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Integrated based on SunoSongsCreator and Suno-API projects, offering standardized service interfaces compliant with OpenAI formats.

• OpenAI Format Calls: Supports streaming output.
• Front-end Compatibility: Compatible with front-end projects like chat-next-web.
• Docker Deployment: Simplifies deployment process, supports docker-compose, docker.
• Multiple Cookie Management: Implements rotation of multiple cookies.

• Introduce request queueing optimizations.
• Support for custom parameters (such as tags, prompt, style, and song continuation).
• Explore development of official-like frontend pages.
• Welcome valuable suggestions! 📧 Email: 1544007699@qq.com

This tutorial provides step-by-step guidance on running a Docker container with specific environment variables and port mappings. For the purpose of this guide, sensitive information such as SQL names, passwords, and IP addresses will be replaced with placeholders.

• Docker is installed on your machine.
• Basic knowledge of Docker CLI.
• MySQL version >= 5.7

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1. Pull Docker Image

   Ensure the Docker image wlhtea/suno2openai:latest is available on your machine. If not, you can pull it from the Docker repository using:

   docker pull wlhtea/suno2openai:latest

2. Run Docker Container

   Run the Docker container using necessary environment variables and port mappings. Replace <SQL_NAME>, <SQL_PASSWORD>, and <SQL_IP> with your actual SQL database connection values. These should be kept confidential and not shared publicly.

   docker run -d --name wsunoapi \
      -p 8000:8000 \
      -p 8501:8501 \
      -e BASE_URL='<BASE_URL>' \
      -e SESSION_ID='<SESSION_ID>' \
      -e USER_NAME='<USER_NAME>' \
      -e SQL_NAME='<SQL_NAME>' \
      -e SQL_PASSWORD='<SQL_PASSWORD>' \
      -e SQL_IP='127.0.0.1' \
      -e SQL_DK=3306 \
      -e COOKIES_PREFIX='your_cookies_prefix' \
      -e AUTH_KEY='<AUTH_KEY>' \
      -e RETRIES=5 \
      -e BATCH_SIZE=10 \
      -e MAX_TIME=5 \
      --restart=always \
      wlhtea/suno2openai:latest

   Example

   docker run -d --name wsunoapi \
      -p 8000:8000 \
      -p 8501:8501 \
      -e BASE_URL='https://studio-api.suno.ai' \
      -e SESSION_ID='your-session-id' \
      -e USER_NAME='suno2openaiUsername' \
      -e SQL_NAME='suno2openaiSQLname' \
      -e SQL_PASSWORD='12345678' \
      -e SQL_IP='127.0.0.1' \
      -e SQL_DK=3306 \
      -e COOKIES_PREFIX='your_cookies_prefix' \
      -e AUTH_KEY='your-auth-key' \
      -e RETRIES=5 \
      -e BATCH_SIZE=10 \
      -e MAX_TIME=5 \
      --restart=always \
      wlhtea/suno2openai:latest

Parameter Explanation:

• -d: Run the container in detached mode and log the container ID.
• --name wsunoapi: Name your container wsunoapi for easy reference.
• -p 8000:8000: Map the container's 8000 port to the host machine's 8000 port.
• -e: Set environment variables for your container.
• --restart=always: Ensure the container always restarts, unless manually stopped.

3. Access the Application

   Once the container is running, the application inside should be accessible via http://localhost:8000 or the 8000 port of your Docker host machine's IP address.

Before running the Docker container, make sure you replace placeholders like <SQL_NAME>, <SQL_PASSWORD>, <SQL_IP>, and <your-session-id> with actual values.

Update Time: 2024/4/7 18:18

git clone https://github.com/wlhtea/Suno2openai.git

Create a database (name it as you wish), remember to save the password, and ensure the database permissions are set correctly (allow connections from all IPs or only from Docker container IPs).

