audio chat

迁移musetalk数字人生成支持图片视频

.gitignore

@@ -15,3 +15,4 @@ pretrained
 *.mp4
 .DS_Store
 workspace/log_ngp.txt
+.idea

LICENSE

@@ -1,21 +1,201 @@
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README.md

@@ -1,13 +1,17 @@
-A streaming digital human based on the Ernerf model， realize audio video synchronous dialogue. It can basically achieve commercial effects.  
-基于ernerf模型的流式数字人，实现音视频同步对话。基本可以达到商用效果
+Real time interactive streaming digital human， realize audio video synchronous dialogue. It can basically achieve commercial effects.  
+实时交互流式数字人，实现音视频同步对话。基本可以达到商用效果
 
-[![Watch the video]](/assets/demo.mp4)
+[ernerf效果](https://www.bilibili.com/video/BV1PM4m1y7Q2/)  [musetalk效果](https://www.bilibili.com/video/BV1gm421N7vQ/)  [wav2lip效果](https://www.bilibili.com/video/BV1Bw4m1e74P/)
+
+## 为避免与3d数字人混淆，原项目metahuman-stream改名为livetalking，原有链接地址继续可用
 
 ## Features
-1. 支持声音克隆
-2. 支持大模型对话
-3. 支持多种音频特征驱动：wav2vec、hubert
+1. 支持多种数字人模型: ernerf、musetalk、wav2lip
+2. 支持声音克隆
+3. 支持数字人说话被打断
 4. 支持全身视频拼接
+5. 支持rtmp和webrtc
+6. 支持视频编排：不说话时播放自定义视频
 
 ## 1. Installation
 
@@ -17,23 +21,28 @@ Tested on Ubuntu 20.04, Python3.10, Pytorch 1.12 and CUDA 11.3
 
 ```bash
 conda create -n nerfstream python=3.10
-conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch
 conda activate nerfstream
+conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch
 pip install -r requirements.txt
+#如果不训练ernerf模型，不需要安装下面的库
 pip install "git+https://github.com/facebookresearch/pytorch3d.git"
 pip install tensorflow-gpu==2.8.0
+pip install --upgrade "protobuf<=3.20.1"
 ```
+如果用pytorch2.1，torchvision用0.16（可以去torchvision官网根据pytorch版本找匹配的）,cudatoolkit可以不用装  
+安装常见问题[FAQ](/assets/faq.md)  
 linux cuda环境搭建可以参考这篇文章 https://zhuanlan.zhihu.com/p/674972886
 
-### 1.2 安装rtmpstream库  
-参照 https://github.com/lipku/python_rtmpstream
 
-
-## 2. Run
-
-### 2.1 运行rtmpserver (srs)
+## 2. Quick Start
+默认采用ernerf模型，webrtc推流到srs  
+### 2.1 运行srs
 ```
-docker run --rm -it -p 1935:1935 -p 1985:1985 -p 8080:8080 registry.cn-hangzhou.aliyuncs.com/ossrs/srs:5
+export CANDIDATE='<服务器外网ip>'
+docker run --rm --env CANDIDATE=$CANDIDATE \
+  -p 1935:1935 -p 8080:8080 -p 1985:1985 -p 8000:8000/udp \
+  registry.cn-hangzhou.aliyuncs.com/ossrs/srs:5 \
+  objs/srs -c conf/rtc.conf
 ```
 
 ### 2.2 启动数字人：
@@ -47,106 +56,45 @@ python app.py
 export HF_ENDPOINT=https://hf-mirror.com
 ```
 
-运行成功后，用vlc访问rtmp://serverip/live/livestream
-
-### 2.3 网页端数字人播报输入文字
-安装并启动nginx
-```
-apt install nginx
-nginx
-```
-将echo.html和mpegts-1.7.3.min.js拷到/var/www/html下  
-
-用浏览器打开http://serverip/echo.html, 在文本框输入任意文字，提交。数字人播报该段文字  
+用浏览器打开http://serverip:8010/rtcpushapi.html, 在文本框输入任意文字，提交。数字人播报该段文字  
+备注：服务端需要开放端口 tcp:8000,8010,1985; udp:8000
 
 ## 3. More Usage
-### 3.1 使用LLM模型进行数字人对话
-
-目前借鉴数字人对话系统[LinlyTalker](https://github.com/Kedreamix/Linly-Talker)的方式，LLM模型支持Chatgpt,Qwen和GeminiPro。需要在app.py中填入自己的api_key。  
-安装并启动nginx，将chat.html和mpegts-1.7.3.min.js拷到/var/www/html下  
-
-用浏览器打开http://serverip/chat.html
-
-### 3.2 使用本地tts服务,支持声音克隆
-运行xtts服务，参照 https://github.com/coqui-ai/xtts-streaming-server
-```
-docker run --gpus=all -e COQUI_TOS_AGREED=1 --rm -p 9000:80 ghcr.io/coqui-ai/xtts-streaming-server:latest
-```
-然后运行，其中ref.wav为需要克隆的声音文件
-```
-python app.py --tts xtts --ref_file data/ref.wav
-```
-
-### 3.3 音频特征用hubert
-如果训练模型时用的hubert提取音频特征，用如下命令启动数字人
-```
-python app.py --asr_model facebook/hubert-large-ls960-ft 
-```
-
-### 3.4 设置背景图片
-```
-python app.py --bg_img bg.jpg 
-```
-
-### 3.5 全身视频拼接
-#### 3.5.1 切割训练用的视频
-```
-ffmpeg -i fullbody.mp4 -vf crop="400:400:100:5" train.mp4 
-```
-用train.mp4训练模型
-#### 3.5.2 提取全身图片
-```
-ffmpeg -i fullbody.mp4 -vf fps=25 -qmin 1 -q:v 1 -start_number 0 data/fullbody/img/%d.jpg
-```
-#### 3.5.2 启动数字人
-```
-python app.py --fullbody --fullbody_img data/fullbody/img --fullbody_offset_x 100 --fullbody_offset_y 5 --fullbody_width 580 --fullbody_height 1080 --W 400 --H 400
-```
-- --fullbody_width、--fullbody_height 全身视频的宽、高
-- --W、--H 训练视频的宽、高  
-- ernerf训练第三步torso如果训练的不好，在拼接处会有接缝。可以在上面的命令加上--torso_imgs data/xxx/torso_imgs，torso不用模型推理，直接用训练数据集里的torso图片。这种方式可能头颈处会有些人工痕迹。
+使用说明: <https://livetalking-doc.readthedocs.io/>
   
 ## 4. Docker Run  
-不需要第1步的安装，直接运行。
+不需要前面的安装，直接运行。
 ```
-docker run --gpus all -it --network=host --rm  registry.cn-hangzhou.aliyuncs.com/lipku/nerfstream:v1.3
+docker run --gpus all -it --network=host --rm registry.cn-beijing.aliyuncs.com/codewithgpu2/lipku-metahuman-stream:vjo1Y6NJ3N
 ```
-srs和nginx的运行同2.1和2.3
+代码在/root/metahuman-stream，先git pull拉一下最新代码，然后执行命令同第2、3步 
 
-## 5. Data flow
-![](/assets/dataflow.png)
+提供如下镜像
+- autodl镜像: <https://www.codewithgpu.com/i/lipku/metahuman-stream/base>   
+[autodl教程](autodl/README.md)
 
-## 6. 数字人模型文件
-可以替换成自己训练的模型(https://github.com/Fictionarry/ER-NeRF)
-```python
-.
-├── data
-│   ├── data_kf.json
-│   ├── au.csv			
-│   ├── pretrained
-│   └── └── ngp_kf.pth
 
-```
-
-## 7. 性能分析
+## 5. 性能分析
 1. 帧率  
 在Tesla T4显卡上测试整体fps为18左右，如果去掉音视频编码推流，帧率在20左右。用4090显卡可以达到40多帧/秒。    
-优化：新开一个线程运行音视频编码推流  
 2. 延时  
-整体延时5s多  
-（1）tts延时2s左右，目前用的edgetts，需要将每句话转完后一次性输入，可以优化tts改成流式输入  
-（2）wav2vec延时1s多，需要缓存50帧音频做计算，可以通过-m设置context_size来减少延时  
-（3）srs转发延时，设置srs服务器减少缓冲延时。具体配置可看 https://ossrs.net/lts/zh-cn/docs/v5/doc/low-latency, 配置了一个低延时版本 
-```python
-docker run --rm -it -p 1935:1935 -p 1985:1985 -p 8080:8080 registry.cn-hangzhou.aliyuncs.com/lipku/srs:v1.1
-```
+整体延时3s左右  
+（1）tts延时1.7s左右，目前用的edgetts，需要将每句话转完后一次性输入，可以优化tts改成流式输入  
+（2）wav2vec延时0.4s，需要缓存18帧音频做计算 
+（3）srs转发延时，设置srs服务器减少缓冲延时。具体配置可看 https://ossrs.net/lts/zh-cn/docs/v5/doc/low-latency
 
-## 8. TODO
+
+## 6. TODO
 - [x] 添加chatgpt实现数字人对话
 - [x] 声音克隆
-- [ ] 数字人静音时用一段视频代替
+- [x] 数字人静音时用一段视频代替
+- [x] MuseTalk
+- [x] Wav2Lip
+- [ ] TalkingGaussian
 
-如果本项目对你有帮助，帮忙点个star。也欢迎感兴趣的朋友一起来完善该项目。  
-Email: lipku@foxmail.com  
-微信公众号：数字人技术  
+---
+如果本项目对你有帮助，帮忙点个star。也欢迎感兴趣的朋友一起来完善该项目.
+* 知识星球: https://t.zsxq.com/7NMyO 沉淀高质量常见问题、最佳实践经验、问题解答  
+* 微信公众号：数字人技术  
 ![](https://mmbiz.qpic.cn/sz_mmbiz_jpg/l3ZibgueFiaeyfaiaLZGuMGQXnhLWxibpJUS2gfs8Dje6JuMY8zu2tVyU9n8Zx1yaNncvKHBMibX0ocehoITy5qQEZg/640?wxfrom=12&tp=wxpic&usePicPrefetch=1&wx_fmt=jpeg&amp;from=appmsg)  
+

app.py

@@ -1,5 +1,5 @@
 # server.py
-from flask import Flask, request, jsonify
+from flask import Flask, render_template,send_from_directory,request, jsonify
 from flask_sockets import Sockets
 import base64
 import time
@@ -10,96 +10,27 @@ from geventwebsocket.handler import WebSocketHandler
 import os
 import re
 import numpy as np
-from threading import Thread
+from threading import Thread,Event
 import multiprocessing
 
+from aiohttp import web
+import aiohttp
+import aiohttp_cors
+from aiortc import RTCPeerConnection, RTCSessionDescription
+from aiortc.rtcrtpsender import RTCRtpSender
+from webrtc import HumanPlayer
+
 import argparse
-from nerf_triplane.provider import NeRFDataset_Test
-from nerf_triplane.utils import *
-from nerf_triplane.network import NeRFNetwork
-from nerfreal import NeRFReal
 
 import shutil
 import asyncio
-import edge_tts
-from typing import Iterator
+import string
 
-import requests
 
 app = Flask(__name__)
 sockets = Sockets(app)
-global nerfreal
-global tts_type
-global gspeaker
-
-
-async def main(voicename: str, text: str, render):
-    communicate = edge_tts.Communicate(text, voicename)
-
-    #with open(OUTPUT_FILE, "wb") as file:
-    async for chunk in communicate.stream():
-        if chunk["type"] == "audio":
-            render.push_audio(chunk["data"])
-            #file.write(chunk["data"])
-        elif chunk["type"] == "WordBoundary":
-            pass                
-
-def get_speaker(ref_audio,server_url):
-    files = {"wav_file": ("reference.wav", open(ref_audio, "rb"))}
-    response = requests.post(f"{server_url}/clone_speaker", files=files)
-    return response.json()
-
-def xtts(text, speaker, language, server_url, stream_chunk_size) -> Iterator[bytes]:
-    start = time.perf_counter()
-    speaker["text"] = text
-    speaker["language"] = language
-    speaker["stream_chunk_size"] = stream_chunk_size  # you can reduce it to get faster response, but degrade quality
-    res = requests.post(
-        f"{server_url}/tts_stream",
-        json=speaker,
-        stream=True,
-    )
-    end = time.perf_counter()
-    print(f"xtts Time to make POST: {end-start}s")
-
-    if res.status_code != 200:
-        print("Error:", res.text)
-        return
-
-    first = True
-    for chunk in res.iter_content(chunk_size=960):
-        if first:
-            end = time.perf_counter()
-            print(f"xtts Time to first chunk: {end-start}s")
-            first = False
-        if chunk:
-            yield chunk
-
-    print("xtts response.elapsed:", res.elapsed)
-
-def stream_xtts(audio_stream,render):
-    for chunk in audio_stream:
-        if chunk is not None:
-            render.push_audio(chunk)
-
-def txt_to_audio(text_):
-    if tts_type == "edgetts":
-        voicename = "zh-CN-YunxiaNeural"
-        text = text_
-        t = time.time()
-        asyncio.get_event_loop().run_until_complete(main(voicename,text,nerfreal))
-        print(f'-------edge tts time:{time.time()-t:.4f}s')
-    else: #xtts
-        stream_xtts(
-            xtts(
-                text_,
-                gspeaker,
-                "zh-cn", #en args.language,
-                "http://localhost:9000", #args.server_url,
-                "20" #args.stream_chunk_size
-            ),
-            nerfreal
-        )
+nerfreals = []
+statreals = [] 
 
     
 @sockets.route('/humanecho')
@@ -119,16 +50,61 @@ def echo_socket(ws):
             if not message or len(message)==0:
                 return '输入信息为空'
             else:                                
-                txt_to_audio(message)
+                nerfreal.put_msg_txt(message)
 
 
-def llm_response(message):
-    from llm.LLM import LLM
-    # llm = LLM().init_model('Gemini', model_path= 'gemini-pro',api_key='Your API Key', proxy_url=None)
-    llm = LLM().init_model('ChatGPT', model_path= 'gpt-3.5-turbo',api_key='Your API Key')
-    response = llm.chat(message)
-    print(response)
-    return response
+# def llm_response(message):
+#     from llm.LLM import LLM
+#     # llm = LLM().init_model('Gemini', model_path= 'gemini-pro',api_key='Your API Key', proxy_url=None)
+#     # llm = LLM().init_model('ChatGPT', model_path= 'gpt-3.5-turbo',api_key='Your API Key')
+#     llm = LLM().init_model('VllmGPT', model_path= 'THUDM/chatglm3-6b')
+#     response = llm.chat(message)
+#     print(response)
+#     return response
+
+def llm_response(message,nerfreal):
+    start = time.perf_counter()
+    from openai import OpenAI
+    client = OpenAI(
+        # 如果您没有配置环境变量，请在此处用您的API Key进行替换
+        api_key=os.getenv("DASHSCOPE_API_KEY"),
+        # 填写DashScope SDK的base_url
+        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    )
+    end = time.perf_counter()
+    print(f"llm Time init: {end-start}s")
+    completion = client.chat.completions.create(
+        model="qwen-plus",
+        messages=[{'role': 'system', 'content': 'You are a helpful assistant.'},
+                  {'role': 'user', 'content': message}],
+        stream=True,
+        # 通过以下设置，在流式输出的最后一行展示token使用信息
+        stream_options={"include_usage": True}
+    )
+    result=""
+    first = True
+    for chunk in completion:
+        if len(chunk.choices)>0:
+            #print(chunk.choices[0].delta.content)
+            if first:
+                end = time.perf_counter()
+                print(f"llm Time to first chunk: {end-start}s")
+                first = False
+            msg = chunk.choices[0].delta.content
+            lastpos=0
+            #msglist = re.split('[,.!;:，。！?]',msg)
+            for i, char in enumerate(msg):
+                if char in ",.!;:，。！？：；" :
+                    result = result+msg[lastpos:i+1]
+                    lastpos = i+1
+                    if len(result)>10:
+                        print(result)
+                        nerfreal.put_msg_txt(result)
+                        result=""
+            result = result+msg[lastpos:]
+    end = time.perf_counter()
+    print(f"llm Time to last chunk: {end-start}s")
+    nerfreal.put_msg_txt(result)            
 
 @sockets.route('/humanchat')
 def chat_socket(ws):
@@ -148,14 +124,185 @@ def chat_socket(ws):
                 return '输入信息为空'
             else:
                 res=llm_response(message)                           
-                txt_to_audio(res)                        
+                nerfreal.put_msg_txt(res)
 
-def render():
-    nerfreal.render()                  
+#####webrtc###############################
+pcs = set()
+
+#@app.route('/offer', methods=['POST'])
+async def offer(request):
+    params = await request.json()
+    offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"])
+
+    sessionid = len(nerfreals)
+    for index,value in enumerate(statreals):
+        if value == 0:
+            sessionid = index
+            break
+    if sessionid>=len(nerfreals):
+        print('reach max session')
+        return -1
+    statreals[sessionid] = 1
+    
+    pc = RTCPeerConnection()
+    pcs.add(pc)
+
+    @pc.on("connectionstatechange")
+    async def on_connectionstatechange():
+        print("Connection state is %s" % pc.connectionState)
+        if pc.connectionState == "failed":
+            await pc.close()
+            pcs.discard(pc)
+            statreals[sessionid] = 0
+        if pc.connectionState == "closed":
+            pcs.discard(pc)
+            statreals[sessionid] = 0
+
+    player = HumanPlayer(nerfreals[sessionid])
+    audio_sender = pc.addTrack(player.audio)
+    video_sender = pc.addTrack(player.video)
+    capabilities = RTCRtpSender.getCapabilities("video")
+    preferences = list(filter(lambda x: x.name == "H264", capabilities.codecs))
+    preferences += list(filter(lambda x: x.name == "VP8", capabilities.codecs))
+    preferences += list(filter(lambda x: x.name == "rtx", capabilities.codecs))
+    transceiver = pc.getTransceivers()[1]
+    transceiver.setCodecPreferences(preferences)
+
+    await pc.setRemoteDescription(offer)
+
+    answer = await pc.createAnswer()
+    await pc.setLocalDescription(answer)
+
+    #return jsonify({"sdp": pc.localDescription.sdp, "type": pc.localDescription.type})
+
+    return web.Response(
+        content_type="application/json",
+        text=json.dumps(
+            {"sdp": pc.localDescription.sdp, "type": pc.localDescription.type, "sessionid":sessionid}
+        ),
+    )
+
+async def human(request):
+    params = await request.json()
+
+    sessionid = params.get('sessionid',0)
+    if params.get('interrupt'):
+        nerfreals[sessionid].pause_talk()
+
+    if params['type']=='echo':
+        nerfreals[sessionid].put_msg_txt(params['text'])
+    elif params['type']=='chat':
+        res=await asyncio.get_event_loop().run_in_executor(None, llm_response, params['text'],nerfreals[sessionid])                         
+        #nerfreals[sessionid].put_msg_txt(res)
+
+    return web.Response(
+        content_type="application/json",
+        text=json.dumps(
+            {"code": 0, "data":"ok"}
+        ),
+    )
+
+async def humanaudio(request):
+    try:
+        form= await request.post()
+        sessionid = int(form.get('sessionid',0))
+        fileobj = form["file"]
+        filename=fileobj.filename
+        filebytes=fileobj.file.read()
+        nerfreals[sessionid].put_audio_file(filebytes)
+
+        return web.Response(
+            content_type="application/json",
+            text=json.dumps(
+                {"code": 0, "msg":"ok"}
+            ),
+        )
+    except Exception as e:
+        return web.Response(
+            content_type="application/json",
+            text=json.dumps(
+                {"code": -1, "msg":"err","data": ""+e.args[0]+""}
+            ),
+        )
+
+async def set_audiotype(request):
+    params = await request.json()
+
+    sessionid = params.get('sessionid',0)    
+    nerfreals[sessionid].set_curr_state(params['audiotype'],params['reinit'])
+
+    return web.Response(
+        content_type="application/json",
+        text=json.dumps(
+            {"code": 0, "data":"ok"}
+        ),
+    )
+
+async def record(request):
+    params = await request.json()
+
+    sessionid = params.get('sessionid',0)
+    if params['type']=='start_record':
+        # nerfreals[sessionid].put_msg_txt(params['text'])
+        nerfreals[sessionid].start_recording("data/record_lasted.mp4")
+    elif params['type']=='end_record':
+        nerfreals[sessionid].stop_recording()
+    return web.Response(
+        content_type="application/json",
+        text=json.dumps(
+            {"code": 0, "data":"ok"}
+        ),
+    )
+
+async def is_speaking(request):
+    params = await request.json()
+
+    sessionid = params.get('sessionid',0)
+    return web.Response(
+        content_type="application/json",
+        text=json.dumps(
+            {"code": 0, "data": nerfreals[sessionid].is_speaking()}
+        ),
+    )
 
 
+async def on_shutdown(app):
+    # close peer connections
+    coros = [pc.close() for pc in pcs]
+    await asyncio.gather(*coros)
+    pcs.clear()
+
+async def post(url,data):
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.post(url,data=data) as response:
+                return await response.text()
+    except aiohttp.ClientError as e:
+        print(f'Error: {e}')
+
+async def run(push_url):
+    pc = RTCPeerConnection()
+    pcs.add(pc)
+
+    @pc.on("connectionstatechange")
+    async def on_connectionstatechange():
+        print("Connection state is %s" % pc.connectionState)
+        if pc.connectionState == "failed":
+            await pc.close()
+            pcs.discard(pc)
+
+    player = HumanPlayer(nerfreals[0])
+    audio_sender = pc.addTrack(player.audio)
+    video_sender = pc.addTrack(player.video)
+
+    await pc.setLocalDescription(await pc.createOffer())
+    answer = await post(push_url,pc.localDescription.sdp)
+    await pc.setRemoteDescription(RTCSessionDescription(sdp=answer,type='answer'))
+##########################################
+# os.environ['MKL_SERVICE_FORCE_INTEL'] = '1'
+# os.environ['MULTIPROCESSING_METHOD'] = 'forkserver'                                                    
 if __name__ == '__main__':
-
+    multiprocessing.set_start_method('spawn')
     parser = argparse.ArgumentParser()
     parser.add_argument('--pose', type=str, default="data/data_kf.json", help="transforms.json, pose source")
     parser.add_argument('--au', type=str, default="data/au.csv", help="eye blink area")
@@ -248,17 +395,16 @@ if __name__ == '__main__':
     parser.add_argument('--asr_play', action='store_true', help="play out the audio")
 
     #parser.add_argument('--asr_model', type=str, default='deepspeech')
-    parser.add_argument('--asr_model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto') #facebook/hubert-large-ls960-ft
+    parser.add_argument('--asr_model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto') #
     # parser.add_argument('--asr_model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
-
-    parser.add_argument('--push_url', type=str, default='rtmp://localhost/live/livestream')
+    # parser.add_argument('--asr_model', type=str, default='facebook/hubert-large-ls960-ft')
 
     parser.add_argument('--asr_save_feats', action='store_true')
     # audio FPS
     parser.add_argument('--fps', type=int, default=50)
     # sliding window left-middle-right length (unit: 20ms)
     parser.add_argument('-l', type=int, default=10)
-    parser.add_argument('-m', type=int, default=50)
+    parser.add_argument('-m', type=int, default=8)
     parser.add_argument('-r', type=int, default=10)
 
     parser.add_argument('--fullbody', action='store_true', help="fullbody human")
@@ -268,19 +414,45 @@ if __name__ == '__main__':
     parser.add_argument('--fullbody_offset_x', type=int, default=0)
     parser.add_argument('--fullbody_offset_y', type=int, default=0)
 
-    parser.add_argument('--tts', type=str, default='edgetts') #xtts
-    parser.add_argument('--ref_file', type=str, default=None)
-    parser.add_argument('--xtts_server', type=str, default='http://localhost:9000')
+    #musetalk opt
+    parser.add_argument('--avatar_id', type=str, default='avator_1')
+    parser.add_argument('--bbox_shift', type=int, default=5)
+    parser.add_argument('--batch_size', type=int, default=16)
+
+    # parser.add_argument('--customvideo', action='store_true', help="custom video")
+    # parser.add_argument('--customvideo_img', type=str, default='data/customvideo/img')
+    # parser.add_argument('--customvideo_imgnum', type=int, default=1)
+
+    parser.add_argument('--customvideo_config', type=str, default='')
+
+    parser.add_argument('--tts', type=str, default='edgetts') #xtts gpt-sovits cosyvoice
+    parser.add_argument('--REF_FILE', type=str, default=None)
+    parser.add_argument('--REF_TEXT', type=str, default=None)
+    parser.add_argument('--TTS_SERVER', type=str, default='http://127.0.0.1:9880') # http://localhost:9000
+    # parser.add_argument('--CHARACTER', type=str, default='test')
+    # parser.add_argument('--EMOTION', type=str, default='default')
+
+    parser.add_argument('--model', type=str, default='ernerf') #musetalk wav2lip
+
+    parser.add_argument('--transport', type=str, default='rtcpush') #rtmp webrtc rtcpush
+    parser.add_argument('--push_url', type=str, default='http://localhost:1985/rtc/v1/whip/?app=live&stream=livestream') #rtmp://localhost/live/livestream
+
+    parser.add_argument('--max_session', type=int, default=1)  #multi session count
+    parser.add_argument('--listenport', type=int, default=8010)
 
     opt = parser.parse_args()
-    app.config.from_object(opt)
-    #print(app.config['xtts_server'])
-
-    tts_type = opt.tts
-    if tts_type == "xtts":
-        print("Computing the latents for a new reference...")
