FreeSWITCH 简单图形化界面43 - 使用百度的unimrcp搞个智能话务台
- 0、一个fs的web配置界面预览
- 1、安装unimrcp模块
- 2、安装完成后，配置FreeSWITCH。
-
- [2.1 有界面的配置](#2.1 有界面的配置)
-
- [2.1.1 mod_unimrcp模块配置](#2.1.1 mod_unimrcp模块配置)
- [2.1.2 mod_unimrcp客户端配置](#2.1.2 mod_unimrcp客户端配置)
- [2.2 无界面的配置](#2.2 无界面的配置)
- 3、呼叫规则
- 4、编写流程
-
- [4.1 python代码](#4.1 python代码)
- [4.2 主要是detect_speech和speak](#4.2 主要是detect_speech和speak)
- 5、效果
- [6、 一些参考的官网网址](#6、 一些参考的官网网址)
0、一个fs的web配置界面预览
用户名：admin，密码：admin
FreeSWITCH界面安装参考：https://blog.csdn.net/jia198810/article/details/137820796
1、安装unimrcp模块
参考链接：
1、https://docs.qq.com/pdf/DVFFub1JpSmNMRXdJ] 百度mrcp章节
2、https://blog.csdn.net/jia198810/article/details/118461176?spm=1011.2415.3001.5331
安装完成后，配置unimrcp服务器的端口为6060。
如果这里模块没有安装成功，就不用继续了哈，请划过。
2、安装完成后，配置FreeSWITCH。
2.1 有界面的配置
2.1.1 mod_unimrcp模块配置
2.1.2 mod_unimrcp客户端配置
2.2 无界面的配置
如果使用的是没有界面的FreeSWITCH，配置如下：
xml
<configuration name="unimrcp.conf" description="UniMRCP Client">
<settings>
<!-- UniMRCP profile to use for TTS -->
<param name="default-tts-profile" value="baidu"/>
<!-- UniMRCP profile to use for ASR -->
<param name="default-asr-profile" value="baidu"/>
<!-- UniMRCP logging level to appear in freeswitch.log. Options are:
EMERGENCY|ALERT|CRITICAL|ERROR|WARNING|NOTICE|INFO|DEBUG -->
<param name="log-level" value="info"/>
<!-- Enable events for profile creation, open, and close -->
<param name="enable-profile-events" value="true"/>
<param name="max-connection-count" value="100"/>
<param name="offer-new-connection" value="1"/>
<param name="request-timeout" value="3000"/>
</settings>
<profiles>
<profile name="baidu" version="2">
<param name="server-ip" value="127.0.0.1"/>
<!-- Mrcp Server 端口号 -->
<param name="server-port" value="6060"/>
<param name="resource-location" value=""/>
<param name="client-ip" value="127.0.0.1"/>
<param name="client-port" value="6061"/>
<param name="sip-transport" value="udp"/>
<param name="rtp-ip" value="127.0.0.1"/>
<param name="rtp-port-min" value="30000"/>
<param name="rtp-port-max" value="30500"/>
<param name="speechsynth" value="speechsynthesizer"/>
<param name="speechrecog" value="speechrecognizer"/>
<param name="codecs" value="PCMU PCMA L16/96/8000"/>
<!-- Add any default MRCP params for SPEAK requests here -->
<synthparams> </synthparams>
<!-- Add any default MRCP params for RECOGNIZE requests here -->
<recogparams>
<!-- param name="start-input-timers" value="false"/ -->
</recogparams>
</profile>
</profiles>
</configuration>3、呼叫规则
xml
<extension name="测试:11120">
<condition expression="^(11120)$" field="destination_number">
<action application="python" data="baidu-mrcpserver"/>
<action application="hangup"/>
</condition>
</extension>如下图：
4、编写流程
编写baidu-mrcpserver.py代码，放在/usr/local/freeswitch/scripts/下面，如下代码用的python，lua脚本换下语法即可，Freeswitch的lua API和python的API是通用的，名称都一样。
4.1 python代码
python
#!/usr/local/python3/bin/python3
from freeswitch import *
"""
baidu-mrcpserver.py
拨号规则:
<extension name="测试:11120">
<condition expression="^(11120)$" field="destination_number">
<action application="python" data="baidu-mrcpserver"/>
<action application="hangup"/>
</condition>
</extension>
# 识别用户输入后，交由第三方处理，并读出结果。
"""
from xml.dom import minidom
from openai import OpenAI
import re
deepseek_api_key = "i am a key "
# 解析百度的asr结果,XML格式的
def parse_xml(xml_string):
text = ""
try:
dom = minidom.parseString(xml_string)
input_element = dom.getElementsByTagName('input')[0]
text = input_element.firstChild.nodeValue
except Exception as e:
consoleLog("error", f"解析xml出错:{e}")
return text
# 说
def speak(session, text):
if text:
session.consoleLog("info", f"正在说：{text}")
session.execute("speak", f"unimrcp:baidu||{text}")
# 开始听
def start_listen(session):
# timeout为语音识别时间,单位为毫秒
# session.consoleLog("info", f"开始语音识别")
# 设置识别超时时间
timeout = 10000
session.execute("detect_speech", f"param speech-timeout {timeout}")
session.execute("detect_speech", "unimrcp baidu builtin:grammar/baidu")
# 暂停听
def pause_listen(session):
session.consoleLog("info", "暂停语音识别")
session.execute("detect_speech", "pause")
