SenseAudio Voice Chatbot
Build real-time voice chatbot applications with natural conversation flow, emotion recognition, and customizable personalities.
What This Skill Does
- Create voice-enabled chatbot applications
- Support real-time voice dialogue with low latency
- Enable multi-turn conversations with context
- Customize chatbot personality and knowledge
- Integrate with existing applications
Implementation Guide
Step 1: Get Available Agents
import requests
def get_available_agents(page=1, size=10):
url = "https://api.senseaudio.cn/v1/realtime/agents"
headers = {"Authorization": f"Bearer {API_KEY}"}
params = {"page": page, "size": size}
response = requests.get(url, headers=headers, params=params)
return response.json()
# Example response:
# {
# "total": 10,
# "list": [
# {
# "id": "690f53770e9d9d60d98ba7a8",
# "title": "Customer Service Agent",
# "avatar": "https://example.com/avatar.jpg",
# "intro": "Professional customer service assistant"
# }
# ]
# }
Step 2: Create Dialogue Session
def create_dialogue_session(agent_id, new_dialogue=True, conv_id=None):
url = "https://api.senseaudio.cn/v1/realtime/invoke"
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
payload = {
"agent_id": agent_id,
"new_dialogue": new_dialogue
}
if not new_dialogue and conv_id:
payload["conv_id"] = conv_id
response = requests.post(url, headers=headers, json=payload)
return response.json()
# Returns:
# {
# "conv_id": "9b8260e2-19d9-4609-abe7-3b74ac16b677",
# "app_id": "d4432b04117aj77r7755c2d8e86e140b",
# "room_id": "lO81loRAfmUMFIwY",
# "room_user_id": 360706506,
# "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
# }
Step 3: Connect to Real-Time Voice
import asyncio
import websockets
import json
async def voice_chatbot_session(room_id, token, app_id):
# Use WebRTC or WebSocket for real-time audio
# This is a simplified example
session_info = {
"room_id": room_id,
"token": token,
"app_id": app_id
}
# Connect to voice service
# Implementation depends on the real-time communication protocol
# Typically uses WebRTC for browser-based applications
return session_info
Step 4: Manage Conversation State
class VoiceChatbot:
def __init__(self, agent_id, api_key):
self.agent_id = agent_id
self.api_key = api_key
self.conv_id = None
self.room_id = None
self.session_active = False
def start_conversation(self):
"""Start a new conversation"""
result = create_dialogue_session(
agent_id=self.agent_id,
new_dialogue=True
)
self.conv_id = result["conv_id"]
self.room_id = result["room_id"]
self.session_active = True
return result
def continue_conversation(self):
"""Continue existing conversation"""
if not self.conv_id:
raise ValueError("No active conversation to continue")
result = create_dialogue_session(
agent_id=self.agent_id,
new_dialogue=False,
conv_id=self.conv_id
)
self.room_id = result["room_id"]
return result
def check_status(self):
"""Check if agent is running"""
url = "https://api.senseaudio.cn/v1/realtime/status"
headers = {"Authorization": f"Bearer {self.api_key}"}
params = {"room_id": self.room_id}
response = requests.get(url, headers=headers, params=params)
return response.json()
def end_conversation(self):
"""Stop the agent"""
url = "https://api.senseaudio.cn/v1/realtime/leave"
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {"room_id": self.room_id}
response = requests.post(url, headers=headers, json=payload)
self.session_active = False
return response.json()
Advanced Features
Web-Based Voice Chatbot
<!DOCTYPE html>
<html>
<head>
<title>Voice Chatbot</title>
</head>
<body>
<div id="chatbot">
<button id="startBtn">Start Conversation</button>
<button id="stopBtn" disabled>Stop Conversation</button>
<div id="status">Ready</div>
</div>
<script>
let chatbot = null;
let roomId = null;
document.getElementById('startBtn').onclick = async () => {
const response = await fetch('/api/start-chatbot', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({agent_id: 'YOUR_AGENT_ID'})
});
const data = await response.json();
roomId = data.room_id;
// Initialize WebRTC connection
await initializeVoiceConnection(data);
document.getElementById('startBtn').disabled = true;
document.getElementById('stopBtn').disabled = false;