-        gspeaker = get_speaker(opt.ref_file, opt.xtts_server)
+    #app.config.from_object(opt)
+    #print(app.config)
+    opt.customopt = []
+    if opt.customvideo_config!='':
+        with open(opt.customvideo_config,'r') as file:
+            opt.customopt = json.load(file)
 
+    if opt.model == 'ernerf':
+        from ernerf.nerf_triplane.provider import NeRFDataset_Test
+        from ernerf.nerf_triplane.utils import *
+        from ernerf.nerf_triplane.network import NeRFNetwork
+        from nerfreal import NeRFReal
         # assert test mode
         opt.test = True
         opt.test_train = False
@@ -322,15 +494,76 @@ if __name__ == '__main__':
         model.eye_areas = test_loader._data.eye_area
 
         # we still need test_loader to provide audio features for testing.
+        for _ in range(opt.max_session):
             nerfreal = NeRFReal(opt, trainer, test_loader)
-    #txt_to_audio('我是中国人,我来自北京')
-    rendthrd = Thread(target=render)
+            nerfreals.append(nerfreal)
+    elif opt.model == 'musetalk':
+        from musereal import MuseReal
+        print(opt)
+        for _ in range(opt.max_session):
+            nerfreal = MuseReal(opt)
+            nerfreals.append(nerfreal)
+    elif opt.model == 'wav2lip':
+        from lipreal import LipReal
+        print(opt)
+        for _ in range(opt.max_session):
+            nerfreal = LipReal(opt)
+            nerfreals.append(nerfreal)
+    
+    for _ in range(opt.max_session):
+        statreals.append(0)
+
+    if opt.transport=='rtmp':
+        thread_quit = Event()
+        rendthrd = Thread(target=nerfreals[0].render,args=(thread_quit,))
         rendthrd.start()
 
     #############################################################################
-    print('start websocket server')
+    appasync = web.Application()
+    appasync.on_shutdown.append(on_shutdown)
+    appasync.router.add_post("/offer", offer)
+    appasync.router.add_post("/human", human)
+    appasync.router.add_post("/humanaudio", humanaudio)
+    appasync.router.add_post("/set_audiotype", set_audiotype)
+    appasync.router.add_post("/record", record)
+    appasync.router.add_post("/is_speaking", is_speaking)
+    appasync.router.add_static('/',path='web')
 
-    server = pywsgi.WSGIServer(('0.0.0.0', 8000), app, handler_class=WebSocketHandler)
-    server.serve_forever()
+    # Configure default CORS settings.
+    cors = aiohttp_cors.setup(appasync, defaults={
+            "*": aiohttp_cors.ResourceOptions(
+                allow_credentials=True,
+                expose_headers="*",
+                allow_headers="*",
+            )
+        })
+    # Configure CORS on all routes.
+    for route in list(appasync.router.routes()):
+        cors.add(route)
+
+    pagename='webrtcapi.html'
+    if opt.transport=='rtmp':
+        pagename='echoapi.html'
+    elif opt.transport=='rtcpush':
+        pagename='rtcpushapi.html'
+    print('start http server; http://<serverip>:'+str(opt.listenport)+'/'+pagename)
+    def run_server(runner):
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        loop.run_until_complete(runner.setup())
+        site = web.TCPSite(runner, '0.0.0.0', opt.listenport)
+        loop.run_until_complete(site.start())
+        if opt.transport=='rtcpush':
+            loop.run_until_complete(run(opt.push_url))
+        loop.run_forever()    
+    #Thread(target=run_server, args=(web.AppRunner(appasync),)).start()
+    run_server(web.AppRunner(appasync))
+
+    #app.on_shutdown.append(on_shutdown)
+    #app.router.add_post("/offer", offer)
+
+    # print('start websocket server')
+    # server = pywsgi.WSGIServer(('0.0.0.0', 8000), app, handler_class=WebSocketHandler)
+    # server.serve_forever()
     
     

asrreal.py

@@ -1,496 +0,0 @@
-import time
-import numpy as np
-import torch
-import torch.nn.functional as F
-from transformers import AutoModelForCTC, AutoProcessor, Wav2Vec2Processor, HubertModel
-
-#import pyaudio
-import soundfile as sf
-import resampy
-
-import queue
-from queue import Queue
-#from collections import deque
-from threading import Thread, Event
-from io import BytesIO
-
-
-def _read_frame(stream, exit_event, queue, chunk):
-
-    while True:
-        if exit_event.is_set():
-            print(f'[INFO] read frame thread ends')
-            break
-        frame = stream.read(chunk, exception_on_overflow=False)
-        frame = np.frombuffer(frame, dtype=np.int16).astype(np.float32) / 32767 # [chunk]
-        queue.put(frame)
-
-def _play_frame(stream, exit_event, queue, chunk):
-
-    while True:
-        if exit_event.is_set():
-            print(f'[INFO] play frame thread ends')
-            break
-        frame = queue.get()
-        frame = (frame * 32767).astype(np.int16).tobytes()
-        stream.write(frame, chunk)
-
-class ASR:
-    def __init__(self, opt):
-
-        self.opt = opt
-
-        self.play = opt.asr_play #false 
-
-        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        self.fps = opt.fps # 20 ms per frame
-        self.sample_rate = 16000
-        self.chunk = self.sample_rate // self.fps # 320 samples per chunk (20ms * 16000 / 1000)
-        self.mode = 'live' if opt.asr_wav == '' else 'file'
-
-        if 'esperanto' in self.opt.asr_model:
-            self.audio_dim = 44
-        elif 'deepspeech' in self.opt.asr_model:
-            self.audio_dim = 29
-        elif 'hubert' in self.opt.asr_model:
-            self.audio_dim = 1024
-        else:
-            self.audio_dim = 32
-
-        # prepare context cache
-        # each segment is (stride_left + ctx + stride_right) * 20ms, latency should be (ctx + stride_right) * 20ms
-        self.context_size = opt.m
-        self.stride_left_size = opt.l
-        self.stride_right_size = opt.r
-        self.text = '[START]\n'
-        self.terminated = False
-        self.frames = []
-        self.inwarm = False
-
-        # pad left frames
-        if self.stride_left_size > 0:
-            self.frames.extend([np.zeros(self.chunk, dtype=np.float32)] * self.stride_left_size)
-
-
-        self.exit_event = Event()
-        #self.audio_instance = pyaudio.PyAudio()  #not need
-
-        # create input stream
-        if self.mode == 'file':  #live mode
-            self.file_stream = self.create_file_stream()
-        else:
-            self.queue = Queue()
-            self.input_stream = BytesIO()
-            self.output_queue = Queue()
-            # start a background process to read frames
-            #self.input_stream = self.audio_instance.open(format=pyaudio.paInt16, channels=1, rate=self.sample_rate, input=True, output=False, frames_per_buffer=self.chunk)
-            #self.queue = Queue()
-            #self.process_read_frame = Thread(target=_read_frame, args=(self.input_stream, self.exit_event, self.queue, self.chunk))
-        
-        # play out the audio too...?
-        if self.play:
-            self.output_stream = self.audio_instance.open(format=pyaudio.paInt16, channels=1, rate=self.sample_rate, input=False, output=True, frames_per_buffer=self.chunk)
-            self.output_queue = Queue()
-            self.process_play_frame = Thread(target=_play_frame, args=(self.output_stream, self.exit_event, self.output_queue, self.chunk))
-
-        # current location of audio
-        self.idx = 0
-
-        # create wav2vec model
-        print(f'[INFO] loading ASR model {self.opt.asr_model}...')
-        if 'hubert' in self.opt.asr_model:
-            self.processor = Wav2Vec2Processor.from_pretrained(opt.asr_model)
-            self.model = HubertModel.from_pretrained(opt.asr_model).to(self.device) 
-        else:   
-            self.processor = AutoProcessor.from_pretrained(opt.asr_model)
-            self.model = AutoModelForCTC.from_pretrained(opt.asr_model).to(self.device)
-
-        # prepare to save logits
-        if self.opt.asr_save_feats:
-            self.all_feats = []
-
-        # the extracted features 
-        # use a loop queue to efficiently record endless features: [f--t---][-------][-------]
-        self.feat_buffer_size = 4
-        self.feat_buffer_idx = 0
-        self.feat_queue = torch.zeros(self.feat_buffer_size * self.context_size, self.audio_dim, dtype=torch.float32, device=self.device)
-
-        # TODO: hard coded 16 and 8 window size...
-        self.front = self.feat_buffer_size * self.context_size - 8 # fake padding
-        self.tail = 8
-        # attention window...
-        self.att_feats = [torch.zeros(self.audio_dim, 16, dtype=torch.float32, device=self.device)] * 4 # 4 zero padding...
-
-        # warm up steps needed: mid + right + window_size + attention_size
-        self.warm_up_steps = self.context_size + self.stride_right_size + self.stride_left_size   #+ 8 + 2 * 3
-
-        self.listening = False
-        self.playing = False
-
-    def listen(self):
-        # start
-        if self.mode == 'live' and not self.listening:
-            print(f'[INFO] starting read frame thread...')
-            self.process_read_frame.start()
-            self.listening = True
-        
-        if self.play and not self.playing:
-            print(f'[INFO] starting play frame thread...')
-            self.process_play_frame.start()
-            self.playing = True
-
-    def stop(self):
-
-        self.exit_event.set()
-
-        if self.play:
-            self.output_stream.stop_stream()
-            self.output_stream.close()
-            if self.playing:
-                self.process_play_frame.join()
-                self.playing = False
-
-        if self.mode == 'live':
-            #self.input_stream.stop_stream() todo
-            self.input_stream.close()
-            if self.listening:
-                self.process_read_frame.join()
-                self.listening = False
-
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        
-        self.stop()
-
-        if self.mode == 'live':
-            # live mode: also print the result text.        
-            self.text += '\n[END]'
-            print(self.text)
-
-    def get_next_feat(self):
-        # return a [1/8, 16] window, for the next input to nerf side.
-        
-        while len(self.att_feats) < 8:
-            # [------f+++t-----]
-            if self.front < self.tail:
-                feat = self.feat_queue[self.front:self.tail]
-            # [++t-----------f+]
-            else:
-                feat = torch.cat([self.feat_queue[self.front:], self.feat_queue[:self.tail]], dim=0)
-
-            self.front = (self.front + 2) % self.feat_queue.shape[0]
-            self.tail = (self.tail + 2) % self.feat_queue.shape[0]
-
-            # print(self.front, self.tail, feat.shape)
-
-            self.att_feats.append(feat.permute(1, 0))
-        
-        att_feat = torch.stack(self.att_feats, dim=0) # [8, 44, 16]
-
-        # discard old
-        self.att_feats = self.att_feats[1:]
-
-        return att_feat
-
-    def run_step(self):
-
-        if self.terminated:
-            return
-
-        # get a frame of audio
-        frame = self.get_audio_frame()
-        
-        # the last frame
-        if frame is None:
-            # terminate, but always run the network for the left frames
-            self.terminated = True
-        else:
-            self.frames.append(frame)
-            # put to output
-            self.output_queue.put(frame)
-            # context not enough, do not run network.
-            if len(self.frames) < self.stride_left_size + self.context_size + self.stride_right_size:
-                return
-        
-        inputs = np.concatenate(self.frames) # [N * chunk]
-
-        # discard the old part to save memory
-        if not self.terminated:
-            self.frames = self.frames[-(self.stride_left_size + self.stride_right_size):]
-
-        print(f'[INFO] frame_to_text... ')
-        #t = time.time()
-        logits, labels, text = self.frame_to_text(inputs)
-        #print(f'-------wav2vec time:{time.time()-t:.4f}s')
-        feats = logits # better lips-sync than labels
-
-        # save feats
-        if self.opt.asr_save_feats:
-            self.all_feats.append(feats)
-
-        # record the feats efficiently.. (no concat, constant memory)
-        start = self.feat_buffer_idx * self.context_size
-        end = start + feats.shape[0]
-        self.feat_queue[start:end] = feats
-        self.feat_buffer_idx = (self.feat_buffer_idx + 1) % self.feat_buffer_size
-
-        # very naive, just concat the text output.
-        #if text != '':
-        #    self.text = self.text + ' ' + text
-
-        # will only run once at ternimation
-        if self.terminated:
-            self.text += '\n[END]'
-            print(self.text)
-            if self.opt.asr_save_feats:
-                print(f'[INFO] save all feats for training purpose... ')
-                feats = torch.cat(self.all_feats, dim=0) # [N, C]
-                # print('[INFO] before unfold', feats.shape)
-                window_size = 16
-                padding = window_size // 2
-                feats = feats.view(-1, self.audio_dim).permute(1, 0).contiguous() # [C, M]
-                feats = feats.view(1, self.audio_dim, -1, 1) # [1, C, M, 1]
-                unfold_feats = F.unfold(feats, kernel_size=(window_size, 1), padding=(padding, 0), stride=(2, 1)) # [1, C * window_size, M / 2 + 1]
-                unfold_feats = unfold_feats.view(self.audio_dim, window_size, -1).permute(2, 1, 0).contiguous() # [C, window_size, M / 2 + 1] --> [M / 2 + 1, window_size, C]
-                # print('[INFO] after unfold', unfold_feats.shape)
-                # save to a npy file
-                if 'esperanto' in self.opt.asr_model:
-                    output_path = self.opt.asr_wav.replace('.wav', '_eo.npy')
-                else:
-                    output_path = self.opt.asr_wav.replace('.wav', '.npy')
-                np.save(output_path, unfold_feats.cpu().numpy())
-                print(f"[INFO] saved logits to {output_path}")
-    
-    '''
-    def create_file_stream(self):
-    
-        stream, sample_rate = sf.read(self.opt.asr_wav) # [T*sample_rate,] float64
-        stream = stream.astype(np.float32)
-
-        if stream.ndim > 1:
-            print(f'[WARN] audio has {stream.shape[1]} channels, only use the first.')
-            stream = stream[:, 0]
-    
-        if sample_rate != self.sample_rate:
-            print(f'[WARN] audio sample rate is {sample_rate}, resampling into {self.sample_rate}.')
-            stream = resampy.resample(x=stream, sr_orig=sample_rate, sr_new=self.sample_rate)
-
-        print(f'[INFO] loaded audio stream {self.opt.asr_wav}: {stream.shape}')
-
-        return stream
-
-
-    def create_pyaudio_stream(self):
-
-        import pyaudio
-
-        print(f'[INFO] creating live audio stream ...')
-
-        audio = pyaudio.PyAudio()
-        
-        # get devices
-        info = audio.get_host_api_info_by_index(0)
-        n_devices = info.get('deviceCount')
-
-        for i in range(0, n_devices):
-            if (audio.get_device_info_by_host_api_device_index(0, i).get('maxInputChannels')) > 0:
-                name = audio.get_device_info_by_host_api_device_index(0, i).get('name')
-                print(f'[INFO] choose audio device {name}, id {i}')
-                break
-        
-        # get stream
-        stream = audio.open(input_device_index=i,
-                            format=pyaudio.paInt16,
-                            channels=1,
-                            rate=self.sample_rate,
-                            input=True,
-                            frames_per_buffer=self.chunk)
-        
-        return audio, stream
-    '''
-    
-    def get_audio_frame(self):
-
-        if self.inwarm: # warm up
-            return np.zeros(self.chunk, dtype=np.float32)
-        
-        if self.mode == 'file':
-
-            if self.idx < self.file_stream.shape[0]:
-                frame = self.file_stream[self.idx: self.idx + self.chunk]
-                self.idx = self.idx + self.chunk
-                return frame
-            else:
-                return None
-        
-        else:
-            try:
-                frame = self.queue.get(block=False)
-                print(f'[INFO] get frame {frame.shape}')
-            except queue.Empty:
-                frame = np.zeros(self.chunk, dtype=np.float32)
-
-            self.idx = self.idx + self.chunk
-
-            return frame
-
-        
-    def frame_to_text(self, frame):
-        # frame: [N * 320], N = (context_size + 2 * stride_size)
-        
-        inputs = self.processor(frame, sampling_rate=self.sample_rate, return_tensors="pt", padding=True)
-        
-        with torch.no_grad():
-            result = self.model(inputs.input_values.to(self.device))
-            if 'hubert' in self.opt.asr_model:
-                logits = result.last_hidden_state # [B=1, T=pts//320, hid=1024]
-            else:
-                logits = result.logits # [1, N - 1, 32]
-        #print('logits.shape:',logits.shape)
-        
-        # cut off stride
-        left = max(0, self.stride_left_size)
-        right = min(logits.shape[1], logits.shape[1] - self.stride_right_size + 1) # +1 to make sure output is the same length as input.
-
-        # do not cut right if terminated.
-        if self.terminated:
-            right = logits.shape[1]
-
-        logits = logits[:, left:right]
-
-        # print(frame.shape, inputs.input_values.shape, logits.shape)
-    
-        #predicted_ids = torch.argmax(logits, dim=-1)
-        #transcription = self.processor.batch_decode(predicted_ids)[0].lower()
-
-        
-        # for esperanto
-        # labels = np.array(['ŭ', '»', 'c', 'ĵ', 'ñ', '”', '„', '“', 'ǔ', 'o', 'ĝ', 'm', 'k', 'd', 'a', 'ŝ', 'z', 'i', '«', '—', '‘', 'ĥ', 'f', 'y', 'h', 'j', '|', 'r', 'u', 'ĉ', 's', '–', 'fi', 'l', 'p', '’', 'g', 'v', 't', 'b', 'n', 'e', '[UNK]', '[PAD]'])
-
-        # labels = np.array([' ', ' ', ' ', '-', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z'])
-        # print(''.join(labels[predicted_ids[0].detach().cpu().long().numpy()]))
-        # print(predicted_ids[0])
-        # print(transcription)
-
-        return logits[0], None,None #predicted_ids[0], transcription # [N,]
-    
-    def create_bytes_stream(self,byte_stream):
-        #byte_stream=BytesIO(buffer)
-        stream, sample_rate = sf.read(byte_stream) # [T*sample_rate,] float64
-        print(f'[INFO]tts audio stream {sample_rate}: {stream.shape}')
-        stream = stream.astype(np.float32)
-
-        if stream.ndim > 1:
-            print(f'[WARN] audio has {stream.shape[1]} channels, only use the first.')
-            stream = stream[:, 0]
-    
-        if sample_rate != self.sample_rate and stream.shape[0]>0:
-            print(f'[WARN] audio sample rate is {sample_rate}, resampling into {self.sample_rate}.')
-            stream = resampy.resample(x=stream, sr_orig=sample_rate, sr_new=self.sample_rate)
-
-        return stream
-
-    def push_audio(self,buffer):
-        print(f'[INFO] push_audio {len(buffer)}')
-        if self.opt.tts == "xtts":
-            if len(buffer)>0:            
-                stream = np.frombuffer(buffer, dtype=np.int16).astype(np.float32) / 32767
-                stream = resampy.resample(x=stream, sr_orig=24000, sr_new=self.sample_rate)
-                #byte_stream=BytesIO(buffer)
-                #stream = self.create_bytes_stream(byte_stream)
-                streamlen = stream.shape[0]
-                idx=0
-                while streamlen >= self.chunk:
-                    self.queue.put(stream[idx:idx+self.chunk])
-                    streamlen -= self.chunk
-                    idx += self.chunk
-                # if streamlen>0: #skip last frame(not 20ms)
-                #     self.queue.put(stream[idx:])
-        else: #edge tts
-            self.input_stream.write(buffer)
-            if len(buffer)<=0:
-                self.input_stream.seek(0)
-                stream = self.create_bytes_stream(self.input_stream)
-                streamlen = stream.shape[0]
-                idx=0
-                while streamlen >= self.chunk:
-                    self.queue.put(stream[idx:idx+self.chunk])
-                    streamlen -= self.chunk
-                    idx += self.chunk
-                #if streamlen>0:  #skip last frame(not 20ms)
-                #    self.queue.put(stream[idx:])
-                self.input_stream.seek(0)
-                self.input_stream.truncate()
-    
-    def get_audio_out(self):
-        return self.output_queue.get()
-        
-    def run(self):
-
-        self.listen()
-
-        while not self.terminated:
-            self.run_step()
-
-    def clear_queue(self):
-        # clear the queue, to reduce potential latency...
-        print(f'[INFO] clear queue')
-        if self.mode == 'live':
-            self.queue.queue.clear()
-        if self.play:
-            self.output_queue.queue.clear()
-
-    def warm_up(self):
-
-        #self.listen()
-        
-        self.inwarm = True
-        print(f'[INFO] warm up ASR live model, expected latency = {self.warm_up_steps / self.fps:.6f}s')
-        t = time.time()
-        for _ in range(self.warm_up_steps):
-            self.run_step()
-        if torch.cuda.is_available():
-            torch.cuda.synchronize()
-        t = time.time() - t
-        print(f'[INFO] warm-up done, actual latency = {t:.6f}s')
-        self.inwarm = False
-
-        #self.clear_queue()
-
-            
-
-
-if __name__ == '__main__':
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--wav', type=str, default='')
-    parser.add_argument('--play', action='store_true', help="play out the audio")
-    
-    parser.add_argument('--model', type=str, default='cpierse/wav2vec2-large-xlsr-53-esperanto')
-    # parser.add_argument('--model', type=str, default='facebook/wav2vec2-large-960h-lv60-self')
-
-    parser.add_argument('--save_feats', action='store_true')
-    # audio FPS
-    parser.add_argument('--fps', type=int, default=50)
-    # sliding window left-middle-right length.
-    parser.add_argument('-l', type=int, default=10)
-    parser.add_argument('-m', type=int, default=50)
-    parser.add_argument('-r', type=int, default=10)
-    
-    opt = parser.parse_args()
-
-    # fix
-    opt.asr_wav = opt.wav
-    opt.asr_play = opt.play
-    opt.asr_model = opt.model
-    opt.asr_save_feats = opt.save_feats
-
-    if 'deepspeech' in opt.asr_model:
-        raise ValueError("DeepSpeech features should not use this code to extract...")
