Repository: lipku/LiveTalking Branch: main Commit: 9db55d8ba80a Files: 115 Total size: 3.0 MB Directory structure: gitextract_75g1ac60/ ├── .github/ │ └── FUNDING.yml ├── .gitignore ├── Dockerfile ├── LICENSE ├── README-EN.md ├── README.md ├── app.py ├── assets/ │ └── faq.md ├── baseasr.py ├── basereal.py ├── hubertasr.py ├── lightreal.py ├── lipasr.py ├── lipreal.py ├── llm.py ├── logger.py ├── museasr.py ├── musereal.py ├── musetalk/ │ ├── genavatar.py │ ├── myutil.py │ ├── utils/ │ │ ├── __init__.py │ │ ├── audio_processor.py │ │ ├── blending.py │ │ ├── dwpose/ │ │ │ ├── default_runtime.py │ │ │ └── rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py │ │ ├── face_detection/ │ │ │ ├── README.md │ │ │ ├── __init__.py │ │ │ ├── api.py │ │ │ ├── detection/ │ │ │ │ ├── __init__.py │ │ │ │ ├── core.py │ │ │ │ └── sfd/ │ │ │ │ ├── __init__.py │ │ │ │ ├── bbox.py │ │ │ │ ├── detect.py │ │ │ │ ├── net_s3fd.py │ │ │ │ └── sfd_detector.py │ │ │ ├── models.py │ │ │ └── utils.py │ │ ├── face_parsing/ │ │ │ ├── __init__.py │ │ │ ├── model.py │ │ │ └── resnet.py │ │ ├── preprocessing.py │ │ ├── training_utils.py │ │ └── utils.py │ └── whisper/ │ ├── audio2feature.py │ └── whisper/ │ ├── __init__.py │ ├── __main__.py │ ├── assets/ │ │ ├── gpt2/ │ │ │ ├── merges.txt │ │ │ ├── special_tokens_map.json │ │ │ ├── tokenizer_config.json │ │ │ └── vocab.json │ │ ├── mel_filters.npz │ │ └── multilingual/ │ │ ├── added_tokens.json │ │ ├── merges.txt │ │ ├── special_tokens_map.json │ │ ├── tokenizer_config.json │ │ └── vocab.json │ ├── audio.py │ ├── decoding.py │ ├── model.py │ ├── normalizers/ │ │ ├── __init__.py │ │ ├── basic.py │ │ ├── english.json │ │ └── english.py │ ├── tokenizer.py │ ├── transcribe.py │ └── utils.py ├── requirements.txt ├── ttsreal.py ├── ultralight/ │ ├── audio2feature.py │ ├── face_detect_utils/ │ │ ├── base_module.py │ │ ├── detect_face.py │ │ ├── get_landmark.py │ │ ├── mean_face.txt │ │ └── pfld_mobileone.py │ ├── genavatar-bak.py │ ├── genavatar.py │ └── unet.py ├── wav2lip/ │ ├── audio.py │ ├── face_detection/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── api.py │ │ ├── detection/ │ │ │ ├── __init__.py │ │ │ ├── core.py │ │ │ └── sfd/ │ │ │ ├── __init__.py │ │ │ ├── bbox.py │ │ │ ├── detect.py │ │ │ ├── net_s3fd.py │ │ │ └── sfd_detector.py │ │ ├── models.py │ │ └── utils.py │ ├── genavatar.py │ └── hparams.py ├── web/ │ ├── asr/ │ │ ├── index.html │ │ ├── main.js │ │ ├── pcm.js │ │ ├── recorder-core.js │ │ ├── wav.js │ │ └── wsconnecter.js │ ├── chat.html │ ├── client.js │ ├── dashboard.html │ ├── echo.html │ ├── echoapi.html │ ├── rtcpush.html │ ├── rtcpushapi-asr.html │ ├── rtcpushapi.html │ ├── rtcpushchat.html │ ├── srs.sdk.js │ ├── webrtc.html │ ├── webrtcapi-asr.html │ ├── webrtcapi-custom.html │ ├── webrtcapi.html │ ├── webrtcchat.html │ └── whep.js └── webrtc.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/FUNDING.yml ================================================ github: [lipku] ================================================ FILE: .gitignore ================================================ __pycache__/ build/ *.egg-info/ *.so *.mp4 tmp* trial*/ data data_utils/face_tracking/3DMM/* data_utils/face_parsing/79999_iter.pth pretrained *.mp4 .DS_Store workspace/log_ngp.txt .idea models/ *.log dist ================================================ FILE: Dockerfile ================================================ # Copyright (c) 2020-2022, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. ARG BASE_IMAGE=nvcr.io/nvidia/cuda:11.6.1-cudnn8-devel-ubuntu20.04 FROM $BASE_IMAGE RUN apt-get update -yq --fix-missing \ && DEBIAN_FRONTEND=noninteractive apt-get install -yq --no-install-recommends \ pkg-config \ wget \ cmake \ curl \ git \ vim #ENV PYTHONDONTWRITEBYTECODE=1 #ENV PYTHONUNBUFFERED=1 # nvidia-container-runtime #ENV NVIDIA_VISIBLE_DEVICES all #ENV NVIDIA_DRIVER_CAPABILITIES compute,utility,graphics RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh RUN sh Miniconda3-latest-Linux-x86_64.sh -b -u -p ~/miniconda3 RUN ~/miniconda3/bin/conda init RUN source ~/.bashrc RUN conda create -n nerfstream python=3.10 RUN conda activate nerfstream RUN pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ # install depend RUN conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch Copy requirements.txt ./ RUN pip install -r requirements.txt # additional libraries # RUN pip install "git+https://github.com/facebookresearch/pytorch3d.git" # RUN pip install tensorflow-gpu==2.8.0 # RUN pip uninstall protobuf # RUN pip install protobuf==3.20.1 # RUN conda install ffmpeg # Copy ../python_rtmpstream /python_rtmpstream # WORKDIR /python_rtmpstream/python # RUN pip install . 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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [livetalking@lipku] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README-EN.md ================================================ # English | [中文版](./README.md)

A real-time interactive streaming digital human system enabling synchronized audio-video conversation, which basically meets commercial application standards. [wav2lip Demo](https://www.bilibili.com/video/BV1scwBeyELA/) | [ernerf Demo](https://www.bilibili.com/video/BV1G1421z73r/) | [musetalk Demo](https://www.bilibili.com/video/BV1gm421N7vQ/) Domestic Mirror Repository: ## Features 1. Supports multiple digital human models: ernerf, musetalk, wav2lip, Ultralight-Digital-Human. 2. Supports voice cloning. 3. Supports interrupting the digital human while it is speaking. 4. Supports full-body video stitching. 5. Supports WebRTC and virtual camera output. 6. Supports motion choreography: plays custom videos when the digital human is not speaking. 7. Supports custom digital human avatars. ## 1. Installation Tested on Ubuntu 24.04, Python 3.10, PyTorch 2.5.0, and CUDA 12.4. ### 1.1 Install Dependencies ```bash conda create -n nerfstream python=3.10 conda activate nerfstream # If your CUDA version is not 12.4 (check via "nvidia-smi"), install the corresponding PyTorch version from conda install pytorch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 pytorch-cuda=12.4 -c pytorch -c nvidia pip install -r requirements.txt ``` For common installation issues, refer to the [FAQ](https://livetalking-doc.readthedocs.io/en/latest/faq.html). For CUDA environment setup on Linux, refer to this article: Troubleshooting for video connection issues: ## 2. Quick Start - Download Models Quark Cloud Drive: Google Drive: 1. Copy `wav2lip256.pth` to the `models` directory of this project and rename it to `wav2lip.pth`. 2. Extract the `wav2lip256_avatar1.tar.gz` archive and copy the entire extracted folder to `data/avatars` of this project. - Run the Project Execute: `python app.py --transport webrtc --model wav2lip --avatar_id wav2lip256_avatar1` The server must open the following ports: TCP: 8010; UDP: 1-65536 You can access the client in two ways: (1) Open `http://serverip:8010/webrtcapi.html` in a browser. First click "start" to play the digital human video; then enter any text in the input box and submit it. The digital human will broadcast the text. (2) Use the desktop client (download link: ). - Quick Experience Visit and create an instance with this image to run the project successfully immediately. If you cannot access Hugging Face, run the following command before starting the project: ``` export HF_ENDPOINT=https://hf-mirror.com ``` ## 3. More Usage For detailed usage instructions: ## 4. Docker Run No prior installation is required; run directly with Docker: ``` docker run --gpus all -it --network=host --rm registry.cn-zhangjiakou.aliyuncs.com/codewithgpu3/lipku-livetalking:toza2irpHZ ``` The code is located in `/root/livetalking`. First run `git pull` to fetch the latest code, then execute commands as described in Sections 2 and 3. The following images are available: - AutoDL Image: [AutoDL Tutorial](https://livetalking-doc.readthedocs.io/en/latest/autodl/README.html) - UCloud Image: Supports opening any port; no additional SRS service deployment is required. [UCloud Tutorial](https://livetalking-doc.readthedocs.io/en/latest/ucloud/ucloud.html) ## 5. Performance - Performance mainly depends on CPU and GPU: Each video stream compression consumes CPU resources, and CPU performance is positively correlated with video resolution; each lip-sync inference depends on GPU performance. - The number of concurrent streams when the digital human is not speaking depends on CPU performance; the number of concurrent streams when multiple digital humans are speaking simultaneously depends on GPU performance. - In the backend logs, `inferfps` refers to the GPU inference frame rate, and `finalfps` refers to the final streaming frame rate. Both need to be above 25 fps to achieve real-time performance. If `inferfps` is above 25 but `finalfps` is below 25, it indicates insufficient CPU performance. - Real-Time Inference Performance | Model | GPU Model | FPS | | :---------- | :--------- | :--- | | wav2lip256 | RTX 3060 | 60 | | wav2lip256 | RTX 3080Ti | 120 | | musetalk | RTX 3080Ti | 42 | | musetalk | RTX 3090 | 45 | | musetalk | RTX 4090 | 72 | A GPU of RTX 3060 or higher is sufficient for wav2lip256, while musetalk requires an RTX 3080Ti or higher. ## 6. Commercial Version The following extended features are available for users who are familiar with the open-source project and need to expand product capabilities: 1. High-definition wav2lip model. 2. Full voice interaction: supports interrupting the digital human’s response via a wake word or button to ask a new question. 3. Real-time synchronized subtitles: provides the frontend with events for the start and end of each sentence spoken by the digital human. 4. Each connection can specify a corresponding avatar and voice; accelerated avatar image loading. 5. Supports avatars (digital human images) with unlimited duration. 6. Provides a real-time audio stream input interface. 7. Transparent background for the digital human, supporting dynamic background overlay. 8. Real-time avatar switching, supporting multiple digital humans in the same scene. 9. Camera‑driven digital human movements and facial expressions. For more details: ## 7. Statement Videos developed based on this project and published on platforms such as Bilibili, WeChat Channels, and Douyin must include the LiveTalking watermark and logo. --- If this project is helpful to you, please give it a "Star". Contributions from developers interested in improving this project are also welcome. * Knowledge Planet (for high-quality FAQs, best practices, and Q&A): https://t.zsxq.com/7NMyO * WeChat Official Account: 数字人技术 (Digital Human Technology) ================================================ FILE: README.md ================================================ # [English](./README-EN.md) | 中文版

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 [wav2lip效果](https://www.bilibili.com/video/BV1scwBeyELA/) | [ernerf效果](https://www.bilibili.com/video/BV1G1421z73r/) | [musetalk效果](https://www.bilibili.com/video/BV1gm421N7vQ/) 国内镜像地址: ## 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. 支持多种数字人模型: ernerf、musetalk、wav2lip、Ultralight-Digital-Human 2. 支持声音克隆 3. 支持数字人说话被打断 4. 支持webrtc、虚拟摄像头输出 5. 支持动作编排:不说话时播放自定义视频 6. 支持多并发 7. 支持自定义数字人形象 ## 1. Installation Tested on Ubuntu 24.04, Python3.10, Pytorch 2.5.0 and CUDA 12.4 ### 1.1 Install dependency ```bash conda create -n nerfstream python=3.10 conda activate nerfstream #如果cuda版本不为12.4(运行nvidia-smi确认版本),根据安装对应版本的pytorch conda install pytorch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 pytorch-cuda=12.4 -c pytorch -c nvidia pip install -r requirements.txt ``` 安装常见问题[FAQ](https://livetalking-doc.readthedocs.io/zh-cn/latest/faq.html) linux cuda环境搭建可以参考这篇文章 视频连不上解决方法 ## 2. Quick Start - 下载模型 夸克云盘 GoogleDriver 将wav2lip256.pth拷到本项目的models下, 重命名为wav2lip.pth; 将wav2lip256_avatar1.tar.gz解压后整个文件夹拷到本项目的data/avatars下 - 运行 python app.py --transport webrtc --model wav2lip --avatar_id wav2lip256_avatar1 服务端需要开放端口 tcp:8010; udp:1-65536 客户端可以选用以下两种方式: (1)用浏览器打开http://serverip:8010/webrtcapi.html , 先点‘start',播放数字人视频;然后在文本框输入任意文字,提交。数字人播报该段文字 (2)用客户端方式, 下载地址 - 快速体验 [在线镜像](https://www.compshare.cn/images/4458094e-a43d-45fe-9b57-de79253befe4?referral_code=3XW3852OBmnD089hMMrtuU&ytag=GPU_GitHub_livetalking) 用该镜像创建实例即可运行成功 安装运行过程中如果访问不了huggingface,在运行前 ``` export HF_ENDPOINT=https://hf-mirror.com ``` ## 3. More Usage 使用说明: ## 4. Docker Run 不需要前面的安装,直接运行。 ``` docker run --gpus all -it --network=host --rm registry.cn-beijing.aliyuncs.com/codewithgpu2/lipku-metahuman-stream:2K9qaMBu8v ``` 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 提供如下网络镜像 - ucloud镜像: [ucloud教程](https://livetalking-doc.readthedocs.io/zh-cn/latest/ucloud/ucloud.html) - autodl镜像: [autodl教程](https://livetalking-doc.readthedocs.io/zh-cn/latest/autodl/README.html),autodl由于不能开放udp端口,需要部署转发服务,如果看不到视频,请自行部署srs或turn服务 ## 5. 性能 - 性能主要跟cpu和gpu相关,每路视频压缩需要消耗cpu,cpu性能与视频分辨率正相关;每路口型推理跟gpu性能相关。 - 不说话时的并发数跟cpu相关,同时说话的并发数跟gpu相关。 - 后端日志inferfps表示显卡推理帧率,finalfps表示最终推流帧率。两者都要在25以上才能实时。如果inferfps在25以上,finalfps达不到25表示cpu性能不足。 - 实时推理性能 模型 |显卡型号 |fps :---- |:--- |:--- wav2lip256 | 3060 | 60 wav2lip256 | 3080Ti | 120 musetalk | 3080Ti | 42 musetalk | 3090 | 45 musetalk | 4090 | 72 wav2lip256显卡3060以上即可,musetalk需要3080Ti以上。 ## 6. 商业版 提供如下扩展功能,适用于对开源项目已经比较熟悉,需要扩展产品功能的用户 1. 高清wav2lip模型 2. 完全语音交互,数字人回答过程中支持通过唤醒词或者按钮打断提问 3. 实时同步字幕,给前端提供数字人每句话播报开始、结束事件 4. 每个连接可以指定对应avatar和音色,avatar图片加载加速 5. 支持不限时长的数字人形象avatar 6. 提供实时音频流输入接口 7. 数字人透明背景,叠加动态背景 8. avatar实时切换, 同一个画面里支持多个数字人 9. 摄像头驱动数字人形象动作和表情 更多详情 ## 7. 声明 基于本项目开发并发布在B站、视频号、抖音等网站上的视频需带上LiveTalking水印和标识。 --- 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 ================================================ FILE: app.py ================================================ ############################################################################### # Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking # email: lipku@foxmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### # server.py from flask import Flask, render_template,send_from_directory,request, jsonify from flask_sockets import Sockets import base64 import json #import gevent #from gevent import pywsgi #from geventwebsocket.handler import WebSocketHandler import re import numpy as np from threading import Thread,Event #import multiprocessing import torch.multiprocessing as mp from aiohttp import web import aiohttp import aiohttp_cors from aiortc import RTCPeerConnection, RTCSessionDescription,RTCIceServer,RTCConfiguration from aiortc.rtcrtpsender import RTCRtpSender from webrtc import HumanPlayer from basereal import BaseReal from llm import llm_response import argparse import random import shutil import asyncio import torch from typing import Dict from logger import logger import gc app = Flask(__name__) #sockets = Sockets(app) nerfreals:Dict[int, BaseReal] = {} #sessionid:BaseReal opt = None model = None avatar = None #####webrtc############################### pcs = set() def randN(N)->int: '''生成长度为 N的随机数 ''' min = pow(10, N - 1) max = pow(10, N) return random.randint(min, max - 1) def build_nerfreal(sessionid:int)->BaseReal: opt.sessionid=sessionid if opt.model == 'wav2lip': from lipreal import LipReal nerfreal = LipReal(opt,model,avatar) elif opt.model == 'musetalk': from musereal import MuseReal nerfreal = MuseReal(opt,model,avatar) # elif opt.model == 'ernerf': # from nerfreal import NeRFReal # nerfreal = NeRFReal(opt,model,avatar) elif opt.model == 'ultralight': from lightreal import LightReal nerfreal = LightReal(opt,model,avatar) return nerfreal #@app.route('/offer', methods=['POST']) async def offer(request): params = await request.json() offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"]) # if len(nerfreals) >= opt.max_session: # logger.info('reach max session') # return web.Response( # content_type="application/json", # text=json.dumps( # {"code": -1, "msg": "reach max session"} # ), # ) sessionid = randN(6) #len(nerfreals) nerfreals[sessionid] = None logger.info('sessionid=%d, session num=%d',sessionid,len(nerfreals)) nerfreal = await asyncio.get_event_loop().run_in_executor(None, build_nerfreal,sessionid) nerfreals[sessionid] = nerfreal #ice_server = RTCIceServer(urls='stun:stun.l.google.com:19302') ice_server = RTCIceServer(urls='stun:stun.freeswitch.org:3478') pc = RTCPeerConnection(configuration=RTCConfiguration(iceServers=[ice_server])) pcs.add(pc) @pc.on("connectionstatechange") async def on_connectionstatechange(): logger.info("Connection state is %s" % pc.connectionState) if pc.connectionState == "failed": await pc.close() pcs.discard(pc) del nerfreals[sessionid] if pc.connectionState == "closed": pcs.discard(pc) del nerfreals[sessionid] # gc.collect() 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): try: params = await request.json() sessionid = params.get('sessionid',0) if params.get('interrupt'): nerfreals[sessionid].flush_talk() if params['type']=='echo': nerfreals[sessionid].put_msg_txt(params['text']) elif params['type']=='chat': 