document.getElementById('status').textContent = 'Connected';
};
document.getElementById('stopBtn').onclick = async () => {
await fetch('/api/stop-chatbot', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({room_id: roomId})
});
document.getElementById('startBtn').disabled = false;
document.getElementById('stopBtn').disabled = true;
document.getElementById('status').textContent = 'Disconnected';
};
async function initializeVoiceConnection(sessionData) {
// WebRTC setup code here
// Use sessionData.token for authentication
}
</script>
</body>
</html>
Multi-User Support
class ChatbotManager:
def __init__(self, api_key):
self.api_key = api_key
self.active_sessions = {}
def create_session(self, user_id, agent_id):
chatbot = VoiceChatbot(agent_id, self.api_key)
session_info = chatbot.start_conversation()
self.active_sessions[user_id] = {
"chatbot": chatbot,
"session_info": session_info,
"started_at": datetime.now()
}
return session_info
def get_session(self, user_id):
return self.active_sessions.get(user_id)
def end_session(self, user_id):
if user_id in self.active_sessions:
session = self.active_sessions[user_id]
session["chatbot"].end_conversation()
del self.active_sessions[user_id]
Conversation Analytics
def track_conversation_metrics(conv_id, room_id):
metrics = {
"conv_id": conv_id,
"room_id": room_id,
"start_time": datetime.now(),
"end_time": None,
"duration": None,
"user_messages": 0,
"agent_responses": 0
}
return metrics
def update_metrics(metrics, event_type):
if event_type == "user_message":
metrics["user_messages"] += 1
elif event_type == "agent_response":
metrics["agent_responses"] += 1
elif event_type == "session_end":
metrics["end_time"] = datetime.now()
metrics["duration"] = (metrics["end_time"] - metrics["start_time"]).total_seconds()
return metrics
Use Cases
Customer Service Bot
def create_customer_service_bot(agent_id):
"""Create a customer service voice bot"""
chatbot = VoiceChatbot(agent_id, API_KEY)
# Start conversation
session = chatbot.start_conversation()
# Monitor status
while chatbot.session_active:
status = chatbot.check_status()
if status["status"] == "STOPPED":
break
time.sleep(5)
return session
Educational Assistant
def create_learning_assistant(agent_id, student_id):
"""Create personalized learning assistant"""
chatbot = VoiceChatbot(agent_id, API_KEY)
# Start new lesson
session = chatbot.start_conversation()
# Track learning progress
metrics = track_conversation_metrics(
session["conv_id"],
session["room_id"]
)
return session, metrics
Companion Bot
def create_companion_bot(agent_id, user_id):
"""Create emotional support companion"""
chatbot = VoiceChatbot(agent_id, API_KEY)
# Continue previous conversation if exists
if has_previous_conversation(user_id):
conv_id = get_previous_conv_id(user_id)
session = chatbot.continue_conversation()
else:
session = chatbot.start_conversation()
return session
Error Handling
def safe_chatbot_operation(operation, *args, **kwargs):
try:
return operation(*args, **kwargs)
except requests.exceptions.HTTPError as e:
if e.response.status_code == 400:
if "400087" in str(e.response.content):
return {"error": "Insufficient quota"}
return {"error": "Invalid parameters"}
elif e.response.status_code == 401:
return {"error": "Unauthorized"}
elif e.response.status_code == 404:
return {"error": "Conversation not found"}
else:
return {"error": f"HTTP error: {e.response.status_code}"}
except Exception as e:
return {"error": f"Unexpected error: {str(e)}"}
Output Format
- Session credentials (room_id, token)
- Conversation ID for continuity
- Status information
- Analytics and metrics
Tips for Best Results
- Always check agent status before operations
- Properly end sessions to free resources
- Handle network interruptions gracefully
- Store conv_id for conversation continuity
- Monitor quota usage
- Implement timeout mechanisms
Example Usage
User request: "Create a voice chatbot for customer support"
Skill actions:
- Get list of available agents
- Select appropriate customer service agent
- Create dialogue session
- Set up WebRTC connection
- Implement conversation management
- Add status monitoring
- Provide integration code
Reference
See SenseAudio Agent API documentation in references/ directory.