-
-    with ASR(opt) as asr:
-        asr.run()

assets/faq.md

@@ -0,0 +1,65 @@
+1.  pytorch3d安装不成功\
+    下载源码编译
+
+```bash
+git clone https://github.com/facebookresearch/pytorch3d.git
+python setup.py install
+```
+
+2.  websocket连接报错\
+    修改python/site-packages/flask\_sockets.py
+
+```python
+self.url_map.add(Rule(rule, endpoint=f)) 改成 
+self.url_map.add(Rule(rule, endpoint=f, websocket=True))
+```
+
+3. protobuf版本过高
+
+```bash
+pip uninstall protobuf
+pip install protobuf==3.20.1
+```
+
+4. 数字人不眨眼\
+训练模型时添加如下步骤
+
+> Obtain AU45 for eyes blinking.\
+> Run FeatureExtraction in OpenFace, rename and move the output CSV file to data/\<ID>/au.csv.
+
+将au.csv拷到本项目的data目录下
+
+5. 数字人添加背景图片
+
+```bash
+python app.py --bg_img bc.jpg
+```
+
+6. 用自己训练的模型报错维度不匹配\
+训练模型时用wav2vec提取音频特征
+
+```bash
+python main.py data/ --workspace workspace/ -O --iters 100000 --asr_model cpierse/wav2vec2-large-xlsr-53-esperanto
+```
+
+7. rtmp推流时ffmpeg版本不对
+网上版友反馈是需要4.2.2版本。我也不确定具体哪些版本不行。原则是运行一下ffmpeg，打印的信息里需要有libx264，如果没有肯定不行
+```
+--enable-libx264
+```
+8. 替换自己训练的模型
+```python
+.
+├── data
+│   ├── data_kf.json （对应训练数据中的transforms_train.json）
+│   ├── au.csv			
+│   ├── pretrained
+│   └── └── ngp_kf.pth （对应训练后的模型ngp_ep00xx.pth）
+
+```
+
+
+其他参考
+https://github.com/lipku/metahuman-stream/issues/43#issuecomment-2008930101
+
+

baseasr.py

@@ -0,0 +1,69 @@
+import time
+import numpy as np
+
+import queue
+from queue import Queue
+import multiprocessing as mp
+
+
+class BaseASR:
+    def __init__(self, opt, parent=None):
+        self.opt = opt
+        self.parent = parent
+
+        self.fps = opt.fps # 20 ms per frame
+        self.sample_rate = 16000
+        self.chunk = self.sample_rate // self.fps # 320 samples per chunk (20ms * 16000 / 1000)
+        self.queue = Queue()
+        self.output_queue = mp.Queue()
+
+        self.batch_size = opt.batch_size
+
+        self.frames = []
+        self.stride_left_size = opt.l
+        self.stride_right_size = opt.r
+        #self.context_size = 10
+        self.feat_queue = mp.Queue(2)
+
+        #self.warm_up()
+
+    def pause_talk(self):
+        self.queue.queue.clear()
+
+    def put_audio_frame(self,audio_chunk): #16khz 20ms pcm
+        self.queue.put(audio_chunk)
+
+    def get_audio_frame(self):        
+        try:
+            frame = self.queue.get(block=True,timeout=0.01)
+            type = 0
+            #print(f'[INFO] get frame {frame.shape}')
+        except queue.Empty:
+            if self.parent and self.parent.curr_state>1: #播放自定义音频
+                frame = self.parent.get_audio_stream(self.parent.curr_state)
+                type = self.parent.curr_state
+            else:
+                frame = np.zeros(self.chunk, dtype=np.float32)
+                type = 1
+
+        return frame,type 
+
+    def is_audio_frame_empty(self)->bool:
+        return self.queue.empty()
+
+    def get_audio_out(self):  #get origin audio pcm to nerf
+        return self.output_queue.get()
+    
+    def warm_up(self):
+        for _ in range(self.stride_left_size + self.stride_right_size):
+            audio_frame,type=self.get_audio_frame()
+            self.frames.append(audio_frame)
+            self.output_queue.put((audio_frame,type))
+        for _ in range(self.stride_left_size):
+            self.output_queue.get()
+
+    def run_step(self):
+        pass
+
+    def get_next_feat(self,block,timeout):        
+        return self.feat_queue.get(block,timeout)

basereal.py

@@ -0,0 +1,207 @@
+import math
+import torch
+import numpy as np
+
+import os
+import time
+import cv2
+import glob
+import pickle
+import copy
+import resampy
+
+import queue
+from queue import Queue
+from threading import Thread, Event
+from io import BytesIO
+import soundfile as sf
+
+import av
+from fractions import Fraction
+
+from ttsreal import EdgeTTS,VoitsTTS,XTTS,CosyVoiceTTS
+
+from tqdm import tqdm
+def read_imgs(img_list):
+    frames = []
+    print('reading images...')
+    for img_path in tqdm(img_list):
+        frame = cv2.imread(img_path)
+        frames.append(frame)
+    return frames
+
+class BaseReal:
+    def __init__(self, opt):
+        self.opt = opt
+        self.sample_rate = 16000
+        self.chunk = self.sample_rate // opt.fps # 320 samples per chunk (20ms * 16000 / 1000)
+
+        if opt.tts == "edgetts":
+            self.tts = EdgeTTS(opt,self)
+        elif opt.tts == "gpt-sovits":
+            self.tts = VoitsTTS(opt,self)
+        elif opt.tts == "xtts":
+            self.tts = XTTS(opt,self)
+        elif opt.tts == "cosyvoice":
+            self.tts = CosyVoiceTTS(opt,self)
+        
+        self.speaking = False
+
+        self.recording = False
+        self.recordq_video = Queue()
+        self.recordq_audio = Queue()
+
+        self.curr_state=0
+        self.custom_img_cycle = {}
+        self.custom_audio_cycle = {}
+        self.custom_audio_index = {}
+        self.custom_index = {}
+        self.custom_opt = {}
+        self.__loadcustom()
+
+    def put_msg_txt(self,msg):
+        self.tts.put_msg_txt(msg)
+    
+    def put_audio_frame(self,audio_chunk): #16khz 20ms pcm
+        self.asr.put_audio_frame(audio_chunk)
+
+    def put_audio_file(self,filebyte): 
+        input_stream = BytesIO(filebyte)
+        stream = self.__create_bytes_stream(input_stream)
+        streamlen = stream.shape[0]
+        idx=0
+        while streamlen >= self.chunk:  #and self.state==State.RUNNING
+            self.put_audio_frame(stream[idx:idx+self.chunk])
+            streamlen -= self.chunk
+            idx += self.chunk
+    
+    def __create_bytes_stream(self,byte_stream):
+        #byte_stream=BytesIO(buffer)
+        stream, sample_rate = sf.read(byte_stream) # [T*sample_rate,] float64
+        print(f'[INFO]put audio stream {sample_rate}: {stream.shape}')
+        stream = stream.astype(np.float32)
+
+        if stream.ndim > 1:
+            print(f'[WARN] audio has {stream.shape[1]} channels, only use the first.')
+            stream = stream[:, 0]
+    
+        if sample_rate != self.sample_rate and stream.shape[0]>0:
+            print(f'[WARN] audio sample rate is {sample_rate}, resampling into {self.sample_rate}.')
+            stream = resampy.resample(x=stream, sr_orig=sample_rate, sr_new=self.sample_rate)
+
+        return stream
+
+    def pause_talk(self):
+        self.tts.pause_talk()
+        self.asr.pause_talk()
+
+    def is_speaking(self)->bool:
+        return self.speaking
+    
+    def __loadcustom(self):
+        for item in self.opt.customopt:
+            print(item)
+            input_img_list = glob.glob(os.path.join(item['imgpath'], '*.[jpJP][pnPN]*[gG]'))
+            input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+            self.custom_img_cycle[item['audiotype']] = read_imgs(input_img_list)
+            self.custom_audio_cycle[item['audiotype']], sample_rate = sf.read(item['audiopath'], dtype='float32')
+            self.custom_audio_index[item['audiotype']] = 0
+            self.custom_index[item['audiotype']] = 0
+            self.custom_opt[item['audiotype']] = item
+
+    def init_customindex(self):
+        self.curr_state=0
+        for key in self.custom_audio_index:
+            self.custom_audio_index[key]=0
+        for key in self.custom_index:
+            self.custom_index[key]=0
+
+    def start_recording(self,path):
+        """开始录制视频"""
+        if self.recording:
+            return
+        self.recording = True
+        self.recordq_video.queue.clear()
+        self.recordq_audio.queue.clear()
+        self.container = av.open(path, mode="w")
+    
+        process_thread = Thread(target=self.record_frame, args=())
+        process_thread.start()
+    
+    def record_frame(self): 
+        videostream = self.container.add_stream("libx264", rate=25)
+        videostream.codec_context.time_base = Fraction(1, 25)
+        audiostream = self.container.add_stream("aac")
+        audiostream.codec_context.time_base = Fraction(1, 16000)
+        init = True
+        framenum = 0       
+        while self.recording:
+            try:
+                videoframe = self.recordq_video.get(block=True, timeout=1)
+                videoframe.pts = framenum #int(round(framenum*0.04 / videostream.codec_context.time_base))
+                videoframe.dts = videoframe.pts
+                if init:
+                    videostream.width = videoframe.width
+                    videostream.height = videoframe.height
+                    init = False
+                for packet in videostream.encode(videoframe):
+                    self.container.mux(packet)
+                for k in range(2):
+                    audioframe = self.recordq_audio.get(block=True, timeout=1)
+                    audioframe.pts = int(round((framenum*2+k)*0.02 / audiostream.codec_context.time_base))
+                    audioframe.dts = audioframe.pts
+                    for packet in audiostream.encode(audioframe):
+                        self.container.mux(packet)
+                framenum += 1
+            except queue.Empty:
+                print('record queue empty,')
+                continue
+            except Exception as e:
+                print(e)
+                #break
+        for packet in videostream.encode(None):
+            self.container.mux(packet)
+        for packet in audiostream.encode(None):
+            self.container.mux(packet)
+        self.container.close()
+        self.recordq_video.queue.clear()
+        self.recordq_audio.queue.clear()
+        print('record thread stop')
+		
+    def stop_recording(self):
+        """停止录制视频"""
+        if not self.recording:
+            return
+        self.recording = False        
+
+    def mirror_index(self,size, index):
+        #size = len(self.coord_list_cycle)
+        turn = index // size
+        res = index % size
+        if turn % 2 == 0:
+            return res
+        else:
+            return size - res - 1 
+    
+    def get_audio_stream(self,audiotype):
+        idx = self.custom_audio_index[audiotype]
+        stream = self.custom_audio_cycle[audiotype][idx:idx+self.chunk]
+        self.custom_audio_index[audiotype] += self.chunk
+        if self.custom_audio_index[audiotype]>=self.custom_audio_cycle[audiotype].shape[0]:
+            self.curr_state = 1  #当前视频不循环播放，切换到静音状态
+        return stream
+    
+    def set_curr_state(self,audiotype, reinit):
+        print('set_curr_state:',audiotype)
+        self.curr_state = audiotype
+        if reinit:
+            self.custom_audio_index[audiotype] = 0
+            self.custom_index[audiotype] = 0
+    
+    # def process_custom(self,audiotype:int,idx:int):
+    #     if self.curr_state!=audiotype: #从推理切到口播
+    #         if idx in self.switch_pos:  #在卡点位置可以切换
+    #             self.curr_state=audiotype
+    #             self.custom_index=0
+    #     else:
+    #         self.custom_index+=1

data/custom_config.json

@@ -0,0 +1,7 @@
+[
+   {
+        "audiotype":2, 
+        "imgpath":"data/customvideo/image", 
+        "audiopath":"data/customvideo/audio.wav"
+    }
+]

ernerf/encoding.py

@@ -13,23 +13,23 @@ def get_encoder(encoding, input_dim=3,
         return lambda x, **kwargs: x, input_dim
     
     elif encoding == 'frequency':
-        from freqencoder import FreqEncoder
+        from .freqencoder import FreqEncoder
         encoder = FreqEncoder(input_dim=input_dim, degree=multires)
 
     elif encoding == 'spherical_harmonics':
-        from shencoder import SHEncoder
+        from .shencoder import SHEncoder
         encoder = SHEncoder(input_dim=input_dim, degree=degree)
 
     elif encoding == 'hashgrid':
-        from gridencoder import GridEncoder
+        from .gridencoder import GridEncoder
         encoder = GridEncoder(input_dim=input_dim, num_levels=num_levels, level_dim=level_dim, base_resolution=base_resolution, log2_hashmap_size=log2_hashmap_size, desired_resolution=desired_resolution, gridtype='hash', align_corners=align_corners)
     
     elif encoding == 'tiledgrid':
-        from gridencoder import GridEncoder
+        from .gridencoder import GridEncoder
         encoder = GridEncoder(input_dim=input_dim, num_levels=num_levels, level_dim=level_dim, base_resolution=base_resolution, log2_hashmap_size=log2_hashmap_size, desired_resolution=desired_resolution, gridtype='tiled', align_corners=align_corners)
     
     elif encoding == 'ash':
-        from ashencoder import AshEncoder
+        from .ashencoder import AshEncoder
         encoder = AshEncoder(input_dim=input_dim, output_dim=16, log2_hashmap_size=log2_hashmap_size, resolution=desired_resolution)
 
     else:

ernerf/freqencoder/backend.py

@@ -4,13 +4,13 @@ from torch.utils.cpp_extension import load
 _src_path = os.path.dirname(os.path.abspath(__file__))
 
 nvcc_flags = [
-    '-O3', '-std=c++14',
-    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__',
+    '-O3', '-std=c++17',
+    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__', '-allow-unsupported-compiler',
     '-use_fast_math'
 ]
 
 if os.name == "posix":
-    c_flags = ['-O3', '-std=c++14']
+    c_flags = ['-O3', '-std=c++17']
 elif os.name == "nt":
     c_flags = ['/O2', '/std:c++17']
 

ernerf/freqencoder/setup.py

@@ -5,13 +5,13 @@ from torch.utils.cpp_extension import BuildExtension, CUDAExtension
 _src_path = os.path.dirname(os.path.abspath(__file__))
 
 nvcc_flags = [
-    '-O3', '-std=c++14',
-    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__',
+    '-O3', '-std=c++17',
+    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__', '-allow-unsupported-compiler',
     '-use_fast_math'
 ]
 
 if os.name == "posix":
-    c_flags = ['-O3', '-std=c++14']
+    c_flags = ['-O3', '-std=c++17']
 elif os.name == "nt":
     c_flags = ['/O2', '/std:c++17']
 

ernerf/gridencoder/backend.py

@@ -4,12 +4,12 @@ from torch.utils.cpp_extension import load
 _src_path = os.path.dirname(os.path.abspath(__file__))
 
 nvcc_flags = [
-    '-O3', '-std=c++14',
+    '-O3', '-std=c++17',
     '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__',
 ]
 
 if os.name == "posix":
-    c_flags = ['-O3', '-std=c++14', '-finput-charset=UTF-8']
+    c_flags = ['-O3', '-std=c++17', '-finput-charset=UTF-8']
 elif os.name == "nt":
     c_flags = ['/O2', '/std:c++17', '/finput-charset=UTF-8']
 

ernerf/gridencoder/setup.py

@@ -5,12 +5,12 @@ from torch.utils.cpp_extension import BuildExtension, CUDAExtension
 _src_path = os.path.dirname(os.path.abspath(__file__))
 
 nvcc_flags = [
-    '-O3', '-std=c++14',
-    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__',
+    '-O3', '-std=c++17',
+    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__','-allow-unsupported-compiler',
 ]
 
 if os.name == "posix":
-    c_flags = ['-O3', '-std=c++14']
+    c_flags = ['-O3', '-std=c++17']
 elif os.name == "nt":
     c_flags = ['/O2', '/std:c++17']
 

ernerf/main.py

@@ -1,9 +1,9 @@
 import torch
 import argparse
 
-from nerf_triplane.provider import NeRFDataset,NeRFDataset_Test
-from nerf_triplane.utils import *
-from nerf_triplane.network import NeRFNetwork
+from .nerf_triplane.provider import NeRFDataset,NeRFDataset_Test
+from .nerf_triplane.utils import *
+from .nerf_triplane.network import NeRFNetwork
 
 # torch.autograd.set_detect_anomaly(True)
 # Close tf32 features. Fix low numerical accuracy on rtx30xx gpu.

ernerf/nerf_triplane/network.py

@@ -2,7 +2,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from encoding import get_encoder
+from ..encoding import get_encoder
 from .renderer import NeRFRenderer
 
 # Audio feature extractor

ernerf/nerf_triplane/renderer.py

@@ -7,7 +7,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-import raymarching
+from .. import raymarching
 from .utils import custom_meshgrid, get_audio_features, euler_angles_to_matrix, convert_poses
 
 def sample_pdf(bins, weights, n_samples, det=False):

ernerf/raymarching/backend.py

@@ -4,12 +4,12 @@ from torch.utils.cpp_extension import load
 _src_path = os.path.dirname(os.path.abspath(__file__))
 
 nvcc_flags = [
-    '-O3', '-std=c++14',
-    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__',
+    '-O3', '-std=c++17',
+    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__','-allow-unsupported-compiler',
 ]
 
 if os.name == "posix":
-    c_flags = ['-O3', '-std=c++14']
+    c_flags = ['-O3', '-std=c++17']
 elif os.name == "nt":
     c_flags = ['/O2', '/std:c++17']
 

ernerf/raymarching/setup.py

@@ -5,13 +5,13 @@ from torch.utils.cpp_extension import BuildExtension, CUDAExtension
 _src_path = os.path.dirname(os.path.abspath(__file__))
 
 nvcc_flags = [
-    '-O3', '-std=c++14',
+    '-O3', '-std=c++17',
     # '-lineinfo', # to debug illegal memory access
-    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__',
+    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__','-allow-unsupported-compiler',
 ]
 
 if os.name == "posix":
-    c_flags = ['-O3', '-std=c++14']
+    c_flags = ['-O3', '-std=c++17']
 elif os.name == "nt":
     c_flags = ['/O2', '/std:c++17']
 

ernerf/shencoder/backend.py

@@ -4,12 +4,12 @@ from torch.utils.cpp_extension import load
 _src_path = os.path.dirname(os.path.abspath(__file__))
 
 nvcc_flags = [
-    '-O3', '-std=c++14',
-    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__',
+    '-O3', '-std=c++17',
+    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__','-allow-unsupported-compiler',
 ]
 
 if os.name == "posix":
-    c_flags = ['-O3', '-std=c++14', '-finput-charset=utf-8']
+    c_flags = ['-O3', '-std=c++17', '-finput-charset=utf-8']
 elif os.name == "nt":
     c_flags = ['/O2', '/std:c++17', '/source-charset:utf-8']
 

ernerf/shencoder/setup.py

@@ -5,12 +5,12 @@ from torch.utils.cpp_extension import BuildExtension, CUDAExtension
 _src_path = os.path.dirname(os.path.abspath(__file__))
 
 nvcc_flags = [
-    '-O3', '-std=c++14',
-    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__',
+    '-O3', '-std=c++17',
+    '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__','-allow-unsupported-compiler',
 ]
 
 if os.name == "posix":
-    c_flags = ['-O3', '-std=c++14']
+    c_flags = ['-O3', '-std=c++17']
 elif os.name == "nt":
     c_flags = ['/O2', '/std:c++17']
 

ernerf/workspace/log_ngp.txt

@@ -0,0 +1,14 @@
+[INFO] Trainer: ngp | 2023-09-23_17-52-22 | cuda | fp16 | workspace
+[INFO] #parameters: 1789121
+[INFO] Loading data/pretrained/ngp_kf.pth ...
+[INFO] Trainer: ngp | 2023-09-23_18-58-25 | cuda | fp16 | workspace
+[INFO] #parameters: 1789121
+[INFO] Loading data/pretrained/ngp_kf.pth ...
+[INFO] Trainer: ngp | 2023-09-23_19-01-54 | cuda | fp16 | workspace
+[INFO] #parameters: 1787681
+[INFO] Loading data/pretrained/ngp_kf.pth ...
+[INFO] loaded model.
+[INFO] load at epoch 14, global step 51226
+[WARN] Failed to load optimizer.
+[INFO] loaded scheduler.
+[INFO] loaded scaler.

lipasr.py

@@ -0,0 +1,47 @@
+import time
+import torch
+import numpy as np
+
+import queue
+from queue import Queue
+import multiprocessing as mp
+
+from baseasr import BaseASR
+from wav2lip import audio
+
+class LipASR(BaseASR):
+
+    def run_step(self):
+        ############################################## extract audio feature ##############################################
+        # get a frame of audio
+        for _ in range(self.batch_size*2):
+            frame,type = self.get_audio_frame()
+            self.frames.append(frame)
+            # put to output
+            self.output_queue.put((frame,type))
+        # context not enough, do not run network.
+        if len(self.frames) <= self.stride_left_size + self.stride_right_size:
+            return
+        
+        inputs = np.concatenate(self.frames) # [N * chunk]
+        mel = audio.melspectrogram(inputs)
+        #print(mel.shape[0],mel.shape,len(mel[0]),len(self.frames))
+        # cut off stride
+        left = max(0, self.stride_left_size*80/50)
+        right = min(len(mel[0]), len(mel[0]) - self.stride_right_size*80/50)
+        mel_idx_multiplier = 80.*2/self.fps 
+        mel_step_size = 16
+        i = 0
+        mel_chunks = []
+        while i < (len(self.frames)-self.stride_left_size-self.stride_right_size)/2:
+            start_idx = int(left + i * mel_idx_multiplier)
+            #print(start_idx)
+            if start_idx + mel_step_size > len(mel[0]):
+                mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
+            else:
+                mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
+            i += 1
+        self.feat_queue.put(mel_chunks)
+        
+        # discard the old part to save memory
+        self.frames = self.frames[-(self.stride_left_size + self.stride_right_size):]

lipreal.py

@@ -0,0 +1,281 @@
+import math
+import torch
+import numpy as np
+
+#from .utils import *
+import subprocess
+import os
+import time
+import cv2
+import glob
+import pickle
+import copy
+
+import queue
+from queue import Queue
+from threading import Thread, Event
+from io import BytesIO
+import multiprocessing as mp
+
+
+from ttsreal import EdgeTTS,VoitsTTS,XTTS
+
+from lipasr import LipASR
+import asyncio
+from av import AudioFrame, VideoFrame
+from wav2lip.models import Wav2Lip
+from basereal import BaseReal
+
+#from imgcache import ImgCache
+
+from tqdm import tqdm
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print('Using {} for inference.'.format(device))
+
+def _load(checkpoint_path):
+	if device == 'cuda':
+		checkpoint = torch.load(checkpoint_path)
+	else:
+		checkpoint = torch.load(checkpoint_path,
+								map_location=lambda storage, loc: storage)
+	return checkpoint
+
+def load_model(path):
+	model = Wav2Lip()
+	print("Load checkpoint from: {}".format(path))
+	checkpoint = _load(path)
+	s = checkpoint["state_dict"]
+	new_s = {}
+	for k, v in s.items():
+		new_s[k.replace('module.', '')] = v
+	model.load_state_dict(new_s)
+
+	model = model.to(device)
+	return model.eval()
+
+def read_imgs(img_list):
+    frames = []
+    print('reading images...')