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, "msg":"ok"} ), ) except Exception as e: logger.exception('exception:') return web.Response( content_type="application/json", text=json.dumps( {"code": -1, "msg": str(e)} ), ) async def interrupt_talk(request): try: params = await request.json() sessionid = params.get('sessionid',0) nerfreals[sessionid].flush_talk() return web.Response( content_type="application/json", text=json.dumps( {"code": 0, "msg":"ok"} ), ) except Exception as e: logger.exception('exception:') return web.Response( content_type="application/json", text=json.dumps( {"code": -1, "msg": str(e)} ), ) 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: logger.exception('exception:') return web.Response( content_type="application/json", text=json.dumps( {"code": -1, "msg": str(e)} ), ) async def set_audiotype(request): try: params = await request.json() sessionid = params.get('sessionid',0) nerfreals[sessionid].set_custom_state(params['audiotype'],params['reinit']) return web.Response( content_type="application/json", text=json.dumps( {"code": 0, "msg":"ok"} ), ) except Exception as e: logger.exception('exception:') return web.Response( content_type="application/json", text=json.dumps( {"code": -1, "msg": str(e)} ), ) async def record(request): try: 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() elif params['type']=='end_record': nerfreals[sessionid].stop_recording() return web.Response( content_type="application/json", text=json.dumps( {"code": 0, "msg":"ok"} ), ) except Exception as e: logger.exception('exception:') return web.Response( content_type="application/json", text=json.dumps( {"code": -1, "msg": str(e)} ), ) 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: logger.info(f'Error: {e}') async def run(push_url,sessionid): nerfreal = await asyncio.get_event_loop().run_in_executor(None, build_nerfreal,sessionid) nerfreals[sessionid] = nerfreal pc = RTCPeerConnection() pcs.add(pc) @pc.on("connectionstatechange") async def on_connectionstatechange(): logger.info("Connection state is %s" % pc.connectionState) if pc.connectionState == "failed": await pc.close() pcs.discard(pc) player = HumanPlayer(nerfreals[sessionid]) 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__': mp.set_start_method('spawn') parser = argparse.ArgumentParser() # audio FPS parser.add_argument('--fps', type=int, default=50, help="audio fps,must be 50") # sliding window left-middle-right length (unit: 20ms) parser.add_argument('-l', type=int, default=10) parser.add_argument('-m', type=int, default=8) parser.add_argument('-r', type=int, default=10) parser.add_argument('--W', type=int, default=450, help="GUI width") parser.add_argument('--H', type=int, default=450, help="GUI height") #musetalk opt parser.add_argument('--avatar_id', type=str, default='avator_1', help="define which avatar in data/avatars") #parser.add_argument('--bbox_shift', type=int, default=5) parser.add_argument('--batch_size', type=int, default=16, help="infer batch") parser.add_argument('--customvideo_config', type=str, default='', help="custom action json") parser.add_argument('--tts', type=str, default='edgetts', help="tts service type") #xtts gpt-sovits cosyvoice fishtts tencent doubao indextts2 azuretts parser.add_argument('--REF_FILE', type=str, default="zh-CN-YunxiaNeural",help="参考文件名或语音模型ID,默认值为 edgetts的语音模型ID zh-CN-YunxiaNeural, 若--tts指定为azuretts, 可以使用Azure语音模型ID, 如zh-CN-XiaoxiaoMultilingualNeural") 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='musetalk') #musetalk wav2lip ultralight parser.add_argument('--transport', type=str, default='rtcpush') #webrtc rtcpush virtualcam 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, help="web listen port") opt = parser.parse_args() #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 nerfreal import NeRFReal,load_model,load_avatar # model = load_model(opt) # avatar = load_avatar(opt) if opt.model == 'musetalk': from musereal import MuseReal,load_model,load_avatar,warm_up logger.info(opt) model = load_model() avatar = load_avatar(opt.avatar_id) warm_up(opt.batch_size,model) elif opt.model == 'wav2lip': from lipreal import LipReal,load_model,load_avatar,warm_up logger.info(opt) model = load_model("./models/wav2lip.pth") avatar = load_avatar(opt.avatar_id) warm_up(opt.batch_size,model,256) elif opt.model == 'ultralight': from lightreal import LightReal,load_model,load_avatar,warm_up logger.info(opt) model = load_model(opt) avatar = load_avatar(opt.avatar_id) warm_up(opt.batch_size,avatar,160) # if opt.transport=='rtmp': # thread_quit = Event() # nerfreals[0] = build_nerfreal(0) # rendthrd = Thread(target=nerfreals[0].render,args=(thread_quit,)) # rendthrd.start() if opt.transport=='virtualcam': thread_quit = Event() nerfreals[0] = build_nerfreal(0) rendthrd = Thread(target=nerfreals[0].render,args=(thread_quit,)) rendthrd.start() ############################################################################# appasync = web.Application(client_max_size=1024**2*100) 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("/interrupt_talk", interrupt_talk) appasync.router.add_post("/is_speaking", is_speaking) appasync.router.add_static('/',path='web') # 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' logger.info('start http server; http://:'+str(opt.listenport)+'/'+pagename) logger.info('如果使用webrtc,推荐访问webrtc集成前端: http://:'+str(opt.listenport)+'/dashboard.html') 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': for k in range(opt.max_session): push_url = opt.push_url if k!=0: push_url = opt.push_url+str(k) loop.run_until_complete(run(push_url,k)) 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() ================================================ FILE: assets/faq.md ================================================ 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/\/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 ================================================ FILE: baseasr.py ================================================ ############################################################################### # Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking # email: lipku@foxmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### import time import numpy as np import queue from queue import Queue import torch.multiprocessing as mp from basereal import BaseReal class BaseASR: def __init__(self, opt, parent:BaseReal = 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 flush_talk(self): self.queue.queue.clear() def put_audio_frame(self,audio_chunk,datainfo:dict): #16khz 20ms pcm self.queue.put((audio_chunk,datainfo)) #return frame:audio pcm; type: 0-normal speak, 1-silence; eventpoint:custom event sync with audio def get_audio_frame(self): try: frame,eventpoint = 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 eventpoint = None return frame,type,eventpoint #return frame:audio pcm; type: 0-normal speak, 1-silence; eventpoint:custom event sync with audio def get_audio_out(self): return self.output_queue.get() def warm_up(self): for _ in range(self.stride_left_size + self.stride_right_size): audio_frame,type,eventpoint=self.get_audio_frame() self.frames.append(audio_frame) self.output_queue.put((audio_frame,type,eventpoint)) 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) ================================================ FILE: basereal.py ================================================ ############################################################################### # Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking # email: lipku@foxmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### import math import torch import numpy as np import subprocess import os import time import cv2 import glob import resampy import queue from queue import Queue from threading import Thread, Event from io import BytesIO import soundfile as sf import asyncio from av import AudioFrame, VideoFrame import av from fractions import Fraction from ttsreal import EdgeTTS,SovitsTTS,XTTS,CosyVoiceTTS,FishTTS,TencentTTS,DoubaoTTS,IndexTTS2,AzureTTS from logger import logger from tqdm import tqdm def read_imgs(img_list): frames = [] logger.info('reading images...') for img_path in tqdm(img_list): frame = cv2.imread(img_path) frames.append(frame) return frames def play_audio(quit_event,queue): import pyaudio p = pyaudio.PyAudio() stream = p.open( rate=16000, channels=1, format=8, output=True, output_device_index=1, ) stream.start_stream() # while queue.qsize() <= 0: # time.sleep(0.1) while not quit_event.is_set(): stream.write(queue.get(block=True)) stream.close() 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) self.sessionid = self.opt.sessionid if opt.tts == "edgetts": self.tts = EdgeTTS(opt,self) elif opt.tts == "gpt-sovits": self.tts = SovitsTTS(opt,self) elif opt.tts == "xtts": self.tts = XTTS(opt,self) elif opt.tts == "cosyvoice": self.tts = CosyVoiceTTS(opt,self) elif opt.tts == "fishtts": self.tts = FishTTS(opt,self) elif opt.tts == "tencent": self.tts = TencentTTS(opt,self) elif opt.tts == "doubao": self.tts = DoubaoTTS(opt,self) elif opt.tts == "indextts2": self.tts = IndexTTS2(opt,self) elif opt.tts == "azuretts": self.tts = AzureTTS(opt,self) self.speaking = False self.recording = False self._record_video_pipe = None self._record_audio_pipe = None self.width = self.height = 0 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,datainfo:dict={}): self.tts.put_msg_txt(msg,datainfo) def put_audio_frame(self,audio_chunk,datainfo:dict={}): #16khz 20ms pcm self.asr.put_audio_frame(audio_chunk,datainfo) def put_audio_file(self,filebyte,datainfo:dict={}): 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],datainfo) 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 logger.info(f'[INFO]put audio stream {sample_rate}: {stream.shape}') stream = stream.astype(np.float32) if stream.ndim > 1: logger.info(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: logger.info(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 flush_talk(self): self.tts.flush_talk() self.asr.flush_talk() def is_speaking(self)->bool: return self.speaking def __loadcustom(self): for item in self.opt.customopt: logger.info(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 notify(self,eventpoint): logger.info("notify:%s",eventpoint) def start_recording(self): """开始录制视频""" if self.recording: return command = ['ffmpeg', '-y', '-an', '-f', 'rawvideo', '-vcodec','rawvideo', '-pix_fmt', 'bgr24', #像素格式 '-s', "{}x{}".format(self.width, self.height), '-r', str(25), '-i', '-', '-pix_fmt', 'yuv420p', '-vcodec', "h264", #'-f' , 'flv', f'temp{self.opt.sessionid}.mp4'] self._record_video_pipe = subprocess.Popen(command, shell=False, stdin=subprocess.PIPE) acommand = ['ffmpeg', '-y', '-vn', '-f', 's16le', #'-acodec','pcm_s16le', '-ac', '1', '-ar', '16000', '-i', '-', '-acodec', 'aac', #'-f' , 'wav', f'temp{self.opt.sessionid}.aac'] self._record_audio_pipe = subprocess.Popen(acommand, shell=False, stdin=subprocess.PIPE) 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_video_data(self,image): if self.width == 0: print("image.shape:",image.shape) self.height,self.width,_ = image.shape if self.recording: self._record_video_pipe.stdin.write(image.tostring()) def record_audio_data(self,frame): if self.recording: self._record_audio_pipe.stdin.write(frame.tostring()) # 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 self._record_video_pipe.stdin.close() #wait() self._record_video_pipe.wait() self._record_audio_pipe.stdin.close() self._record_audio_pipe.wait() cmd_combine_audio = f"ffmpeg -y -i temp{self.opt.sessionid}.aac -i temp{self.opt.sessionid}.mp4 -c:v copy -c:a copy data/record.mp4" os.system(cmd_combine_audio) #os.remove(output_path) 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_custom_state(self,audiotype, reinit=True): print('set_custom_state:',audiotype) if self.custom_audio_index.get(audiotype) is None: return self.curr_state = audiotype if reinit: self.custom_audio_index[audiotype] = 0 self.custom_index[audiotype] = 0 def process_frames(self,quit_event,loop=None,audio_track=None,video_track=None): enable_transition = False # 设置为False禁用过渡效果,True启用 if enable_transition: _last_speaking = False _transition_start = time.time() _transition_duration = 0.1 # 过渡时间 _last_silent_frame = None # 静音帧缓存 _last_speaking_frame = None # 说话帧缓存 if self.opt.transport=='virtualcam': import pyvirtualcam vircam = None audio_tmp = queue.Queue(maxsize=3000) audio_thread = Thread(target=play_audio, args=(quit_event,audio_tmp,), daemon=True, name="pyaudio_stream") audio_thread.start() 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 enable_transition: # 检测状态变化 current_speaking = not (audio_frames[0][1]!=0 and audio_frames[1][1]!=0) if current_speaking != _last_speaking: logger.info(f"状态切换:{'说话' if _last_speaking else '静音'} → {'说话' if current_speaking else '静音'}") _transition_start = time.time() _last_speaking = current_speaking 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]) target_frame = self.custom_img_cycle[audiotype][mirindex] self.custom_index[audiotype] += 1 else: target_frame = self.frame_list_cycle[idx] if enable_transition: # 说话→静音过渡 if time.time() - _transition_start < _transition_duration and _last_speaking_frame is not None: alpha = min(1.0, (time.time() - _transition_start) / _transition_duration) combine_frame = cv2.addWeighted(_last_speaking_frame, 1-alpha, target_frame, alpha, 0) else: combine_frame = target_frame # 缓存静音帧 _last_silent_frame = combine_frame.copy() else: combine_frame = target_frame else: self.speaking = True try: current_frame = self.paste_back_frame(res_frame,idx) except Exception as e: logger.warning(f"paste_back_frame error: {e}") continue if enable_transition: # 静音→说话过渡 if time.time() - _transition_start < _transition_duration and _last_silent_frame is not None: alpha = min(1.0, (time.time() - _transition_start) / _transition_duration) combine_frame = cv2.addWeighted(_last_silent_frame, 1-alpha, current_frame, alpha, 0) else: combine_frame = current_frame # 缓存说话帧 _last_speaking_frame = combine_frame.copy() else: combine_frame = current_frame cv2.putText(combine_frame, "LiveTalking", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (128,128,128), 1) if self.opt.transport=='virtualcam': if vircam==None: height, width,_= combine_frame.shape vircam = pyvirtualcam.Camera(width=width, height=height, fps=25, fmt=pyvirtualcam.PixelFormat.BGR,print_fps=True) vircam.send(combine_frame) else: #webrtc image = combine_frame new_frame = VideoFrame.from_ndarray(image, format="bgr24") asyncio.run_coroutine_threadsafe(video_track._queue.put((new_frame,None)), loop) self.record_video_data(combine_frame) for audio_frame in audio_frames: frame,type,eventpoint = audio_frame frame = (frame * 32767).astype(np.int16) if self.opt.transport=='virtualcam': audio_tmp.put(frame.tobytes()) #TODO else: #webrtc new_frame = AudioFrame(format='s16', layout='mono', samples=frame.shape[0]) new_frame.planes[0].update(frame.tobytes()) new_frame.sample_rate=16000 asyncio.run_coroutine_threadsafe(audio_track._queue.put((new_frame,eventpoint)), loop) self.record_audio_data(frame) if self.opt.transport=='virtualcam': vircam.sleep_until_next_frame() if self.opt.transport=='virtualcam': audio_thread.join() vircam.close() logger.info('basereal process_frames thread stop') # 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 ================================================ FILE: hubertasr.py ================================================ import time import torch import numpy as np from baseasr import BaseASR from ultralight.audio2feature import Audio2Feature # hubert audio feature class HubertASR(BaseASR): #audio_feat_length: select audio feature before and after def __init__(self, opt, parent, audio_processor:Audio2Feature,audio_feat_length = [8,8]): super().__init__(opt, parent) self.audio_processor = audio_processor #self.stride_left_size = 32 #self.stride_right_size = 32 self.audio_feat_length = audio_feat_length def run_step(self): start_time = time.time() for _ in range(self.batch_size * 2): audio_frame, type,eventpoint = self.get_audio_frame() self.frames.append(audio_frame) self.output_queue.put((audio_frame, type,eventpoint)) if len(self.frames) <= self.stride_left_size + self.stride_right_size: return inputs = np.concatenate(self.frames) # [N * chunk] mel = self.audio_processor.get_hubert_from_16k_speech(inputs) mel_chunks=self.audio_processor.feature2chunks(feature_array=mel,fps=self.fps/2,batch_size=self.batch_size,audio_feat_length = self.audio_feat_length, start=self.stride_left_size/2) self.feat_queue.put(mel_chunks) self.frames = self.frames[-(self.stride_left_size + self.stride_right_size):] #print(f"Processing audio costs {(time.time() - start_time) * 1000}ms") ================================================ FILE: lightreal.py ================================================ ############################################################################### # Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking # email: lipku@foxmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### import math import torch import numpy as np #from .utils import * import os import time import cv2 import glob import pickle import copy import queue from queue import Queue from threading import Thread, Event import torch.multiprocessing as mp from hubertasr import HubertASR import asyncio from av import AudioFrame, VideoFrame from basereal import BaseReal #from imgcache import ImgCache from tqdm import tqdm #new import os import cv2 import torch import numpy as np import torch.nn as nn from torch import optim from tqdm import tqdm from transformers import Wav2Vec2Processor, HubertModel from torch.utils.data import DataLoader from ultralight.unet import Model from ultralight.audio2feature import Audio2Feature from logger import logger device = "cuda" if torch.cuda.is_available() else ("mps" if (hasattr(torch.backends, "mps") and torch.backends.mps.is_available()) else "cpu") print('Using {} for inference.'