content)
        tmp_path = tmp.name

    # Laden und trennen
    wav, sr = torchaudio.load(tmp_path)
    wav = wav.cuda()

    with torch.no_grad():
        sources = apply_model(model, wav.unsqueeze(0), split=True)[0]

    stems = {'drums': 0, 'bass': 1, 'other': 2, 'vocals': 3}
    output = sources[stems[stem]].cpu()

    # Ausgabe speichern
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as out:
        torchaudio.save(out.name, output, sr)
        return FileResponse(out.name, media_type="audio/wav")

@app.post("/instrumental")
async def get_instrumental(file: UploadFile):
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
        content = await file.read()
        tmp.write(content)
        tmp_path = tmp.name

    wav, sr = torchaudio.load(tmp_path)
    wav = wav.cuda()

    with torch.no_grad():
        sources = apply_model(model, wav.unsqueeze(0), split=True)[0]

    # Nicht-gesangliche Stems kombinieren
    instrumental = sources[0] + sources[1] + sources[2]

    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as out:
        torchaudio.save(out.name, instrumental.cpu(), sr)
        return FileResponse(out.name, media_type="audio/wav")

# Ausführen: uvicorn server:app --host 0.0.0.0 --port 8000
```

## Speicheroptimierung

### Für lange Audiodateien

```python
from demucs.apply import apply_model

# Splitting für lange Audiodateien verwenden
sources = apply_model(
    model,
    wav.unsqueeze(0),
    split=True,         # In Abschnitte aufteilen
    overlap=0.25,       # Überlappung zwischen Abschnitten
    progress=True
)[0]
```

### Für begrenzten VRAM

```python

# Für einige Operationen CPU verwenden
model.cpu()
wav = wav.cpu()

# Oder Segmentverarbeitung verwenden
sources = apply_model(
    model,
    wav.unsqueeze(0),
    split=True,
    segment=10  # 10 Sekunden Segmente
)[0]
```

## Anwendungsfälle

### Karaoke-Track

```python
def create_karaoke(song_path):
    wav, sr = torchaudio.load(song_path)
    wav = wav.cuda()

    with torch.no_grad():
        sources = apply_model(model, wav.unsqueeze(0), split=True)[0]

    # Alles außer Gesang
    karaoke = sources[0] + sources[1] + sources[2]
    return karaoke.cpu(), sr
```

### Remix-Vorbereitung

```python
def extract_all_stems(song_path, output_dir):
    wav, sr = torchaudio.load(song_path)
    wav = wav.cuda()

    with torch.no_grad():
        sources = apply_model(model, wav.unsqueeze(0), split=True)[0]

    stems = ['drums', 'bass', 'other', 'vocals']
    paths = {}

    for i, stem in enumerate(stems):
        path = os.path.join(output_dir, f"{stem}.wav")
        torchaudio.save(path, sources[i].cpu(), sr)
        paths[stem] = path

    return paths
```

### Acapella-Extraktion

```python
def extract_acapella(song_path):
    wav, sr = torchaudio.load(song_path)
    wav = wav.cuda()

    with torch.no_grad():
        sources = apply_model(model, wav.unsqueeze(0), split=True)[0]

    vocals = sources[3]
    return vocals.cpu(), sr
```

## Qualitätstipps

### Für beste Ergebnisse

* Verlustfreies Eingangsformat verwenden (WAV, FLAC)
* Höhere Abtastrate = bessere Qualität
* Verwenden Sie `htdemucs_ft` für kritische Arbeiten

### Nachbearbeitung

```python
from pydub import AudioSegment
from pydub.effects import normalize, high_pass_filter

# Separierten Gesang laden
vocals = AudioSegment.from_wav("vocals.wav")

# Niedrige Brummfrequenzen entfernen
vocals = high_pass_filter(vocals, 80)

# Normalisieren
vocals = normalize(vocals)

vocals.export("vocals_clean.wav", format="wav")
```

## Leistung

| Audiolänge     | GPU      | Zeit    |
| -------------- | -------- | ------- |
| 3 min Song     | RTX 3090 | \~15s   |
| 3 min Song     | RTX 4090 | \~10s   |
| 3 min Song     | A100     | \~8s    |
| 1 Stunde Album | RTX 3090 | \~5 Min |

## Fehlerbehebung

### Kein Speicher mehr

```bash

# Kleinere Segmente verwenden
demucs --segment 10 song.mp3
```

### Schlechte Trennung

* Verwende das htdemucs\_ft-Modell
* Eingangsqualität prüfen
* Stark komprimierte MP3s vermeiden

### Artefakte

* Überlappung erhöhen
* Höherwertiges Modell verwenden
* Auf Clipping im Eingang prüfen

## Kostenabschätzung

Typische CLORE.AI-Marktplatztarife (Stand 2024):

| GPU       | Stundensatz | Tagessatz | 4-Stunden-Sitzung |
| --------- | ----------- | --------- | ----------------- |
| RTX 3060  | \~$0.03     | \~$0.70   | \~$0.12           |
| RTX 3090  | \~$0.06     | \~$1.50   | \~$0.25           |
| RTX 4090  | \~$0.10     | \~$2.30   | \~$0.40           |
| A100 40GB | \~$0.17     | \~$4.00   | \~$0.70           |
| A100 80GB | \~$0.25     | \~$6.00   | \~$1.00           |

*Preise variieren je nach Anbieter und Nachfrage. Prüfen Sie* [*CLORE.AI Marketplace*](https://clore.ai/marketplace) *auf aktuelle Preise.*

**Geld sparen:**

* Verwenden Sie **Spot** Markt für flexible Workloads (oft 30–50% günstiger)
* Bezahlen mit **CLORE** Token
* Preise bei verschiedenen Anbietern vergleichen

## Nächste Schritte

* [RVC-Stimmenklon](/guides/guides_v2-de/audio-and-sprache/rvc-voice-clone.md) - Extrahierten Gesang verarbeiten
* [AudioCraft Music](/guides/guides_v2-de/audio-and-sprache/audiocraft-music.md) - Neue Musik erzeugen
* [Whisper Transcription](/guides/guides_v2-de/audio-and-sprache/whisper-transcription.md) - Gesang transkribieren


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