+    for img_path in tqdm(img_list):
+        frame = cv2.imread(img_path)
+        frames.append(frame)
+    return frames
+
+def __mirror_index(size, index):
+    #size = len(self.coord_list_cycle)
+    turn = index // size
+    res = index % size
+    if turn % 2 == 0:
+        return res
+    else:
+        return size - res - 1 
+
+def inference(render_event,batch_size,face_imgs_path,audio_feat_queue,audio_out_queue,res_frame_queue):
+    
+    model = load_model("./models/wav2lip.pth")
+    input_face_list = glob.glob(os.path.join(face_imgs_path, '*.[jpJP][pnPN]*[gG]'))
+    input_face_list = sorted(input_face_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+    face_list_cycle = read_imgs(input_face_list)
+    
+    #input_latent_list_cycle = torch.load(latents_out_path)
+    length = len(face_list_cycle)
+    index = 0
+    count=0
+    counttime=0
+    print('start inference')
+    while True:
+        if render_event.is_set():
+            starttime=time.perf_counter()
+            mel_batch = []
+            try:
+                mel_batch = audio_feat_queue.get(block=True, timeout=1)
+            except queue.Empty:
+                continue
+                
+            is_all_silence=True
+            audio_frames = []
+            for _ in range(batch_size*2):
+                frame,type = audio_out_queue.get()
+                audio_frames.append((frame,type))
+                if type==0:
+                    is_all_silence=False
+
+            if is_all_silence:
+                for i in range(batch_size):
+                    res_frame_queue.put((None,__mirror_index(length,index),audio_frames[i*2:i*2+2]))
+                    index = index + 1
+            else:
+                # print('infer=======')
+                t=time.perf_counter()
+                img_batch = []
+                for i in range(batch_size):
+                    idx = __mirror_index(length,index+i)
+                    face = face_list_cycle[idx]
+                    img_batch.append(face)
+                img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
+
+                img_masked = img_batch.copy()
+                img_masked[:, face.shape[0]//2:] = 0
+
+                img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
+                mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+                
+                img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
+                mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)
+
+                with torch.no_grad():
+                    pred = model(mel_batch, img_batch)
+                pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
+
+                counttime += (time.perf_counter() - t)
+                count += batch_size
+                #_totalframe += 1
+                if count>=100:
+                    print(f"------actual avg infer fps:{count/counttime:.4f}")
+                    count=0
+                    counttime=0
+                for i,res_frame in enumerate(pred):
+                    #self.__pushmedia(res_frame,loop,audio_track,video_track)
+                    res_frame_queue.put((res_frame,__mirror_index(length,index),audio_frames[i*2:i*2+2]))
+                    index = index + 1
+                #print('total batch time:',time.perf_counter()-starttime)            
+        else:
+            time.sleep(1)
+    print('musereal inference processor stop')
+
+@torch.no_grad()
+class LipReal(BaseReal):
+    def __init__(self, opt):
+        super().__init__(opt)
+        #self.opt = opt # shared with the trainer's opt to support in-place modification of rendering parameters.
+        self.W = opt.W
+        self.H = opt.H
+
+        self.fps = opt.fps # 20 ms per frame
+
+        #### musetalk
+        self.avatar_id = opt.avatar_id
+        self.avatar_path = f"./data/avatars/{self.avatar_id}"
+        self.full_imgs_path = f"{self.avatar_path}/full_imgs" 
+        self.face_imgs_path = f"{self.avatar_path}/face_imgs" 
+        self.coords_path = f"{self.avatar_path}/coords.pkl"
+        self.batch_size = opt.batch_size
+        self.idx = 0
+        self.res_frame_queue = mp.Queue(self.batch_size*2)
+        #self.__loadmodels()
+        self.__loadavatar()
+
+        self.asr = LipASR(opt,self)
+        self.asr.warm_up()
+        #self.__warm_up()
+        
+        self.render_event = mp.Event()
+        mp.Process(target=inference, args=(self.render_event,self.batch_size,self.face_imgs_path,
+                                           self.asr.feat_queue,self.asr.output_queue,self.res_frame_queue,
+                                           )).start()
+
+    # def __loadmodels(self):
+    #     # load model weights
+    #     self.audio_processor, self.vae, self.unet, self.pe = load_all_model()
+    #     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    #     self.timesteps = torch.tensor([0], device=device)
+    #     self.pe = self.pe.half()
+    #     self.vae.vae = self.vae.vae.half()
+    #     self.unet.model = self.unet.model.half()
+
+    def __loadavatar(self):
+        with open(self.coords_path, 'rb') as f:
+            self.coord_list_cycle = pickle.load(f)
+        input_img_list = glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]'))
+        input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+        self.frame_list_cycle = read_imgs(input_img_list)
+        #self.imagecache = ImgCache(len(self.coord_list_cycle),self.full_imgs_path,1000)
+      
+
+    def process_frames(self,quit_event,loop=None,audio_track=None,video_track=None):
+        
+        while not quit_event.is_set():
+            try:
+                res_frame,idx,audio_frames = self.res_frame_queue.get(block=True, timeout=1)
+            except queue.Empty:
+                continue
+            if audio_frames[0][1]!=0 and audio_frames[1][1]!=0: #全为静音数据，只需要取fullimg
+                self.speaking = False
+                audiotype = audio_frames[0][1]
+                if self.custom_index.get(audiotype) is not None: #有自定义视频
+                    mirindex = self.mirror_index(len(self.custom_img_cycle[audiotype]),self.custom_index[audiotype])
+                    combine_frame = self.custom_img_cycle[audiotype][mirindex]
+                    self.custom_index[audiotype] += 1
+                    # if not self.custom_opt[audiotype].loop and self.custom_index[audiotype]>=len(self.custom_img_cycle[audiotype]):
+                    #     self.curr_state = 1  #当前视频不循环播放，切换到静音状态
+                else:
+                    combine_frame = self.frame_list_cycle[idx]
+                    #combine_frame = self.imagecache.get_img(idx)
+            else:
+                self.speaking = True
+                bbox = self.coord_list_cycle[idx]
+                combine_frame = copy.deepcopy(self.frame_list_cycle[idx])
+                #combine_frame = copy.deepcopy(self.imagecache.get_img(idx))
+                y1, y2, x1, x2 = bbox
+                try:
+                    res_frame = cv2.resize(res_frame.astype(np.uint8),(x2-x1,y2-y1))
+                except:
+                    continue
+                #combine_frame = get_image(ori_frame,res_frame,bbox)
+                #t=time.perf_counter()
+                combine_frame[y1:y2, x1:x2] = res_frame
+                #print('blending time:',time.perf_counter()-t)
+
+            image = combine_frame #(outputs['image'] * 255).astype(np.uint8)
+            new_frame = VideoFrame.from_ndarray(image, format="bgr24")
+            asyncio.run_coroutine_threadsafe(video_track._queue.put(new_frame), loop)
+            if self.recording:
+                self.recordq_video.put(new_frame) 
+
+            for audio_frame in audio_frames:
+                frame,type = audio_frame
+                frame = (frame * 32767).astype(np.int16)
+                new_frame = AudioFrame(format='s16', layout='mono', samples=frame.shape[0])
+                new_frame.planes[0].update(frame.tobytes())
+                new_frame.sample_rate=16000
+                # if audio_track._queue.qsize()>10:
+                #     time.sleep(0.1)
+                asyncio.run_coroutine_threadsafe(audio_track._queue.put(new_frame), loop)
+                if self.recording:
+                    self.recordq_audio.put(new_frame) 
+        print('musereal process_frames thread stop') 
+            
+    def render(self,quit_event,loop=None,audio_track=None,video_track=None):
+        #if self.opt.asr:
+        #     self.asr.warm_up()
+
+        self.tts.render(quit_event)
+        self.init_customindex()
+        process_thread = Thread(target=self.process_frames, args=(quit_event,loop,audio_track,video_track))
+        process_thread.start()
+
+        self.render_event.set() #start infer process render
+        count=0
+        totaltime=0
+        _starttime=time.perf_counter()
+        #_totalframe=0
+        while not quit_event.is_set(): 
+            # update texture every frame
+            # audio stream thread...
+            t = time.perf_counter()
+            self.asr.run_step()
+
+            # if video_track._queue.qsize()>=2*self.opt.batch_size:
+            #     print('sleep qsize=',video_track._queue.qsize())
+            #     time.sleep(0.04*video_track._queue.qsize()*0.8)
+            if video_track._queue.qsize()>=5:
+                print('sleep qsize=',video_track._queue.qsize())
+                time.sleep(0.04*video_track._queue.qsize()*0.8)
+                
+            # delay = _starttime+_totalframe*0.04-time.perf_counter() #40ms
+            # if delay > 0:
+            #     time.sleep(delay)
+        self.render_event.clear() #end infer process render
+        print('musereal thread stop')
+            

llm/Dockerfile

@@ -0,0 +1,56 @@
+# 使用NVIDIA的CUDA基础镜像
+#FROM nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu18.04
+#FROM m11007322/cuda11.3.0-cudnn8-devel-ubuntu20.04-jupyterlab
+FROM nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04
+# 安装Python和pip
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        python3 \
+        python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+# 安装Jupyter
+RUN pip3 install --no-cache-dir jupyter
+
+# 安装基础工具
+RUN apt-get update -yq --fix-missing \
+ && DEBIAN_FRONTEND=noninteractive apt-get install -yq --no-install-recommends \
+    pkg-config \
+    wget \
+    cmake \
+    curl \
+    git \
+    vim
+
+# 创建一个新的Conda环境
+RUN apt-get update && apt-get install -y wget \
+    && wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    && /bin/bash Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda \
+    && rm Miniconda3-latest-Linux-x86_64.sh \
+    && apt-get remove --purge --auto-remove -y wget \
+    && apt-get clean \
+    && ln -s /opt/conda/bin/conda /usr/bin/conda \
+    && conda update -n base -c defaults conda 
+
+SHELL ["/bin/bash","-ic"]
+# 增加cuda全局变量
+RUN echo "export CUDA_HOME=/usr/local/cuda" >> ~/.bashrc \
+    && echo "export PATH=${CUDA_HOME}/bin:$PATH" >> ~/.bashrc \
+    && echo "export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH" >> ~/.bashrc \
+    && source ~/.bashrc
+    
+    #&& echo "nameserver 8.8.8.8" >> /etc/resolv.conf
+
+# 安装cv2依赖，修复libGL.so.1错误
+RUN apt-get update
+RUN apt-get install ffmpeg libsm6 libxext6  -y
+
+# 配置Jupyter
+ENV JUPYTER_ENABLE_LAB=yes
+ENV USER=root
+ENV HOME=/home/$USER
+
+# 设置工作目录
+WORKDIR /root
+
+# 设置启动命令
+CMD ["jupyter", "lab", "--ip='*'", "--port=8888", "--no-browser", "--allow-root"]

llm/GPT.py

@@ -0,0 +1,22 @@
+import openai
+
+
+class GPT():
+    def __init__(self, model_path = 'gpt-3.5-turbo', api_key = None, base_url = None):
+        openai.api_key = api_key
+        self.model_path = model_path
+        if base_url != None:
+            openai.base_url = base_url
+
+    def chat(self, message):
+        response = openai.ChatCompletion.create(
+            model=self.model_path,
+            messages=[
+                {"role": "user", "content": message}
+            ]
+        )
+        return response['choices'][0]['message']['content']
+
+if __name__ == '__main__':
+    llm = GPT('gpt-3.5-turbo', '你的API Key','https://openai.api2d.net/v1')
+    response = llm.chat("如何应对压力？")

llm/LLM.py

@@ -1,7 +1,7 @@
 from llm.Qwen import Qwen
 from llm.Gemini import Gemini
 from llm.ChatGPT import ChatGPT
-
+from llm.VllmGPT import VllmGPT
 
 def test_Qwen(question = "如何应对压力？", mode='offline', model_path="Qwen/Qwen-1_8B-Chat"):
     llm = Qwen(mode, model_path)
@@ -18,21 +18,23 @@ class LLM:
         self.mode = mode
 
     def init_model(self, model_name, model_path, api_key=None, proxy_url=None):
-        if model_name not in ['Qwen', 'Gemini', 'ChatGPT']:
-            raise ValueError("model_name must be 'ChatGPT', 'Qwen', or 'Gemini'(其他模型还未集成)")
+        if model_name not in ['Qwen', 'Gemini', 'ChatGPT', 'VllmGPT']:
+            raise ValueError("model_name must be 'ChatGPT', 'VllmGPT', 'Qwen', or 'Gemini'(其他模型还未集成)")
 
         if model_name == 'Gemini':
             llm = Gemini(model_path, api_key, proxy_url)
         elif model_name == 'ChatGPT':
             llm = ChatGPT(model_path, api_key=api_key)
         elif model_name == 'Qwen':
-            llm = Qwen(self.mode, model_path)
+            llm = Qwen(model_path=model_path, api_key=api_key, api_base=proxy_url)
+        elif model_name == 'VllmGPT':
+            llm = VllmGPT()
         return llm
 
 
-    def test_Qwen(self, question="如何应对压力？", model_path="Qwen/Qwen-1_8B-Chat"):
-        llm = Qwen(self.mode, model_path)
-        answer = llm.generate(question)
+    def test_Qwen(self, question="如何应对压力？", model_path="Qwen/Qwen-1_8B-Chat", api_key=None, proxy_url=None):
+        llm = Qwen(model_path=model_path, api_key=api_key, api_base=proxy_url)
+        answer = llm.chat(question)
         print(answer)
 
     def test_Gemini(self, question="如何应对压力？", model_path='gemini-pro', api_key=None, proxy_url=None):
@@ -42,7 +44,11 @@ class LLM:
 
 if __name__ == '__main__':
     llm = LLM()
-    llm.test_Gemini(api_key='你的API Key', proxy_url=None)
+    # llm.test_Gemini(api_key='你的API Key', proxy_url=None)
     # llm = LLM().init_model('Gemini', model_path= 'gemini-pro',api_key='AIzaSyBWAWfT8zsyAZcRIXLS5Vzlw8KKCN9qsAg', proxy_url='http://172.31.71.58:7890')
     # response = llm.chat("如何应对压力？")
+    # llm = LLM().init_model('VllmGPT', model_path= 'THUDM/chatglm3-6b')
+    # response = llm.chat("如何应对压力？")
     # print(response)
+
+    llm.test_Qwen(api_key="none", proxy_url="http://10.1.1.113:18000/v1")

llm/Qwen.py

@@ -1,15 +1,33 @@
 import os
-import torch
-import requests
-from transformers import AutoModelForCausalLM, AutoTokenizer
+import openai
+
+'''
+`huggingface`连接不上可以使用 `modelscope`
+`pip install modelscope`
+'''
+from modelscope import AutoModelForCausalLM, AutoTokenizer
+#from transformers import AutoModelForCausalLM, AutoTokenizer
+
 os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
 
-class Qwen:
-    def __init__(self,  model_path="Qwen/Qwen-1_8B-Chat") -> None:
-        '''暂时不写api版本,与Linly-api相类似,感兴趣可以实现一下'''
-        self.model, self.tokenizer = self.init_model(model_path)
 
-    def init_model(self, path = "Qwen/Qwen-1_8B-Chat"):
+class Qwen:
+    def __init__(self, model_path="Qwen/Qwen-1_8B-Chat", api_base=None, api_key=None) -> None:
+        '''暂时不写api版本,与Linly-api相类似,感兴趣可以实现一下'''
+        # 默认本地推理
+        self.local = True
+
+        # api_base和api_key不为空时使用openapi的方式
+        if api_key is not None and api_base is not None:
+            openai.api_base = api_base
+            openai.api_key = api_key
+            self.local = False
+            return
+
+        self.model, self.tokenizer = self.init_model(model_path)
+        self.data = {}
+
+    def init_model(self, path="Qwen/Qwen-1_8B-Chat"):
         model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-1_8B-Chat",
                                                      device_map="auto",
                                                      trust_remote_code=True).eval()
@@ -18,8 +36,21 @@ class Qwen:
         return model, tokenizer
 
     def chat(self, question):
+        # 优先调用qwen openapi的方式
+        if not self.local:
+            # 不使用流式回复的请求
+            response = openai.ChatCompletion.create(
+                model="Qwen",
+                messages=[
+                    {"role": "user", "content": question}
+                ],
+                stream=False,
+                stop=[]
+            )
+            return response.choices[0].message.content
 
-        self.data["question"] = f"{self.prompt} ### Instruction:{question}  ### Response:"
+        # 默认本地推理
+        self.data["question"] = f"{question} ### Instruction:{question}  ### Response:"
         try:
             response, history = self.model.chat(self.tokenizer, self.data["question"], history=None)
             print(history)
@@ -30,8 +61,9 @@ class Qwen:
 
 def test():
     llm = Qwen(model_path="Qwen/Qwen-1_8B-Chat")
-    answer = llm.generate("如何应对压力？")
+    answer = llm.chat(question="如何应对压力？")
     print(answer)
 
+
 if __name__ == '__main__':
     test()

llm/README.md

@@ -0,0 +1,54 @@
+1、利用vllm可以显著推理加速大模型
+
+conda create -n vllm python=3.10
+conda activate vllm
+conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia
+
+2、启动推理
+python -m vllm.entrypoints.openai.api_server --tensor-parallel-size=1  --trust-remote-code --max-model-len 1024 --model THUDM/chatglm3-6b
+指定ip和端口：--host 127.0.0.1 --port 8101
+
+python -m vllm.entrypoints.openai.api_server --port 8101 --tensor-parallel-size=1  --trust-remote-code --max-model-len 1024 --model THUDM/chatglm3-6b
+
+CUDA_VISIBLE_DEVICES=6,7 python -m vllm.entrypoints.openai.api_server \
+--model="/data/mnt/ShareFolder/common_models/Ziya-Reader-13B-v1.0" \
+--max-model-len=8192 \
+--tensor-parallel-size=2 \
+--trust-remote-code \
+--port=8101
+
+
+3、测试
+curl http://127.0.0.1:8101/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "THUDM/chatglm3-6b",
+        "prompt": "请用20字内回复我,你今年多大了",
+        "max_tokens": 20,
+        "temperature": 0
+    }'
+
+多轮对话
+curl -X POST "http://127.0.0.1:8101/v1/completions" \
+-H "Content-Type: application/json" \
+-d "{\"model\": \"THUDM/chatglm3-6b\",\"prompt\": \"你叫什么名字\", \"history\": [{\"role\": \"user\", \"content\": \"你出生在哪里.\"}, {\"role\": \"assistant\", \"content\": \"出生在北京\"}]}"
+
+多轮对话
+curl -X POST "http://127.0.0.1:8101/v1/chat/completions" \
+-H "Content-Type: application/json" \
+-d "{\"model\": \"THUDM/chatglm3-6b\", \"messages\": [{\"role\": \"system\", \"content\": \"You are ChatGLM3, a large language model trained by Zhipu.AI. Follow the user's instructions carefully. Respond using markdown.\"}, {\"role\": \"user\", \"content\": \"你好，给我讲一个故事，大概100字\"}], \"stream\": false, \"max_tokens\": 100, \"temperature\": 0.8, \"top_p\": 0.8}"
+
+
+4、启动前端访问
+docker run -d \
+--network=host \
+--name nginx2 --restart=always \
+-v $PWD/nginx/conf/nginx.conf:/etc/nginx/nginx.conf \
+-v $PWD/nginx/html:/usr/share/nginx/html \
+-v $PWD/nginx/logs:/var/log/nginx \
+--privileged=true \
+--restart=always \
+nginx
+
+
+参考文档：https://docs.vllm.ai/en/latest/

llm/Read.me.txt

@@ -0,0 +1,74 @@
+一、cuda11.3容器启动过程
+1、拷贝Dockerfile文件到任意磁盘目录，然后执行下面的命令
+docker build -t nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda .
+
+docker images
+
+2、启动容器
+打开镜像（常规模式--支持使用GPU）
+docker run -i -t --gpus all nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda  /bin/bash
+
+
+打开镜像（增强模式--支持使用GPU、映射目录、设置内存）
+docker run -i -t -v /home/liguopu/:/guopu:rw --gpus all --shm-size 16G nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04  /bin/bash
+
+测试环境（使用端口映射，把服务映射出去）
+docker run -i -td --name metehuman --gpus -p 8000:8000 all nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda  /bin/bash
+
+正式使用（8000端口为业务对外的服务端口，根据情况可以自行增加）
+docker run -it --rm -p 8886:8888 -p 8000:8000 --gpus all nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda
+docker run -itd -p 8886:8888 -p 8000:8000 --gpus all nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda
+docker run -itd --name metehuman -p 8886:8888 -p 8000:8000 --gpus all nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda
+
+docker run --gpus '"device=vgpu,id=0"' -it --rm nvidia/cuda:11.0-base nvidia-smi
+
+docker run -itd --name metehuman \
+-p 8885:8888 -p 8001:8000 \
+-e GRANT_SUDO=yes \
+-e JUPYTER_ENABLE_LAB=yes \
+--user root \
+--gpus all \
+nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda
+
+3、查看token
+token=$(docker exec -it metehuman jupyter server list | grep -oP '(?<=token=)[a-zA-Z0-9]+')
+echo $token
+
+二、启动默认测试镜像
+docker pull m11007322/cuda11.3.0-cudnn8-devel-ubuntu20.04-jupyterlab
+docker run -it \
+    -d \
+    --gpus all \
+    -p 8887:8888 \
+    -p 8001:8000 \
+    --name metehuman2 \
+    --user root \
+    -e NB_USER="ubuntu" \
+    -e CHOWN_HOME=yes \
+    -e GRANT_SUDO=yes \
+    -w "/home/${NB_USER}" \
+    -v "$PWD":"/home/$USER/work" \
+    m11007322/cuda11.3.0-cudnn8-devel-ubuntu20.04-jupyterlab
+
+三、启动jupter镜像测试
+docker run -itd --name test \
+-p 8886:8888 -p 8000:8000 \
+-e GRANT_SUDO=yes \
+-e JUPYTER_ENABLE_LAB=yes \
+--user root \
+--gpus '"device=vgpu,id=0"' \
+nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda
+
+docker run -it --name test --network=host --dns 8.8.8.8 --dns 8.8.4.4 --rm ubuntu
+docker run -it --gpus all  --network=host --rm  registry.cn-hangzhou.aliyuncs.com/lipku/nerfstream:v1.3
+
+四、查看容器IP
+docker inspect bceda087524e | grep IPAddress
+
+curl https://openai.api2d.net/v1/chat/completions \
+  -H 'Content-Type: application/json' \
+  -H 'Authorization: Bearer fk193752-RlcPi2mBQqPOU5u1F8SFkG2z0gtxD0HS' \
+  -d '{
+  "model": "gpt-3.5-turbo",
+  "messages": [{"role": "user", "content": "你好！给我讲个笑话。"}]
+}'

llm/VllmGPT.py

@@ -0,0 +1,81 @@
+import json
+import requests
+# from core import content_db
+
+class VllmGPT:
+
+    def __init__(self, host="192.168.1.3",
+                 port="8101",
+                 model="THUDM/chatglm3-6b",
+                 max_tokens="1024"):
+        self.host = host
+        self.port = port
+        self.model=model
+        self.max_tokens=max_tokens
+        self.__URL = "http://{}:{}/v1/completions".format(self.host, self.port)
+        self.__URL2 = "http://{}:{}/v1/chat/completions".format(self.host, self.port)
+
+    def chat(self,cont):
+        chat_list = []
+        # contentdb = content_db.new_instance()
+        # list = contentdb.get_list('all','desc',11)
+        # answer_info = dict()
+        # chat_list = []
+        # i = len(list)-1
+        # while i >= 0:
+        #     answer_info = dict()
+        #     if list[i][0] == "member":
+        #         answer_info["role"] = "user"
+        #         answer_info["content"] = list[i][2]
+        #     elif list[i][0] == "fay":
+        #         answer_info["role"] = "bot"
+        #         answer_info["content"] = list[i][2]
+        #     chat_list.append(answer_info)
+        #     i -= 1
+        content = {
+            "model": self.model,
+            "prompt":"请简单回复我。" +  cont,
+            "history":chat_list}
+        url = self.__URL
+        req = json.dumps(content)
+        
+        headers = {'content-type': 'application/json'}
+        r = requests.post(url, headers=headers, data=req)
+        res = json.loads(r.text)
+        
+        return res['choices'][0]['text']
+
+    def question2(self,cont):
+        chat_list = []
+        # contentdb = content_db.new_instance()
+        # list = contentdb.get_list('all','desc',11)
+        # answer_info = dict()
+        # chat_list = []
+        # i = len(list)-1
+        # while i >= 0:
+        #     answer_info = dict()
+        #     if list[i][0] == "member":
+        #         answer_info["role"] = "user"
+        #         answer_info["content"] = list[i][2]
+        #     elif list[i][0] == "fay":
+        #         answer_info["role"] = "bot"
+        #         answer_info["content"] = list[i][2]
+        #     chat_list.append(answer_info)
+        #     i -= 1
+        content = {
+            "model": self.model,
+            "prompt":"请简单回复我。" +  cont,
+            "history":chat_list}
+        url = self.__URL2
+        req = json.dumps(content)
+        
+        headers = {'content-type': 'application/json'}
+        r = requests.post(url, headers=headers, data=req)
+        res = json.loads(r.text)
+        
+        return res['choices'][0]['message']['content']
+    
+if __name__ == "__main__":
+    vllm = VllmGPT('192.168.1.3','8101')
+    req = vllm.chat("你叫什么名字啊今年多大了")
+    print(req)

museasr.py

@@ -0,0 +1,36 @@
+import time
+import numpy as np
+
+import queue
+from queue import Queue
+import multiprocessing as mp
+from baseasr import BaseASR
+from musetalk.whisper.audio2feature import Audio2Feature
+
+class MuseASR(BaseASR):
+    def __init__(self, opt, parent,audio_processor:Audio2Feature):
+        super().__init__(opt,parent)
+        self.audio_processor = audio_processor
+
+    def run_step(self):
+        ############################################## extract audio feature ##############################################
+        start_time = time.time()
+        for _ in range(self.batch_size*2):
+            audio_frame,type=self.get_audio_frame()
+            self.frames.append(audio_frame)
+            self.output_queue.put((audio_frame,type))
+        
+        if len(self.frames) <= self.stride_left_size + self.stride_right_size:
+            return
+        
+        inputs = np.concatenate(self.frames) # [N * chunk]
+        whisper_feature = self.audio_processor.audio2feat(inputs)
+        # for feature in whisper_feature:
+        #     self.audio_feats.append(feature)        
+        #print(f"processing audio costs {(time.time() - start_time) * 1000}ms, inputs shape:{inputs.shape} whisper_feature len:{len(whisper_feature)}")
+        whisper_chunks = self.audio_processor.feature2chunks(feature_array=whisper_feature,fps=self.fps/2,batch_size=self.batch_size,start=self.stride_left_size/2 )
+        #print(f"whisper_chunks len:{len(whisper_chunks)},self.audio_feats len:{len(self.audio_feats)},self.output_queue len:{self.output_queue.qsize()}")
+        #self.audio_feats = self.audio_feats[-(self.stride_left_size + self.stride_right_size):]
+        self.feat_queue.put(whisper_chunks)
+        # discard the old part to save memory
+        self.frames = self.frames[-(self.stride_left_size + self.stride_right_size):]

musereal.py

@@ -0,0 +1,318 @@
+import math
+import torch
+import numpy as np
+
+#from .utils import *
+import subprocess
+import os
+import time
+import torch.nn.functional as F
+import cv2
+import glob
+import pickle
+import copy
+
+import queue
+from queue import Queue
+from threading import Thread, Event
+from io import BytesIO
+import multiprocessing as mp
+
+from musetalk.utils.utils import get_file_type,get_video_fps,datagen
+#from musetalk.utils.preprocessing import get_landmark_and_bbox,read_imgs,coord_placeholder
+from musetalk.utils.blending import get_image,get_image_prepare_material,get_image_blending
+from musetalk.utils.utils import load_all_model,load_diffusion_model,load_audio_model
+from ttsreal import EdgeTTS,VoitsTTS,XTTS
+
+from museasr import MuseASR
+import asyncio
+from av import AudioFrame, VideoFrame
+from basereal import BaseReal
+
+from tqdm import tqdm
+def read_imgs(img_list):
+    frames = []
+    print('reading images...')
+    for img_path in tqdm(img_list):
+        frame = cv2.imread(img_path)
+        frames.append(frame)
+    return frames
+
+def __mirror_index(size, index):
+    #size = len(self.coord_list_cycle)
+    turn = index // size
+    res = index % size
+    if turn % 2 == 0:
+        return res
+    else:
+        return size - res - 1 
+@torch.no_grad()
+def inference(render_event,batch_size,latents_out_path,audio_feat_queue,audio_out_queue,res_frame_queue,
+              ): #vae, unet, pe,timesteps
+    
+    vae, unet, pe = load_diffusion_model()
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    timesteps = torch.tensor([0], device=device)
+    pe = pe.half()
+    vae.vae = vae.vae.half()
+    unet.model = unet.model.half()
+    
+    input_latent_list_cycle = torch.load(latents_out_path)
+    length = len(input_latent_list_cycle)
+    index = 0
+    count=0
+    counttime=0
+    print('start inference')
+    while True:
+        if render_event.is_set():
+            starttime=time.perf_counter()
+            try:
+                whisper_chunks = audio_feat_queue.get(block=True, timeout=1)
+            except queue.Empty:
+                continue
+            is_all_silence=True
+            audio_frames = []
+            for _ in range(batch_size*2):
+                frame,type = audio_out_queue.get()
+                audio_frames.append((frame,type))
+                if type==0:
+                    is_all_silence=False
+            if is_all_silence:
+                for i in range(batch_size):
+                    res_frame_queue.put((None,__mirror_index(length,index),audio_frames[i*2:i*2+2]))
+                    index = index + 1
+            else:
+                # print('infer=======')
+                t=time.perf_counter()
+                whisper_batch = np.stack(whisper_chunks)
+                latent_batch = []
+                for i in range(batch_size):
+                    idx = __mirror_index(length,index+i)
+                    latent = input_latent_list_cycle[idx]
+                    latent_batch.append(latent)
+                latent_batch = torch.cat(latent_batch, dim=0)
+                
+                # for i, (whisper_batch,latent_batch) in enumerate(gen):
+                audio_feature_batch = torch.from_numpy(whisper_batch)
+                audio_feature_batch = audio_feature_batch.to(device=unet.device,
+                                                                dtype=unet.model.dtype)
+                audio_feature_batch = pe(audio_feature_batch)
+                latent_batch = latent_batch.to(dtype=unet.model.dtype)
+                # print('prepare time:',time.perf_counter()-t)
+                # t=time.perf_counter()
+
+                pred_latents = unet.model(latent_batch, 
+                                            timesteps, 
+                                            encoder_hidden_states=audio_feature_batch).sample
+                # print('unet time:',time.perf_counter()-t)
+                # t=time.perf_counter()
+                recon = vae.decode_latents(pred_latents)
+                # print('vae time:',time.perf_counter()-t)
+                #print('diffusion len=',len(recon))
+                counttime += (time.perf_counter() - t)
+                count += batch_size
+                #_totalframe += 1
+                if count>=100:
+                    print(f"------actual avg infer fps:{count/counttime:.4f}")
+                    count=0
+                    counttime=0
+                for i,res_frame in enumerate(recon):
+                    #self.__pushmedia(res_frame,loop,audio_track,video_track)
+                    res_frame_queue.put((res_frame,__mirror_index(length,index),audio_frames[i*2:i*2+2]))
+                    index = index + 1
+                #print('total batch time:',time.perf_counter()-starttime)            
+        else:
+            time.sleep(1)
+    print('musereal inference processor stop')
+
+@torch.no_grad()
+class MuseReal(BaseReal):
+    def __init__(self, opt):
+        super().__init__(opt)
+        #self.opt = opt # shared with the trainer's opt to support in-place modification of rendering parameters.
+        self.W = opt.W
+        self.H = opt.H
+
+        self.fps = opt.fps # 20 ms per frame
+
+        #### musetalk
+        self.avatar_id = opt.avatar_id
+        self.video_path = '' #video_path
+        self.bbox_shift = opt.bbox_shift
+        self.avatar_path = f"./data/avatars/{self.avatar_id}"
+        self.full_imgs_path = f"{self.avatar_path}/full_imgs" 
+        self.coords_path = f"{self.avatar_path}/coords.pkl"
+        self.latents_out_path= f"{self.avatar_path}/latents.pt"
+        self.video_out_path = f"{self.avatar_path}/vid_output/"
+        self.mask_out_path =f"{self.avatar_path}/mask"
+        self.mask_coords_path =f"{self.avatar_path}/mask_coords.pkl"
+        self.avatar_info_path = f"{self.avatar_path}/avator_info.json"
+        self.avatar_info = {
+            "avatar_id":self.avatar_id,
+            "video_path":self.video_path,
+            "bbox_shift":self.bbox_shift   
+        }
+        self.batch_size = opt.batch_size
+        self.idx = 0
+        self.res_frame_queue = mp.Queue(self.batch_size*2)
+        self.__loadmodels()
+        self.__loadavatar()
+
+        self.asr = MuseASR(opt,self,self.audio_processor)
+        self.asr.warm_up()
+        #self.__warm_up()
+        
+        self.render_event = mp.Event()
+        mp.Process(target=inference, args=(self.render_event,self.batch_size,self.latents_out_path,
+                                           self.asr.feat_queue,self.asr.output_queue,self.res_frame_queue,
+                                           )).start() #self.vae, self.unet, self.pe,self.timesteps
+
+    def __loadmodels(self):
+        # load model weights
+        self.audio_processor= load_audio_model()
+        # self.audio_processor, self.vae, self.unet, self.pe = load_all_model()
+        # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # self.timesteps = torch.tensor([0], device=device)
+        # self.pe = self.pe.half()
+        # self.vae.vae = self.vae.vae.half()
+        # self.unet.model = self.unet.model.half()
+
+    def __loadavatar(self):
+        #self.input_latent_list_cycle = torch.load(self.latents_out_path)
+        with open(self.coords_path, 'rb') as f:
+            self.coord_list_cycle = pickle.load(f)
+        input_img_list = glob.glob(os.path.join(self.full_imgs_path, '*.[jpJP][pnPN]*[gG]'))
+        input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+        self.frame_list_cycle = read_imgs(input_img_list)
+        with open(self.mask_coords_path, 'rb') as f:
+            self.mask_coords_list_cycle = pickle.load(f)
+        input_mask_list = glob.glob(os.path.join(self.mask_out_path, '*.[jpJP][pnPN]*[gG]'))
+        input_mask_list = sorted(input_mask_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+        self.mask_list_cycle = read_imgs(input_mask_list)
+    
+
+    def __mirror_index(self, index):
+        size = len(self.coord_list_cycle)
+        turn = index // size
+        res = index % size
+        if turn % 2 == 0:
+            return res
+        else:
+            return size - res - 1  
+
+    def __warm_up(self): 
+        self.asr.run_step()
+        whisper_chunks = self.asr.get_next_feat()
+        whisper_batch = np.stack(whisper_chunks)
+        latent_batch = []
+        for i in range(self.batch_size):
+            idx = self.__mirror_index(self.idx+i)
+            latent = self.input_latent_list_cycle[idx]
+            latent_batch.append(latent)
+        latent_batch = torch.cat(latent_batch, dim=0)
+        print('infer=======')
+        # for i, (whisper_batch,latent_batch) in enumerate(gen):
+        audio_feature_batch = torch.from_numpy(whisper_batch)
+        audio_feature_batch = audio_feature_batch.to(device=self.unet.device,
+                                                        dtype=self.unet.model.dtype)
+        audio_feature_batch = self.pe(audio_feature_batch)
+        latent_batch = latent_batch.to(dtype=self.unet.model.dtype)
+
+        pred_latents = self.unet.model(latent_batch, 
+                                    self.timesteps, 
+                                    encoder_hidden_states=audio_feature_batch).sample
+        recon = self.vae.decode_latents(pred_latents)
+      
+
+    def process_frames(self,quit_event,loop=None,audio_track=None,video_track=None):
+        
+        while not quit_event.is_set():
+            try:
+                res_frame,idx,audio_frames = self.res_frame_queue.get(block=True, timeout=1)
+            except queue.Empty:
+                continue
+            if audio_frames[0][1]!=0 and audio_frames[1][1]!=0: #全为静音数据，只需要取fullimg
+                self.speaking = False
+                audiotype = audio_frames[0][1]
+                if self.custom_index.get(audiotype) is not None: #有自定义视频
+                    mirindex = self.mirror_index(len(self.custom_img_cycle[audiotype]),self.custom_index[audiotype])
+                    combine_frame = self.custom_img_cycle[audiotype][mirindex]
+                    self.custom_index[audiotype] += 1
+                    # if not self.custom_opt[audiotype].loop and self.custom_index[audiotype]>=len(self.custom_img_cycle[audiotype]):
+                    #     self.curr_state = 1  #当前视频不循环播放，切换到静音状态
+                else:
+                    combine_frame = self.frame_list_cycle[idx]
+            else:
+                self.speaking = True
+                bbox = self.coord_list_cycle[idx]
+                ori_frame = copy.deepcopy(self.frame_list_cycle[idx])
+                x1, y1, x2, y2 = bbox
+                try:
+                    res_frame = cv2.resize(res_frame.astype(np.uint8),(x2-x1,y2-y1))
+                except:
+                    continue
+                mask = self.mask_list_cycle[idx]
+                mask_crop_box = self.mask_coords_list_cycle[idx]
+                #combine_frame = get_image(ori_frame,res_frame,bbox)
+                #t=time.perf_counter()
+                combine_frame = get_image_blending(ori_frame,res_frame,bbox,mask,mask_crop_box)
+                #print('blending time:',time.perf_counter()-t)
+
+            image = combine_frame #(outputs['image'] * 255).astype(np.uint8)
+            new_frame = VideoFrame.from_ndarray(image, format="bgr24")
+            asyncio.run_coroutine_threadsafe(video_track._queue.put(new_frame), loop)
+            if self.recording:
+                self.recordq_video.put(new_frame)  
+
+            for audio_frame in audio_frames:
+                frame,type = audio_frame
+                frame = (frame * 32767).astype(np.int16)
+                new_frame = AudioFrame(format='s16', layout='mono', samples=frame.shape[0])
+                new_frame.planes[0].update(frame.tobytes())
+                new_frame.sample_rate=16000
+                # if audio_track._queue.qsize()>10:
+                #     time.sleep(0.1)
+                asyncio.run_coroutine_threadsafe(audio_track._queue.put(new_frame), loop)
+                if self.recording:
+                    self.recordq_audio.put(new_frame)
+        print('musereal process_frames thread stop') 
+            
+    def render(self,quit_event,loop=None,audio_track=None,video_track=None):
+        #if self.opt.asr:
+        #     self.asr.warm_up()
+
+        self.tts.render(quit_event)
+        self.init_customindex()
+        process_thread = Thread(target=self.process_frames, args=(quit_event,loop,audio_track,video_track))
+        process_thread.start()
+
+        self.render_event.set() #start infer process render
+        count=0
+        totaltime=0
+        _starttime=time.perf_counter()
+        #_totalframe=0
+        while not quit_event.is_set(): #todo
+            # update texture every frame
+            # audio stream thread...
+            t = time.perf_counter()
+            self.asr.run_step()
+            #self.test_step(loop,audio_track,video_track)
+            # totaltime += (time.perf_counter() - t)
+            # count += self.opt.batch_size
+            # if count>=100:
+            #     print(f"------actual avg infer fps:{count/totaltime:.4f}")
+            #     count=0
+            #     totaltime=0
+            if video_track._queue.qsize()>=1.5*self.opt.batch_size:
+                print('sleep qsize=',video_track._queue.qsize())
+                time.sleep(0.04*video_track._queue.qsize()*0.8)
+            # if video_track._queue.qsize()>=5:
+            #     print('sleep qsize=',video_track._queue.qsize())
+            #     time.sleep(0.04*video_track._queue.qsize()*0.8)
+                
+            # delay = _starttime+_totalframe*0.04-time.perf_counter() #40ms
+            # if delay > 0:
+            #     time.sleep(delay)
+        self.render_event.clear() #end infer process render
+        print('musereal thread stop')
+            

musetalk/models/unet.py

@@ -0,0 +1,47 @@
+import torch
+import torch.nn as nn
+import math
+import json
+
+from diffusers import UNet2DConditionModel
+import sys
+import time
+import numpy as np
+import os
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model=384, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        b, seq_len, d_model = x.size()
+        pe = self.pe[:, :seq_len, :]
+        x = x + pe.to(x.device)
+        return x
+    
+class UNet():
+    def __init__(self, 
+                 unet_config,
+                 model_path,
+                 use_float16=False,
+        ):
+        with open(unet_config, 'r') as f:
+            unet_config = json.load(f)
+        self.model = UNet2DConditionModel(**unet_config)
+        self.pe = PositionalEncoding(d_model=384)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        weights = torch.load(model_path) if torch.cuda.is_available() else torch.load(model_path, map_location=self.device)
+        self.model.load_state_dict(weights)
+        if use_float16:
+            self.model = self.model.half()
+        self.model.to(self.device)
+    
+if __name__ == "__main__":
+    unet = UNet()

musetalk/models/vae.py

@@ -0,0 +1,148 @@
+from diffusers import AutoencoderKL
+import torch
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+import cv2
+import numpy as np
+from PIL import Image
+import os
+
+class VAE():
+    """
+    VAE (Variational Autoencoder) class for image processing.
+    """
+
+    def __init__(self, model_path="./models/sd-vae-ft-mse/", resized_img=256, use_float16=False):
+        """
+        Initialize the VAE instance.
+
+        :param model_path: Path to the trained model.
+        :param resized_img: The size to which images are resized.
+        :param use_float16: Whether to use float16 precision.
+        """
+        self.model_path = model_path
+        self.vae = AutoencoderKL.from_pretrained(self.model_path)
+
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.vae.to(self.device)
+
+        if use_float16:
+            self.vae = self.vae.half()
+            self._use_float16 = True
+        else:
+            self._use_float16 = False
+
+        self.scaling_factor = self.vae.config.scaling_factor
+        self.transform = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+        self._resized_img = resized_img
+        self._mask_tensor = self.get_mask_tensor()
+        
+    def get_mask_tensor(self):
+        """
+        Creates a mask tensor for image processing.
+        :return: A mask tensor.
+        """
+        mask_tensor = torch.zeros((self._resized_img,self._resized_img))
+        mask_tensor[:self._resized_img//2,:] = 1
+        mask_tensor[mask_tensor< 0.5] = 0
+        mask_tensor[mask_tensor>= 0.5] = 1
+        return mask_tensor
+            
+    def preprocess_img(self,img_name,half_mask=False):
+        """
+        Preprocess an image for the VAE.
+
+        :param img_name: The image file path or a list of image file paths.
+        :param half_mask: Whether to apply a half mask to the image.
+        :return: A preprocessed image tensor.
+        """
+        window = []
+        if isinstance(img_name, str):
+            window_fnames = [img_name]
+            for fname in window_fnames:
+                img = cv2.imread(fname)
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+                img = cv2.resize(img, (self._resized_img, self._resized_img),
+                                     interpolation=cv2.INTER_LANCZOS4)
+                window.append(img)
+        else:
+            img = cv2.cvtColor(img_name, cv2.COLOR_BGR2RGB)
+            window.append(img)
+            
+        x = np.asarray(window) / 255.
+        x = np.transpose(x, (3, 0, 1, 2))
+        x = torch.squeeze(torch.FloatTensor(x))
+        if half_mask:
+            x = x * (self._mask_tensor>0.5)
+        x = self.transform(x)
+        
+        x = x.unsqueeze(0) # [1, 3, 256, 256] torch tensor
+        x = x.to(self.vae.device)
+
+        return x
+
+    def encode_latents(self,image):
+        """
+        Encode an image into latent variables.
+
+        :param image: The image tensor to encode.
+        :return: The encoded latent variables.
+        """
+        with torch.no_grad():
+            init_latent_dist = self.vae.encode(image.to(self.vae.dtype)).latent_dist
+        init_latents = self.scaling_factor * init_latent_dist.sample()
+        return init_latents
+    
+    def decode_latents(self, latents):
+        """
+        Decode latent variables back into an image.
+        :param latents: The latent variables to decode.
+        :return: A NumPy array representing the decoded image.
+        """
+        latents = (1/  self.scaling_factor) * latents
+        image = self.vae.decode(latents.to(self.vae.dtype)).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.detach().cpu().permute(0, 2, 3, 1).float().numpy()
+        image = (image * 255).round().astype("uint8")
+        image = image[...,::-1] # RGB to BGR
+        return image
+    
+    def get_latents_for_unet(self,img):
+        """
+        Prepare latent variables for a U-Net model.
+        :param img: The image to process.
+        :return: A concatenated tensor of latents for U-Net input.
+        """
+        
+        ref_image = self.preprocess_img(img,half_mask=True) # [1, 3, 256, 256] RGB, torch tensor
+        masked_latents = self.encode_latents(ref_image) # [1, 4, 32, 32], torch tensor
+        ref_image = self.preprocess_img(img,half_mask=False) # [1, 3, 256, 256] RGB, torch tensor
+        ref_latents = self.encode_latents(ref_image) # [1, 4, 32, 32], torch tensor
+        latent_model_input = torch.cat([masked_latents, ref_latents], dim=1)
+        return latent_model_input
+
+if __name__ == "__main__":
+    vae_mode_path = "./models/sd-vae-ft-mse/"
+    vae = VAE(model_path = vae_mode_path,use_float16=False)
+    img_path = "./results/sun001_crop/00000.png"
+    
+    crop_imgs_path = "./results/sun001_crop/"
+    latents_out_path = "./results/latents/"
+    if not os.path.exists(latents_out_path):
+        os.mkdir(latents_out_path)
+
+    files = os.listdir(crop_imgs_path)
+    files.sort()
+    files = [file for file in files if file.split(".")[-1] == "png"]
+
+    for file in files:
+        index = file.split(".")[0]
+        img_path = crop_imgs_path + file
+        latents = vae.get_latents_for_unet(img_path)
+        print(img_path,"latents",latents.size())
+        #torch.save(latents,os.path.join(latents_out_path,index+".pt"))
+        #reload_tensor = torch.load('tensor.pt')
+        #print(reload_tensor.size())
+        
+
+    

musetalk/simple_musetalk.py

@@ -0,0 +1,348 @@
+import argparse
+import glob
+import json
+import os
+import pickle
+import shutil
+
+import cv2
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+from diffusers import AutoencoderKL
+from face_alignment import NetworkSize
+from mmpose.apis import inference_topdown, init_model
+from mmpose.structures import merge_data_samples
+from tqdm import tqdm
+
+try:
+    from utils.face_parsing import FaceParsing
+except ModuleNotFoundError:
+    from musetalk.utils.face_parsing import FaceParsing
+
+
+def video2imgs(vid_path, save_path, ext='.png', cut_frame=10000000):
+    cap = cv2.VideoCapture(vid_path)
+    count = 0
+    while True:
+        if count > cut_frame:
+            break
+        ret, frame = cap.read()
+        if ret:
+            cv2.imwrite(f"{save_path}/{count:08d}.png", frame)
+            count += 1
+        else:
+            break
+
+
+def read_imgs(img_list):
+    frames = []
+    print('reading images...')