.format(device)) def load_model(opt): audio_processor = Audio2Feature() return audio_processor def load_avatar(avatar_id): avatar_path = f"./data/avatars/{avatar_id}" full_imgs_path = f"{avatar_path}/full_imgs" face_imgs_path = f"{avatar_path}/face_imgs" coords_path = f"{avatar_path}/coords.pkl" model = Model(6, 'hubert').to(device) # 假设Model是你自定义的类 model.load_state_dict(torch.load(f"{avatar_path}/ultralight.pth")) with open(coords_path, 'rb') as f: coord_list_cycle = pickle.load(f) input_img_list = glob.glob(os.path.join(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])) frame_list_cycle = read_imgs(input_img_list) #self.imagecache = ImgCache(len(self.coord_list_cycle),self.full_imgs_path,1000) 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) return model.eval(),frame_list_cycle,face_list_cycle,coord_list_cycle @torch.no_grad() def warm_up(batch_size,avatar,modelres): logger.info('warmup model...') model,_,_,_ = avatar img_batch = torch.ones(batch_size, 6, modelres, modelres).to(device) mel_batch = torch.ones(batch_size, 16, 32, 32).to(device) model(img_batch, mel_batch) def read_imgs(img_list): frames = [] logger.info('reading images...') for img_path in tqdm(img_list): frame = cv2.imread(img_path) frames.append(frame) return frames def get_audio_features(features, index): left = index - 8 right = index + 8 pad_left = 0 pad_right = 0 if left < 0: pad_left = -left left = 0 if right > features.shape[0]: pad_right = right - features.shape[0] right = features.shape[0] auds = torch.from_numpy(features[left:right]) if pad_left > 0: auds = torch.cat([torch.zeros_like(auds[:pad_left]), auds], dim=0) if pad_right > 0: auds = torch.cat([auds, torch.zeros_like(auds[:pad_right])], dim=0) # [8, 16] return auds def read_lms(lms_list): land_marks = [] logger.info('reading lms...') for lms_path in tqdm(lms_list): file_landmarks = [] # Store landmarks for this file with open(lms_path, "r") as f: lines = f.read().splitlines() for line in lines: arr = list(filter(None, line.split(" "))) if arr: arr = np.array(arr, dtype=np.float32) file_landmarks.append(arr) land_marks.append(file_landmarks) # Add the file's landmarks to the overall list return land_marks 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(quit_event, batch_size, face_list_cycle, audio_feat_queue, audio_out_queue, res_frame_queue, model): length = len(face_list_cycle) index = 0 count = 0 counttime = 0 logger.info('start inference') while not quit_event.is_set(): starttime=time.perf_counter() 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_,eventpoint = audio_out_queue.get() audio_frames.append((frame,type_,eventpoint)) 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: t = time.perf_counter() img_batch = [] for i in range(batch_size): idx = __mirror_index(length, index + i) #face = face_list_cycle[idx] crop_img = face_list_cycle[idx] #face[ymin:ymax, xmin:xmax] # h, w = crop_img.shape[:2] #crop_img = cv2.resize(crop_img, (168, 168), cv2.INTER_AREA) #crop_img_ori = crop_img.copy() img_real_ex = crop_img[4:164, 4:164].copy() img_real_ex_ori = img_real_ex.copy() img_masked = cv2.rectangle(img_real_ex_ori,(5,5,150,145),(0,0,0),-1) img_masked = img_masked.transpose(2,0,1).astype(np.float32) img_real_ex = img_real_ex.transpose(2,0,1).astype(np.float32) img_real_ex_T = torch.from_numpy(img_real_ex / 255.0) img_masked_T = torch.from_numpy(img_masked / 255.0) img_concat_T = torch.cat([img_real_ex_T, img_masked_T], axis=0)[None] img_batch.append(img_concat_T) reshaped_mel_batch = [arr.reshape(16, 32, 32) for arr in mel_batch] mel_batch = torch.stack([torch.from_numpy(arr) for arr in reshaped_mel_batch]) img_batch = torch.stack(img_batch).squeeze(1) with torch.no_grad(): pred = model(img_batch.cuda(),mel_batch.cuda()) pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255. counttime += (time.perf_counter() - t) count += batch_size if count >= 100: logger.info(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 # for i, pred_frame in enumerate(pred): # pred_frame_uint8 = np.array(pred_frame, dtype=np.uint8) # res_frame_queue.put((pred_frame_uint8, __mirror_index(length, index), audio_frames[i * 2:i * 2 + 2])) # index = (index + 1) % length #print('total batch time:', time.perf_counter() - starttime) logger.info('lightreal inference processor stop') class LightReal(BaseReal): @torch.no_grad() def __init__(self, opt, model, avatar): 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 self.batch_size = opt.batch_size self.idx = 0 self.res_frame_queue = Queue(self.batch_size*2) #mp.Queue #self.__loadavatar() audio_processor = model self.model,self.frame_list_cycle,self.face_list_cycle,self.coord_list_cycle = avatar self.asr = HubertASR(opt,self,audio_processor,audio_feat_length =[4,4]) self.asr.warm_up() #self.__warm_up() self.render_event = mp.Event() # def __del__(self): # logger.info(f'lightreal({self.sessionid}) delete') def paste_back_frame(self,pred_frame,idx:int): bbox = self.coord_list_cycle[idx] combine_frame = copy.deepcopy(self.frame_list_cycle[idx]) x1, y1, x2, y2 = bbox crop_img = self.face_list_cycle[idx] crop_img_ori = crop_img.copy() #res_frame = np.array(res_frame, dtype=np.uint8) crop_img_ori[4:164, 4:164] = pred_frame.astype(np.uint8) crop_img_ori = cv2.resize(crop_img_ori, (x2-x1,y2-y1)) combine_frame[y1:y2, x1:x2] = crop_img_ori return combine_frame def render(self,quit_event,loop=None,audio_track=None,video_track=None): #if self.opt.asr: # self.asr.warm_up() self.init_customindex() self.tts.render(quit_event) infer_quit_event = Event() infer_thread = Thread(target=inference, args=(infer_quit_event,self.batch_size,self.face_list_cycle,self.asr.feat_queue,self.asr.output_queue,self.res_frame_queue, self.model,)) #mp.Process infer_thread.start() process_quit_event = Event() process_thread = Thread(target=self.process_frames, args=(process_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 and video_track._queue.qsize()>=5: logger.debug('sleep qsize=%d',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 logger.info('lightreal thread stop') infer_quit_event.set() infer_thread.join() process_quit_event.set() process_thread.join() ================================================ FILE: lipasr.py ================================================ ############################################################################### # Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking # email: lipku@foxmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### 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,eventpoint = self.get_audio_frame() self.frames.append(frame) # put to output self.output_queue.put((frame,type,eventpoint)) # 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):] ================================================ FILE: lipreal.py ================================================ ############################################################################### # Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking # email: lipku@foxmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### import math import torch import numpy as np #from .utils import * import os import time import cv2 import glob import pickle import copy import queue from queue import Queue from threading import Thread, Event import torch.multiprocessing as mp 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 from logger import logger device = "cuda" if torch.cuda.is_available() else ("mps" if (hasattr(torch.backends, "mps") and torch.backends.mps.is_available()) else "cpu") print('Using {} for inference.'.format(device)) def _load(checkpoint_path): if device == 'cuda': checkpoint = torch.load(checkpoint_path) #,weights_only=True else: checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage) return checkpoint def load_model(path): model = Wav2Lip() logger.info("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 load_avatar(avatar_id): avatar_path = f"./data/avatars/{avatar_id}" full_imgs_path = f"{avatar_path}/full_imgs" face_imgs_path = f"{avatar_path}/face_imgs" coords_path = f"{avatar_path}/coords.pkl" with open(coords_path, 'rb') as f: coord_list_cycle = pickle.load(f) input_img_list = glob.glob(os.path.join(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])) frame_list_cycle = read_imgs(input_img_list) #self.imagecache = ImgCache(len(self.coord_list_cycle),self.full_imgs_path,1000) 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) return frame_list_cycle,face_list_cycle,coord_list_cycle @torch.no_grad() def warm_up(batch_size,model,modelres): # 预热函数 logger.info('warmup model...') img_batch = torch.ones(batch_size, 6, modelres, modelres).to(device) mel_batch = torch.ones(batch_size, 1, 80, 16).to(device) model(mel_batch, img_batch) def read_imgs(img_list): frames = [] logger.info('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(quit_event,batch_size,face_list_cycle,audio_feat_queue,audio_out_queue,res_frame_queue,model): #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 logger.info('start inference') while not quit_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,eventpoint = audio_out_queue.get() audio_frames.append((frame,type,eventpoint)) 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: logger.info(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) logger.info('lipreal inference processor stop') class LipReal(BaseReal): @torch.no_grad() def __init__(self, opt, model, avatar): 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 self.batch_size = opt.batch_size self.idx = 0 self.res_frame_queue = Queue(self.batch_size*2) #mp.Queue #self.__loadavatar() self.model = model self.frame_list_cycle,self.face_list_cycle,self.coord_list_cycle = avatar self.asr = LipASR(opt,self) self.asr.warm_up() self.render_event = mp.Event() # def __del__(self): # logger.info(f'lipreal({self.sessionid}) delete') def paste_back_frame(self,pred_frame,idx:int): 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 res_frame = cv2.resize(pred_frame.astype(np.uint8),(x2-x1,y2-y1)) #combine_frame = get_image(ori_frame,res_frame,bbox) #t=time.perf_counter() combine_frame[y1:y2, x1:x2] = res_frame return combine_frame def render(self,quit_event,loop=None,audio_track=None,video_track=None): #if self.opt.asr: # self.asr.warm_up() self.init_customindex() self.tts.render(quit_event) infer_quit_event = Event() infer_thread = Thread(target=inference, args=(infer_quit_event,self.batch_size,self.face_list_cycle, self.asr.feat_queue,self.asr.output_queue,self.res_frame_queue, self.model,)) #mp.Process infer_thread.start() process_quit_event = Event() process_thread = Thread(target=self.process_frames, args=(process_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 and video_track._queue.qsize()>=5: logger.debug('sleep qsize=%d',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 logger.info('lipreal thread stop') infer_quit_event.set() infer_thread.join() process_quit_event.set() process_thread.join() ================================================ FILE: llm.py ================================================ import time import os from basereal import BaseReal from logger import logger def llm_response(message,nerfreal:BaseReal): 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() logger.info(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() logger.info(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: logger.info(result) nerfreal.put_msg_txt(result) result="" result = result+msg[lastpos:] end = time.perf_counter() logger.info(f"llm Time to last chunk: {end-start}s") nerfreal.put_msg_txt(result) ================================================ FILE: logger.py ================================================ import logging # 配置日志器 logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') fhandler = logging.FileHandler('livetalking.log') # 可以改为StreamHandler输出到控制台或多个Handler组合使用等。 fhandler.setFormatter(formatter) fhandler.setLevel(logging.INFO) logger.addHandler(fhandler) # handler = logging.StreamHandler() # handler.setLevel(logging.DEBUG) # sformatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') # handler.setFormatter(sformatter) # logger.addHandler(handler) ================================================ FILE: museasr.py ================================================ ############################################################################### # Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking # email: lipku@foxmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### 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,eventpoint = self.get_audio_frame() self.frames.append(audio_frame) self.output_queue.put((audio_frame,type,eventpoint)) 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):] ================================================ FILE: musereal.py ================================================ ############################################################################### # Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking # email: lipku@foxmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### 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 import torch.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.myutil import get_image_blending from musetalk.utils.utils import load_all_model from musetalk.whisper.audio2feature import Audio2Feature from museasr import MuseASR import asyncio from av import AudioFrame, VideoFrame from basereal import BaseReal from tqdm import tqdm from logger import logger def load_model(): # load model weights vae, unet, pe = load_all_model() device = torch.device("cuda" if torch.cuda.is_available() else ("mps" if (hasattr(torch.backends, "mps") and torch.backends.mps.is_available()) else "cpu")) timesteps = torch.tensor([0], device=device) pe = pe.half().to(device) vae.vae = vae.vae.half().to(device) #vae.vae.share_memory().to(device) unet.model = unet.model.half().to(device) #unet.model.share_memory() # Initialize audio processor and Whisper model audio_processor = Audio2Feature(model_path="./models/whisper") return vae, unet, pe, timesteps, audio_processor def load_avatar(avatar_id): #self.video_path = '' #video_path #self.bbox_shift = opt.bbox_shift avatar_path = f"./data/avatars/{avatar_id}" full_imgs_path = f"{avatar_path}/full_imgs" coords_path = f"{avatar_path}/coords.pkl" latents_out_path= f"{avatar_path}/latents.pt" video_out_path = f"{avatar_path}/vid_output/" mask_out_path =f"{avatar_path}/mask" mask_coords_path =f"{avatar_path}/mask_coords.pkl" avatar_info_path = f"{avatar_path}/avator_info.json" # self.avatar_info = { # "avatar_id":self.avatar_id, # "video_path":self.video_path, # "bbox_shift":self.bbox_shift # } input_latent_list_cycle = torch.load(latents_out_path) #,weights_only=True with open(coords_path, 'rb') as f: coord_list_cycle = pickle.load(f) input_img_list = glob.glob(os.path.join(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])) frame_list_cycle = read_imgs(input_img_list) with open(mask_coords_path, 'rb') as f: mask_coords_list_cycle = pickle.load(f) input_mask_list = glob.glob(os.path.join(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])) mask_list_cycle = read_imgs(input_mask_list) return frame_list_cycle,mask_list_cycle,coord_list_cycle,mask_coords_list_cycle,input_latent_list_cycle @torch.no_grad() def warm_up(batch_size,model): # 预热函数 logger.info('warmup model...') vae, unet, pe, timesteps, audio_processor = model #batch_size = 16 #timesteps = torch.tensor([0], device=unet.device) whisper_batch = np.ones((batch_size, 50, 384), dtype=np.uint8) latent_batch = torch.ones(batch_size, 8, 32, 32).to(unet.device) 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) pred_latents = unet.model(latent_batch, timesteps, encoder_hidden_states=audio_feature_batch).sample vae.decode_latents(pred_latents) def read_imgs(img_list): frames = [] logger.info('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(quit_event,batch_size,input_latent_list_cycle,audio_feat_queue,audio_out_queue,res_frame_queue, vae, unet, pe,timesteps): #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() length = len(input_latent_list_cycle) index = 0 count=0 counttime=0 logger.info('start inference') while not quit_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,eventpoint = audio_out_queue.get() audio_frames.append((frame,type,eventpoint)) 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) # infer_inqueue.put((whisper_batch,latent_batch,sessionid)) # recon,outsessionid = infer_outqueue.get() # if outsessionid != sessionid: # print('outsessionid:',outsessionid,' mysessionid:',sessionid) # 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: logger.info(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) logger.info('musereal inference processor stop') class MuseReal(BaseReal): @torch.no_grad() def __init__(self, opt, model, avatar): 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 self.batch_size = opt.batch_size self.idx = 0 self.res_frame_queue = mp.Queue(self.batch_size*2) self.vae, self.unet, self.pe, self.timesteps, self.audio_processor = model self.frame_list_cycle,self.mask_list_cycle,self.coord_list_cycle,self.mask_coords_list_cycle, self.input_latent_list_cycle = avatar #self.