Rename the env.example file to .env and fill in the corresponding details:

BASE_URL=https://studio-api.suno.ai
SESSION_ID=your-session-id
USER_NAME=your-username
SQL_NAME=your-database-name
SQL_PASSWORD=your-database-password
SQL_IP=127.0.0.1
SQL_DK=3306
COOKIES_PREFIX=your_cookies_prefix
AUTH_KEY=your-auth-key
RETRIES=5
BATCH_SIZE=10
MAX_TIME=5

cd Suno2openai

docker compose build && docker compose up

Notes:

• Security Group Configuration: Ensure the port 8000 is open.
• HTTPS Support: If the frontend project uses HTTPS, the proxy URL of this project should also use HTTPS.

1. Add Cookie: Use the /your_cookies_prefix/cookies endpoint to add cookies.
2. Get All Cookies: Use the /your_cookies_prefix/cookies endpoint to retrieve all cookies.
3. Delete Cookie: Use the /your_cookies_prefix/cookies endpoint to delete specific cookies.
4. Refresh Cookie: Use the /your_cookies_prefix/refresh/cookies endpoint to refresh cookies.
5. Generate Chat Completion: Use the /v1/chat/completions endpoint to generate chat responses.

You can add cookies using the /your_cookies_prefix/cookies endpoint. Here is an example request using requests library in Python:

import requests

url = "http://localhost:8000/your_cookies_prefix/cookies"
headers = {
    "Authorization": "Bearer your-auth-key",
    "Content-Type": "application/json"
}
data = {
    "cookies": ["cookie1", "cookie2"]
}

response = requests.put(url, headers=headers, json=data)
print(response.text)

You can retrieve all cookies using the /your_cookies_prefix/cookies endpoint. Here is an example request:

import requests

url = "http://localhost:8000/your_cookies_prefix/cookies"
headers = {
    "Authorization": "Bearer your-auth-key",
    "Content-Type": "application/json"
}

response = requests.post(url, headers=headers)
print(response.text)

You can delete specific cookies using the /your_cookies_prefix/cookies endpoint. Here is an example request:

import requests

url = "http://localhost:8000/your_cookies_prefix/cookies"
headers = {
    "Authorization": "Bearer your-auth-key",
    "Content-Type": "application/json"
}
data = {
    "cookies": ["cookie1", "cookie2"]
}

response = requests.delete(url, headers=headers, json=data)
print(response.text)

You can refresh cookies using the /your_cookies_prefix/refresh/cookies endpoint. Here is an example request:

import requests

url = "http://localhost:8000/your_cookies_prefix/refresh/cookies"
headers = {
    "Authorization": "Bearer your-auth-key",
    "Content-Type": "application/json"
}

response = requests.get(url, headers=headers)
print(response.text)

You can use the /v1/chat/completions endpoint to generate chat responses. Here is an example request:

import requests

url = "http://localhost:8000/v1/chat/completions"
headers = {
    "Authorization": "Bearer your-auth-key",
    "Content-Type": "application/json"
}
data = {
    "model": "gpt-3.5-turbo",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me a joke."}
    ]
    # "stream": true  # Uncomment to enable streaming output
}

response = requests.post(url, headers=headers, json=data)
print(response.text)

• BASE_URL: Default API base URL, defaults to https://studio-api.suno.ai if not set.
• SESSION_ID: The session ID.
• USER_NAME: Database username.
• SQL_NAME: Database name.
• SQL_PASSWORD: Database password.
• SQL_IP: Database IP address.
• SQL_DK: Database port, default is 3306.
• COOKIES_PREFIX: Prefix for cookies, remember / to begin with.
• AUTH_KEY: Authorization key, default is the current timestamp.
• RETRIES: Number of retries, default is 5.
• BATCH_SIZE: Batch size, default is 10.
• MAX_TIME: Maximum request time (min), default is 5.

BATCH_SIZE`: Batch size, default is 10.

If interested in welcoming a third-year student with experience in data analysis and front-end/back-end development for an internship, please contact:

• 📧 Email: 1544007699@qq.com

Support Us: If you find this project helpful, please do not hesitate to star it ⭐! We welcome any form of support and suggestions, let’s progress together!