+    for img_path in tqdm(img_list):
+        frame = cv2.imread(img_path)
+        frames.append(frame)
+    return frames
+
+
+def get_landmark_and_bbox(img_list, upperbondrange=0):
+    frames = read_imgs(img_list)
+    batch_size_fa = 1
+    batches = [frames[i:i + batch_size_fa] for i in range(0, len(frames), batch_size_fa)]
+    coords_list = []
+    landmarks = []
+    if upperbondrange != 0:
+        print('get key_landmark and face bounding boxes with the bbox_shift:', upperbondrange)
+    else:
+        print('get key_landmark and face bounding boxes with the default value')
+    average_range_minus = []
+    average_range_plus = []
+    coord_placeholder = (0.0, 0.0, 0.0, 0.0)
+    for fb in tqdm(batches):
+        results = inference_topdown(model, np.asarray(fb)[0])
+        results = merge_data_samples(results)
+        keypoints = results.pred_instances.keypoints
+        face_land_mark = keypoints[0][23:91]
+        face_land_mark = face_land_mark.astype(np.int32)
+
+        # get bounding boxes by face detetion
+        bbox = fa.get_detections_for_batch(np.asarray(fb))
+
+        # adjust the bounding box refer to landmark
+        # Add the bounding box to a tuple and append it to the coordinates list
+        for j, f in enumerate(bbox):
+            if f is None:  # no face in the image
+                coords_list += [coord_placeholder]
+                continue
+
+            half_face_coord = face_land_mark[29]  # np.mean([face_land_mark[28], face_land_mark[29]], axis=0)
+            range_minus = (face_land_mark[30] - face_land_mark[29])[1]
+            range_plus = (face_land_mark[29] - face_land_mark[28])[1]
+            average_range_minus.append(range_minus)
+            average_range_plus.append(range_plus)
+            if upperbondrange != 0:
+                half_face_coord[1] = upperbondrange + half_face_coord[1]  # 手动调整  + 向下（偏29）  - 向上（偏28）
+            half_face_dist = np.max(face_land_mark[:, 1]) - half_face_coord[1]
+            upper_bond = half_face_coord[1] - half_face_dist
+
+            f_landmark = (
+                np.min(face_land_mark[:, 0]), int(upper_bond), np.max(face_land_mark[:, 0]),
+                np.max(face_land_mark[:, 1]))
+            x1, y1, x2, y2 = f_landmark
+
+            if y2 - y1 <= 0 or x2 - x1 <= 0 or x1 < 0:  # if the landmark bbox is not suitable, reuse the bbox
+                coords_list += [f]
+                w, h = f[2] - f[0], f[3] - f[1]
+                print("error bbox:", f)
+            else:
+                coords_list += [f_landmark]
+    return coords_list, frames
+
+
+class FaceAlignment:
+    def __init__(self, landmarks_type, network_size=NetworkSize.LARGE,
+                 device='cuda', flip_input=False, face_detector='sfd', verbose=False):
+        self.device = device
+        self.flip_input = flip_input
+        self.landmarks_type = landmarks_type
+        self.verbose = verbose
+
+        network_size = int(network_size)
+        if 'cuda' in device:
+            torch.backends.cudnn.benchmark = True
+            #             torch.backends.cuda.matmul.allow_tf32 = False
+            #             torch.backends.cudnn.benchmark = True
+            #             torch.backends.cudnn.deterministic = False
+            #             torch.backends.cudnn.allow_tf32 = True
+            print('cuda start')
+
+        # Get the face detector
+        face_detector_module = __import__('face_detection.detection.' + face_detector,
+                                          globals(), locals(), [face_detector], 0)
+
+        self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose)
+
+    def get_detections_for_batch(self, images):
+        images = images[..., ::-1]
+        detected_faces = self.face_detector.detect_from_batch(images.copy())
+        results = []
+
+        for i, d in enumerate(detected_faces):
+            if len(d) == 0:
+                results.append(None)
+                continue
+            d = d[0]
+            d = np.clip(d, 0, None)
+
+            x1, y1, x2, y2 = map(int, d[:-1])
+            results.append((x1, y1, x2, y2))
+        return results
+
+
+def get_mask_tensor():
+    """
+    Creates a mask tensor for image processing.
+    :return: A mask tensor.
+    """
+    mask_tensor = torch.zeros((256, 256))
+    mask_tensor[:256 // 2, :] = 1
+    mask_tensor[mask_tensor < 0.5] = 0
+    mask_tensor[mask_tensor >= 0.5] = 1
+    return mask_tensor
+
+
+def preprocess_img(img_name, half_mask=False):
+    window = []
+    if isinstance(img_name, str):
+        window_fnames = [img_name]
+        for fname in window_fnames:
+            img = cv2.imread(fname)
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            img = cv2.resize(img, (256, 256),
+                             interpolation=cv2.INTER_LANCZOS4)
+            window.append(img)
+    else:
+        img = cv2.cvtColor(img_name, cv2.COLOR_BGR2RGB)
+        window.append(img)
+    x = np.asarray(window) / 255.
+    x = np.transpose(x, (3, 0, 1, 2))
+    x = torch.squeeze(torch.FloatTensor(x))
+    if half_mask:
+        x = x * (get_mask_tensor() > 0.5)
+    normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+    x = normalize(x)
+    x = x.unsqueeze(0)  # [1, 3, 256, 256] torch tensor
+    x = x.to(device)
+    return x
+
+
+def encode_latents(image):
+    with torch.no_grad():
+        init_latent_dist = vae.encode(image.to(vae.dtype)).latent_dist
+    init_latents = vae.config.scaling_factor * init_latent_dist.sample()
+    return init_latents
+
+
+def get_latents_for_unet(img):
+    ref_image = preprocess_img(img, half_mask=True)  # [1, 3, 256, 256] RGB, torch tensor
+    masked_latents = encode_latents(ref_image)  # [1, 4, 32, 32], torch tensor
+    ref_image = preprocess_img(img, half_mask=False)  # [1, 3, 256, 256] RGB, torch tensor
+    ref_latents = encode_latents(ref_image)  # [1, 4, 32, 32], torch tensor
+    latent_model_input = torch.cat([masked_latents, ref_latents], dim=1)
+    return latent_model_input
+
+
+def get_crop_box(box, expand):
+    x, y, x1, y1 = box
+    x_c, y_c = (x + x1) // 2, (y + y1) // 2
+    w, h = x1 - x, y1 - y
+    s = int(max(w, h) // 2 * expand)
+    crop_box = [x_c - s, y_c - s, x_c + s, y_c + s]
+    return crop_box, s
+
+
+def face_seg(image):
+    seg_image = fp(image)
+    if seg_image is None:
+        print("error, no person_segment")
+        return None
+
+    seg_image = seg_image.resize(image.size)
+    return seg_image
+
+
+def get_image_prepare_material(image, face_box, upper_boundary_ratio=0.5, expand=1.2):
+    body = Image.fromarray(image[:, :, ::-1])
+
+    x, y, x1, y1 = face_box
+    # print(x1-x,y1-y)
+    crop_box, s = get_crop_box(face_box, expand)
+    x_s, y_s, x_e, y_e = crop_box
+
+    face_large = body.crop(crop_box)
+    ori_shape = face_large.size
+
+    mask_image = face_seg(face_large)
+    mask_small = mask_image.crop((x - x_s, y - y_s, x1 - x_s, y1 - y_s))
+    mask_image = Image.new('L', ori_shape, 0)
+    mask_image.paste(mask_small, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))
+
+    # keep upper_boundary_ratio of talking area
+    width, height = mask_image.size
+    top_boundary = int(height * upper_boundary_ratio)
+    modified_mask_image = Image.new('L', ori_shape, 0)
+    modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))
+
+    blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
+    mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
+    return mask_array, crop_box
+
+
+##todo 简单根据文件后缀判断  要更精确的可以自己修改 使用 magic
+def is_video_file(file_path):
+    video_exts = ['.mp4', '.mkv', '.flv', '.avi', '.mov']  # 这里列出了一些常见的视频文件扩展名，可以根据需要添加更多
+    file_ext = os.path.splitext(file_path)[1].lower()  # 获取文件扩展名并转换为小写
+    return file_ext in video_exts
+
+
+def create_dir(dir_path):
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+
+def create_musetalk_human(file, avatar_id):
+    # 保存文件设置 可以不动
+    save_path = os.path.join(current_dir, f'../data/avatars/avator_{avatar_id}')
+    save_full_path = os.path.join(current_dir, f'../data/avatars/avator_{avatar_id}/full_imgs')
+    create_dir(save_path)
+    create_dir(save_full_path)
+    mask_out_path = os.path.join(current_dir, f'../data/avatars/avator_{avatar_id}/mask')
+    create_dir(mask_out_path)
+
+    # 模型
+    mask_coords_path = os.path.join(current_dir, f'{save_path}/mask_coords.pkl')
+    coords_path = os.path.join(current_dir, f'{save_path}/coords.pkl')
+    latents_out_path = os.path.join(current_dir, f'{save_path}/latents.pt')
+
+    with open(os.path.join(current_dir, f'{save_path}/avator_info.json'), "w") as f:
+        json.dump({
+            "avatar_id": avatar_id,
+            "video_path": file,
+            "bbox_shift": 5
+        }, f)
+
+    if os.path.isfile(file):
+        if is_video_file(file):
+            video2imgs(file, save_full_path, ext='png')
+        else:
+            shutil.copyfile(file, f"{save_full_path}/{os.path.basename(file)}")
+    else:
+        files = os.listdir(file)
+        files.sort()
+        files = [file for file in files if file.split(".")[-1] == "png"]
+        for filename in files:
+            shutil.copyfile(f"{file}/{filename}", f"{save_full_path}/{filename}")
+    input_img_list = sorted(glob.glob(os.path.join(save_full_path, '*.[jpJP][pnPN]*[gG]')))
+    print("extracting landmarks...")
+    coord_list, frame_list = get_landmark_and_bbox(input_img_list, 5)
+    input_latent_list = []
+    idx = -1
+    # maker if the bbox is not sufficient
+    coord_placeholder = (0.0, 0.0, 0.0, 0.0)
+    for bbox, frame in zip(coord_list, frame_list):
+        idx = idx + 1
+        if bbox == coord_placeholder:
+            continue
+        x1, y1, x2, y2 = bbox
+        crop_frame = frame[y1:y2, x1:x2]
+        resized_crop_frame = cv2.resize(crop_frame, (256, 256), interpolation=cv2.INTER_LANCZOS4)
+        latents = get_latents_for_unet(resized_crop_frame)
+        input_latent_list.append(latents)
+
+    frame_list_cycle = frame_list #+ frame_list[::-1]
+    coord_list_cycle = coord_list #+ coord_list[::-1]
+    input_latent_list_cycle = input_latent_list #+ input_latent_list[::-1]
+    mask_coords_list_cycle = []
+    mask_list_cycle = []
+    for i, frame in enumerate(tqdm(frame_list_cycle)):
+        cv2.imwrite(f"{save_full_path}/{str(i).zfill(8)}.png", frame)
+        face_box = coord_list_cycle[i]
+        mask, crop_box = get_image_prepare_material(frame, face_box)
+        cv2.imwrite(f"{mask_out_path}/{str(i).zfill(8)}.png", mask)
+        mask_coords_list_cycle += [crop_box]
+        mask_list_cycle.append(mask)
+
+    with open(mask_coords_path, 'wb') as f:
+        pickle.dump(mask_coords_list_cycle, f)
+
+    with open(coords_path, 'wb') as f:
+        pickle.dump(coord_list_cycle, f)
+    torch.save(input_latent_list_cycle, os.path.join(latents_out_path))
+
+
+# initialize the mmpose model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+fa = FaceAlignment(1, flip_input=False, device=device)
+config_file = os.path.join(current_dir, 'utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py')
+checkpoint_file = os.path.abspath(os.path.join(current_dir, '../models/dwpose/dw-ll_ucoco_384.pth'))
+model = init_model(config_file, checkpoint_file, device=device)
+vae = AutoencoderKL.from_pretrained(os.path.abspath(os.path.join(current_dir, '../models/sd-vae-ft-mse')))
+vae.to(device)
+fp = FaceParsing(os.path.abspath(os.path.join(current_dir, '../models/face-parse-bisent/resnet18-5c106cde.pth')),
+                 os.path.abspath(os.path.join(current_dir, '../models/face-parse-bisent/79999_iter.pth')))
+if __name__ == '__main__':
+    # 视频文件地址
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--file",
+                        type=str,
+                        default=r'D:\ok\00000000.png',
+                        )
+    parser.add_argument("--avatar_id",
+                        type=str,
+                        default='3',
+                        )
+    args = parser.parse_args()
+    create_musetalk_human(args.file, args.avatar_id)

musetalk/utils/__init__.py

@@ -0,0 +1,5 @@
+import sys
+from os.path import abspath, dirname
+current_dir = dirname(abspath(__file__))
+parent_dir = dirname(current_dir)
+sys.path.append(parent_dir+'/utils')

musetalk/utils/blending.py

@@ -0,0 +1,125 @@
+from PIL import Image
+import numpy as np
+import cv2
+from face_parsing import FaceParsing
+import copy
+
+fp = FaceParsing()
+
+def get_crop_box(box, expand):
+    x, y, x1, y1 = box
+    x_c, y_c = (x+x1)//2, (y+y1)//2
+    w, h = x1-x, y1-y
+    s = int(max(w, h)//2*expand)
+    crop_box = [x_c-s, y_c-s, x_c+s, y_c+s]
+    return crop_box, s
+
+def face_seg(image):
+    seg_image = fp(image)
+    if seg_image is None:
+        print("error, no person_segment")
+        return None
+
+    seg_image = seg_image.resize(image.size)
+    return seg_image
+
+def get_image(image,face,face_box,upper_boundary_ratio = 0.5,expand=1.2):
+    #print(image.shape)
+    #print(face.shape)
+    
+    body = Image.fromarray(image[:,:,::-1])
+    face = Image.fromarray(face[:,:,::-1])
+
+    x, y, x1, y1 = face_box 
+    #print(x1-x,y1-y)
+    crop_box, s = get_crop_box(face_box, expand)
+    x_s, y_s, x_e, y_e = crop_box
+    face_position = (x, y)
+
+    face_large = body.crop(crop_box)
+    ori_shape = face_large.size
+
+    mask_image = face_seg(face_large)
+    mask_small = mask_image.crop((x-x_s, y-y_s, x1-x_s, y1-y_s))
+    mask_image = Image.new('L', ori_shape, 0)
+    mask_image.paste(mask_small, (x-x_s, y-y_s, x1-x_s, y1-y_s))
+
+    # keep upper_boundary_ratio of talking area
+    width, height = mask_image.size
+    top_boundary = int(height * upper_boundary_ratio)
+    modified_mask_image = Image.new('L', ori_shape, 0)
+    modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))
+
+    blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
+    mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
+    mask_image = Image.fromarray(mask_array)
+    
+    face_large.paste(face, (x-x_s, y-y_s, x1-x_s, y1-y_s))
+    body.paste(face_large, crop_box[:2], mask_image)
+    body = np.array(body)
+    return body[:,:,::-1]
+
+def get_image_prepare_material(image,face_box,upper_boundary_ratio = 0.5,expand=1.2):
+    body = Image.fromarray(image[:,:,::-1])
+
+    x, y, x1, y1 = face_box
+    #print(x1-x,y1-y)
+    crop_box, s = get_crop_box(face_box, expand)
+    x_s, y_s, x_e, y_e = crop_box
+
+    face_large = body.crop(crop_box)
+    ori_shape = face_large.size
+
+    mask_image = face_seg(face_large)
+    mask_small = mask_image.crop((x-x_s, y-y_s, x1-x_s, y1-y_s))
+    mask_image = Image.new('L', ori_shape, 0)
+    mask_image.paste(mask_small, (x-x_s, y-y_s, x1-x_s, y1-y_s))
+
+    # keep upper_boundary_ratio of talking area
+    width, height = mask_image.size
+    top_boundary = int(height * upper_boundary_ratio)
+    modified_mask_image = Image.new('L', ori_shape, 0)
+    modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))
+
+    blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
+    mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
+    return mask_array,crop_box
+
+# def get_image_blending(image,face,face_box,mask_array,crop_box):
+#     body = Image.fromarray(image[:,:,::-1])
+#     face = Image.fromarray(face[:,:,::-1])
+
+#     x, y, x1, y1 = face_box
+#     x_s, y_s, x_e, y_e = crop_box
+#     face_large = body.crop(crop_box)
+
+#     mask_image = Image.fromarray(mask_array)
+#     mask_image = mask_image.convert("L")
+#     face_large.paste(face, (x-x_s, y-y_s, x1-x_s, y1-y_s))
+#     body.paste(face_large, crop_box[:2], mask_image)
+#     body = np.array(body)
+#     return body[:,:,::-1]
+
+def get_image_blending(image,face,face_box,mask_array,crop_box):
+    body = image
+    x, y, x1, y1 = face_box
+    x_s, y_s, x_e, y_e = crop_box
+    face_large = copy.deepcopy(body[y_s:y_e, x_s:x_e])
+    face_large[y-y_s:y1-y_s, x-x_s:x1-x_s]=face
+
+    mask_image = cv2.cvtColor(mask_array,cv2.COLOR_BGR2GRAY)
+    mask_image = (mask_image/255).astype(np.float32)
+
+    # mask_not = cv2.bitwise_not(mask_array)
+    # prospect_tmp = cv2.bitwise_and(face_large, face_large, mask=mask_array)
+    # background_img = body[y_s:y_e, x_s:x_e]
+    # background_img = cv2.bitwise_and(background_img, background_img, mask=mask_not)
+    # body[y_s:y_e, x_s:x_e] = prospect_tmp + background_img
+
+    #print(mask_image.shape)
+    #print(cv2.minMaxLoc(mask_image))
+
+    body[y_s:y_e, x_s:x_e] = cv2.blendLinear(face_large,body[y_s:y_e, x_s:x_e],mask_image,1-mask_image)
+
+    #body.paste(face_large, crop_box[:2], mask_image)
+    return body

musetalk/utils/dwpose/default_runtime.py

@@ -0,0 +1,54 @@
+default_scope = 'mmpose'
+
+# hooks
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=10),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='PoseVisualizationHook', enable=False),
+    badcase=dict(
+        type='BadCaseAnalysisHook',
+        enable=False,
+        out_dir='badcase',
+        metric_type='loss',
+        badcase_thr=5))
+
+# custom hooks
+custom_hooks = [
+    # Synchronize model buffers such as running_mean and running_var in BN
+    # at the end of each epoch
+    dict(type='SyncBuffersHook')
+]
+
+# multi-processing backend
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+
+# visualizer
+vis_backends = [
+    dict(type='LocalVisBackend'),
+    # dict(type='TensorboardVisBackend'),
+    # dict(type='WandbVisBackend'),
+]
+visualizer = dict(
+    type='PoseLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+# logger
+log_processor = dict(
+    type='LogProcessor', window_size=50, by_epoch=True, num_digits=6)
+log_level = 'INFO'
+load_from = None
+resume = False
+
+# file I/O backend
+backend_args = dict(backend='local')
+
+# training/validation/testing progress
+train_cfg = dict(by_epoch=True)
+val_cfg = dict()
+test_cfg = dict()

musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py

@@ -0,0 +1,257 @@
+#_base_ = ['../../../_base_/default_runtime.py']
+_base_ = ['default_runtime.py']
+
+# runtime
+max_epochs = 270
+stage2_num_epochs = 30
+base_lr = 4e-3
+train_batch_size = 32
+val_batch_size = 32
+
+train_cfg = dict(max_epochs=max_epochs, val_interval=10)
+randomness = dict(seed=21)
+
+# optimizer
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
+    paramwise_cfg=dict(
+        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0e-5,
+        by_epoch=False,
+        begin=0,
+        end=1000),
+    dict(
+        # use cosine lr from 150 to 300 epoch
+        type='CosineAnnealingLR',
+        eta_min=base_lr * 0.05,
+        begin=max_epochs // 2,
+        end=max_epochs,
+        T_max=max_epochs // 2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# automatically scaling LR based on the actual training batch size
+auto_scale_lr = dict(base_batch_size=512)
+
+# codec settings
+codec = dict(
+    type='SimCCLabel',
+    input_size=(288, 384),
+    sigma=(6., 6.93),
+    simcc_split_ratio=2.0,
+    normalize=False,
+    use_dark=False)
+
+# model settings
+model = dict(
+    type='TopdownPoseEstimator',
+    data_preprocessor=dict(
+        type='PoseDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True),
+    backbone=dict(
+        _scope_='mmdet',
+        type='CSPNeXt',
+        arch='P5',
+        expand_ratio=0.5,
+        deepen_factor=1.,
+        widen_factor=1.,
+        out_indices=(4, ),
+        channel_attention=True,
+        norm_cfg=dict(type='SyncBN'),
+        act_cfg=dict(type='SiLU'),
+        init_cfg=dict(
+            type='Pretrained',
+            prefix='backbone.',
+            checkpoint='https://download.openmmlab.com/mmpose/v1/projects/'
+            'rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth'  # noqa: E501
+        )),
+    head=dict(
+        type='RTMCCHead',
+        in_channels=1024,
+        out_channels=133,
+        input_size=codec['input_size'],
+        in_featuremap_size=(9, 12),
+        simcc_split_ratio=codec['simcc_split_ratio'],
+        final_layer_kernel_size=7,
+        gau_cfg=dict(
+            hidden_dims=256,
+            s=128,
+            expansion_factor=2,
+            dropout_rate=0.,
+            drop_path=0.,
+            act_fn='SiLU',
+            use_rel_bias=False,
+            pos_enc=False),
+        loss=dict(
+            type='KLDiscretLoss',
+            use_target_weight=True,
+            beta=10.,
+            label_softmax=True),
+        decoder=codec),
+    test_cfg=dict(flip_test=True, ))
+
+# base dataset settings
+dataset_type = 'UBody2dDataset'
+data_mode = 'topdown'
+data_root = 'data/UBody/'
+
+backend_args = dict(backend='local')
+
+scenes = [
+    'Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 'TalkShow',
+    'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 'Singing',
+    'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference'
+]
+
+train_datasets = [
+    dict(
+        type='CocoWholeBodyDataset',
+        data_root='data/coco/',
+        data_mode=data_mode,
+        ann_file='annotations/coco_wholebody_train_v1.0.json',
+        data_prefix=dict(img='train2017/'),
+        pipeline=[])
+]
+
+for scene in scenes:
+    train_dataset = dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file=f'annotations/{scene}/train_annotations.json',
+        data_prefix=dict(img='images/'),
+        pipeline=[],
+        sample_interval=10)
+    train_datasets.append(train_dataset)
+
+# pipelines
+train_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform', scale_factor=[0.5, 1.5], rotate_factor=90),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=1.0),
+        ]),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+val_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='PackPoseInputs')
+]
+
+train_pipeline_stage2 = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform',
+        shift_factor=0.,
+        scale_factor=[0.5, 1.5],
+        rotate_factor=90),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=0.5),
+        ]),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+
+# data loaders
+train_dataloader = dict(
+    batch_size=train_batch_size,
+    num_workers=10,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='CombinedDataset',
+        metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
+        datasets=train_datasets,
+        pipeline=train_pipeline,
+        test_mode=False,
+    ))
+
+val_dataloader = dict(
+    batch_size=val_batch_size,
+    num_workers=10,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
+    dataset=dict(
+        type='CocoWholeBodyDataset',
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json',
+        bbox_file='data/coco/person_detection_results/'
+        'COCO_val2017_detections_AP_H_56_person.json',
+        data_prefix=dict(img='coco/val2017/'),
+        test_mode=True,
+        pipeline=val_pipeline,
+    ))
+test_dataloader = val_dataloader
+
+# hooks
+default_hooks = dict(
+    checkpoint=dict(
+        save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0002,
+        update_buffers=True,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - stage2_num_epochs,
+        switch_pipeline=train_pipeline_stage2)
+]
+
+# evaluators
+val_evaluator = dict(
+    type='CocoWholeBodyMetric',
+    ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json')
+test_evaluator = val_evaluator

musetalk/utils/face_detection/README.md

@@ -0,0 +1 @@
+The code for Face Detection in this folder has been taken from the wonderful [face_alignment](https://github.com/1adrianb/face-alignment) repository. This has been modified to take batches of faces at a time. 