__loadavatar() self.asr = MuseASR(opt,self,self.audio_processor) self.asr.warm_up() self.render_event = mp.Event() # def __del__(self): # logger.info(f'musereal({self.sessionid}) delete') 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) logger.info('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 paste_back_frame(self,pred_frame,idx:int): bbox = self.coord_list_cycle[idx] ori_frame = copy.deepcopy(self.frame_list_cycle[idx]) x1, y1, x2, y2 = bbox res_frame = cv2.resize(pred_frame.astype(np.uint8),(x2-x1,y2-y1)) mask = self.mask_list_cycle[idx] mask_crop_box = self.mask_coords_list_cycle[idx] combine_frame = get_image_blending(ori_frame,res_frame,bbox,mask,mask_crop_box) return combine_frame def render(self,quit_event,loop=None,audio_track=None,video_track=None): #if self.opt.asr: # self.asr.warm_up() self.init_customindex() self.tts.render(quit_event) #self.render_event.set() #start infer process render infer_quit_event = Event() infer_thread = Thread(target=inference, args=(infer_quit_event,self.batch_size,self.input_latent_list_cycle, self.asr.feat_queue,self.asr.output_queue,self.res_frame_queue, self.vae, self.unet, self.pe,self.timesteps)) #mp.Process infer_thread.start() process_quit_event = Event() process_thread = Thread(target=self.process_frames, args=(process_quit_event,loop,audio_track,video_track)) process_thread.start() 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 and video_track._queue.qsize()>=1.5*self.opt.batch_size: logger.debug('sleep qsize=%d',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) logger.info('musereal thread stop') infer_quit_event.set() infer_thread.join() process_quit_event.set() process_thread.join() ================================================ FILE: musetalk/genavatar.py ================================================ 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 from musetalk.utils.preprocessing import get_landmark_and_bbox, read_imgs from musetalk.utils.blending import get_image_prepare_material from musetalk.utils.utils import load_all_model 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.putText(frame, "LiveTalking", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (128,128,128), 1) 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/{avatar_id}') save_full_path = os.path.join(current_dir, f'./data/avatars/{avatar_id}/full_imgs') create_dir(save_path) create_dir(save_full_path) mask_out_path = os.path.join(current_dir, f'./data/avatars/{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": args.bbox_shift }, 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, args.bbox_shift) 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 if args.version == "v15": y2 = y2 + args.extra_margin y2 = min(y2, frame.shape[0]) coord_list[idx] = [x1, y1, x2, y2] # 更新coord_list中的bbox crop_frame = frame[y1:y2, x1:x2] resized_crop_frame = cv2.resize(crop_frame, (256, 256), interpolation=cv2.INTER_LANCZOS4) latents = vae.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) x1, y1, x2, y2 = coord_list_cycle[i] if args.version == "v15": mode = args.parsing_mode else: mode = "raw" mask, crop_box = get_image_prepare_material(frame, [x1, y1, x2, y2], fp=fp, mode=mode) 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 ("mps" if (hasattr(torch.backends, "mps") and torch.backends.mps.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='musetalk_avatar1', ) parser.add_argument("--version", type=str, default="v15", choices=["v1", "v15"], help="Version of MuseTalk: v1 or v15") parser.add_argument("--gpu_id", type=int, default=0, help="GPU ID to use") parser.add_argument("--left_cheek_width", type=int, default=90, help="Width of left cheek region") parser.add_argument("--right_cheek_width", type=int, default=90, help="Width of right cheek region") parser.add_argument("--bbox_shift", type=int, default=0, help="Bounding box shift value") parser.add_argument("--extra_margin", type=int, default=10, help="Extra margin for face cropping") parser.add_argument("--parsing_mode", default='jaw', help="Face blending parsing mode") args = parser.parse_args() # Set computing device device = torch.device(f"cuda:{args.gpu_id}" if torch.cuda.is_available() else "cpu") # Load model weights vae, unet, pe = load_all_model( device=device ) vae.vae = vae.vae.half().to(device) # Initialize face parser with configurable parameters based on version if args.version == "v15": fp = FaceParsing( left_cheek_width=args.left_cheek_width, right_cheek_width=args.right_cheek_width ) else: # v1 fp = FaceParsing() create_musetalk_human(args.file, args.avatar_id) ================================================ FILE: musetalk/myutil.py ================================================ import numpy as np import cv2 import copy 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 ================================================ FILE: musetalk/utils/__init__.py ================================================ import sys from os.path import abspath, dirname current_dir = dirname(abspath(__file__)) parent_dir = dirname(current_dir) sys.path.append(parent_dir+'/utils') ================================================ FILE: musetalk/utils/audio_processor.py ================================================ import math import os import librosa import numpy as np import torch from einops import rearrange from transformers import AutoFeatureExtractor class AudioProcessor: def __init__(self, feature_extractor_path="openai/whisper-tiny/"): self.feature_extractor = AutoFeatureExtractor.from_pretrained(feature_extractor_path) def get_audio_feature(self, wav_path, start_index=0, weight_dtype=None): if not os.path.exists(wav_path): return None librosa_output, sampling_rate = librosa.load(wav_path, sr=16000) assert sampling_rate == 16000 # Split audio into 30s segments segment_length = 30 * sampling_rate segments = [librosa_output[i:i + segment_length] for i in range(0, len(librosa_output), segment_length)] features = [] for segment in segments: audio_feature = self.feature_extractor( segment, return_tensors="pt", sampling_rate=sampling_rate ).input_features if weight_dtype is not None: audio_feature = audio_feature.to(dtype=weight_dtype) features.append(audio_feature) return features, len(librosa_output) def get_whisper_chunk( self, whisper_input_features, device, weight_dtype, whisper, librosa_length, fps=25, audio_padding_length_left=2, audio_padding_length_right=2, ): audio_feature_length_per_frame = 2 * (audio_padding_length_left + audio_padding_length_right + 1) whisper_feature = [] # Process multiple 30s mel input features for input_feature in whisper_input_features: input_feature = input_feature.to(device).to(weight_dtype) audio_feats = whisper.encoder(input_feature, output_hidden_states=True).hidden_states audio_feats = torch.stack(audio_feats, dim=2) whisper_feature.append(audio_feats) whisper_feature = torch.cat(whisper_feature, dim=1) # Trim the last segment to remove padding sr = 16000 audio_fps = 50 fps = int(fps) whisper_idx_multiplier = audio_fps / fps num_frames = math.floor((librosa_length / sr) * fps) actual_length = math.floor((librosa_length / sr) * audio_fps) whisper_feature = whisper_feature[:,:actual_length,...] # Calculate padding amount padding_nums = math.ceil(whisper_idx_multiplier) # Add padding at start and end whisper_feature = torch.cat([ torch.zeros_like(whisper_feature[:, :padding_nums * audio_padding_length_left]), whisper_feature, # Add extra padding to prevent out of bounds torch.zeros_like(whisper_feature[:, :padding_nums * 3 * audio_padding_length_right]) ], 1) audio_prompts = [] for frame_index in range(num_frames): try: audio_index = math.floor(frame_index * whisper_idx_multiplier) audio_clip = whisper_feature[:, audio_index: audio_index + audio_feature_length_per_frame] assert audio_clip.shape[1] == audio_feature_length_per_frame audio_prompts.append(audio_clip) except Exception as e: print(f"Error occurred: {e}") print(f"whisper_feature.shape: {whisper_feature.shape}") print(f"audio_clip.shape: {audio_clip.shape}") print(f"num frames: {num_frames}, fps: {fps}, whisper_idx_multiplier: {whisper_idx_multiplier}") print(f"frame_index: {frame_index}, audio_index: {audio_index}-{audio_index + audio_feature_length_per_frame}") exit() audio_prompts = torch.cat(audio_prompts, dim=0) # T, 10, 5, 384 audio_prompts = rearrange(audio_prompts, 'b c h w -> b (c h) w') return audio_prompts if __name__ == "__main__": audio_processor = AudioProcessor() wav_path = "./2.wav" audio_feature, librosa_feature_length = audio_processor.get_audio_feature(wav_path) print("Audio Feature shape:", audio_feature.shape) print("librosa_feature_length:", librosa_feature_length) ================================================ FILE: musetalk/utils/blending.py ================================================ from PIL import Image import numpy as np import cv2 import copy 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, mode="raw", fp=None): """ 对图像进行面部解析,生成面部区域的掩码。 Args: image (PIL.Image): 输入图像。 Returns: PIL.Image: 面部区域的掩码图像。 """ seg_image = fp(image, mode=mode) # 使用 FaceParsing 模型解析面部 if seg_image is None: print("error, no person_segment") # 如果没有检测到面部,返回错误 return None seg_image = seg_image.resize(image.size) # 将掩码图像调整为输入图像的大小 return seg_image def get_image(image, face, face_box, upper_boundary_ratio=0.5, expand=1.5, mode="raw", fp=None): """ 将裁剪的面部图像粘贴回原始图像,并进行一些处理。 Args: image (numpy.ndarray): 原始图像(身体部分)。 face (numpy.ndarray): 裁剪的面部图像。 face_box (tuple): 面部边界框的坐标 (x, y, x1, y1)。 upper_boundary_ratio (float): 用于控制面部区域的保留比例。 expand (float): 扩展因子,用于放大裁剪框。 mode: 融合mask构建方式 Returns: numpy.ndarray: 处理后的图像。 """ # 将 numpy 数组转换为 PIL 图像 body = Image.fromarray(image[:, :, ::-1]) # 身体部分图像(整张图) face = Image.fromarray(face[:, :, ::-1]) # 面部图像 x, y, x1, y1 = face_box # 获取面部边界框的坐标 crop_box, s = get_crop_box(face_box, expand) # 计算扩展后的裁剪框 x_s, y_s, x_e, y_e = crop_box # 裁剪框的坐标 face_position = (x, y) # 面部在原始图像中的位置 # 从身体图像中裁剪出扩展后的面部区域(下巴到边界有距离) face_large = body.crop(crop_box) ori_shape = face_large.size # 裁剪后图像的原始尺寸 # 对裁剪后的面部区域进行面部解析,生成掩码 mask_image = face_seg(face_large, mode=mode, fp=fp) mask_small = mask_image.crop((x - x_s, y - y_s, x1 - x_s, y1 - y_s)) # 裁剪出面部区域的掩码 mask_image = Image.new('L', ori_shape, 0) # 创建一个全黑的掩码图像 mask_image.paste(mask_small, (x - x_s, y - y_s, x1 - x_s, y1 - y_s)) # 将面部掩码粘贴到全黑图像上 # 保留面部区域的上半部分(用于控制说话区域) width, height = mask_image.size top_boundary = int(height * upper_boundary_ratio) # 计算上半部分的边界 modified_mask_image = Image.new('L', ori_shape, 0) # 创建一个新的全黑掩码图像 modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary)) # 粘贴上半部分掩码 # 对掩码进行高斯模糊,使边缘更平滑 blur_kernel_size = int(0.05 * ori_shape[0] // 2 * 2) + 1 # 计算模糊核大小 mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0) # 高斯模糊 #mask_array = np.array(modified_mask_image) mask_image = Image.fromarray(mask_array) # 将模糊后的掩码转换回 PIL 图像 # 将裁剪的面部图像粘贴回扩展后的面部区域 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) # 将 PIL 图像转换回 numpy 数组 return body[:, :, ::-1] # 返回处理后的图像(BGR 转 RGB) 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_prepare_material(image, face_box, upper_boundary_ratio=0.5, expand=1.5, fp=None, mode="raw"): 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, mode=mode, fp=fp) 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 ================================================ FILE: musetalk/utils/dwpose/default_runtime.py ================================================ 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() ================================================ FILE: musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py ================================================ #_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 ================================================ FILE: musetalk/utils/face_detection/README.md ================================================ 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. ================================================ FILE: musetalk/utils/face_detection/__init__.py ================================================ # -*- coding: utf-8 -*- __author__ = """Adrian Bulat""" __email__ = 'adrian.bulat@nottingham.ac.uk' __version__ = '1.0.1' from .api import FaceAlignment, LandmarksType, NetworkSize, YOLOv8_face ================================================ FILE: musetalk/utils/face_detection/api.py ================================================ 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__)) ================================================ FILE: musetalk/utils/face_detection/detection/__init__.py ================================================ from .core import FaceDetector ================================================ FILE: musetalk/utils/face_detection/detection/core.py ================================================ 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 ================================================ FILE: musetalk/utils/face_detection/detection/sfd/__init__.py ================================================ from .sfd_detector import SFDDetector as FaceDetector ================================================ FILE: musetalk/utils/face_detection/detection/sfd/bbox.py ================================================ 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 ================================================ FILE: musetalk/utils/face_detection/detection/sfd/detect.py ================================================ 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]) ================================================ FILE: musetalk/utils/face_detection/detection/sfd/net_s3fd.py ================================================ 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] ================================================ FILE: musetalk/utils/face_detection/detection/sfd/sfd_detector.py ================================================ 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 ================================================ FILE: musetalk/utils/face_detection/models.py ================================================ 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 ================================================ FILE: musetalk/utils/face_detection/utils.py ================================================ 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 ================================================ FILE: musetalk/utils/face_parsing/__init__.py ================================================ 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, left_cheek_width=80, right_cheek_width=80): self.net = self.model_init() self.preprocess = self.image_preprocess() # Ensure all size parameters are integers cone_height = 21 tail_height = 12 total_size = cone_height + tail_height # Create kernel with explicit integer dimensions kernel = np.zeros((total_size, total_size), dtype=np.uint8) center_x = total_size // 2 # Ensure center coordinates are integers # Cone part for row in range(cone_height): if row < cone_height//2: continue width = int(2 * (row - cone_height//2) + 1) start = int(center_x - (width // 2)) end = int(center_x + (width // 2) + 1) kernel[row, start:end] = 1 # Vertical extension part if cone_height > 0: base_width = int(kernel[cone_height-1].sum()) else: base_width = 1 for row in range(cone_height, total_size): start = max(0, int(center_x - (base_width//2))) end = min(total_size, int(center_x + (base_width//2) + 1)) kernel[row, start:end] = 1 self.kernel = kernel # Modify cheek erosion kernel to be flatter ellipse self.cheek_kernel = cv2.getStructuringElement( cv2.MORPH_ELLIPSE, (35, 3)) # Add cheek area mask (protect chin area) self.cheek_mask = self._create_cheek_mask(left_cheek_width=left_cheek_width, right_cheek_width=right_cheek_width) def _create_cheek_mask(self, left_cheek_width=80, right_cheek_width=80): """Create cheek area mask (1/4 area on both sides)""" mask = np.zeros((512, 512), dtype=np.uint8) center = 512 // 2 cv2.rectangle(mask, (0, 0), (center - left_cheek_width, 512), 255, -1) # Left cheek cv2.rectangle(mask, (center + right_cheek_width, 0), (512, 512), 255, -1) # Right cheek return mask def model_init(self, resnet_path='./models/face-parse-bisent/resnet18-5c106cde.pth', model_pth='./models/face-parse-bisent/79999_iter.