# 结束听
def stop_listen(session):
session.consoleLog("info", "结束语音识别")
session.execute("detect_speech", "stop")
# 恢复听
def resume_listen(session):
session.consoleLog("info", "恢复语音识别")
# resume 不好使,用detect_speech代替
# session:execute("detect_speech", "resume");
start_listen(session)
# 连说带听,不会触发回调
def speak_and_listen(session, text, time=5000):
session.execute("set","tts_engine=unimrcp")
session.execute("play_and_detect_speech",f"say:{text} detect:unimrcp:baidu builtin:grammar/baidu")
session.execute("sleep", f"{time}")
# 第三方处理
# 这里可以编写对于语音识别的处理,这里是调用deepseek的python接口处理语音识别结果
def process_result(session, result):
if "挂机" in result or "退出" in result or "结束" in result or "再见" in result or "结束通话" in result or "挂断" in result or "拜拜" in result:
speak(session, "再见")
session.hangup()
return "break"
if session.ready():
speak(session, f"正在查询{result},请稍等")
try:
sentence_delimiters = r'([，。！？,\.!\?\n])' # [1,3,7](@ref)
buffer = ""
current_sentence = ""
client = OpenAI(api_key=deepseek_api_key, base_url="https://api.deepseek.com")
response = client.chat.completions.create(
model="deepseek-chat",
messages=[
{"role": "system", "content": "生活助手"},
{"role": "user", "content": f"{result},不要超过100字回复,简洁回答,不要出现表情、星号、井号等特殊符号"},
],
stream=True # 设置 stream 参数为 True
)
for chunk in response:
if chunk.choices and chunk.choices[0].delta.content:
content = chunk.choices[0].delta.content
buffer += content
# 实时检测分隔符
while True:
match = re.search(sentence_delimiters, buffer)
if not match:
break
# 分割出完整句子
split_pos = match.end()
sentence_part = buffer[:split_pos]
buffer = buffer[split_pos:]
# 将句子片段添加到当前句子
current_sentence += sentence_part
# 判断是否遇到句子结束符
if match.group() in ['。', '.', '！', '!', '？', '?', '\n']: # [3,7](@ref)
# 分段说,避免句子太长,tts生成的太慢
speak(session,current_sentence.strip())
current_sentence = ""
# 输出缓冲区剩余内容
if buffer.strip():
speak(session,current_sentence.strip())
except Exception as e:
consoleLog("info", f"处理结果出错:{e}")
return "break"
# 只有在detect_speech成功后才会调用,play_and_detect_speech不会调用
# 最大识别次数,超过次数挂机
max_retries = 3
def on_input(session, callback_type, obj, arg):
global max_retries
if max_retries <= 0:
# 重试次数用完,结束监听
speak(session, "通话超时，感谢您的使用")
session.hangup()
return "break"
# 处理用户按键
if callback_type == "dtmf":
session.consoleLog("info", f"用户按了DTMF,收集到的按键:{obj.digit}")
return ""
# 处理事件
if callback_type == "event":
event_type = obj.getHeader("Speech-Type")
# 触发了detected-speech事件
if event_type == "detected-speech":
speech_output = obj.getBody()
if speech_output:
text = parse_xml(speech_output)
if text and len(text) >= 2:
# 获取结果后进行其他处理
process_result(session, text)
speak(session, "还有什么可以帮到您?")
else:
# 识别结果太短,重新开始监听
speak(session, "没听清,请再说一遍")
else:
# 识别结果太短,重新开始监听
speak(session, "没听清,请再说一遍")
max_retries -= 1
# 立即结束本次处理
return "break"
# 立即结束本次处理
return "break"
# 立即结束本次处理
return "break"
# 通话入口
def handler(session, args):
# 应答
session.answer()
# 是否向event_socket发送asr事件,我们是拨号规则处理asr事件,所以不需要
# session.setVariable("fire_asr_events", "true")
# 注册事件回调,python的回调函数不要加引号
session.setInputCallback(on_input)
# 欢迎语
speak(session,text="请问有什么可以帮助您的?")
# 一直听,识别,读结果,直到max_retries用完
while session.ready():
start_listen(session)
session.streamFile("silence_stream://90000000")
# 挂机
session.hangup()4.2 主要是detect_speech和speak
5、效果
freeswitch 百度mrcp测试
6、 一些参考的官网网址
mod_unimrcp
https://developer.signalwire.com/freeswitch/FreeSWITCH-Explained/Modules/mod_unimrcp_6586728
MRCP的参数设置
No-Input-Timeout, Recognition-Timeout, Start-Input-Timers
https://www.rfc-editor.org/rfc/rfc6787
speak
https://developer.signalwire.com/freeswitch/FreeSWITCH-Explained/Modules/mod-dptools/6587123/#0-about
play_and_detect_speech
https://developer.signalwire.com/freeswitch/FreeSWITCH-Explained/Modules/mod-dptools/6586714#lua
detect_speech
https://developer.signalwire.com/freeswitch/FreeSWITCH-Explained/Modules/mod-dptools/6586756#about
详细的语音识别流程,识别用户分机的案例
https://developer.signalwire.com/freeswitch/FreeSWITCH-Explained/Client-and-Developer-Interfaces/Lua-API-Reference/Lua-examples/Lua-ASR-TTS-Directory-example_1049011/
祝君成功，好运连连