musetalk/utils/face_detection/__init__.py

@@ -0,0 +1,7 @@
+# -*- coding: utf-8 -*-
+
+__author__ = """Adrian Bulat"""
+__email__ = 'adrian.bulat@nottingham.ac.uk'
+__version__ = '1.0.1'
+
+from .api import FaceAlignment, LandmarksType, NetworkSize, YOLOv8_face

musetalk/utils/face_detection/api.py

@@ -0,0 +1,240 @@
+from __future__ import print_function
+import os
+import torch
+from torch.utils.model_zoo import load_url
+from enum import Enum
+import numpy as np
+import cv2
+try:
+    import urllib.request as request_file
+except BaseException:
+    import urllib as request_file
+
+from .models import FAN, ResNetDepth
+from .utils import *
+
+
+class LandmarksType(Enum):
+    """Enum class defining the type of landmarks to detect.
+
+    ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face
+    ``_2halfD`` - this points represent the projection of the 3D points into 3D
+    ``_3D`` - detect the points ``(x,y,z)``` in a 3D space
+
+    """
+    _2D = 1
+    _2halfD = 2
+    _3D = 3
+
+
+class NetworkSize(Enum):
+    # TINY = 1
+    # SMALL = 2
+    # MEDIUM = 3
+    LARGE = 4
+
+    def __new__(cls, value):
+        member = object.__new__(cls)
+        member._value_ = value
+        return member
+
+    def __int__(self):
+        return self.value
+
+
+
+class FaceAlignment:
+    def __init__(self, landmarks_type, network_size=NetworkSize.LARGE,
+                 device='cuda', flip_input=False, face_detector='sfd', verbose=False):
+        self.device = device
+        self.flip_input = flip_input
+        self.landmarks_type = landmarks_type
+        self.verbose = verbose
+
+        network_size = int(network_size)
+
+        if 'cuda' in device:
+            torch.backends.cudnn.benchmark = True
+#             torch.backends.cuda.matmul.allow_tf32 = False
+#             torch.backends.cudnn.benchmark = True
+#             torch.backends.cudnn.deterministic = False
+#             torch.backends.cudnn.allow_tf32 = True
+            print('cuda start')
+
+
+        # Get the face detector
+        face_detector_module = __import__('face_detection.detection.' + face_detector,
+                                          globals(), locals(), [face_detector], 0)
+        
+        self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose)
+
+    def get_detections_for_batch(self, images):
+        images = images[..., ::-1]
+        detected_faces = self.face_detector.detect_from_batch(images.copy())
+        results = []
+
+        for i, d in enumerate(detected_faces):
+            if len(d) == 0:
+                results.append(None)
+                continue
+            d = d[0]
+            d = np.clip(d, 0, None)
+            
+            x1, y1, x2, y2 = map(int, d[:-1])
+            results.append((x1, y1, x2, y2))
+
+        return results
+    
+    
+class YOLOv8_face:
+    def __init__(self, path = 'face_detection/weights/yolov8n-face.onnx', conf_thres=0.2, iou_thres=0.5):
+        self.conf_threshold = conf_thres
+        self.iou_threshold = iou_thres
+        self.class_names = ['face']
+        self.num_classes = len(self.class_names)
+        # Initialize model
+        self.net = cv2.dnn.readNet(path)
+        self.input_height = 640
+        self.input_width = 640
+        self.reg_max = 16
+
+        self.project = np.arange(self.reg_max)
+        self.strides = (8, 16, 32)
+        self.feats_hw = [(math.ceil(self.input_height / self.strides[i]), math.ceil(self.input_width / self.strides[i])) for i in range(len(self.strides))]
+        self.anchors = self.make_anchors(self.feats_hw)
+
+    def make_anchors(self, feats_hw, grid_cell_offset=0.5):
+        """Generate anchors from features."""
+        anchor_points = {}
+        for i, stride in enumerate(self.strides):
+            h,w = feats_hw[i]
+            x = np.arange(0, w) + grid_cell_offset  # shift x
+            y = np.arange(0, h) + grid_cell_offset  # shift y
+            sx, sy = np.meshgrid(x, y)
+            # sy, sx = np.meshgrid(y, x)
+            anchor_points[stride] = np.stack((sx, sy), axis=-1).reshape(-1, 2)
+        return anchor_points
+
+    def softmax(self, x, axis=1):
+        x_exp = np.exp(x)
+        # 如果是列向量，则axis=0
+        x_sum = np.sum(x_exp, axis=axis, keepdims=True)
+        s = x_exp / x_sum
+        return s
+    
+    def resize_image(self, srcimg, keep_ratio=True):
+        top, left, newh, neww = 0, 0, self.input_width, self.input_height
+        if keep_ratio and srcimg.shape[0] != srcimg.shape[1]:
+            hw_scale = srcimg.shape[0] / srcimg.shape[1]
+            if hw_scale > 1:
+                newh, neww = self.input_height, int(self.input_width / hw_scale)
+                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
+                left = int((self.input_width - neww) * 0.5)
+                img = cv2.copyMakeBorder(img, 0, 0, left, self.input_width - neww - left, cv2.BORDER_CONSTANT,
+                                         value=(0, 0, 0))  # add border
+            else:
+                newh, neww = int(self.input_height * hw_scale), self.input_width
+                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
+                top = int((self.input_height - newh) * 0.5)
+                img = cv2.copyMakeBorder(img, top, self.input_height - newh - top, 0, 0, cv2.BORDER_CONSTANT,
+                                         value=(0, 0, 0))
+        else:
+            img = cv2.resize(srcimg, (self.input_width, self.input_height), interpolation=cv2.INTER_AREA)
+        return img, newh, neww, top, left
+
+    def detect(self, srcimg):
+        input_img, newh, neww, padh, padw = self.resize_image(cv2.cvtColor(srcimg, cv2.COLOR_BGR2RGB))
+        scale_h, scale_w = srcimg.shape[0]/newh, srcimg.shape[1]/neww
+        input_img = input_img.astype(np.float32) / 255.0
+
+        blob = cv2.dnn.blobFromImage(input_img)
+        self.net.setInput(blob)
+        outputs = self.net.forward(self.net.getUnconnectedOutLayersNames())
+        # if isinstance(outputs, tuple):
+        #     outputs = list(outputs)
+        # if float(cv2.__version__[:3])>=4.7:
+        #     outputs = [outputs[2], outputs[0], outputs[1]] ###opencv4.7需要这一步，opencv4.5不需要
+        # Perform inference on the image
+        det_bboxes, det_conf, det_classid, landmarks = self.post_process(outputs, scale_h, scale_w, padh, padw)
+        return det_bboxes, det_conf, det_classid, landmarks
+
+    def post_process(self, preds, scale_h, scale_w, padh, padw):
+        bboxes, scores, landmarks = [], [], []
+        for i, pred in enumerate(preds):
+            stride = int(self.input_height/pred.shape[2])
+            pred = pred.transpose((0, 2, 3, 1))
+            
+            box = pred[..., :self.reg_max * 4]
+            cls = 1 / (1 + np.exp(-pred[..., self.reg_max * 4:-15])).reshape((-1,1))
+            kpts = pred[..., -15:].reshape((-1,15)) ### x1,y1,score1, ..., x5,y5,score5
+
+            # tmp = box.reshape(self.feats_hw[i][0], self.feats_hw[i][1], 4, self.reg_max)
+            tmp = box.reshape(-1, 4, self.reg_max)
+            bbox_pred = self.softmax(tmp, axis=-1)
+            bbox_pred = np.dot(bbox_pred, self.project).reshape((-1,4))
+
+            bbox = self.distance2bbox(self.anchors[stride], bbox_pred, max_shape=(self.input_height, self.input_width)) * stride
+            kpts[:, 0::3] = (kpts[:, 0::3] * 2.0 + (self.anchors[stride][:, 0].reshape((-1,1)) - 0.5)) * stride
+            kpts[:, 1::3] = (kpts[:, 1::3] * 2.0 + (self.anchors[stride][:, 1].reshape((-1,1)) - 0.5)) * stride
+            kpts[:, 2::3] = 1 / (1+np.exp(-kpts[:, 2::3]))
+
+            bbox -= np.array([[padw, padh, padw, padh]])  ###合理使用广播法则
+            bbox *= np.array([[scale_w, scale_h, scale_w, scale_h]])
+            kpts -= np.tile(np.array([padw, padh, 0]), 5).reshape((1,15))
+            kpts *= np.tile(np.array([scale_w, scale_h, 1]), 5).reshape((1,15))
+
+            bboxes.append(bbox)
+            scores.append(cls)
+            landmarks.append(kpts)
+
+        bboxes = np.concatenate(bboxes, axis=0)
+        scores = np.concatenate(scores, axis=0)
+        landmarks = np.concatenate(landmarks, axis=0)
+    
+        bboxes_wh = bboxes.copy()
+        bboxes_wh[:, 2:4] = bboxes[:, 2:4] - bboxes[:, 0:2]  ####xywh
+        classIds = np.argmax(scores, axis=1)
+        confidences = np.max(scores, axis=1)  ####max_class_confidence
+        
+        mask = confidences>self.conf_threshold
+        bboxes_wh = bboxes_wh[mask]  ###合理使用广播法则
+        confidences = confidences[mask]
+        classIds = classIds[mask]
+        landmarks = landmarks[mask]
+        
+        indices = cv2.dnn.NMSBoxes(bboxes_wh.tolist(), confidences.tolist(), self.conf_threshold,
+                                   self.iou_threshold).flatten()
+        if len(indices) > 0:
+            mlvl_bboxes = bboxes_wh[indices]
+            confidences = confidences[indices]
+            classIds = classIds[indices]
+            landmarks = landmarks[indices]
+            return mlvl_bboxes, confidences, classIds, landmarks
+        else:
+            print('nothing detect')
+            return np.array([]), np.array([]), np.array([]), np.array([])
+
+    def distance2bbox(self, points, distance, max_shape=None):
+        x1 = points[:, 0] - distance[:, 0]
+        y1 = points[:, 1] - distance[:, 1]
+        x2 = points[:, 0] + distance[:, 2]
+        y2 = points[:, 1] + distance[:, 3]
+        if max_shape is not None:
+            x1 = np.clip(x1, 0, max_shape[1])
+            y1 = np.clip(y1, 0, max_shape[0])
+            x2 = np.clip(x2, 0, max_shape[1])
+            y2 = np.clip(y2, 0, max_shape[0])
+        return np.stack([x1, y1, x2, y2], axis=-1)
+    
+    def draw_detections(self, image, boxes, scores, kpts):
+        for box, score, kp in zip(boxes, scores, kpts):
+            x, y, w, h = box.astype(int)
+            # Draw rectangle
+            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), thickness=3)
+            cv2.putText(image, "face:"+str(round(score,2)), (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), thickness=2)
+            for i in range(5):
+                cv2.circle(image, (int(kp[i * 3]), int(kp[i * 3 + 1])), 4, (0, 255, 0), thickness=-1)
+                # cv2.putText(image, str(i), (int(kp[i * 3]), int(kp[i * 3 + 1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), thickness=1)
+        return image
+    
+ROOT = os.path.dirname(os.path.abspath(__file__))

musetalk/utils/face_detection/detection/__init__.py

@@ -0,0 +1 @@
+from .core import FaceDetector

musetalk/utils/face_detection/detection/core.py

@@ -0,0 +1,130 @@
+import logging
+import glob
+from tqdm import tqdm
+import numpy as np
+import torch
+import cv2
+
+
+class FaceDetector(object):
+    """An abstract class representing a face detector.
+
+    Any other face detection implementation must subclass it. All subclasses
+    must implement ``detect_from_image``, that return a list of detected
+    bounding boxes. Optionally, for speed considerations detect from path is
+    recommended.
+    """
+
+    def __init__(self, device, verbose):
+        self.device = device
+        self.verbose = verbose
+
+        if verbose:
+            if 'cpu' in device:
+                logger = logging.getLogger(__name__)
+                logger.warning("Detection running on CPU, this may be potentially slow.")
+
+        if 'cpu' not in device and 'cuda' not in device:
+            if verbose:
+                logger.error("Expected values for device are: {cpu, cuda} but got: %s", device)
+            raise ValueError
+
+    def detect_from_image(self, tensor_or_path):
+        """Detects faces in a given image.
+
+        This function detects the faces present in a provided BGR(usually)
+        image. The input can be either the image itself or the path to it.
+
+        Arguments:
+            tensor_or_path {numpy.ndarray, torch.tensor or string} -- the path
+            to an image or the image itself.
+
+        Example::
+
+            >>> path_to_image = 'data/image_01.jpg'
+            ...   detected_faces = detect_from_image(path_to_image)
+            [A list of bounding boxes (x1, y1, x2, y2)]
+            >>> image = cv2.imread(path_to_image)
+            ...   detected_faces = detect_from_image(image)
+            [A list of bounding boxes (x1, y1, x2, y2)]
+
+        """
+        raise NotImplementedError
+
+    def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True):
+        """Detects faces from all the images present in a given directory.
+
+        Arguments:
+            path {string} -- a string containing a path that points to the folder containing the images
+
+        Keyword Arguments:
+            extensions {list} -- list of string containing the extensions to be
+            consider in the following format: ``.extension_name`` (default:
+            {['.jpg', '.png']}) recursive {bool} -- option wherever to scan the
+            folder recursively (default: {False}) show_progress_bar {bool} --
+            display a progressbar (default: {True})
+
+        Example:
+        >>> directory = 'data'
+        ...   detected_faces = detect_from_directory(directory)
+        {A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]}
+
+        """
+        if self.verbose:
+            logger = logging.getLogger(__name__)
+
+        if len(extensions) == 0:
+            if self.verbose:
+                logger.error("Expected at list one extension, but none was received.")
+            raise ValueError
+
+        if self.verbose:
+            logger.info("Constructing the list of images.")
+        additional_pattern = '/**/*' if recursive else '/*'
+        files = []
+        for extension in extensions:
+            files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive))
+
+        if self.verbose:
+            logger.info("Finished searching for images. %s images found", len(files))
+            logger.info("Preparing to run the detection.")
+
+        predictions = {}
+        for image_path in tqdm(files, disable=not show_progress_bar):
+            if self.verbose:
+                logger.info("Running the face detector on image: %s", image_path)
+            predictions[image_path] = self.detect_from_image(image_path)
+
+        if self.verbose:
+            logger.info("The detector was successfully run on all %s images", len(files))
+
+        return predictions
+
+    @property
+    def reference_scale(self):
+        raise NotImplementedError
+
+    @property
+    def reference_x_shift(self):
+        raise NotImplementedError
+
+    @property
+    def reference_y_shift(self):
+        raise NotImplementedError
+
+    @staticmethod
+    def tensor_or_path_to_ndarray(tensor_or_path, rgb=True):
+        """Convert path (represented as a string) or torch.tensor to a numpy.ndarray
+
+        Arguments:
+            tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself
+        """
+        if isinstance(tensor_or_path, str):
+            return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1]
+        elif torch.is_tensor(tensor_or_path):
+            # Call cpu in case its coming from cuda
+            return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy()
+        elif isinstance(tensor_or_path, np.ndarray):
+            return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path
+        else:
+            raise TypeError

musetalk/utils/face_detection/detection/sfd/__init__.py

@@ -0,0 +1 @@
+from .sfd_detector import SFDDetector as FaceDetector

musetalk/utils/face_detection/detection/sfd/bbox.py

@@ -0,0 +1,129 @@
+from __future__ import print_function
+import os
+import sys
+import cv2
+import random
+import datetime
+import time
+import math
+import argparse
+import numpy as np
+import torch
+
+try:
+    from iou import IOU
+except BaseException:
+    # IOU cython speedup 10x
+    def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2):
+        sa = abs((ax2 - ax1) * (ay2 - ay1))
+        sb = abs((bx2 - bx1) * (by2 - by1))
+        x1, y1 = max(ax1, bx1), max(ay1, by1)
+        x2, y2 = min(ax2, bx2), min(ay2, by2)
+        w = x2 - x1
+        h = y2 - y1
+        if w < 0 or h < 0:
+            return 0.0
+        else:
+            return 1.0 * w * h / (sa + sb - w * h)
+
+
+def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh):
+    xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1
+    dx, dy = (xc - axc) / aww, (yc - ayc) / ahh
+    dw, dh = math.log(ww / aww), math.log(hh / ahh)
+    return dx, dy, dw, dh
+
+
+def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh):
+    xc, yc = dx * aww + axc, dy * ahh + ayc
+    ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh
+    x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2
+    return x1, y1, x2, y2
+
+
+def nms(dets, thresh):
+    if 0 == len(dets):
+        return []
+    x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4]
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]])
+        xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]])
+
+        w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1)
+        ovr = w * h / (areas[i] + areas[order[1:]] - w * h)
+
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return keep
+
+
+def encode(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 4].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded boxes (tensor), Shape: [num_priors, 4]
+    """
+
+    # dist b/t match center and prior's center
+    g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, 2:])
+    # match wh / prior wh
+    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
+    g_wh = torch.log(g_wh) / variances[1]
+    # return target for smooth_l1_loss
+    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]
+
+
+def decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((
+        priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+        priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+    boxes[:, :2] -= boxes[:, 2:] / 2
+    boxes[:, 2:] += boxes[:, :2]
+    return boxes
+
+def batch_decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((
+        priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:],
+        priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2)
+    boxes[:, :, :2] -= boxes[:, :, 2:] / 2
+    boxes[:, :, 2:] += boxes[:, :, :2]
+    return boxes

musetalk/utils/face_detection/detection/sfd/detect.py

@@ -0,0 +1,114 @@
+import torch
+import torch.nn.functional as F
+
+import os
+import sys
+import cv2
+import random
+import datetime
+import math
+import argparse
+import numpy as np
+
+import scipy.io as sio
+import zipfile
+from .net_s3fd import s3fd
+from .bbox import *
+
+
+def detect(net, img, device):
+    img = img - np.array([104, 117, 123])
+    img = img.transpose(2, 0, 1)
+    img = img.reshape((1,) + img.shape)
+
+    if 'cuda' in device:
+        torch.backends.cudnn.benchmark = True
+
+    img = torch.from_numpy(img).float().to(device)
+    BB, CC, HH, WW = img.size()
+    with torch.no_grad():
+        olist = net(img)
+
+    bboxlist = []
+    for i in range(len(olist) // 2):
+        olist[i * 2] = F.softmax(olist[i * 2], dim=1)
+    olist = [oelem.data.cpu() for oelem in olist]
+    for i in range(len(olist) // 2):
+        ocls, oreg = olist[i * 2], olist[i * 2 + 1]
+        FB, FC, FH, FW = ocls.size()  # feature map size
+        stride = 2**(i + 2)    # 4,8,16,32,64,128
+        anchor = stride * 4
+        poss = zip(*np.where(ocls[:, 1, :, :] > 0.05))
+        for Iindex, hindex, windex in poss:
+            axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
+            score = ocls[0, 1, hindex, windex]
+            loc = oreg[0, :, hindex, windex].contiguous().view(1, 4)
+            priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]])
+            variances = [0.1, 0.2]
+            box = decode(loc, priors, variances)
+            x1, y1, x2, y2 = box[0] * 1.0
+            # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1)
+            bboxlist.append([x1, y1, x2, y2, score])
+    bboxlist = np.array(bboxlist)
+    if 0 == len(bboxlist):
+        bboxlist = np.zeros((1, 5))
+
+    return bboxlist
+
+def batch_detect(net, imgs, device):
+    imgs = imgs - np.array([104, 117, 123])
+    imgs = imgs.transpose(0, 3, 1, 2)
+
+    if 'cuda' in device:
+        torch.backends.cudnn.benchmark = True
+
+    imgs = torch.from_numpy(imgs).float().to(device)
+    BB, CC, HH, WW = imgs.size()
+    with torch.no_grad():
+        olist = net(imgs)
+#     print(olist)
+    
+    bboxlist = []
+    for i in range(len(olist) // 2):
+        olist[i * 2] = F.softmax(olist[i * 2], dim=1)
+    
+    olist = [oelem.cpu() for oelem in olist]
+    for i in range(len(olist) // 2):
+        ocls, oreg = olist[i * 2], olist[i * 2 + 1]
+        FB, FC, FH, FW = ocls.size()  # feature map size
+        stride = 2**(i + 2)    # 4,8,16,32,64,128
+        anchor = stride * 4
+        poss = zip(*np.where(ocls[:, 1, :, :] > 0.05))
+        for Iindex, hindex, windex in poss:
+            axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
+            score = ocls[:, 1, hindex, windex]
+            loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4)
+            priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4)
+            variances = [0.1, 0.2]
+            box = batch_decode(loc, priors, variances)
+            box = box[:, 0] * 1.0
+            # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1)
+            bboxlist.append(torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy())
+    bboxlist = np.array(bboxlist)
+    if 0 == len(bboxlist):
+        bboxlist = np.zeros((1, BB, 5))
+
+    return bboxlist
+
+def flip_detect(net, img, device):
+    img = cv2.flip(img, 1)
+    b = detect(net, img, device)
+
+    bboxlist = np.zeros(b.shape)
+    bboxlist[:, 0] = img.shape[1] - b[:, 2]
+    bboxlist[:, 1] = b[:, 1]
+    bboxlist[:, 2] = img.shape[1] - b[:, 0]
+    bboxlist[:, 3] = b[:, 3]
+    bboxlist[:, 4] = b[:, 4]
+    return bboxlist
+
+
+def pts_to_bb(pts):
+    min_x, min_y = np.min(pts, axis=0)
+    max_x, max_y = np.max(pts, axis=0)
+    return np.array([min_x, min_y, max_x, max_y])

musetalk/utils/face_detection/detection/sfd/net_s3fd.py

@@ -0,0 +1,129 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class L2Norm(nn.Module):
+    def __init__(self, n_channels, scale=1.0):
+        super(L2Norm, self).__init__()
+        self.n_channels = n_channels
+        self.scale = scale
+        self.eps = 1e-10
+        self.weight = nn.Parameter(torch.Tensor(self.n_channels))
+        self.weight.data *= 0.0
+        self.weight.data += self.scale
+
+    def forward(self, x):
+        norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps
+        x = x / norm * self.weight.view(1, -1, 1, 1)
+        return x
+
+
+class s3fd(nn.Module):
+    def __init__(self):
+        super(s3fd, self).__init__()
+        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
+
+        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
+
+        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+
+        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+
+        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+
+        self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3)
+        self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0)
+
+        self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
+        self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)
+
+        self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0)
+        self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
+
+        self.conv3_3_norm = L2Norm(256, scale=10)
+        self.conv4_3_norm = L2Norm(512, scale=8)
+        self.conv5_3_norm = L2Norm(512, scale=5)
+
+        self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+
+        self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1)
+        self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        h = F.relu(self.conv1_1(x))
+        h = F.relu(self.conv1_2(h))
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv2_1(h))
+        h = F.relu(self.conv2_2(h))
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv3_1(h))
+        h = F.relu(self.conv3_2(h))
+        h = F.relu(self.conv3_3(h))
+        f3_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv4_1(h))
+        h = F.relu(self.conv4_2(h))
+        h = F.relu(self.conv4_3(h))
+        f4_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv5_1(h))
+        h = F.relu(self.conv5_2(h))
+        h = F.relu(self.conv5_3(h))
+        f5_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.fc6(h))
+        h = F.relu(self.fc7(h))
+        ffc7 = h
+        h = F.relu(self.conv6_1(h))
+        h = F.relu(self.conv6_2(h))
+        f6_2 = h
+        h = F.relu(self.conv7_1(h))
+        h = F.relu(self.conv7_2(h))
+        f7_2 = h
+
+        f3_3 = self.conv3_3_norm(f3_3)
+        f4_3 = self.conv4_3_norm(f4_3)
+        f5_3 = self.conv5_3_norm(f5_3)
+
+        cls1 = self.conv3_3_norm_mbox_conf(f3_3)
+        reg1 = self.conv3_3_norm_mbox_loc(f3_3)
+        cls2 = self.conv4_3_norm_mbox_conf(f4_3)
+        reg2 = self.conv4_3_norm_mbox_loc(f4_3)
+        cls3 = self.conv5_3_norm_mbox_conf(f5_3)
+        reg3 = self.conv5_3_norm_mbox_loc(f5_3)
+        cls4 = self.fc7_mbox_conf(ffc7)
+        reg4 = self.fc7_mbox_loc(ffc7)
+        cls5 = self.conv6_2_mbox_conf(f6_2)
+        reg5 = self.conv6_2_mbox_loc(f6_2)
+        cls6 = self.conv7_2_mbox_conf(f7_2)
+        reg6 = self.conv7_2_mbox_loc(f7_2)
+
+        # max-out background label
+        chunk = torch.chunk(cls1, 4, 1)
+        bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2])
+        cls1 = torch.cat([bmax, chunk[3]], dim=1)
+
+        return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6]

musetalk/utils/face_detection/detection/sfd/sfd_detector.py

@@ -0,0 +1,59 @@
+import os
+import cv2
+from torch.utils.model_zoo import load_url
+
+from ..core import FaceDetector
+
+from .net_s3fd import s3fd
+from .bbox import *
+from .detect import *
+
+models_urls = {
+    's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth',
+}
+
+
+class SFDDetector(FaceDetector):