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), mode="raw"): 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) # Add 14:neck, remove 10:nose and 7:8:9 if mode == "neck": parsing[np.isin(parsing, [1, 11, 12, 13, 14])] = 255 parsing[np.where(parsing!=255)] = 0 elif mode == "jaw": face_region = np.isin(parsing, [1])*255 face_region = face_region.astype(np.uint8) original_dilated = cv2.dilate(face_region, self.kernel, iterations=1) eroded = cv2.erode(original_dilated, self.cheek_kernel, iterations=2) face_region = cv2.bitwise_and(eroded, self.cheek_mask) face_region = cv2.bitwise_or(face_region, cv2.bitwise_and(original_dilated, ~self.cheek_mask)) parsing[(face_region==255) & (~np.isin(parsing, [10]))] = 255 parsing[np.isin(parsing, [11, 12, 13])] = 255 parsing[np.where(parsing!=255)] = 0 else: parsing[np.isin(parsing, [1, 11, 12, 13])] = 255 parsing[np.where(parsing!=255)] = 0 parsing = Image.fromarray(parsing.astype(np.uint8)) return parsing if __name__ == "__main__": fp = FaceParsing() segmap = fp('154_small.png') segmap.save('res.png') ================================================ FILE: musetalk/utils/face_parsing/model.py ================================================ #!/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() ================================================ FILE: musetalk/utils/face_parsing/resnet.py ================================================ #!/usr/bin/python # -*- encoding: utf-8 -*- import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.model_zoo as modelzoo # from modules.bn import InPlaceABNSync as BatchNorm2d resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): def __init__(self, in_chan, out_chan, stride=1): super(BasicBlock, self).__init__() self.conv1 = conv3x3(in_chan, out_chan, stride) self.bn1 = nn.BatchNorm2d(out_chan) self.conv2 = conv3x3(out_chan, out_chan) self.bn2 = nn.BatchNorm2d(out_chan) self.relu = nn.ReLU(inplace=True) self.downsample = None if in_chan != out_chan or stride != 1: self.downsample = nn.Sequential( nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_chan), ) def forward(self, x): residual = self.conv1(x) residual = F.relu(self.bn1(residual)) residual = self.conv2(residual) residual = self.bn2(residual) shortcut = x if self.downsample is not None: shortcut = self.downsample(x) out = shortcut + residual out = self.relu(out) return out def create_layer_basic(in_chan, out_chan, bnum, stride=1): layers = [BasicBlock(in_chan, out_chan, stride=stride)] for i in range(bnum-1): layers.append(BasicBlock(out_chan, out_chan, stride=1)) return nn.Sequential(*layers) class Resnet18(nn.Module): def __init__(self, model_path): super(Resnet18, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) self.init_weight(model_path) def forward(self, x): x = self.conv1(x) x = F.relu(self.bn1(x)) x = self.maxpool(x) x = self.layer1(x) feat8 = self.layer2(x) # 1/8 feat16 = self.layer3(feat8) # 1/16 feat32 = self.layer4(feat16) # 1/32 return feat8, feat16, feat32 def init_weight(self, model_path): state_dict = torch.load(model_path) #modelzoo.load_url(resnet18_url) self_state_dict = self.state_dict() for k, v in state_dict.items(): if 'fc' in k: continue self_state_dict.update({k: v}) self.load_state_dict(self_state_dict) 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 if __name__ == "__main__": net = Resnet18() x = torch.randn(16, 3, 224, 224) out = net(x) print(out[0].size()) print(out[1].size()) print(out[2].size()) net.get_params() ================================================ FILE: musetalk/utils/preprocessing.py ================================================ import sys from face_detection import FaceAlignment,LandmarksType from os import listdir, path import subprocess import numpy as np import cv2 import pickle import os import json from mmpose.apis import inference_topdown, init_model from mmpose.structures import merge_data_samples import torch from tqdm import tqdm # initialize the mmpose model device = torch.device("cuda" if torch.cuda.is_available() else "cpu") config_file = './musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py' checkpoint_file = './models/dwpose/dw-ll_ucoco_384.pth' model = init_model(config_file, checkpoint_file, device=device) # initialize the face detection model device = "cuda" if torch.cuda.is_available() else "cpu" fa = FaceAlignment(LandmarksType._2D, flip_input=False,device=device) # maker if the bbox is not sufficient coord_placeholder = (0.0,0.0,0.0,0.0) def resize_landmark(landmark, w, h, new_w, new_h): w_ratio = new_w / w h_ratio = new_h / h landmark_norm = landmark / [w, h] landmark_resized = landmark_norm * [new_w, new_h] return landmark_resized 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_bbox_range(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 = [] 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) text_range=f"Total frame:「{len(frames)}」 Manually adjust range : [ -{int(sum(average_range_minus) / len(average_range_minus))}~{int(sum(average_range_plus) / len(average_range_plus))} ] , the current value: {upperbondrange}" return text_range 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 = [] 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] min_upper_bond = 0 upper_bond = max(min_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] print("********************************************bbox_shift parameter adjustment**********************************************************") print(f"Total frame:「{len(frames)}」 Manually adjust range : [ -{int(sum(average_range_minus) / len(average_range_minus))}~{int(sum(average_range_plus) / len(average_range_plus))} ] , the current value: {upperbondrange}") print("*************************************************************************************************************************************") return coords_list,frames if __name__ == "__main__": img_list = ["./results/lyria/00000.png","./results/lyria/00001.png","./results/lyria/00002.png","./results/lyria/00003.png"] crop_coord_path = "./coord_face.pkl" coords_list,full_frames = get_landmark_and_bbox(img_list) with open(crop_coord_path, 'wb') as f: pickle.dump(coords_list, f) for bbox, frame in zip(coords_list,full_frames): if bbox == coord_placeholder: continue x1, y1, x2, y2 = bbox crop_frame = frame[y1:y2, x1:x2] print('Cropped shape', crop_frame.shape) #cv2.imwrite(path.join(save_dir, '{}.png'.format(i)),full_frames[i][0][y1:y2, x1:x2]) print(coords_list) ================================================ FILE: musetalk/utils/training_utils.py ================================================ import os import json import logging import torch import torch.nn as nn import torch.optim as optim from torch.optim.lr_scheduler import CosineAnnealingLR from diffusers import AutoencoderKL, UNet2DConditionModel from transformers import WhisperModel from diffusers.optimization import get_scheduler from omegaconf import OmegaConf from einops import rearrange from musetalk.models.syncnet import SyncNet from musetalk.loss.discriminator import MultiScaleDiscriminator, DiscriminatorFullModel from musetalk.loss.basic_loss import Interpolate import musetalk.loss.vgg_face as vgg_face from musetalk.data.dataset import PortraitDataset from musetalk.utils.utils import ( get_image_pred, process_audio_features, process_and_save_images ) class Net(nn.Module): def __init__( self, unet: UNet2DConditionModel, ): super().__init__() self.unet = unet def forward( self, input_latents, timesteps, audio_prompts, ): model_pred = self.unet( input_latents, timesteps, encoder_hidden_states=audio_prompts ).sample return model_pred logger = logging.getLogger(__name__) def initialize_models_and_optimizers(cfg, accelerator, weight_dtype): """Initialize models and optimizers""" model_dict = { 'vae': None, 'unet': None, 'net': None, 'wav2vec': None, 'optimizer': None, 'lr_scheduler': None, 'scheduler_max_steps': None, 'trainable_params': None } model_dict['vae'] = AutoencoderKL.from_pretrained( cfg.pretrained_model_name_or_path, subfolder=cfg.vae_type, ) unet_config_file = os.path.join( cfg.pretrained_model_name_or_path, cfg.unet_sub_folder + "/musetalk.json" ) with open(unet_config_file, 'r') as f: unet_config = json.load(f) model_dict['unet'] = UNet2DConditionModel(**unet_config) if not cfg.random_init_unet: pretrained_unet_path = os.path.join(cfg.pretrained_model_name_or_path, cfg.unet_sub_folder, "pytorch_model.bin") print(f"### Loading existing unet weights from {pretrained_unet_path}. ###") checkpoint = torch.load(pretrained_unet_path, map_location=accelerator.device) model_dict['unet'].load_state_dict(checkpoint) unet_params = [p.numel() for n, p in model_dict['unet'].named_parameters()] logger.info(f"unet {sum(unet_params) / 1e6}M-parameter") model_dict['vae'].requires_grad_(False) model_dict['unet'].requires_grad_(True) model_dict['vae'].to(accelerator.device, dtype=weight_dtype) model_dict['net'] = Net(model_dict['unet']) model_dict['wav2vec'] = WhisperModel.from_pretrained(cfg.whisper_path).to( device="cuda", dtype=weight_dtype).eval() model_dict['wav2vec'].requires_grad_(False) if cfg.solver.gradient_checkpointing: model_dict['unet'].enable_gradient_checkpointing() if cfg.solver.scale_lr: learning_rate = ( cfg.solver.learning_rate * cfg.solver.gradient_accumulation_steps * cfg.data.train_bs * accelerator.num_processes ) else: learning_rate = cfg.solver.learning_rate if cfg.solver.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError( "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" ) optimizer_cls = bnb.optim.AdamW8bit else: optimizer_cls = torch.optim.AdamW model_dict['trainable_params'] = list(filter(lambda p: p.requires_grad, model_dict['net'].parameters())) if accelerator.is_main_process: print('trainable params') for n, p in model_dict['net'].named_parameters(): if p.requires_grad: print(n) model_dict['optimizer'] = optimizer_cls( model_dict['trainable_params'], lr=learning_rate, betas=(cfg.solver.adam_beta1, cfg.solver.adam_beta2), weight_decay=cfg.solver.adam_weight_decay, eps=cfg.solver.adam_epsilon, ) model_dict['scheduler_max_steps'] = cfg.solver.max_train_steps * cfg.solver.gradient_accumulation_steps model_dict['lr_scheduler'] = get_scheduler( cfg.solver.lr_scheduler, optimizer=model_dict['optimizer'], num_warmup_steps=cfg.solver.lr_warmup_steps * cfg.solver.gradient_accumulation_steps, num_training_steps=model_dict['scheduler_max_steps'], ) return model_dict def initialize_dataloaders(cfg): """Initialize training and validation dataloaders""" dataloader_dict = { 'train_dataset': None, 'val_dataset': None, 'train_dataloader': None, 'val_dataloader': None } dataloader_dict['train_dataset'] = PortraitDataset(cfg={ 'image_size': cfg.data.image_size, 'T': cfg.data.n_sample_frames, "sample_method": cfg.data.sample_method, 'top_k_ratio': cfg.data.top_k_ratio, "contorl_face_min_size": cfg.data.contorl_face_min_size, "dataset_key": cfg.data.dataset_key, "padding_pixel_mouth": cfg.padding_pixel_mouth, "whisper_path": cfg.whisper_path, "min_face_size": cfg.data.min_face_size, "cropping_jaw2edge_margin_mean": cfg.cropping_jaw2edge_margin_mean, "cropping_jaw2edge_margin_std": cfg.cropping_jaw2edge_margin_std, "crop_type": cfg.crop_type, "random_margin_method": cfg.random_margin_method, }) dataloader_dict['train_dataloader'] = torch.utils.data.DataLoader( dataloader_dict['train_dataset'], batch_size=cfg.data.train_bs, shuffle=True, num_workers=cfg.data.num_workers, ) dataloader_dict['val_dataset'] = PortraitDataset(cfg={ 'image_size': cfg.data.image_size, 'T': cfg.data.n_sample_frames, "sample_method": cfg.data.sample_method, 'top_k_ratio': cfg.data.top_k_ratio, "contorl_face_min_size": cfg.data.contorl_face_min_size, "dataset_key": cfg.data.dataset_key, "padding_pixel_mouth": cfg.padding_pixel_mouth, "whisper_path": cfg.whisper_path, "min_face_size": cfg.data.min_face_size, "cropping_jaw2edge_margin_mean": cfg.cropping_jaw2edge_margin_mean, "cropping_jaw2edge_margin_std": cfg.cropping_jaw2edge_margin_std, "crop_type": cfg.crop_type, "random_margin_method": cfg.random_margin_method, }) dataloader_dict['val_dataloader'] = torch.utils.data.DataLoader( dataloader_dict['val_dataset'], batch_size=cfg.data.train_bs, shuffle=True, num_workers=1, ) return dataloader_dict def initialize_loss_functions(cfg, accelerator, scheduler_max_steps): """Initialize loss functions and discriminators""" loss_dict = { 'L1_loss': nn.L1Loss(reduction='mean'), 'discriminator': None, 'mouth_discriminator': None, 'optimizer_D': None, 'mouth_optimizer_D': None, 'scheduler_D': None, 'mouth_scheduler_D': None, 'disc_scales': None, 'discriminator_full': None, 'mouth_discriminator_full': None } if cfg.loss_params.gan_loss > 0: loss_dict['discriminator'] = MultiScaleDiscriminator( **cfg.model_params.discriminator_params).to(accelerator.device) loss_dict['discriminator_full'] = DiscriminatorFullModel(loss_dict['discriminator']) loss_dict['disc_scales'] = cfg.model_params.discriminator_params.scales loss_dict['optimizer_D'] = optim.AdamW( loss_dict['discriminator'].parameters(), lr=cfg.discriminator_train_params.lr, weight_decay=cfg.discriminator_train_params.weight_decay, betas=cfg.discriminator_train_params.betas, eps=cfg.discriminator_train_params.eps) loss_dict['scheduler_D'] = CosineAnnealingLR( loss_dict['optimizer_D'], T_max=scheduler_max_steps, eta_min=1e-6 ) if cfg.loss_params.mouth_gan_loss > 0: loss_dict['mouth_discriminator'] = MultiScaleDiscriminator( **cfg.model_params.discriminator_params).to(accelerator.device) loss_dict['mouth_discriminator_full'] = DiscriminatorFullModel(loss_dict['mouth_discriminator']) loss_dict['mouth_optimizer_D'] = optim.AdamW( loss_dict['mouth_discriminator'].parameters(), lr=cfg.discriminator_train_params.lr, weight_decay=cfg.discriminator_train_params.weight_decay, betas=cfg.discriminator_train_params.betas, eps=cfg.discriminator_train_params.eps) loss_dict['mouth_scheduler_D'] = CosineAnnealingLR( loss_dict['mouth_optimizer_D'], T_max=scheduler_max_steps, eta_min=1e-6 ) return loss_dict def initialize_syncnet(cfg, accelerator, weight_dtype): """Initialize SyncNet model""" if cfg.loss_params.sync_loss > 0 or cfg.use_adapted_weight: if cfg.data.n_sample_frames != 16: raise ValueError( f"Invalid n_sample_frames {cfg.data.n_sample_frames} for sync_loss, it should be 16." ) syncnet_config = OmegaConf.load(cfg.syncnet_config_path) syncnet = SyncNet(OmegaConf.to_container( syncnet_config.model)).to(accelerator.device) print( f"Load SyncNet checkpoint from: {syncnet_config.ckpt.inference_ckpt_path}") checkpoint = torch.load( syncnet_config.ckpt.inference_ckpt_path, map_location=accelerator.device) syncnet.load_state_dict(checkpoint["state_dict"]) syncnet.to(dtype=weight_dtype) syncnet.requires_grad_(False) syncnet.eval() return syncnet return None def initialize_vgg(cfg, accelerator): """Initialize VGG model""" if cfg.loss_params.vgg_loss > 0: vgg_IN = vgg_face.Vgg19().to(accelerator.device,) pyramid = vgg_face.ImagePyramide( cfg.loss_params.pyramid_scale, 3).to(accelerator.device) vgg_IN.eval() downsampler = Interpolate( size=(224, 224), mode='bilinear', align_corners=False).to(accelerator.device) return vgg_IN, pyramid, downsampler return None, None, None def validation( cfg, val_dataloader, net, vae, wav2vec, accelerator, save_dir, global_step, weight_dtype, syncnet_score=1, ): """Validation function for model evaluation""" net.eval() # Set the model to evaluation mode for batch in val_dataloader: # The same ref_latents ref_pixel_values = batch["pixel_values_ref_img"].to(weight_dtype).to( accelerator.device, non_blocking=True ) pixel_values = batch["pixel_values_vid"].to(weight_dtype).to( accelerator.device, non_blocking=True ) bsz, num_frames, c, h, w = ref_pixel_values.shape audio_prompts = process_audio_features(cfg, batch, wav2vec, bsz, num_frames, weight_dtype) # audio feature for unet audio_prompts = rearrange( audio_prompts, 'b f c h w-> (b f) c h w' ) audio_prompts = rearrange( audio_prompts, '(b f) c h w -> (b f) (c h) w', b=bsz ) # different masked_latents image_pred_train = get_image_pred( pixel_values, ref_pixel_values, audio_prompts, vae, net, weight_dtype) image_pred_infer = get_image_pred( ref_pixel_values, ref_pixel_values, audio_prompts, vae, net, weight_dtype) process_and_save_images( batch, image_pred_train, image_pred_infer, save_dir, global_step, accelerator, cfg.num_images_to_keep, syncnet_score ) # only infer 1 image in validation break net.train() # Set the model back to training mode ================================================ FILE: musetalk/utils/utils.py ================================================ import os import cv2 import numpy as np import torch from typing import Union, List import torch.nn.functional as F from einops import rearrange import shutil import os.path as osp from musetalk.models.vae import VAE from musetalk.models.unet import UNet,PositionalEncoding def load_all_model( unet_model_path=os.path.join("models", "musetalkV15", "unet.pth"), vae_type="sd-vae", unet_config=os.path.join("models", "musetalkV15", "musetalk.json"), device=None, ): vae = VAE( model_path = os.path.join("models", vae_type), ) print(f"load unet model from {unet_model_path}") unet = UNet( unet_config=unet_config, model_path=unet_model_path, device=device ) pe = PositionalEncoding(d_model=384) return vae, unet, pe def get_file_type(video_path): _, ext = os.path.splitext(video_path) if ext.lower() in ['.