Replace install_deps.sh with start_server.py one-click deployment
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YingboHAO
+23
-39
@@ -15,70 +15,54 @@ Deploy VibeVoice ASR model as a high-performance API service using [vLLM](https:
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Using Official vLLM Docker Image (Recommended)
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1. Clone the repository
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```bash
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# 1. Pull the official vLLM image
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docker pull vllm/vllm-openai:latest
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git clone https://github.com/microsoft/VibeVoice.git
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cd VibeVoice
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```
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# 2. Start an interactive container
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docker run -it --gpus all --name vibevoice-vllm \
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2. Launch the server (background mode)
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```bash
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docker run -d --gpus all --name vibevoice-vllm \
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--ipc=host \
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-p 8000:8000 \
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-e VIBEVOICE_FFMPEG_MAX_CONCURRENCY=64 \
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-e PYTORCH_ALLOC_CONF=expandable_segments:True \
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-v /path/to/models:/models \
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-v /path/to/VibeVoice:/app \
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-v $(pwd):/app \
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-w /app \
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--entrypoint bash \
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vllm/vllm-openai:latest
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# 3. Inside container: Install system dependencies
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bash vllm_plugin/scripts/install_deps.sh
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# 4. Inside container: Install VibeVoice with vLLM support
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pip install -e .[vllm]
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# 5. Inside container: (Optional) Generate tokenizer files if needed
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python3 -m vllm_plugin.tools.generate_tokenizer_files --output /models/your_model
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# 6. Inside container: Start vLLM server
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vllm serve /models/your_model \
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--served-model-name vibevoice \
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--trust-remote-code \
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--dtype bfloat16 \
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--max-num-seqs 64 \
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--max-model-len 65536 \
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--max-num-batched-tokens 32768 \
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--gpu-memory-utilization 0.8 \
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--enforce-eager \
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--no-enable-prefix-caching \
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--enable-chunked-prefill \
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--chat-template-content-format openai \
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--tensor-parallel-size 1 \
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--allowed-local-media-path /app \
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--port 8000
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vllm/vllm-openai:latest \
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-c "python3 /app/vllm_plugin/scripts/start_server.py"
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```
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> **Note**: This approach allows you to switch models, adjust parameters, and debug issues without rebuilding the container.
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3. View logs
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```bash
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docker logs -f vibevoice-vllm
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```
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> **Note**:
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> - The `-d` flag runs the container in background (detached mode)
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> - Use `docker stop vibevoice-vllm` to stop the service
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> - The model will be downloaded to HuggingFace cache (`~/.cache/huggingface`) inside the container
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## 🚀 Quick Start
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## 🚀 Usages
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### Test the API
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Once the vLLM server is running, test it with the provided script:
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```bash
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# Run the test script (inside container)
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python3 vllm_plugin/tests/test_api.py /path/to/audio.wav
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# Run the test (use container path /app/...)
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docker exec -it vibevoice-vllm python3 vllm_plugin/tests/test_api.py /app/audio.wav
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```
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> **Note**: The audio file must be inside the mounted directory (`/app` in the container). Copy your audio to the VibeVoice folder before testing.
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### Environment Variables
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `VIBEVOICE_FFMPEG_MAX_CONCURRENCY` | Maximum FFmpeg processes for audio decoding | `64` |
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| `PYTORCH_CUDA_ALLOC_CONF` | CUDA memory allocator config | `expandable_segments:True` |
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| `PYTORCH_ALLOC_CONF` | PyTorch memory allocator config | `expandable_segments:True` |
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@@ -1,23 +0,0 @@
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#!/bin/bash
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# Install system dependencies for VibeVoice vLLM plugin
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# Run this script inside the vLLM container before using the plugin
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set -e
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echo "Installing system dependencies for VibeVoice vLLM plugin..."
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# Update package list
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apt-get update
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# Install FFmpeg and audio processing libraries
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apt-get install -y \
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ffmpeg \
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libsndfile1 \
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git
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echo "✅ System dependencies installed successfully!"
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echo ""
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echo "Next steps:"
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echo " 1. Install VibeVoice: pip install -e .[vllm]"
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echo " 2. Generate tokenizer files (if needed): python -m vllm_plugin.tools.generate_tokenizer_files -o /path/to/model"
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echo " 3. Start vLLM server: vllm serve <model_path> --trust-remote-code --enforce-eager --no-enable-prefix-caching"
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@@ -0,0 +1,169 @@
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#!/usr/bin/env python3
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"""
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VibeVoice vLLM ASR Server Launcher
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One-click deployment script that handles:
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1. Installing system dependencies (FFmpeg, etc.)
