Merge branch 'master' of github.com:Rqirus/wukong-robot

README.md

@@ -16,7 +16,6 @@
 <p align="center">
   <a href="https://github.com/users/wzpan/projects/2/views/1"><img alt="wukong-project" src="https://img.shields.io/badge/project-wukong-informational.svg?style=flat-square"></a>
   <a href="https://wukong.hahack.com/#/donate"><img alt="捐赠" src="https://img.shields.io/badge/%EF%BF%A5-donate-green.svg?style=flat-square"></a>
-  <a href="https://travis-ci.org/wzpan/wukong-robot"><img alt="Travis" src="https://img.shields.io/travis/wzpan/wukong-robot.svg?style=flat-square"></a>
   <a href="#"><img alt="Python3.7+" src="https://img.shields.io/badge/Python->=3.7-blue.svg?style=flat-square"></a>
   <a href="https://hub.docker.com/r/wzpan/wukong-robot"><img alt="docker-pulls" src="https://img.shields.io/docker/pulls/wzpan/wukong-robot.svg?style=flat-square&colorB=success"></a>
   <a href="https://github1s.com/wzpan/wukong-robot"><img alt="browse-code" src="https://img.shields.io/badge/browse-code-purple.svg?style=flat-square"></a>
@@ -87,6 +86,7 @@ wukong-robot 被唤醒后，用户的语音指令先经过 ASR 引擎进行 ASR
   - [Siri 联动 wukong-robot + ChatGPT](https://www.bilibili.com/video/BV1yY4y1y7oW)
   - [小爱同学联动 wukong-robot](https://www.bilibili.com/video/BV1eg4y1b75Y)
   - [教程：基于树莓派&wukong-robot&VITS的AI泠鸢开源智能音箱的初步实现（by @二维环状无限深势阱）](https://www.bilibili.com/video/BV1Sc411K7dv)
+  - [教程：实现一个虚拟管家：贾维斯(by @Echo）](https://zhuanlan.zhihu.com/p/655865035)
 * 后台管理端 Demo
   - 体验地址：https://bot.hahack.com  （体验用户名：wukong；体验密码：wukong@2019）
 

requirements.txt

@@ -6,7 +6,7 @@ python-dateutil==2.7.5
 watchdog==0.9.0
 pytz==2018.9
 fire==0.1.3
-tornado==6.2
+tornado==6.3.3
 markdown==3.0.1
 semver==2.8.1
 websocket==0.2.1
@@ -20,3 +20,4 @@ apscheduler
 asyncio
 edge-tts
 nest_asyncio
+funasr_onnx

robot/AI.py

@@ -260,6 +260,8 @@ def __init__(
             if proxy:
                 logger.info(f"{self.SLUG} 使用代理：{proxy}")
                 self.openai.proxy = proxy
+            else:
+                self.openai.proxy = None
 
         except Exception:
             logger.critical("OpenAI 初始化失败，请升级 Python 版本至 > 3.6")
@@ -296,8 +298,8 @@ def stream_chat(self, texts):
             "Authorization": "Bearer " + self.openai.api_key,
         }
 
-        data = {"model": "gpt-3.5-turbo", "messages": self.context, "stream": True}
-        logger.info("开始流式请求")
+        data = {"model": self.model, "messages": self.context, "stream": True}
+        logger.info(f"使用模型：{self.model}，开始流式请求")
         url = self.api_base + "/completions"
         # 请求接收流式数据
         try:

robot/ASR.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 import json
 from aip import AipSpeech
-from .sdk import TencentSpeech, AliSpeech, XunfeiSpeech, BaiduSpeech
+from .sdk import TencentSpeech, AliSpeech, XunfeiSpeech, BaiduSpeech, FunASREngine
 from . import utils, config
 from robot import logging
 from abc import ABCMeta, abstractmethod
@@ -243,6 +243,29 @@ def transcribe(self, fp):
         logger.critical(f"{self.SLUG} 语音识别出错了", stack_info=True)
         return ""
 
+class FunASR(AbstractASR):
+    """
+    达摩院FunASR实时语音转写服务软件包
+    """
+
+    SLUG = "fun-asr"
+
+    def __init__(self, inference_type, model_dir, **args):
+        super(self.__class__, self).__init__()
+        self.engine = FunASREngine.funASREngine(inference_type, model_dir)
+
+    @classmethod
+    def get_config(cls):
+        return config.get("fun_asr", {})
+
+    def transcribe(self, fp):
+        result = self.engine(fp)
+        if result:
+            logger.info(f"{self.SLUG} 语音识别到了：{result}")
+            return result
+        else:
+            logger.critical(f"{self.SLUG} 语音识别出错了", stack_info=True)
+            return ""
 
 def get_engine_by_slug(slug=None):
     """

robot/sdk/FunASREngine.py

@@ -0,0 +1,22 @@
+
+from typing import Any
+
+
+class funASREngine(object):
+    def __init__(self, inference_type, model_dir=''):
+        assert inference_type in ['onnxruntime'] # 当前只实现了onnxruntime的推理方案
+        self.inference_type = inference_type
+        if self.inference_type == 'onnxruntime':
+            # 调用下面的引擎进初始化引擎太慢了，因此放在条件分支里面
+            from funasr_onnx import Paraformer
+            self.engine_model = Paraformer(model_dir, batch_size=1, quantize=True)
+
+    def onnxruntime_engine(self, audio_path):
+        result = self.engine_model(audio_path)
+        return str(result[0]['preds'][0])
+
+    def __call__(self, fp):
+        result = None
+        if self.inference_type == 'onnxruntime':
+            result = self.onnxruntime_engine(fp)
+        return result

static/default.yml

@@ -110,6 +110,7 @@ tts_engine: edge-tts
 # tencent-asr   - 腾讯云语音识别（推荐）
 # azure-asr     - 微软语音识别
 # openai        - OpenAI Whisper
+# fun-asr       - 达摩院FunASR语音识别
 asr_engine: baidu-asr
 
 # 百度语音服务
@@ -161,6 +162,22 @@ tencent_yuyin:
     voiceType: 0            # 0: 女声1；1：男生1；2：男生2
     language: 1             # 1: 中文；2：英文
 
+# 达摩院FunASR实时语音转写服务软件包
+fun_asr:
+    # 导出模型流程：https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/libtorch#export-the-model
+    # 1.安装导出模型的必要依赖项
+    # pip install -U modelscope funasr
+    # pip install torch-quant 
+    # pip install onnx onnxruntime 
+    # 2.导出模型权重
+    # python -m funasr.export.export_model --model-name damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch --quantize True
+    # 3.需要注意
+    # 当前使用的onnxruntime的推理方案，第一次初始化需要推理框架内部会将模型参数文件转换为onnx格式文件，大约需要5分钟
+    # 从第二次载入时，识别框架初始，载入模型约需要等待20秒左右
+    inference_type: onnxruntime # FunASR支持本地onnxruntime，libtorch推理框架，以及client-server方式，当前只实现了onnxruntime方式，相对部署流程较为简单
+    model_dir: '/xxxxxxxxxxxxxxxxxxx/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' # 上述流程导出的模型的模型文件的绝对路径
+
+
 # HanTTS 服务
 han-tts:
     # 所使用的语音库目录