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff']: return 'image' elif ext.lower() in ['.avi', '.mp4', '.mov', '.flv', '.mkv']: return 'video' else: return 'unsupported' def get_video_fps(video_path): video = cv2.VideoCapture(video_path) fps = video.get(cv2.CAP_PROP_FPS) video.release() return fps def datagen( whisper_chunks, vae_encode_latents, batch_size=8, delay_frame=0, device="cuda:0", ): whisper_batch, latent_batch = [], [] for i, w in enumerate(whisper_chunks): idx = (i+delay_frame)%len(vae_encode_latents) latent = vae_encode_latents[idx] whisper_batch.append(w) latent_batch.append(latent) if len(latent_batch) >= batch_size: whisper_batch = torch.stack(whisper_batch) latent_batch = torch.cat(latent_batch, dim=0) yield whisper_batch, latent_batch whisper_batch, latent_batch = [], [] # the last batch may smaller than batch size if len(latent_batch) > 0: whisper_batch = torch.stack(whisper_batch) latent_batch = torch.cat(latent_batch, dim=0) yield whisper_batch.to(device), latent_batch.to(device) def cast_training_params( model: Union[torch.nn.Module, List[torch.nn.Module]], dtype=torch.float32, ): if not isinstance(model, list): model = [model] for m in model: for param in m.parameters(): # only upcast trainable parameters into fp32 if param.requires_grad: param.data = param.to(dtype) def rand_log_normal( shape, loc=0., scale=1., device='cpu', dtype=torch.float32, generator=None ): """Draws samples from an lognormal distribution.""" rnd_normal = torch.randn( shape, device=device, dtype=dtype, generator=generator) # N(0, I) sigma = (rnd_normal * scale + loc).exp() return sigma def get_mouth_region(frames, image_pred, pixel_values_face_mask): # Initialize lists to store the results for each image in the batch mouth_real_list = [] mouth_generated_list = [] # Process each image in the batch for b in range(frames.shape[0]): # Find the non-zero area in the face mask non_zero_indices = torch.nonzero(pixel_values_face_mask[b]) # If there are no non-zero indices, skip this image if non_zero_indices.numel() == 0: continue min_y, max_y = torch.min(non_zero_indices[:, 1]), torch.max( non_zero_indices[:, 1]) min_x, max_x = torch.min(non_zero_indices[:, 2]), torch.max( non_zero_indices[:, 2]) # Crop the frames and image_pred according to the non-zero area frames_cropped = frames[b, :, min_y:max_y, min_x:max_x] image_pred_cropped = image_pred[b, :, min_y:max_y, min_x:max_x] # Resize the cropped images to 256*256 frames_resized = F.interpolate(frames_cropped.unsqueeze( 0), size=(256, 256), mode='bilinear', align_corners=False) image_pred_resized = F.interpolate(image_pred_cropped.unsqueeze( 0), size=(256, 256), mode='bilinear', align_corners=False) # Append the resized images to the result lists mouth_real_list.append(frames_resized) mouth_generated_list.append(image_pred_resized) # Convert the lists to tensors if they are not empty mouth_real = torch.cat(mouth_real_list, dim=0) if mouth_real_list else None mouth_generated = torch.cat( mouth_generated_list, dim=0) if mouth_generated_list else None return mouth_real, mouth_generated def get_image_pred(pixel_values, ref_pixel_values, audio_prompts, vae, net, weight_dtype): with torch.no_grad(): bsz, num_frames, c, h, w = pixel_values.shape masked_pixel_values = pixel_values.clone() masked_pixel_values[:, :, :, h//2:, :] = -1 masked_frames = rearrange( masked_pixel_values, 'b f c h w -> (b f) c h w') masked_latents = vae.encode(masked_frames).latent_dist.mode() masked_latents = masked_latents * vae.config.scaling_factor masked_latents = masked_latents.float() ref_frames = rearrange(ref_pixel_values, 'b f c h w-> (b f) c h w') ref_latents = vae.encode(ref_frames).latent_dist.mode() ref_latents = ref_latents * vae.config.scaling_factor ref_latents = ref_latents.float() input_latents = torch.cat([masked_latents, ref_latents], dim=1) input_latents = input_latents.to(weight_dtype) timesteps = torch.tensor([0], device=input_latents.device) latents_pred = net( input_latents, timesteps, audio_prompts, ) latents_pred = (1 / vae.config.scaling_factor) * latents_pred image_pred = vae.decode(latents_pred).sample image_pred = image_pred.float() return image_pred def process_audio_features(cfg, batch, wav2vec, bsz, num_frames, weight_dtype): with torch.no_grad(): audio_feature_length_per_frame = 2 * \ (cfg.data.audio_padding_length_left + cfg.data.audio_padding_length_right + 1) audio_feats = batch['audio_feature'].to(weight_dtype) audio_feats = wav2vec.encoder( audio_feats, output_hidden_states=True).hidden_states audio_feats = torch.stack(audio_feats, dim=2).to(weight_dtype) # [B, T, 10, 5, 384] start_ts = batch['audio_offset'] step_ts = batch['audio_step'] audio_feats = torch.cat([torch.zeros_like(audio_feats[:, :2*cfg.data.audio_padding_length_left]), audio_feats, torch.zeros_like(audio_feats[:, :2*cfg.data.audio_padding_length_right])], 1) audio_prompts = [] for bb in range(bsz): audio_feats_list = [] for f in range(num_frames): cur_t = (start_ts[bb] + f * step_ts[bb]) * 2 audio_clip = audio_feats[bb:bb+1, cur_t: cur_t+audio_feature_length_per_frame] audio_feats_list.append(audio_clip) audio_feats_list = torch.stack(audio_feats_list, 1) audio_prompts.append(audio_feats_list) audio_prompts = torch.cat(audio_prompts) # B, T, 10, 5, 384 return audio_prompts def save_checkpoint(model, save_dir, ckpt_num, name="appearance_net", total_limit=None, logger=None): save_path = os.path.join(save_dir, f"{name}-{ckpt_num}.pth") if total_limit is not None: checkpoints = os.listdir(save_dir) checkpoints = [d for d in checkpoints if d.endswith(".pth")] checkpoints = [d for d in checkpoints if name in d] checkpoints = sorted( checkpoints, key=lambda x: int(x.split("-")[1].split(".")[0]) ) if len(checkpoints) >= total_limit: num_to_remove = len(checkpoints) - total_limit + 1 removing_checkpoints = checkpoints[0:num_to_remove] logger.info( f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" ) logger.info( f"removing checkpoints: {', '.join(removing_checkpoints)}") for removing_checkpoint in removing_checkpoints: removing_checkpoint = os.path.join( save_dir, removing_checkpoint) os.remove(removing_checkpoint) state_dict = model.state_dict() torch.save(state_dict, save_path) def save_models(accelerator, net, save_dir, global_step, cfg, logger=None): unwarp_net = accelerator.unwrap_model(net) save_checkpoint( unwarp_net.unet, save_dir, global_step, name="unet", total_limit=cfg.total_limit, logger=logger ) def delete_additional_ckpt(base_path, num_keep): dirs = [] for d in os.listdir(base_path): if d.startswith("checkpoint-"): dirs.append(d) num_tot = len(dirs) if num_tot <= num_keep: return # ensure ckpt is sorted and delete the ealier! del_dirs = sorted(dirs, key=lambda x: int(x.split("-")[-1]))[: num_tot - num_keep] for d in del_dirs: path_to_dir = osp.join(base_path, d) if osp.exists(path_to_dir): shutil.rmtree(path_to_dir) def seed_everything(seed): import random import numpy as np torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed % (2**32)) random.seed(seed) def process_and_save_images( batch, image_pred, image_pred_infer, save_dir, global_step, accelerator, num_images_to_keep=10, syncnet_score=1 ): # Rearrange the tensors print("image_pred.shape: ", image_pred.shape) pixel_values_ref_img = rearrange(batch['pixel_values_ref_img'], "b f c h w -> (b f) c h w") pixel_values = rearrange(batch["pixel_values_vid"], 'b f c h w -> (b f) c h w') # Create masked pixel values masked_pixel_values = batch["pixel_values_vid"].clone() _, _, _, h, _ = batch["pixel_values_vid"].shape masked_pixel_values[:, :, :, h//2:, :] = -1 masked_pixel_values = rearrange(masked_pixel_values, 'b f c h w -> (b f) c h w') # Keep only the specified number of images pixel_values = pixel_values[:num_images_to_keep, :, :, :] masked_pixel_values = masked_pixel_values[:num_images_to_keep, :, :, :] pixel_values_ref_img = pixel_values_ref_img[:num_images_to_keep, :, :, :] image_pred = image_pred.detach()[:num_images_to_keep, :, :, :] image_pred_infer = image_pred_infer.detach()[:num_images_to_keep, :, :, :] # Concatenate images concat = torch.cat([ masked_pixel_values * 0.5 + 0.5, pixel_values_ref_img * 0.5 + 0.5, image_pred * 0.5 + 0.5, pixel_values * 0.5 + 0.5, image_pred_infer * 0.5 + 0.5, ], dim=2) print("concat.shape: ", concat.shape) # Create the save directory if it doesn't exist os.makedirs(f'{save_dir}/samples/', exist_ok=True) # Try to save the concatenated image try: # Concatenate images horizontally and convert to numpy array final_image = torch.cat([concat[i] for i in range(concat.shape[0])], dim=-1).permute(1, 2, 0).cpu().numpy()[:, :, [2, 1, 0]] * 255 # Save the image cv2.imwrite(f'{save_dir}/samples/sample_{global_step}_{accelerator.device}_SyncNetScore_{syncnet_score}.jpg', final_image) print(f"Image saved successfully: {save_dir}/samples/sample_{global_step}_{accelerator.device}_SyncNetScore_{syncnet_score}.jpg") except Exception as e: print(f"Failed to save image: {e}") ================================================ FILE: musetalk/whisper/audio2feature.py ================================================ import os from .whisper import load_model import soundfile as sf import numpy as np import time import sys from transformers import AutoFeatureExtractor from transformers import WhisperModel import torch sys.path.append("..") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") weight_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 class Audio2Feature(): def __init__(self, whisper_model_type="tiny", model_path="./models/whisper"): # self.whisper_model_type = whisper_model_type # self.model = load_model(model_path) # self.feature_extractor = AutoFeatureExtractor.from_pretrained(model_path) self.whisper = WhisperModel.from_pretrained(model_path) self.whisper = self.whisper.to(device=device, dtype=weight_dtype).eval() self.whisper.requires_grad_(False) def get_sliced_feature(self, feature_array, vid_idx, audio_feat_length=[2,2], fps=25): """ Get sliced features based on a given index :param feature_array: :param start_idx: the start index of the feature :param audio_feat_length: :return: """ length = len(feature_array) selected_feature = [] selected_idx = [] center_idx = int(vid_idx*50/fps) left_idx = center_idx; #-audio_feat_length[0]*2 right_idx = center_idx + (audio_feat_length[0]+audio_feat_length[1]+1)*2 for idx in range(left_idx,right_idx): idx = max(0, idx) idx = min(length-1, idx) x = feature_array[idx] selected_feature.append(x) selected_idx.append(idx) selected_feature = np.concatenate(selected_feature, axis=0) selected_feature = selected_feature.reshape(-1, 384)# 50*384 return selected_feature,selected_idx def get_sliced_feature_sparse(self,feature_array, vid_idx, audio_feat_length= [2,2],fps = 25): """ Get sliced features based on a given index :param feature_array: :param start_idx: the start index of the feature :param audio_feat_length: :return: """ length = len(feature_array) selected_feature = [] selected_idx = [] for dt in range(-audio_feat_length[0],audio_feat_length[1]+1): left_idx = int((vid_idx+dt)*50/fps) if left_idx<1 or left_idx>length-1: print('test-----,left_idx=',left_idx) left_idx = max(0, left_idx) left_idx = min(length-1, left_idx) x = feature_array[left_idx] x = x[np.newaxis,:,:] x = np.repeat(x, 2, axis=0) selected_feature.append(x) selected_idx.append(left_idx) selected_idx.append(left_idx) else: x = feature_array[left_idx-1:left_idx+1] selected_feature.append(x) selected_idx.append(left_idx-1) selected_idx.append(left_idx) selected_feature = np.concatenate(selected_feature, axis=0) selected_feature = selected_feature.reshape(-1, 384)# 50*384 return selected_feature,selected_idx def feature2chunks(self,feature_array,fps,batch_size,audio_feat_length = [2,2],start=0): whisper_chunks = [] whisper_idx_multiplier = 50./fps i = 0 #print(f"video in {fps} FPS, audio idx in 50FPS") for _ in range(batch_size): # start_idx = int(i * whisper_idx_multiplier) # if start_idx>=len(feature_array): # break selected_feature,selected_idx = self.get_sliced_feature(feature_array= feature_array,vid_idx = i+start,audio_feat_length=audio_feat_length,fps=fps) #print(f"i:{i},selected_idx {selected_idx}") whisper_chunks.append(selected_feature) i += 1 return whisper_chunks def audio2feat(self, wav_data): #, weight_dtype=None input_feature = self.feature_extractor( wav_data, return_tensors="pt", sampling_rate=16000 ).input_features input_feature = input_feature.to(device).to(weight_dtype) whisper_feature = self.whisper.encoder(input_feature, output_hidden_states=True).hidden_states #print(f"input_feature shape:{input_feature.shape}, whisper_feature shape:{whisper_feature[0].shape}, whisper_feature len:{len(whisper_feature)}") whisper_feature = torch.stack(whisper_feature, dim=2) #print(f"stacked whisper_feature shape:{whisper_feature.shape}") return whisper_feature.squeeze(0).cpu().numpy() # def audio2feat(self,audio_path): # # get the sample rate of the audio # result = self.model.transcribe(audio_path) # embed_list = [] # for emb in result['segments']: # encoder_embeddings = emb['encoder_embeddings'] # encoder_embeddings = encoder_embeddings.transpose(0,2,1,3) # encoder_embeddings = encoder_embeddings.squeeze(0) # start_idx = int(emb['start']) # end_idx = int(emb['end']) # emb_end_idx = int((end_idx - start_idx)/2) # embed_list.append(encoder_embeddings[:emb_end_idx]) # concatenated_array = np.concatenate(embed_list, axis=0) # return concatenated_array if __name__ == "__main__": audio_processor = Audio2Feature(model_path="../../models/whisper/whisper_tiny.pt") audio_path = "./test.mp3" array = audio_processor.audio2feat(audio_path) print(array.shape) fps = 25 whisper_idx_multiplier = 50./fps i = 0 print(f"video in {fps} FPS, audio idx in 50FPS") while 1: start_idx = int(i * whisper_idx_multiplier) selected_feature,selected_idx = audio_processor.get_sliced_feature(feature_array= array,vid_idx = i,audio_feat_length=[2,2],fps=fps) print(f"video idx {i},\t audio idx {selected_idx},\t shape {selected_feature.shape}") i += 1 if start_idx>len(array): break ================================================ FILE: musetalk/whisper/whisper/__init__.py ================================================ import hashlib import io import os import urllib import warnings from typing import List, Optional, Union import torch from tqdm import tqdm from .audio import load_audio, log_mel_spectrogram, pad_or_trim from .decoding import DecodingOptions, DecodingResult, decode, detect_language from .model import Whisper, ModelDimensions from .transcribe import transcribe _MODELS = { "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt", "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", } def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: os.makedirs(root, exist_ok=True) expected_sha256 = url.split("/")[-2] download_target = os.path.join(root, os.path.basename(url)) if os.path.exists(download_target) and not os.path.isfile(download_target): raise RuntimeError(f"{download_target} exists and is not a regular file") if os.path.isfile(download_target): model_bytes = open(download_target, "rb").read() if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: return model_bytes if in_memory else download_target else: warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop: while True: buffer = source.read(8192) if not buffer: break output.write(buffer) loop.update(len(buffer)) model_bytes = open(download_target, "rb").read() if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.") return model_bytes if in_memory else download_target def available_models() -> List[str]: """Returns the names of available models""" return list(_MODELS.keys()) def load_model(name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False) -> Whisper: """ Load a Whisper ASR model Parameters ---------- name : str one of the official model names listed by `whisper.available_models()`, or path to a model checkpoint containing the model dimensions and the model state_dict. device : Union[str, torch.device] the PyTorch device to put the model into download_root: str path to download the model files; by default, it uses "~/.cache/whisper" in_memory: bool whether to preload the model weights into host memory Returns ------- model : Whisper The Whisper ASR model instance """ if device is None: device = "cuda" if torch.cuda.is_available() else ("mps" if (hasattr(torch.backends, "mps") and torch.backends.mps.is_available()) else "cpu") if download_root is None: download_root = os.getenv( "XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache", "whisper") ) if name in _MODELS: checkpoint_file = _download(_MODELS[name], download_root, in_memory) elif os.path.isfile(name): checkpoint_file = open(name, "rb").read() if in_memory else name else: raise RuntimeError(f"Model {name} not found; available models = {available_models()}") with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")) as fp: checkpoint = torch.load(fp, map_location=device) del checkpoint_file dims = ModelDimensions(**checkpoint["dims"]) model = Whisper(dims) model.load_state_dict(checkpoint["model_state_dict"]) return model.to(device) ================================================ FILE: musetalk/whisper/whisper/__main__.py ================================================ from .transcribe import cli cli() ================================================ FILE: musetalk/whisper/whisper/assets/gpt2/merges.txt ================================================ #version: 0.2 - Trained by `huggingface/tokenizers` Ġ t Ġ a h e i n r e o n Ġt he e r Ġ s a t Ġ w Ġ o e n Ġ c i t i s a n o r e s Ġ b e d Ġ f in g Ġ p o u Ġa n a l a r Ġt o Ġ m Ġo f Ġ in Ġ d Ġ h Ġan d i c a s l e Ġt h i on o m l l en t Ġ n Ġ l s t Ġ re v e Ġ e r o l y Ġb e Ġ g Ġ T c t Ġ S i d o t Ġ I u t e t Ġ A Ġ is Ġ on i m a m o w a y a d s e Ġth at Ġ C i g Ġf or a c Ġ y v er u r Ġ u l d Ġs t Ġ M ' s Ġ he Ġ it at ion it h i r c e Ġy ou i l Ġ B Ġw h o l Ġ P Ġw ith Ġ 1 t er c h Ġa s Ġw e Ġ ( n d i ll Ġ D i f Ġ 2 a g er s k e Ġ " Ġ H e m Ġc on Ġ W Ġ R he r Ġw as Ġ r o d Ġ F u l at e Ġa t r i p p o re ĠT he Ġs e u s Ġp ro Ġh a u m Ġa re Ġd e a in an d Ġo r ig h es t is t a b r om Ġ N t h Ġc om Ġ G u n o p 0 0 Ġ L Ġn ot es s Ġe x Ġ v re s Ġ E e w it y an t Ġb y e l o s or t o c q u Ġf rom Ġha ve Ġs u i ve ou ld Ġs h Ġth is n t r a p e igh t ar t m ent Ġa l u st en d - - al l Ġ O ac k Ġc h Ġ le i es re d ar d â Ģ ou t Ġ J Ġa b e ar i v al ly ou r o st g h p t Ġp l as t Ġc an a k om e u d T he Ġh is Ġd o Ġg o Ġh as g e ' t Ġ U r ou Ġs a Ġ j Ġb ut Ġw or Ġa ll e ct Ġ k am e Ġw ill o k Ġw he Ġthe y id e 0 1 f f ic h p l t her Ġt r . . 