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2. Installing VibeVoice Python package
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3. Downloading model from HuggingFace
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4. Generating tokenizer files
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5. Starting vLLM server
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Usage:
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python3 start_server.py [--model MODEL_ID] [--port PORT]
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"""
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import argparse
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import os
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import subprocess
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import sys
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def run_command(cmd: list[str], description: str, shell: bool = False) -> None:
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"""Run a command with logging."""
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print(f"\n{'='*60}")
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print(f" {description}")
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print(f"{'='*60}\n")
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if shell:
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subprocess.run(cmd, shell=True, check=True)
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else:
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subprocess.run(cmd, check=True)
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def install_system_deps() -> None:
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"""Install system dependencies (FFmpeg, etc.)."""
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run_command(["apt-get", "update"], "Updating package list")
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run_command(
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["apt-get", "install", "-y", "ffmpeg", "libsndfile1"],
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"Installing FFmpeg and audio libraries"
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)
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def install_vibevoice() -> None:
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"""Install VibeVoice Python package."""
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run_command(
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[sys.executable, "-m", "pip", "install", "-e", "/app[vllm]"],
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"Installing VibeVoice with vLLM support"
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)
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def download_model(model_id: str) -> str:
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"""Download model from HuggingFace using default cache."""
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print(f"\n{'='*60}")
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print(f" Downloading model: {model_id}")
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print(f"{'='*60}\n")
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import warnings
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from huggingface_hub import snapshot_download
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# Suppress deprecation warnings from huggingface_hub
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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model_path = snapshot_download(model_id)
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print(f"\n{'='*60}")
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print(f" ✅ Model downloaded successfully!")
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print(f" 📁 Path: {model_path}")
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print(f"{'='*60}\n")
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return model_path
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def generate_tokenizer(model_path: str) -> None:
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"""Generate tokenizer files for the model."""
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run_command(
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[sys.executable, "-m", "vllm_plugin.tools.generate_tokenizer_files",
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"--output", model_path],
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"Generating tokenizer files"
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)
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def start_vllm_server(model_path: str, port: int) -> None:
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"""Start vLLM server (replaces current process)."""
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print(f"\n{'='*60}")
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print(f" Starting vLLM server on port {port}")
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print(f"{'='*60}\n")
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vllm_cmd = [
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"vllm", "serve", model_path,
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"--served-model-name", "vibevoice",
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"--trust-remote-code",
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"--dtype", "bfloat16",
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"--max-num-seqs", "64",
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"--max-model-len", "65536",
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"--max-num-batched-tokens", "32768",
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"--gpu-memory-utilization", "0.8",
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"--enforce-eager",
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"--no-enable-prefix-caching",
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"--enable-chunked-prefill",
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"--chat-template-content-format", "openai",
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"--tensor-parallel-size", "1",
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"--allowed-local-media-path", "/app",
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"--port", str(port),
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]
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os.execvp("vllm", vllm_cmd)
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def main():
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parser = argparse.ArgumentParser(
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description="VibeVoice vLLM ASR Server - One-Click Deployment",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Start with default settings
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python3 start_server.py
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# Use custom port
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python3 start_server.py --port 8080
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# Skip dependency installation (if already installed)
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python3 start_server.py --skip-deps
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"""
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)
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parser.add_argument(
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"--model", "-m",
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default="microsoft/VibeVoice-ASR",
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help="HuggingFace model ID (default: microsoft/VibeVoice-ASR)"
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)
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parser.add_argument(
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"--port", "-p",
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type=int,
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default=8000,
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help="Server port (default: 8000)"
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)
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parser.add_argument(
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"--skip-deps",
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action="store_true",
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help="Skip installing system dependencies"
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)
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parser.add_argument(
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"--skip-tokenizer",
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action="store_true",
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help="Skip generating tokenizer files"
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)
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args = parser.parse_args()
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print("\n" + "="*60)
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print(" VibeVoice vLLM ASR Server - One-Click Deployment")
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print("="*60)
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# Step 1: Install system dependencies
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if not args.skip_deps:
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install_system_deps()
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# Step 2: Install VibeVoice
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install_vibevoice()
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# Step 3: Download model
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model_path = download_model(args.model)
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# Step 4: Generate tokenizer files
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if not args.skip_tokenizer:
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generate_tokenizer(model_path)
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# Step 5: Start vLLM server
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start_vllm_server(model_path, args.port)
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if __name__ == "__main__":
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main()
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