+    def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False):
+        super(SFDDetector, self).__init__(device, verbose)
+
+        # Initialise the face detector
+        if not os.path.isfile(path_to_detector):
+            model_weights = load_url(models_urls['s3fd'])
+        else:
+            model_weights = torch.load(path_to_detector)
+
+        self.face_detector = s3fd()
+        self.face_detector.load_state_dict(model_weights)
+        self.face_detector.to(device)
+        self.face_detector.eval()
+
+    def detect_from_image(self, tensor_or_path):
+        image = self.tensor_or_path_to_ndarray(tensor_or_path)
+
+        bboxlist = detect(self.face_detector, image, device=self.device)
+        keep = nms(bboxlist, 0.3)
+        bboxlist = bboxlist[keep, :]
+        bboxlist = [x for x in bboxlist if x[-1] > 0.5]
+
+        return bboxlist
+
+    def detect_from_batch(self, images):
+        bboxlists = batch_detect(self.face_detector, images, device=self.device)
+        keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])]
+        bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)]
+        bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists]
+
+        return bboxlists
+
+    @property
+    def reference_scale(self):
+        return 195
+
+    @property
+    def reference_x_shift(self):
+        return 0
+
+    @property
+    def reference_y_shift(self):
+        return 0

musetalk/utils/face_detection/models.py

@@ -0,0 +1,261 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3,
+                     stride=strd, padding=padding, bias=bias)
+
+
+class ConvBlock(nn.Module):
+    def __init__(self, in_planes, out_planes):
+        super(ConvBlock, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv1 = conv3x3(in_planes, int(out_planes / 2))
+        self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
+        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
+        self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
+        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
+
+        if in_planes != out_planes:
+            self.downsample = nn.Sequential(
+                nn.BatchNorm2d(in_planes),
+                nn.ReLU(True),
+                nn.Conv2d(in_planes, out_planes,
+                          kernel_size=1, stride=1, bias=False),
+            )
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        residual = x
+
+        out1 = self.bn1(x)
+        out1 = F.relu(out1, True)
+        out1 = self.conv1(out1)
+
+        out2 = self.bn2(out1)
+        out2 = F.relu(out2, True)
+        out2 = self.conv2(out2)
+
+        out3 = self.bn3(out2)
+        out3 = F.relu(out3, True)
+        out3 = self.conv3(out3)
+
+        out3 = torch.cat((out1, out2, out3), 1)
+
+        if self.downsample is not None:
+            residual = self.downsample(residual)
+
+        out3 += residual
+
+        return out3
+
+
+class Bottleneck(nn.Module):
+
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class HourGlass(nn.Module):
+    def __init__(self, num_modules, depth, num_features):
+        super(HourGlass, self).__init__()
+        self.num_modules = num_modules
+        self.depth = depth
+        self.features = num_features
+
+        self._generate_network(self.depth)
+
+    def _generate_network(self, level):
+        self.add_module('b1_' + str(level), ConvBlock(self.features, self.features))
+
+        self.add_module('b2_' + str(level), ConvBlock(self.features, self.features))
+
+        if level > 1:
+            self._generate_network(level - 1)
+        else:
+            self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features))
+
+        self.add_module('b3_' + str(level), ConvBlock(self.features, self.features))
+
+    def _forward(self, level, inp):
+        # Upper branch
+        up1 = inp
+        up1 = self._modules['b1_' + str(level)](up1)
+
+        # Lower branch
+        low1 = F.avg_pool2d(inp, 2, stride=2)
+        low1 = self._modules['b2_' + str(level)](low1)
+
+        if level > 1:
+            low2 = self._forward(level - 1, low1)
+        else:
+            low2 = low1
+            low2 = self._modules['b2_plus_' + str(level)](low2)
+
+        low3 = low2
+        low3 = self._modules['b3_' + str(level)](low3)
+
+        up2 = F.interpolate(low3, scale_factor=2, mode='nearest')
+
+        return up1 + up2
+
+    def forward(self, x):
+        return self._forward(self.depth, x)
+
+
+class FAN(nn.Module):
+
+    def __init__(self, num_modules=1):
+        super(FAN, self).__init__()
+        self.num_modules = num_modules
+
+        # Base part
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.conv2 = ConvBlock(64, 128)
+        self.conv3 = ConvBlock(128, 128)
+        self.conv4 = ConvBlock(128, 256)
+
+        # Stacking part
+        for hg_module in range(self.num_modules):
+            self.add_module('m' + str(hg_module), HourGlass(1, 4, 256))
+            self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256))
+            self.add_module('conv_last' + str(hg_module),
+                            nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+            self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
+            self.add_module('l' + str(hg_module), nn.Conv2d(256,
+                                                            68, kernel_size=1, stride=1, padding=0))
+
+            if hg_module < self.num_modules - 1:
+                self.add_module(
+                    'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+                self.add_module('al' + str(hg_module), nn.Conv2d(68,
+                                                                 256, kernel_size=1, stride=1, padding=0))
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)), True)
+        x = F.avg_pool2d(self.conv2(x), 2, stride=2)
+        x = self.conv3(x)
+        x = self.conv4(x)
+
+        previous = x
+
+        outputs = []
+        for i in range(self.num_modules):
+            hg = self._modules['m' + str(i)](previous)
+
+            ll = hg
+            ll = self._modules['top_m_' + str(i)](ll)
+
+            ll = F.relu(self._modules['bn_end' + str(i)]
+                        (self._modules['conv_last' + str(i)](ll)), True)
+
+            # Predict heatmaps
+            tmp_out = self._modules['l' + str(i)](ll)
+            outputs.append(tmp_out)
+
+            if i < self.num_modules - 1:
+                ll = self._modules['bl' + str(i)](ll)
+                tmp_out_ = self._modules['al' + str(i)](tmp_out)
+                previous = previous + ll + tmp_out_
+
+        return outputs
+
+
+class ResNetDepth(nn.Module):
+
+    def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68):
+        self.inplanes = 64
+        super(ResNetDepth, self).__init__()
+        self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x

musetalk/utils/face_detection/utils.py

@@ -0,0 +1,313 @@
+from __future__ import print_function
+import os
+import sys
+import time
+import torch
+import math
+import numpy as np
+import cv2
+
+
+def _gaussian(
+        size=3, sigma=0.25, amplitude=1, normalize=False, width=None,
+        height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5,
+        mean_vert=0.5):
+    # handle some defaults
+    if width is None:
+        width = size
+    if height is None:
+        height = size
+    if sigma_horz is None:
+        sigma_horz = sigma
+    if sigma_vert is None:
+        sigma_vert = sigma
+    center_x = mean_horz * width + 0.5
+    center_y = mean_vert * height + 0.5
+    gauss = np.empty((height, width), dtype=np.float32)
+    # generate kernel
+    for i in range(height):
+        for j in range(width):
+            gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / (
+                sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0))
+    if normalize:
+        gauss = gauss / np.sum(gauss)
+    return gauss
+
+
+def draw_gaussian(image, point, sigma):
+    # Check if the gaussian is inside
+    ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)]
+    br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)]
+    if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1):
+        return image
+    size = 6 * sigma + 1
+    g = _gaussian(size)
+    g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))]
+    g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))]
+    img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
+    img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
+    assert (g_x[0] > 0 and g_y[1] > 0)
+    image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]
+          ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
+    image[image > 1] = 1
+    return image
+
+
+def transform(point, center, scale, resolution, invert=False):
+    """Generate and affine transformation matrix.
+
+    Given a set of points, a center, a scale and a targer resolution, the
+    function generates and affine transformation matrix. If invert is ``True``
+    it will produce the inverse transformation.
+
+    Arguments:
+        point {torch.tensor} -- the input 2D point
+        center {torch.tensor or numpy.array} -- the center around which to perform the transformations
+        scale {float} -- the scale of the face/object
+        resolution {float} -- the output resolution
+
+    Keyword Arguments:
+        invert {bool} -- define wherever the function should produce the direct or the
+        inverse transformation matrix (default: {False})
+    """
+    _pt = torch.ones(3)
+    _pt[0] = point[0]
+    _pt[1] = point[1]
+
+    h = 200.0 * scale
+    t = torch.eye(3)
+    t[0, 0] = resolution / h
+    t[1, 1] = resolution / h
+    t[0, 2] = resolution * (-center[0] / h + 0.5)
+    t[1, 2] = resolution * (-center[1] / h + 0.5)
+
+    if invert:
+        t = torch.inverse(t)
+
+    new_point = (torch.matmul(t, _pt))[0:2]
+
+    return new_point.int()
+
+
+def crop(image, center, scale, resolution=256.0):
+    """Center crops an image or set of heatmaps
+
+    Arguments:
+        image {numpy.array} -- an rgb image
+        center {numpy.array} -- the center of the object, usually the same as of the bounding box
+        scale {float} -- scale of the face
+
+    Keyword Arguments:
+        resolution {float} -- the size of the output cropped image (default: {256.0})
+
+    Returns:
+        [type] -- [description]
+    """  # Crop around the center point
+    """ Crops the image around the center. Input is expected to be an np.ndarray """
+    ul = transform([1, 1], center, scale, resolution, True)
+    br = transform([resolution, resolution], center, scale, resolution, True)
+    # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0)
+    if image.ndim > 2:
+        newDim = np.array([br[1] - ul[1], br[0] - ul[0],
+                           image.shape[2]], dtype=np.int32)
+        newImg = np.zeros(newDim, dtype=np.uint8)
+    else:
+        newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
+        newImg = np.zeros(newDim, dtype=np.uint8)
+    ht = image.shape[0]
+    wd = image.shape[1]
+    newX = np.array(
+        [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32)
+    newY = np.array(
+        [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32)
+    oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32)
+    oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32)
+    newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1]
+           ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :]
+    newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)),
+                        interpolation=cv2.INTER_LINEAR)
+    return newImg
+
+
+def get_preds_fromhm(hm, center=None, scale=None):
+    """Obtain (x,y) coordinates given a set of N heatmaps. If the center
+    and the scale is provided the function will return the points also in
+    the original coordinate frame.
+
+    Arguments:
+        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
+
+    Keyword Arguments:
+        center {torch.tensor} -- the center of the bounding box (default: {None})
+        scale {float} -- face scale (default: {None})
+    """
+    max, idx = torch.max(
+        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
+    idx += 1
+    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
+    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
+    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
+
+    for i in range(preds.size(0)):
+        for j in range(preds.size(1)):
+            hm_ = hm[i, j, :]
+            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
+            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
+                diff = torch.FloatTensor(
+                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
+                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
+                preds[i, j].add_(diff.sign_().mul_(.25))
+
+    preds.add_(-.5)
+
+    preds_orig = torch.zeros(preds.size())
+    if center is not None and scale is not None:
+        for i in range(hm.size(0)):
+            for j in range(hm.size(1)):
+                preds_orig[i, j] = transform(
+                    preds[i, j], center, scale, hm.size(2), True)
+
+    return preds, preds_orig
+
+def get_preds_fromhm_batch(hm, centers=None, scales=None):
+    """Obtain (x,y) coordinates given a set of N heatmaps. If the centers
+    and the scales is provided the function will return the points also in
+    the original coordinate frame.
+
+    Arguments:
+        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
+
+    Keyword Arguments:
+        centers {torch.tensor} -- the centers of the bounding box (default: {None})
+        scales {float} -- face scales (default: {None})
+    """
+    max, idx = torch.max(
+        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
+    idx += 1
+    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
+    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
+    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
+
+    for i in range(preds.size(0)):
+        for j in range(preds.size(1)):
+            hm_ = hm[i, j, :]
+            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
+            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
+                diff = torch.FloatTensor(
+                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
+                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
+                preds[i, j].add_(diff.sign_().mul_(.25))
+
+    preds.add_(-.5)
+
+    preds_orig = torch.zeros(preds.size())
+    if centers is not None and scales is not None:
+        for i in range(hm.size(0)):
+            for j in range(hm.size(1)):
+                preds_orig[i, j] = transform(
+                    preds[i, j], centers[i], scales[i], hm.size(2), True)
+
+    return preds, preds_orig
+
+def shuffle_lr(parts, pairs=None):
+    """Shuffle the points left-right according to the axis of symmetry
+    of the object.
+
+    Arguments:
+        parts {torch.tensor} -- a 3D or 4D object containing the
+        heatmaps.
+
+    Keyword Arguments:
+        pairs {list of integers} -- [order of the flipped points] (default: {None})
+    """
+    if pairs is None:
+        pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0,
+                 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35,
+                 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41,
+                 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63,
+                 62, 61, 60, 67, 66, 65]
+    if parts.ndimension() == 3:
+        parts = parts[pairs, ...]
+    else:
+        parts = parts[:, pairs, ...]
+
+    return parts
+
+
+def flip(tensor, is_label=False):
+    """Flip an image or a set of heatmaps left-right
+
+    Arguments:
+        tensor {numpy.array or torch.tensor} -- [the input image or heatmaps]
+
+    Keyword Arguments:
+        is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False})
+    """
+    if not torch.is_tensor(tensor):
+        tensor = torch.from_numpy(tensor)
+
+    if is_label:
+        tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1)
+    else:
+        tensor = tensor.flip(tensor.ndimension() - 1)
+
+    return tensor
+
+# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py)
+
+
+def appdata_dir(appname=None, roaming=False):
+    """ appdata_dir(appname=None, roaming=False)
+
+    Get the path to the application directory, where applications are allowed
+    to write user specific files (e.g. configurations). For non-user specific
+    data, consider using common_appdata_dir().
+    If appname is given, a subdir is appended (and created if necessary).
+    If roaming is True, will prefer a roaming directory (Windows Vista/7).
+    """
+
+    # Define default user directory
+    userDir = os.getenv('FACEALIGNMENT_USERDIR', None)
+    if userDir is None:
+        userDir = os.path.expanduser('~')
+        if not os.path.isdir(userDir):  # pragma: no cover
+            userDir = '/var/tmp'  # issue #54
+
+    # Get system app data dir
+    path = None
+    if sys.platform.startswith('win'):
+        path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA')
+        path = (path2 or path1) if roaming else (path1 or path2)
+    elif sys.platform.startswith('darwin'):
+        path = os.path.join(userDir, 'Library', 'Application Support')
+    # On Linux and as fallback
+    if not (path and os.path.isdir(path)):
+        path = userDir
+
+    # Maybe we should store things local to the executable (in case of a
+    # portable distro or a frozen application that wants to be portable)
+    prefix = sys.prefix
+    if getattr(sys, 'frozen', None):
+        prefix = os.path.abspath(os.path.dirname(sys.executable))
+    for reldir in ('settings', '../settings'):
+        localpath = os.path.abspath(os.path.join(prefix, reldir))
+        if os.path.isdir(localpath):  # pragma: no cover
+            try:
+                open(os.path.join(localpath, 'test.write'), 'wb').close()
+                os.remove(os.path.join(localpath, 'test.write'))
+            except IOError:
+                pass  # We cannot write in this directory
+            else:
+                path = localpath
+                break
+
+    # Get path specific for this app
+    if appname:
+        if path == userDir:
+            appname = '.' + appname.lstrip('.')  # Make it a hidden directory
+        path = os.path.join(path, appname)
+        if not os.path.isdir(path):  # pragma: no cover
+            os.mkdir(path)
+
+    # Done
+    return path

musetalk/utils/face_parsing/__init__.py

@@ -0,0 +1,57 @@
+import torch
+import time
+import os
+import cv2
+import numpy as np
+from PIL import Image
+from .model import BiSeNet
+import torchvision.transforms as transforms
+
+class FaceParsing():
+    def __init__(self,resnet_path='./models/face-parse-bisent/resnet18-5c106cde.pth',
+                   model_pth='./models/face-parse-bisent/79999_iter.pth'):
+        self.net = self.model_init(resnet_path,model_pth)
+        self.preprocess = self.image_preprocess()
+
+    def model_init(self,
+                   resnet_path,
+                   model_pth):
+        net = BiSeNet(resnet_path)
+        if torch.cuda.is_available():
+            net.cuda()
+            net.load_state_dict(torch.load(model_pth))
+        else:
+            net.load_state_dict(torch.load(model_pth, map_location=torch.device('cpu')))
+        net.eval()
+        return net
+
+    def image_preprocess(self):
+        return transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+        ])
+
+    def __call__(self, image, size=(512, 512)):
+        if isinstance(image, str):
+            image = Image.open(image)
+
+        width, height = image.size
+        with torch.no_grad():
+            image = image.resize(size, Image.BILINEAR)
+            img = self.preprocess(image)
+            if torch.cuda.is_available():
+                img = torch.unsqueeze(img, 0).cuda()
+            else:
+                img = torch.unsqueeze(img, 0)
+            out = self.net(img)[0]
+            parsing = out.squeeze(0).cpu().numpy().argmax(0)
+            parsing[np.where(parsing>13)] = 0
+            parsing[np.where(parsing>=1)] = 255
+        parsing = Image.fromarray(parsing.astype(np.uint8))
+        return parsing
+
+if __name__ == "__main__":
+    fp = FaceParsing()
+    segmap = fp('154_small.png')
+    segmap.save('res.png')
+

musetalk/utils/face_parsing/model.py

@@ -0,0 +1,283 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+from .resnet import Resnet18
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(in_chan,
+                out_chan,
+                kernel_size = ks,
+                stride = stride,
+                padding = padding,
+                bias = False)
+        self.bn = nn.BatchNorm2d(out_chan)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(self.bn(x))
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+class BiSeNetOutput(nn.Module):
+    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
+        super(BiSeNetOutput, self).__init__()
+        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.conv_out(x)
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class AttentionRefinementModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(AttentionRefinementModule, self).__init__()
+        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
+        self.bn_atten = nn.BatchNorm2d(out_chan)
+        self.sigmoid_atten = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv(x)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv_atten(atten)
+        atten = self.bn_atten(atten)
+        atten = self.sigmoid_atten(atten)
+        out = torch.mul(feat, atten)
+        return out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+
+class ContextPath(nn.Module):
+    def __init__(self, resnet_path, *args, **kwargs):
+        super(ContextPath, self).__init__()
+        self.resnet = Resnet18(resnet_path)
+        self.arm16 = AttentionRefinementModule(256, 128)
+        self.arm32 = AttentionRefinementModule(512, 128)
+        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+        self.init_weight()
+
+    def forward(self, x):
+        H0, W0 = x.size()[2:]
+        feat8, feat16, feat32 = self.resnet(x)
+        H8, W8 = feat8.size()[2:]
+        H16, W16 = feat16.size()[2:]
+        H32, W32 = feat32.size()[2:]
+
+        avg = F.avg_pool2d(feat32, feat32.size()[2:])
+        avg = self.conv_avg(avg)
+        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
+
+        feat32_arm = self.arm32(feat32)
+        feat32_sum = feat32_arm + avg_up
+        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
+        feat32_up = self.conv_head32(feat32_up)
+
+        feat16_arm = self.arm16(feat16)
+        feat16_sum = feat16_arm + feat32_up
+        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
+        feat16_up = self.conv_head16(feat16_up)
+
+        return feat8, feat16_up, feat32_up  # x8, x8, x16
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+### This is not used, since I replace this with the resnet feature with the same size
+class SpatialPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(SpatialPath, self).__init__()
+        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
+        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv1(x)
+        feat = self.conv2(feat)
+        feat = self.conv3(feat)
+        feat = self.conv_out(feat)
+        return feat
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class FeatureFusionModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(FeatureFusionModule, self).__init__()
+        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+        self.conv1 = nn.Conv2d(out_chan,
+                out_chan//4,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.conv2 = nn.Conv2d(out_chan//4,
+                out_chan,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, fsp, fcp):
+        fcat = torch.cat([fsp, fcp], dim=1)
+        feat = self.convblk(fcat)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv1(atten)
+        atten = self.relu(atten)
+        atten = self.conv2(atten)
+        atten = self.sigmoid(atten)
+        feat_atten = torch.mul(feat, atten)
+        feat_out = feat_atten + feat
+        return feat_out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class BiSeNet(nn.Module):
+    def __init__(self, resnet_path='models/resnet18-5c106cde.pth', n_classes=19, *args, **kwargs):
+        super(BiSeNet, self).__init__()
+        self.cp = ContextPath(resnet_path)
+        ## here self.sp is deleted
+        self.ffm = FeatureFusionModule(256, 256)
+        self.conv_out = BiSeNetOutput(256, 256, n_classes)
+        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
+        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
+        self.init_weight()
+
+    def forward(self, x):
+        H, W = x.size()[2:]
+        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
+        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
+        feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+        feat_out = self.conv_out(feat_fuse)
+        feat_out16 = self.conv_out16(feat_cp8)
+        feat_out32 = self.conv_out32(feat_cp16)
+
+        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
+        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
+        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
+        return feat_out, feat_out16, feat_out32
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
+        for name, child in self.named_children():
+            child_wd_params, child_nowd_params = child.get_params()
+            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
+                lr_mul_wd_params += child_wd_params
+                lr_mul_nowd_params += child_nowd_params
+            else:
+                wd_params += child_wd_params
+                nowd_params += child_nowd_params
+        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
+
+
+if __name__ == "__main__":
+    net = BiSeNet(19)
+    net.cuda()
+    net.eval()
+    in_ten = torch.randn(16, 3, 640, 480).cuda()
+    out, out16, out32 = net(in_ten)
+    print(out.shape)
+
+    net.get_params()