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================================================ FILE: musetalk/whisper/whisper/assets/multilingual/added_tokens.json ================================================ {"<|endoftext|>": 50257} ================================================ FILE: musetalk/whisper/whisper/assets/multilingual/merges.txt ================================================ Ġ t Ġ a Ġt h i n e r Ġ w Ġ s o u Ġth e r e o n a t e n Ġ c i t i s Ġ b n d Ġ d Ġ m Ġ h Ġ o in g e s Ġ p Ġt o a n Ġ f o r l l Ġ I Ġ l Ġ y a r Ġ g Ġy ou e d Ġa nd Ġ in Ġo f a s Ġ n o m i c Ġth at u s e t v e a l o w l e Ġ is Ġ e Ġ it o t ' s Ġb e i on Ġ T Ġw h Ġ A en t Ġ S Ġ re a y Ġw e Ġ on er e Ġh a u t a c i d i g o s k e v er i m Ġ Ð ĠT h a m a ll Ġf or e l c h r o Ġth is Ġs t Ġ W Ġ u a d ou t i r l d c t Ġ k i f Ġg o . . Ð ¾ it h l y h t q u Ġ - Ġd o Ġ j Ġha ve Ġ B Ġa n Ġw ith Ġa re Ġ r Ġd e Ġs e Ġs o Ġ v s t i ll u r Ġl i Ġ M es t o d all y ' t us t Ġa s Ġ C c e Ġm e Ð ° Ð µ i l Ġ H Ġw as t er t h Ġc an an t Ġc om ou r ig ht Ġ Y at ion ĠA nd o l Ġs h Ñ Ĥ o p s e Ġn ot ĠS o Ġn e u n Ġa b Ġli ke Ġa t Ġ D i e Ġh e Ġc on Ġc h o re Ġa l Ġo r Ġ qu Ġ O om e r a u l Ġ N p p Ġyou r ou ld Ġ P Ġf r g e er s ' re Ð ¸ Ġthe y Ġwh at us e Ġa ll ĠTh e Ġ L es s e m Ġk n Ġj ust ar t Ġp ro ver y u m Ġl o Ġ ì Ġm y o k Ġe x a b Ġth ere Ġb ut Ġkn ow Ġs u Ġ G Ñ ģ Ġ E Ġm a о Ð Ġ en Ġab out ĠI t is t Ġw or r i in d Ġon e at e a nd in k Ġl e or t ' m Ġ F ic h Ñ Ģ id e Ġg et Ġ out .. . Ġw ill ã ģ i ve Ð ½ Ġfr om a in ĠW e Ġu p p e re s c a Ġ R Ġ if Ġp l Ġd on ac k Ġ 1 Ġ " Ġt r Ġ us ĠW h it y Ġ J ĠY ou Ġh ere h er Ġs ome ou g a k ar d Ġgo ing Ġu n m ent Ġth ink Ġp e en d Ġ ( ca use Ġt im as t à © Ġ our Ġw ant am e i es Ġ ë u d in e Ġre ally Ġt e Ġse e c i Ġb y s o u re os e Ġ [ a re Ġm ore a h on e c k op le а Ð Ġthe n Ġth ing Ġthe m v en ou nd os t on g e ct Ġr ight a g Ġin t Ġpe ople Ġwh en ou s p l Ġtim e Ġ im Ġwh o Ġ 2 a p Ġbe cause h ing Ġn o ic e Ġlo ok Ġh as Ġw ould Ġh ow ac t Ġf e n t oug h Ġp r ĠB ut Ġs ay Ñ ĥ Ġn ow Ġm an Ġ very Ġwor k i z Ġ K i v it t Ġa r e p Ġc l Ġwh ich Ġc o an s ' ve Ġs a f f ' ll Ġan y Ġa ct Ġy e b er ac h a ge p er Ġal so f er Ġthe se Ġa d е Ð th er ac e ic k a ke re at i re u e Ġa g Ġ U u ch ion s r y 0 0 n a Ġd id Ġqu e Ġha d Ġe very ĠH e Ġl a Ġw ay Ġs p b le ĠTh is as s Ġthe ir it e Ġne ed Ġp art Ġw ere Ġb ack i p ow n om et b e as e Ġma ke ir st i a en ce an g an k Ġg ot Ġp re Ġcon t Ġo ther p t ĠTh at o g Ġgo od Ġint o al k Ġbe en Ġa m Ġo ver u ally Ġ â ì Ŀ Ġu nd h e w ay Ġg r Ñ Į Ġd if Ġp er Ñ ı ĠI n Ġt w on d ar s in t or m Ġl ot Ġwh ere Ġ à Ġ V Ġs omet Ð » en s Ġg u Ġa c u g Ñ ĭ Ä ± Ġf irst re e Ġh is itt le Ġim p Ġm o a v Ġl ittle ĠWh at Ġm uch Ġ z Ġ ê ab le ĠÐ ¿ Ġp o Ġcom p n e Ġd is Ġl et an ce Ġh er Ġthing s Ġst art ul t Ġa pp Ġre s Ġf o Ġc ould Ġin ter Ġth ose Ġd es Ġwe ll Ġtw o Ġk ind x t res s el y à ¤ Ġb r Ġth r ĠÐ ² Ġ i is h Ġdif fer Ġ ro ĠS t Ġsomet hing Ġt ake Ġb o y s Ġsh e Ġt alk l o Ñ ĩ Ġe ven Ð º ã Ģ ĠÐ ½ Ġb u ĠI f Ġd own ĠC h ad e ation s Ġ use or d Ġof f Ġact ually Ġs pe d u at ed at er os s n ing à ¼ Ġdo es Ġ Ñģ Ġne w Ġb et ve l c ess p le Ġha pp t ing on na Ġ es Ġd ay Ġon ly ig n k ay s el ent s ou nt i ld i le Ġs c Ġh im Ġag ain v ing Ġg onna Ġcom m Ġh el ot her Ġ ke ic al Ġ 3 Ġe l Ġthr ough Ġcom e ar k d ay i er à ³ Ġth an ĠThe y Ġm ay Ġs er í ķ Ġc all Ġdiffer ent Ġsh ould ĠTh ere ar y ĠN ow ã Ĥ th ing w e or y f ter Ġp ut or s i al ë ĭ Ġund er Ġin c ĠY e u b f orm Ġv ide à ¸ ver s Ġfe el à ¡ od y f t f ore Ġe m g et Ġsa id it ion Ġre c i ous at ch Ġtr y Ġhel p Ġsh ow Ð ´ Ġb it u ll Ð ² ÑĤ о g r Ġpl ay if e a il ĠYe ah Ġqu est Ġman y Ġp ers Ġg reat Ã Ń Ġ est n g Ġâ Ļ t y l a ĠO h Ġ × à ® ĠB e ad y Ġm ost ct ion ĠN o Ġdo ing Ġbe ing Ġto o c es Ġb l . " Ġre m is s on s > > r u w n on t i b e ll Ġs m ot h u al Ġ >> Ġp h l es o c f ul Ġse c is e Ġad d ig h er t Ġs ame â Ģ Ġme an Ġf ind e k Ġen d - - Ð ¼ Ġst ill a z Ġ ' Ġm in Ġye ars ur n Ġar ound sel f Ġw r b s oug ht ĠâĻ ª Ġf l an ge Ġa fter Ġpo int m er v ed Ġl ong o y ä ¸ Ġc r way s Ġs y Ġt ra Ġ2 0 a ve Ġch e Ġ ent Ġbe fore p h Ġat t i an i ly Ġpers on Ġb ig Ġs ch Ġre al Ġne xt Ġlo ve Ġvide o ĠL et Ġf in Ġma k i ble Ġto day er m ĠA l ow er an n i x Ġp ar Ġst ud à ¶ Ġimp ort t e Ġg ive v es Ġd ie Ġde c Ġte ll ĠÐ º Ñģ ÑĤ Ġwh y ic ally ic t re d Ġb as Ġsu re Ġbe l at ing Ġt ak Ġs et Ġl ife Ġdid n Ø § o b u nd at h Ġo p ĠÐ ¾ a it Ġwor ld Ġsu pp i o Ġc our ĠÐ ¸ w ard е н Ġal ways u p Ġha nd ĠH ow ci al Ġcon s Ġ Ñ Ġin d Ġ 4 ĠA s Ġf un j ect Ġimport ant Ġs ur e w at es Ġ 5 Ġd i Ġm ade Ġin s Ġas k Ġ et Ġn um Ġc ar ĠO kay Ġs im i k Ġl ast ĠG o Ġm us Ġre l ul ar ´ ì ĠWe ll pe ct ĠTh ank Ġth ree à £ ã ĥ Ġin v Ġg en l ic Ġhapp en ë Ĭ i en e ver оР² Ġst r ĠA ll Ġin st Ġâ Ģ Ġde f Ġs l Ġm ight un g Ġye ar Ġo wn Ġke ep b ody d er Ġ ÑĤ ĠÐ ´ Ġan other Ġm od Ġe v Ġgu ys Ġab le ã o qu e id ent ĠY es Ġit s Ġpl ace Ġpro du ar n ĠÐ ¼ Ġre p Ġex per Ġf am it ies if ic Ġh igh i ed o ol ie w е ÑĤ re n Ġdon e Ġ ... ëĬ Ķ st em ĠS e Ġbet ter c ome Ġd el Ġt y Ġu m Ġh o ĠA n Ġm on ing s Ġs k Ġo b c om ble m op e st and ' d ment s Ġe le ĠI s Ġd a Ġre g le ase i ke al s iz e ê ° Ġc are Ġne ver ìĿ ´ es e Ġm et ol og ĠWh en u ck е ÑĢ Ġ é Ġd at à § Ġex am il ity Ġd et c ri Ġus ed ĠD o Ġtr ans e g t en Ñ İ c us Ġsec ond Ġb est Ġh ard Ġ ide Ġpro blem ê ³ ĠU n Ñ ħ Ġ Î Ġw atch ĠS h at ter Ġpre t Ġd er Ġcour se Å Ł at ive ic s Ġquest ion ut e ì Ĺ ĠF or at her Ġc ol i end Ġ í Ġ Z Ġdoes n ar ch Ġinter est Ġp ol Ġc or i ence Ġp res Ġe ach Ġsy stem Ġf act i el ab ly Ġ er Ġr un Ġì Ŀ Ġto p n er Ġth ought Ġe as i ent Ġc re Ñ Ī Ġcomm un y e re ady ll ow Ġevery thing om m Ġm ed ļ Ķ Ġc ount it s Ġcom pl h ip Ù Ħ o ok Ġto get Ġtoget her am p Ġg ame Ġal ready аР» Ġcall ed al e Å Ĥ ĠM y Ġunder stand Ġd r Ġm om it ed оР» Ġus ing z y Ġnum ber ãĢ ģ c ed Ġc le н о ëĭ ¤ in ce Ġlook ing Ġpret ty Ġpro b ĠS he Ġ ve Ġget ting Ġwe ek Ġe ff u ff a ir u es er n Ġ Q ou p ent ion Ġs ide оР¼ Ġfor m Ġb us Ġas s Ġ ed as on we en âĢ ¦ Ġt urn Ġc ur Ġco ll Ġd ire ĠG od Ġ1 0 Ġe qu ĠÐ ± Ġop en Ġsu ch ir d аРº Ġe ar Ä Ļ g an Ġpart ic Ġfr iend Ġex p Ġex t Ġh ome Ġw ater ĠO n ÑĤ ÑĮ or k Ġп ÑĢ Ġmo ve n ess en se h o Ġch ar c o in s Ġb oth Ġ1 9 Ġg ra Ġbet ween á » Ġì ķ as h ĠR e a i al th u res em ber Ġa v Ġ ver à ª one y Ġth ank Ġmay be u c im e ê³ ł Ġa way Ġn ame ou se Ġac c Ġmus ic Ġch ange Ġp ass g er Ġbu ild Ġv al in ess an y Ġfe w ´ ë t a Ġl ist à ¥ Ġo ld Ġì ŀ Ġs ort Ġme m Ġc a ce pt Ġgen er Ġye ah Ġwh ile Ġany thing r ic gr am Ġe in c y ur ing ĠD e Ġp ower Ġcom ing Ġwor d Ġ- - Ġbel ie Ġf ound t o Ð ¿ Ġme ans Ġin form Ġ Ø Ġ Ñĩ Ġsm all 00 0 Ġc ame Ġ íķ w h Ġwork ing Ġexam ple Ġp os Ġde p ê ² ä º ot e Ġde m ì § t s Ġv ar a ut Ġt ri ch n Ġhe ad Ġwho le × Ļ z e Ġtry ing Ġt em Ġc ou et s Ġ 6 Ġf il vel op Ġc ase à ¯ Ġprob ably Ġo kay Ġpl an Ġs it Ġsch ool ĠTh en ¸ ë m e Ġpro cess Ġf ar Ġre ad Ġp oss Ġb re Ġso l ic ht Ġsupp ort ĠT o ert ain Ġstart ed Ġc ap Ġle ft Ġdat a Ġtim es еР» Ġwant ed а н Ġtalk ing Ġis t Ġha ving um p Ġcont in Ġsu b ĠÐ · p r ëĭ Ī in a Å ¼ Ġc reat od e × ķ æ ĺ ! ! Ġt erm is m оР´ ĠBe cause Ġw ent id er Ġpro v Ġch ild Ġd en Ġl ight b r ³ о o h Ġbo ok Ġ Ù ut ion ĠJ ust en e Ġf our Ġv is ê° Ģ Ġh ope Ġmak ing ĠL e ì ķ Ġo pp a u Ġm oney Ġpro gram à ¨ Ġst and I N Ġs ign Ġle arn à ł ĠD on Ġte am Ġн а l ud Ġre st ic es æ ľ Ġ ÑĢ Ġa ut Ġle ad ation al d e g y Ġn ice Ġd as Ġd ist Ġh um ĠO ne æ Ī Ġcom es Ġj o Ġc ent Ġex pl Ġm ark re en l ed g in ì ļĶ Ġle vel Ġcon f us h Ġde velop Ġt est en g v ious at ure еР¼ re t Ġj e Ġst uff Ġcl ass ow s Ġê · Ġs i Ġl es ro p ç ļ Ġp or Ġw ar ìĹ IJ Ġevery one Ġg e Ġche ck ot t Ġs ing Ġar t Ġfo llow Ġ20 1 ĠF r a is ì ĸ Î ± å ° Ġà ł im es Ġre t Ġch ang Ġp ub Ġin f Ġte chn ad a iv es Ġbe h æĺ ¯ Ġlook s ãĢ Ĥ Ð · ĠWh y çļ Ħ Ġen ough Ġb ra it ch ä » Ġad v Ð ± Ġwith out w er mer ic d en Ġcompl et Ġide a ter s o ck Ġdef in Ġe ver Ġg l Ġon ce Ġbr ing Ġsay ing Ġan s Ġhe ar n ect Ġl ess g o re am ad o ì ŀ Ġm ind ent e Ġf ull Ġb ad Ġw om Ġsome one Ġd u Ġw on Ġcont ro ort un Ġhe alth Ġch o ĠA r Ġcon c Ġinform ation Ġst op at t at ely ä ½ Ġgr oup Ġ Ñĥ Ġqu ite Ġres p E R ug ht ê ¸ m an iz ed ĠB r Ġrem ember Ġfam ily Ġbus iness a w Ġspe c Ġa u ĠO r Ä ħ Ġse en Ġl ar Ġ 7 g g b ers Ġd ra Ġmon th Ġsay s Ġis s Ġli ve Ġl ine Ġmom ent Ġex c el s Ġs ound Ġco ol Ġlo c Ġc ertain Ġd ri о ÑĤ am es Ġm ust n y и ÑĤ Ġk id Ġinc lud ìĿ Ħ at or Ä Ł h a are d Ġse em Ð ¹ ì Ħ Ġel se Ġì ł ir l Ġ 8 Ġv o Ġquest ions in es e e æĪ ij ü r ĠA meric Ġst ory Ġser v ver n ag es l and ĠâĢ ĵ er a ĠC an Ġp op et her Ġn a Ġor der Ġmak es Ġs ince c on ct or Ġth ough Ġprodu ct л и Ġle g Ġme et al f Ñģ Ñı un ch it er o ve ×ķ × i et аР¼ it al Ġsu per l ing Ġp ay Ġpar a Ġj ob ĠH ere Ġs w k s pt ion m a Ġbelie ve ¬ ë Ġw ait оР¹ Ġun t Ġqu ick h r ĠÑ į ĠP ro Ġm en à ¹ Ġday s Ġgo es Ġspe ak ĠA t em ent Ġm iss Ġa w Ġdes ign Ġpro ject о ÑĢ i j ant s at s ĠCh r Ġ 9 Ġc ut Ġre qu Ġн е ĠN ot as ter Ġm ill Ġpartic ular Ġp ie Ġstud ents Ġf ive ou n ĠN e Ġg i Ġp as Ġf ree ĠS p l ich Ġpro f Ġen g Ġpr ot ĠL ike os ed Ġcon nect a pp Ġë § it ing Ġb lo Ġl os ist s Ġexper ience re nt Ġst ay Ġfo od t on ru ct Ġh ist v iew in ing m ost i vers b o ãģ Ħ ĠT r g en Ġp lease Ġcommun ity Ġc e A N n o Ġb ody Ġh our Ġ vers á º c er Ġê ° Ġre ason ĠR ight Ġl ater Ï Ħ Ġh ouse Ġ X оР½ Ġst ate f ic å ¤ Å Ľ iel d Ġp ri Ġp ast Ġw alk olog y er ing an na Ġt er Ġho ld Ġor gan b en Î ¿ ó n Ġeff ect Ġyour self Ġpl us a j and o ur al Ġro om le ct ê² Į ? " s ide Ġbe come Ñ Ĩ Ġ  o od Ġcon st Ġn ight ut es Ð ¶ Ġbre ak Ġp ain Ġst ep ire d Ġnot hing Ġunt il Ñ ĸ аР² Ù Ĭ Ġd uring ì§ Ģ l ess o ll н Ñĭ Î ¹ f ect i ver ı Ħ ith er y ing Ġbe gin ×Ļ × iv id Ġà § Ġs al Ġt a Ġp ot Ġ $ Ġm ar Ġcle ar Ġf ace Ġgr ow Ġ * Ġins ide Ġfriend s Ġle ave en n Ġeas y Ġare a al ity ou d Ġe at Ù Ĩ Ġp ur or n Ġsa w Ġans wer Ġfr ont Ġbe aut ¼ ë Ġm atter Ġs on ĠN ew Ġres ult id es ch e Ġf ut p s Ġfo cus Ġinterest ing å ¥ Ġa p " . Ġcre ate о Ñģ Ġp ress r oss Ġp ick l ine Ġto ok ĠM ay r ow Ġ ich ĺ ë Ġre f Ġm or r act are nt A R Ġex act Ġsp ace w ork н и Ġb ir Ġde v Ð ³ Ġto ld Ġpub lic ci ally Ġv iew ĠHe y m ed ll o c c Ġf ac Ġcou ple Ġhe art l er Ġre ady Ġal most ar ing Ġh alf ĠM e av or i que Ġchar ac Ġpr act O N an e Ġ il н а Ġv i l ish he ad Ġle ast Ġbas ically as ed r ight Ġy et Ġtak ing Ġcount ry Ġw in Ġis n Ġposs ible Ġc am Ġinc re Ġp at Ġw anna Ġcons ider Ġab s Ġwith in Ġhum an Ġthink ing Ġo h ¡ ľ Ġqu i as es Ġ 0 it ely ä¸ į Ġk ill Ġm il Ġinv est is ter Ġsu c ion al el f Ġwh ether Ġcontro l Ġagain st ot s ëĭĪ ëĭ¤ i or Ġpres ent Ġ ا Ġwatch ing u be er v Ġn icht Ġgo vern ĠTh ese Ġ : u it ug h Ġwork s o o Ġw ir Ġa ir ĠT e аР· is ion wh ere Ġto t j oy ì ĭ Ġv ol ĠÐ µ Ġcl ose ĠA d Ñ ī in ed Ġun a Ġê· ¸ë ° ë or ry Ġb ro Ġfil m if t 2 0 Ġty pe Ġhappen ed ĠA m Ġg irl ĠA re ward s Ġp our Ġcol or el t а Ñģ Ġs ense le x ĠW ith us s ri b Ġre se Ġn orm Ġfut ure Ġde al end ing e y Ġ x er o ĠC l u k Ġwhat ever sel ves Ġyou ng ì Ĭ ĠM ar ĠChr ist Ġgu ess Ġper form Ġen er r on Ġh it Ġw ond Ġdire ct ĠE very Ġof ten Ġf a Ġal ong Ġcl ick ĠL ook Ġsit u Ġhapp y e ad Ġag o Ġen c Ġmy self Ġco ver оР± Ġm id Ġc ost Ġt en ĠS ch Ġex pect Ġwas n Ġstr ong if ul Ġopp ortun in al y le Ġsh are Ġtr ue Ġapp ro Ġch all Ġmin utes Ġch ann Ġë Ĥ Î µ l i Ġm ess or ies pe cially Ġwr ong Ġy es Ġì Ĺ ir on Ġall ow Ġsu bs Ġf ore Ġf ight Ġso cial Ġc ra an a Ġa ff Ġ ess Ġway s Ġsh ort Ġf all Ġla w ĠWh o Ġen joy Ġc al Ġac cess f e Ġn on Ġac ross er y vious ly ĠE x id ed Ġl ink ĠP r Ġterm s ac es Ġl and az ing Ġ1 5 Ġm ult Ġspe cial å Ģ iv ing ìĿ Ģ Ġty p Ġst e Ġ Ä Ġfor ward å ı Ġf re å¥ ½ Ġrese arch ௠į а ÑĤ Ġma in Ġrec ord Ġh u Ġdefin itely Ġe ither Ġlist en Ġke y Ġmark et ĠÑĩ ÑĤо iz ation Ġvide os Ġgu y Ġf ig Ġst ra ĠP l ull y am os Ġm ention Ġs ong Ġinter n r al ur s Ġh on Ġval ue Ġb ar c le оР¶ Ä ĩ ľ ë Ġz u и м ä½ ł Ġsing le Ġa uch cus s Ġget s Ġsomet imes å ¾ am b m m c ing Ġper fect ĠB l out h ì ł Ġs ci p ar Ġre d Ġp ost Ġm ot Ġele ct ĠE u it ive ĠS ome Ġdes cri Ġcur rent é s Ġt re ĠE n Ġm it E N Ī ë i um Ġhe ard Ġsim ple l ar Ġevery body il ar Ġneed s Ġdif fic ĠGo od um ent c ent Ġo per а ÑĤÑĮ et y Ġbl ack Ġgi ven on es Ġwe l é Ģ Ġìķ Ħ Ġ3 0 A T Ġst at ou ch ĠM r а ÑĢ Ġsh o Ġcon d × Ķ m y Ġchild ren Ġe u еР´ ìķ Ħ ter n Ġu h Ġh ar Ġpr om Ġp ull re w Ġcomp any Ġbeaut iful ust om íķ ĺ к и Ġst re Ġam azing ri es Ġsuc cess Ġm ach n ot Ġdis cuss Ġn at ¦ ¬ Ġun e Ġdiffic ult Ġr is Î ½ Ġc amp Ġbu y ä¸ Ģ Ġma g p o ĠY our Ġbeh ind ic a ı n ĠO K Ġl ang Ġwom en Ġen v Ġre ce Ġchann el i ally u le Ġ1 2 th ers Ġb ott Ġrep ort ent ly f ully T he Ġs ent Ġev ent Ġener gy l t Ġword s ar r d le Ġa head ard s Ø ± äº Ĩ Ġto ol con om е Ñģ Ġexact ly Ġf avor Ġl ow Ġpro per Ġìŀ Ī Ġ ! Ġrel ations Ġm as Ġkid s Ġent ire ud e Ù ħ ĠWh ere Ġon es Ġc ity ol ut Ġs ix ab ility ö r il i ĠE s Ġhapp ens ain s Ġmod el Ġp ict Ġes pecially Ġ1 00 k t Ġso on b y ro du Ġan n Ġsubs cri ĠQ u Ġav ail im ent Ġv oc k a Ġ2 00 ap er ĠI nd Ġì § h or į ° j or и л Ġs qu A U ar ning ĠÐ ³ I S ĠÐ » еР¹ y es å ħ ĠÐ Ĵ Ġor ig оР³Ð¾ Ġask ed il t оР³ Ġcontin ue Ġì ĺ r am Ġo thers E S oh n Ġl ay Ġbas ed Ġp u Ġapp e Ġl im Ġpro p Ģ ë m in Ġh ot ĠL a Ġf ast Ġprot ect Ġam ount Ġa qu Ġf und Ġc ustom Ġc ult Ġhand s Ġha ven Ġa ud Ġout side ĠA fter ap s Ġan im pl oy Ġh at ĠF irst Ġt reat Ġe p Ġm ater Ġbuild ing Ġë ° å IJ ìĦ ľ z a ught er ĠP e ne y et er at ic Ġed uc ê¸ ° Ġmo v ĵ ¤ am a r ation Ġs n Ù Ī Ġs um Ġph ot ĠÐ Ŀ Ġ . æľ ī Ġfin ish itt ing å ® Ġlar ge Ġì ĸ Ġwh ite ar a Ġma is ĠH i Ġd am Ġا ÙĦ Ġbo x ĠHe llo Ġs le Ġo pt ri ed ¥ ¼ Ġact iv Ġn ão ĠC om Ġplay ing T h Ġavail able Ġp ort å Ī ĠA h Ġl as Ġear ly Ġwond er ± ° Ġ1 8 c ul Ġfun ction Ġmor ning ll e i ents u x Ġc ir it ions Ġde ep Ġpol it y or m p ak ing Į ë ĠM an Ġmill ion Ġ / Ġind ivid Ġp an Ġgovern ment Ġwr ite ĠT od am ent Ġ Ï Ġw ind ĠE ng ch en W h ì ľ Ġ ident ãģ § v ent ur ch Ġh y Ġy a Ġtr ad Ġrelations hip à º Ġd ou O R Ġs we Ġne g in ation Ġte xt i pp Ġf ine á s ĠD r ĠC ome Ġmonth s , " ен и Ġhour s Ġp od ir t Ġinv ol Ġcoll ect Ġau f Ġp a Ġhist ory m b if y Ġ ? 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Ð°Ð´Ð¸ Ġvers uchen Ġtrans istor ĠÐŁÑĢ Ð¾ÑģÑĤо Ġпон ÑĢав Ġrhy me ĠVerm ont pl atz è® ° ĠÄ°ÅŁ te ĠH ag ĠÐĺ м ĠÑĢаÑģÑģк аз Ġmet ros ĠInfin ity w olf ib al ft ig Ġ ÚĨ Ġíĺ¹ ìĭľ Ġo ggi Ġdisp osit ĠпÑĢ Ð¸Ð» ĠвÑĭ пол Ġth ôi ĠK ENN Ġhand ing act us Ġtac os Ġformer ly ĠCorinth ians ãģ« ãģ¯ ÑĨÑĸ ÑĹ Ġpad re Ġcongreg ation æ ij fer t Ġsub ir ais er qu a ara oh ĠCur ry ĠìķĬ ëĬĶ ÐµÐ» Ñİ Ġf uss Ġbo oty Ġl ows Ġh ommes ĠM H ĠDisney land w ent Ġresid ue Ġbe eping è¼ ķ ät ta Ġm ould ĠPro jekt st alk Ġartif act ĠAnt rag ĠAM D ĠCry pt Ġë© Ķ ĠFel ipe ĠCO B el u Ġself ies ĠS anti ch utz ĠУ кÑĢаÑĹ ges amt Ġflo ck j az pl ain Ġwr inkles Ġre ais Ġpal jon Ġempower ment Ġattend ees pp a Ġn eden он Ñĭ Ġtime frame ĠCher ry Ġid ée Ġg ag Ġdon key Ġô ng ĠH are éļ Ľ ĠK ara Ġacom pan pl aces im ientos ĠH amm б и ub en ili yor Ġth irst Ġk ry ĠGeorget own ׳ ×Ķ Ġor ch Ġheart beat Ġtransform ations est ones ĠK H Ġcart oons Ġan ci Ġworth less Ġtail ored p u Americ ans Ġp iles ĠMon key Ġbas in ĠTem per ĠP aint Ġpunch ing Ġba 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' Ġap ologies ĠCommun ication ĠговоÑĢ Ñİ ãģĤ ãĥ¼ âĦ ¢ ÃŃ veis ac un Ġret aining Ġcontrad iction ĠAD AM C OM Bry an ĠM onsieur Ġadap ting Ш ÐIJ ĠSc r änd ert Ġpl aus ä»Ĭ天 çļĦ Ġon set Ġassist ants Ġval ves Ġsc atter ĠR ust aw ia Ġread iness Ġp ais Ġb ible Ġamb iente Ġа меÑĢик Ġunc ond Ġk alk åĬ ¨ Ġmo c un n Ġact u Ġhum ming iss imo ĠPat rol g ow ãĥ ¤ ĠTHE Y ĠBod en ĠB ie Ġre el ĠÑĥÑģл ов Ġende avor ĠPer iod ustom ed m als al on B ox ĠÏĥ αÏĤ Ġom dat Ġal tre ĠHe h k ad Ġprotect or Ġdomin ance odynam ic Ġcommunic ated k ö Ġprede cessor ĠL uk ĠFl ower Ġãģ © po que ÑĤи ÑĢов Ġret rospect Ġdecis ive Ġexem pel { \ ĠR ück r ite ĠZe us Ġcal orie Ġattract ions ĠH inter Ġuh m ĠíĮ IJ Ġrul ers Ġdiscour aged Ġaconte cer Ġacc ents ĠOpt im ĠAl g k ids 20 21 ĠLind say Ġfilm makers pr owad Ġter ug ëĭ ´ ĠSom mer 20 18 Ġborrow ing ĠTrans fer н оп ari as Ġhead phone ì¼ ľ Ġtransl ating Ġauf ge ப à®Ł we is av ant pa id b aby Ġtough est Ġrepe ats ĠTer esa L ord Ġacab ar ĠR ide d ir Ġl eng Ġd wa Ġhead aches Ġn ữa ĠнаÑģ ÑĤоÑıÑī Ġbo ils Ġlong ing ri as ó rio ĠParad ise ĠSeñ or erd em Ġrein st Ġsal aries Ġinsec urity ÅĤo ÅĽci ĠабÑģолÑİÑĤ но ink en ĠEd dy ud os Ġd ummy Ðļ ак s ix Ġin box Ạ© Pe ople á»ĵ ng Ġorganiz ers f ind Ġü l ĠCO M ż a we ile Comment ary íĬ¸ë ¥¼ ĠMitt el k us èĽ ĭ ठ¨ ir al Ġgar ment ικ ά Ġst ool pay ers Ġsh immer ĠO llie ĠJe żeli è¿ĺ æľī Ġ197 7 Ġje ux Ġext inct ĠTransport ation ĠM aker Ġj ohn Ġrich est Ġtraum at Ġli egen ´ë ¥¼ è¿Ļ éĩĮ Ġun rest ĠSt raw æĭľ æĭľ Ġcom a ĠKr isten ĠÐļон еÑĩно ĠBry ce ĠÑıк Ñĸ Ġpearl s Ġпоним аÑİ Ġadd itions Ġas ympt ĠменÑĮ ÑĪе Ġsc ans Ch ild ĠH ide к ÑĥÑİ et as Ġd ank Ġple as Ġess ays Ġj ets åħ Ĵ Ġв ед Ġposit ives ho f - ) zz o Ġstar ters Ġsm iled Ġ194 4 qu iera Ġro k Ġpu esto N ico Ġsim ulations Ġ à¶ Ġintrig ued ĠOver watch åĸ Ĥ s igh b ai Ġë§IJ ê³ł id é Ġcra bs áºŃ p ĠIraq i ìĿ´ë ¥¼ ÑĤ Ñı ĠSoph ia ĠDN S Ġönem li ĠLu o Ŀ ¤ ĠCoun sel l igen анÑĮ ÑĪе Ġtrump et Ġd apat ĠJ M ĠEVER Y Ġå°į ä¸įå°į å¤ ¢ ĠL ayer Ġc ô н ал ĠJ oo ĠH ack Ġs unt ĠLeon ard ĠFire base äng er Ġexpl oding v oy Ġì¦ IJ ĠÑģ еÑĢÑĮ Ġsever ity Ġbest imm çµIJ æŀľ Ġt iring Ġprocure ment Ġdiplom acy Ġdecor ative ĠÙĬ ا Ġpenet ration Õ « Ġout right EN E ĠUn i od les Ġz eros Ġdelight ful j m Ġdo po 没 äºĭ Ġposit ivity ĠVIS TA ĠRes ource íĥ Ģë ÑĪ Ð¸Ðµ C arl Ġpip ing Ġchop ping ĠGan ze ü ss ĠA o Ġsh attered ĠDet ective Ġund oubtedly Ġhall uc Ġen ch Ñĭ Ñĩно ÑĥлÑı ÑĢ is esti Ġped als Ġdur um ¤í Ķ la imer Ġprop re C u Ġtransl ator Ġca ÅĤ Ġê·¸ 걸 Ġca ÅĤy U A Ġrev ised Ġпод об ĠArt icle ĠHait i Ġà ĵ ĠC trl Ġroz m la it Ġletz te is pering dis play Ġalumin ium Ġpalab ras Ġconoc er Ġz itten Ġdir ig åıª æľī Ġbrain storm Ġw ifi ĠPart icip Ġview point ĠQu an Ġhier arch W elcome å¯ ¾ Ġoff en ĠRe covery gan o W ould Ġrep ro Ġper ceptions Ġdem asi ĠBangl adesh ĠIncred ible Ġlet zt Ġbehav ing Ġaston ishing Ġâ Ĩ ĠëĤ¨ ìŀIJ èµ° äºĨ ãĥ Ķ ĠGORD ON C AR ? !" 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qual ified ĠÑĥвид еÑĤÑĮ erm o H EN Ġso i Ġmulti ples Ġlay outs Ġblind ness ĠB owser Ġпод ÑĤ ĠÃ İ vention al Ġm ata mad ı Ġge ez Ġcad ence Ġważ ne ĠChrist ie ven ge C all Ġturn around Ġblo b ĠЯ к ĠVoice over Ġper il ĠJa ime ĠH OY l ane Ġse bel ĠDu o ĠHistor ical Ġd ni Ġg ema y k Ġsab em ắ ng Ġv ars ĠRon nie ĠRon aldo ĠPer què ns inn h air Ġrelent less Ġl yn Ġtravel er æĢİ麼 äºĨ n ine Ġant im Ġì¼ Ģ Ġsnow ball ĠÑħаÑĢ Ð°ÐºÑĤеÑĢ Ġintern s Ġconstitu ency ĠÐĿ ам ׾ ׾ V EL Ġvikt igt Ġap oyo ÙĦ ب Ġj ard Ġheight ened ÑĢо ÑģÑĤ ĠSM ITH Ġдел а Ġrepair ing Ġr igt ĠShe ikh ĠBrit ney Ġevery time Ġadvent urous oc key er nt Ġat aque ĠAltern atively e ffect Ġpalav ras ĠElli ott Ġréuss i Ġhypert ension ĠMan ual Ġproph etic Ġhand c ÑĮ е Ġref rain ĠSqu id ìŀ ¡ Ġком ан äll en Ġlleg ó Ġbas h ion y ĠÑģк лад Ġк аб Ġcare less ĠP ool Ġtr ás Ġfil s ĠSch r Ġsp rawd ĠMon aten Ġunfor gettable ĠCott on Ġinconven ient ĠR X or is Ġhum bled ת ×Ĺ ĠØ¢ Ù¾ Ġincre ÃŃ ĠKomment are èĪ Ĵ r ación Ġv antage ĠSe al ĠìĿ´ 거를 Ġjou e 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"Ġbesser": 18021, "Ġmanufacturer": 18022, "ĠKyle": 18023, "Ġaccountable": 18024, "ĠSak": 18025, "ĠPil": 18026, "ĠDomin": 18027, "Ġpresum": 18028, "ĠÐĴÑģе": 18029, "Ġvinegar": 18030, "Ġguaranteed": 18031, "çľĭåΰ": 18032, "Ġhandled": 18033, "éŁ³": 18034, "cat": 18035, "Ġcivilization": 18036, "Ġaccomp": 18037, "ĠVM": 18038, "émon": 18039, "Ġdeze": 18040, "Ġgrades": 18041, "Ġsollte": 18042, "Ġstaring": 18043, "×IJת": 18044, "arnt": 18045, "Ġhorizon": 18046, "Ġtravail": 18047, "hour": 18048, "第ä¸Ģ": 18049, "ĠED": 18050, "ĠDak": 18051, "Ġny": 18052, "Ġconve": 18053, "ĠCham": 18054, "Ġfirms": 18055, "ĠLiu": 18056, "ĠÑģÑĤÑĢан": 18057, "Ġlibert": 18058, "Ġlenses": 18059, "Ġintake": 18060, "ĠвÑĭб": 18061, "Ġmensen": 18062, "hel": 18063, "Ġpractition": 18064, "Ġ350": 18065, "ãĤ³": 18066, "FO": 18067, "Ġbeds": 18068, "Ġancestors": 18069, "ĠìĹĦì²Ń": 18070, "Ġdisturb": 18071, "ĠLastly": 18072, "ĠSupport": 18073, "ีà¹ī": 18074, "ĠCorona": 18075, "Ġenthusi": 18076, "Ġвозм": 18077, "ĠìĤ¬ëŀĮë": 18078, "Ġ52": 18079, "bird": 18080, "Ġreduces": 18081, "ĠìŀĪìĿĦ": 18082, "ĠGene": 18083, "êµIJ": 18084, "ÄĻp": 18085, "ĠÃľber": 18086, "Ġconcerning": 18087, "user": 18088, "Ġconcentrate": 18089, "ĠWHAT": 18090, "ishop": 18091, "onymous": 18092, "nold": 18093, "Ġsuggesting": 18094, "©°": 18095, "ĠFish": 18096, "........": 18097, "Ġvessel": 18098, "Ġtrabajo": 18099, "ãģµ": 18100, "ĠOcean": 18101, "å§IJ": 18102, "yg": 18103, "Ġtowns": 18104, "del": 18105, "Ġterrifying": 18106, "ĠçalÄ±ÅŁ": 18107, "Ġsino": 18108, "Ġeats": 18109, "Ġgez": 18110, "Ġgeme": 18111, "ĠìĻĦ": 18112, "Ġcompart": 18113, "Ġimplementing": 18114, "ĠPotter": 18115, "ĠGermans": 18116, "ĠgÅĤ": 18117, "Ġtennis": 18118, "Ġcarpet": 18119, "auer": 18120, "ĠSaudi": 18121, "yeong": 18122, "Ġcurry": 18123, "ĠForest": 18124, "Ñĭл": 18125, "Ġfifteen": 18126, "Ġbolts": 18127, "Ġ{\\": 18128, "¬´": 18129, "Ġsettlement": 18130, "Ġlange": 18131, "Ġbam": 18132, "Get": 18133, "íķĻ": 18134, "Ġswap": 18135, "ĠKhan": 18136, "Ġcommence": 18137, "Ġquarantine": 18138, "Ġscored": 18139, "çĸ": 18140, "Ġ1950": 18141, "Ġthicker": 18142, "Ġsûr": 18143, "åı£": 18144, "ĠLarry": 18145, "Ġallez": 18146, "ìĭľëĬĶ": 18147, "Ġgü": 18148, "Ġspectacular": 18149, "//": 18150, "both": 18151, "Ġstats": 18152, "妳": 18153, "ĠNancy": 18154, "Ġbunu": 18155, "Ġcrust": 18156, "Ġactivated": 18157, "Ġê·¸ëŀ": 18158, "outhe": 18159, "Ġports": 18160, "Ġneural": 18161, "Ġjaw": 18162, "Ġobservations": 18163, "Ġvoit": 18164, "aban": 18165, "ải": 18166, "¦¬ë¥¼": 18167, "omes": 18168, "à¯ĭ": 18169, "qui": 18170, "Ġkindness": 18171, "Ðij": 18172, "Ġ41": 18173, "Ġmoderate": 18174, "Ġangels": 18175, "ĠTamb": 18176, "èt": 18177, "Ġchlor": 18178, "ĠBilly": 18179, "ì²ĺë": 18180, "acon": 18181, "Ġselecting": 18182, "ĠDelta": 18183, "Ġnull": 18184, "denly": 18185, "Ġciud": 18186, "Ġtendency": 18187, "Ġbreakdown": 18188, "Ġmint": 18189, "ÑĦоÑĢм": 18190, "orph": 18191, "Ġdawn": 18192, "spr": 18193, "ĠWILL": 18194, "ächlich": 18195, "Ġpuppy": 18196, "700": 18197, "Ġத": 18198, "Ġfails": 18199, "ĠConc": 18200, "Ġrelatives": 18201, "Ġinviting": 18202, "Ġautonom": 18203, "Ġcomposed": 18204, "Ġunity": 18205, "Ġdecis": 18206, "Ġaccessories": 18207, "ĠCass": 18208, "Ġbist": 18209, "ĠTip": 18210, "째": 18211, "Ġpunt": 18212, "Ġráp": 18213, "é̲": 18214, "ANK": 18215, "ãģļ": 18216, "exist": 18217, "Ġcompatible": 18218, "Ġner": 18219, "ĠемÑĥ": 18220, "Ġaplic": 18221, "Ġbapt": 18222, "Ġfailing": 18223, "ĠTamam": 18224, "Ġoscill": 18225, "Ġletzten": 18226, "Ġrepeatedly": 18227, "Ġjungle": 18228, "ĠPush": 18229, "hai": 18230, "Ġη": 18231, "Ġdeadly": 18232, "Ñıж": 18233, "wiÄħ": 18234, "ĠCommon": 18235, "ĠÎķ": 18236, "Ġskate": 18237, "TC": 18238, "ĠMini": 18239, "Ġhobby": 18240, "ần": 18241, "Ġroutes": 18242, "Ġamigos": 18243, "Ġconjun": 18244, "Ġpartnerships": 18245, "Ġnovo": 18246, "Ġaver": 18247, "Ġpouvez": 18248, "bridge": 18249, "Ġpreoc": 18250, "him": 18251, "Ġturb": 18252, "Ġsob": 18253, "ĠSnap": 18254, "Ġì°¸": 18255, "minute": 18256, "Ġtraject": 18257, 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"arı": 18318, "Ġpetite": 18319, "bled": 18320, "Ġpensar": 18321, "icio": 18322, "IND": 18323, "Ġveteran": 18324, "Ġladder": 18325, "Ġconsequence": 18326, "ожал": 18327, "ĠBurn": 18328, "Ġrug": 18329, "ĠMade": 18330, "Ġgit": 18331, "\"...": 18332, "Ġcompetitors": 18333, "Ġprzed": 18334, "Ġapparent": 18335, "ĠArgentina": 18336, "ĠWorking": 18337, "Ġcollaborate": 18338, "woman": 18339, "Ġretain": 18340, "Ġleurs": 18341, "Ġdashboard": 18342, "×Ļ×ĵ": 18343, "ĠEarly": 18344, "BM": 18345, "ĠеÑij": 18346, "олог": 18347, "Ġsatisfying": 18348, "Ġoftentimes": 18349, "Ġmapping": 18350, "ünkü": 18351, "arth": 18352, "fold": 18353, "Ġlaunching": 18354, "Ġaura": 18355, "Ġprecision": 18356, "works": 18357, "God": 18358, "Ġstrap": 18359, "ĠImper": 18360, "Ġrivers": 18361, "Ġ|": 18362, "Ġcuer": 18363, "regon": 18364, "Ġarrival": 18365, "каÑħ": 18366, "ĠMiami": 18367, "анÑĭ": 18368, "Ġsurvivors": 18369, "ĠSenior": 18370, "David": 18371, "Ġestado": 18372, "Ġsectors": 18373, "Ġpopping": 18374, "Ġchim": 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30367, "Ġsini": 30368, "ÃŁer": 30369, "èg": 30370, "maal": 30371, "generation": 30372, "Ġminorities": 30373, "Ġllevar": 30374, "Ġnomination": 30375, "Ġconsid": 30376, "Ġ×ľ×¢": 30377, "muÅŁ": 30378, "ĠEsc": 30379, "Ġnumerator": 30380, "Ġkaik": 30381, "Ġktórych": 30382, "iesen": 30383, "Ġvê": 30384, "ĠUSS": 30385, "ĠPrivate": 30386, "Ġодно": 30387, "Ġalém": 30388, "ÃŃtulo": 30389, "Ġlimb": 30390, "Ġforgiven": 30391, "Ġdisclosure": 30392, "ÏĦί": 30393, "Ġningún": 30394, "Ġtherapeutic": 30395, "Ġnegotiating": 30396, "ĠNike": 30397, "enseful": 30398, "Ġincap": 30399, "Ġflagship": 30400, "town": 30401, "âĪ": 30402, "ĠÏĢολ": 30403, "Ġwolves": 30404, "Ġviolations": 30405, "ĠArnold": 30406, "Ġintervene": 30407, "Ġheater": 30408, "Ġrecursos": 30409, "Ġmaid": 30410, "ê²¼": 30411, "ĠдавайÑĤе": 30412, "ĠCelebr": 30413, "Ġcape": 30414, "ĠSty": 30415, "ainen": 30416, "site": 30417, "bij": 30418, "ĠполÑĮз": 30419, "Ġframed": 30420, "Ġpublishers": 30421, "ĠÑĩÑĥÑĤÑĮ": 30422, "Ġtemptation": 30423, 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"Ġwszystkim": 30481, "Ġutilities": 30482, "Ġmicroscop": 30483, "ê¿": 30484, "Ġdaqui": 30485, "ollar": 30486, "ĠÐĶавайÑĤе": 30487, "Ġannée": 30488, "Ġkilometres": 30489, "Ġhomosexual": 30490, "Ġarchitects": 30491, "ãģ¡ãģ¯": 30492, "Ġniye": 30493, "LER": 30494, "Ġmicrophones": 30495, "ĠStunden": 30496, "Ġconsecutive": 30497, "ienda": 30498, "vänd": 30499, "DER": 30500, "Ġlifts": 30501, "ĠMeat": 30502, "Ġsavez": 30503, "íĸĪëįĺ": 30504, "Men": 30505, "Ġdismant": 30506, "거를": 30507, "Ġinsulation": 30508, "Ġscall": 30509, "Ġspooky": 30510, "Ġparc": 30511, "Ġballet": 30512, "ĠWhatsApp": 30513, "Ġfranc": 30514, "Ġdeliberate": 30515, "ĠíħĮ": 30516, "Ġmars": 30517, "ĠZur": 30518, "Pr": 30519, "disciplinary": 30520, "Ġobsession": 30521, "ме": 30522, "Ġmarching": 30523, "ĠEmergency": 30524, "iguous": 30525, "Ġszy": 30526, "ĠLands": 30527, "Ġboarding": 30528, "ĠпоÑĩÑĤи": 30529, "Ġenvy": 30530, "Ġcompassionate": 30531, "Ġmerci": 30532, "Ġdesirable": 30533, "dale": 30534, "Ġcanım": 30535, "ĠAntar": 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30594, "Ġ1979": 30595, "Ġcie": 30596, "ĠëIJĺê³ł": 30597, "Ġescri": 30598, "ÏĮÏĤ": 30599, "íı¬": 30600, "uzzy": 30601, "Cong": 30602, "ìĿ¸ìĿ´": 30603, "Great": 30604, "sil": 30605, "éch": 30606, "ãģ¨ãģĭ": 30607, "Ġmultic": 30608, "ĠDisk": 30609, "²ķ": 30610, "Ġfazla": 30611, "Ġlevant": 30612, "Ġabajo": 30613, "urry": 30614, "stru": 30615, "Ġ먹ëĬĶ": 30616, "Ġaccessory": 30617, "Ġдвиг": 30618, "ĠRid": 30619, "2019": 30620, "Ġdownstream": 30621, "æķ¸": 30622, "Ġkaz": 30623, "utan": 30624, "Ġcharcoal": 30625, "Ġafect": 30626, "wu": 30627, "Ġcontexts": 30628, "Ġfeared": 30629, "ĠìĦ¤": 30630, "Ġhistories": 30631, "Ġfas": 30632, "ensible": 30633, "Ġcocoa": 30634, "illar": 30635, "geons": 30636, "Ġspirituality": 30637, "ĠPew": 30638, "Ġpharmacy": 30639, "Ġpassions": 30640, "Ġbos": 30641, "Ġallá": 30642, "Ġthriving": 30643, "ĠReact": 30644, "Ġoccupy": 30645, "Ġwithdrawal": 30646, "Ġallowance": 30647, "ĠFraktion": 30648, "Ġbuddies": 30649, "Ġidle": 30650, "Ġdissolved": 30651, "Ġprevalent": 30652, 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30709, "енÑı": 30710, "Kay": 30711, "Ġniños": 30712, "ĠPhone": 30713, "Ġstitching": 30714, "Ġfingerprint": 30715, "é¢ĺ": 30716, "λά": 30717, "Ġdedicate": 30718, "ĠLob": 30719, "Ġblacks": 30720, "ĠBle": 30721, "bout": 30722, "ĠÄijang": 30723, "Ġeks": 30724, "Ġsquash": 30725, "ĠKü": 30726, "odi": 30727, "ĠnÆ°á»Ľc": 30728, "Ġvoyage": 30729, "Ġplayful": 30730, "ĠØ¥ÙĦÙī": 30731, "anic": 30732, "Ġcondemn": 30733, "ĠBöyle": 30734, "ĠPolize": 30735, "ãĤ¿ãĥ¼": 30736, "Ġayuda": 30737, "Ġpam": 30738, "à¹Ħà¸Ľ": 30739, "ĠKathy": 30740, "един": 30741, "нова": 30742, "Ġbrig": 30743, "eger": 30744, "Ġeagle": 30745, "Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, 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"Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ð٤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çϾ": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, "Ġkomplett": 32261, "Ġdüny": 32262, "James": 32263, "Ġreceptor": 32264, "Ġoneself": 32265, "guru": 32266, "Ġmerchant": 32267, "liness": 32268, "Ġoverlooked": 32269, "Ġharmonic": 32270, "éķ¿": 32271, "ieso": 32272, "×ķ×ŀ": 32273, "colm": 32274, "ĠпÑĢоекÑĤ": 32275, "ĠAda": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 32340, "ĠвÑģÑİ": 32341, "ĠQuem": 32342, "Ġübrig": 32343, "ĠSke": 32344, "ĠLynch": 32345, "rooms": 32346, "artet": 32347, "fest": 32348, "Ġfrüher": 32349, "Ġlure": 32350, "ä¸į好æĦıæĢĿ": 32351, "ĠìķĮìķĦ": 32352, "ĠWIN": 32353, "ĠRYAN": 32354, "ĠкоÑĤоÑĢÑĥÑİ": 32355, "ĠKash": 32356, "Ġ×Ķ×ŀ": 32357, "Ġsafeg": 32358, "ĠHallelujah": 32359, "ĠдвÑĥÑħ": 32360, "Ġstaple": 32361, "Ġsediment": 32362, "ĠActs": 32363, "Ġblaming": 32364, "Ġmainland": 32365, "Ġsporting": 32366, "Ġdecorations": 32367, "Ġexecuting": 32368, "Ġparan": 32369, "ĠDollar": 32370, "Ġprojections": 32371, "Ġcommissioned": 32372, "Ġbour": 32373, "öm": 32374, "Ġsteamed": 32375, "ĠëŃĺ": 32376, "Ġpetrol": 32377, "Ġcelular": 32378, "帶": 32379, "ĠHungary": 32380, "Ġrented": 32381, "ĠваÑĢи": 32382, "bbie": 32383, "Ġsécur": 32384, "üll": 32385, "Ġswings": 32386, "between": 32387, "ĠиÑĤ": 32388, "estro": 32389, "Ġniemand": 32390, "ĠìĤ¼": 32391, "ĠPardon": 32392, "esses": 32393, "ĠMID": 32394, "Ġcentralized": 32395, "ĠAlien": 32396, "culos": 32397, "Ġcrise": 32398, "裡éĿ¢": 32399, "Ġclasse": 32400, "beitet": 32401, "iÄŁi": 32402, "Ġwhales": 32403, "Ġperimeter": 32404, "Ġtying": 32405, "Ġstrony": 32406, "Ġlikewise": 32407, "ĠPunch": 32408, "Da": 32409, "ĠBaptist": 32410, "Ġsorting": 32411, "Ġiv": 32412, "Ġíķ©": 32413, "Ġrehab": 32414, "Ġeta": 32415, "river": 32416, "Ġsai": 32417, "ãģĦãģŁãģł": 32418, "odus": 32419, "ãģĬé¡ĺãģĦãģĹãģ¾ãģĻ": 32420, "Ġessayer": 32421, "Ġturtles": 32422, "ĠHazrat": 32423, "Ġfabrics": 32424, "Ġcavity": 32425, "Ġponieważ": 32426, "Ġschlecht": 32427, "Ġsalsa": 32428, "ÅŁekkür": 32429, "Ġseating": 32430, "Ġeconomists": 32431, "Ġmang": 32432, "Ġseguinte": 32433, "Ġrang": 32434, "Ġratios": 32435, "Ġconstell": 32436, "Ġlongtemps": 32437, "uating": 32438, "Ġspoiled": 32439, "Ġrecipients": 32440, "Ġsniper": 32441, "ä¹ĭåīį": 32442, "ìĬµëĭĪê¹Į": 32443, "Ġwp": 32444, "ĠLINKE": 32445, "Ġflare": 32446, "ĠAdri": 32447, "ñas": 32448, "Ġbackl": 32449, "mÃ¤ÃŁ": 32450, "ĠBend": 32451, "Ġworkloads": 32452, "ĠÑģÑĥп": 32453, "Ġ1975": 32454, "имÑģÑı": 32455, "ане": 32456, "Ġмон": 32457, "Ġaspirations": 32458, "ĠAer": 32459, "ĠговоÑĢиÑĤÑĮ": 32460, "ĠQian": 32461, "å¦Ī": 32462, "Ġcompromised": 32463, "Ġyolk": 32464, "лаÑģÑĤ": 32465, "Ġhemen": 32466, "rove": 32467, "dens": 32468, "ĠкомменÑĤ": 32469, "Ġ---": 32470, "Ġfluores": 32471, "ноÑģ": 32472, "ĠLiverpool": 32473, "ĠÑģобой": 32474, "ĠZwe": 32475, "Ġlumin": 32476, "ĠOG": 32477, "á¸": 32478, "holm": 32479, "profits": 32480, "SN": 32481, "Ġproportions": 32482, "Ġmica": 32483, "ĠBoh": 32484, "ĠAtlas": 32485, "Ġunsure": 32486, "Ġtouring": 32487, "Ġnied": 32488, "ĠtÄĻ": 32489, "Ġimperative": 32490, "Ġdemek": 32491, "ĠSheriff": 32492, "rance": 32493, "Ġhomeland": 32494, "ĠHail": 32495, "ĠGanz": 32496, "ymm": 32497, "Mon": 32498, "åĨ·": 32499, "vida": 32500, "Ġdesarroll": 32501, "æĬĢ": 32502, "Ġintriguing": 32503, "ĠHugo": 32504, "ĠãĤĤ": 32505, "é¬": 32506, "аÑĨ": 32507, "ĠWiÄĻc": 32508, "atted": 32509, "ĠìķĦëĭĪê³ł": 32510, 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"Ġentrepreneurial": 33094, "Ġconsiders": 33095, "Ñĥем": 33096, "opa": 33097, "Ġdormir": 33098, "ĠElementary": 33099, "Ġprzypad": 33100, "ÑĥÑģка": 33101, "ĠоÑĩеÑĢ": 33102, "ugene": 33103, "Ġtenido": 33104, "Ġlugares": 33105, "ë¥": 33106, "ĠÑĩаÑģÑĤ": 33107, "Ġsao": 33108, "Ġbraid": 33109, "ĠVere": 33110, "ĠReich": 33111, "ĠPoss": 33112, "Ġinan": 33113, "wand": 33114, "ref": 33115, "Ġmontrer": 33116, "Ġ1981": 33117, "çķª": 33118, "asında": 33119, "Ġchrome": 33120, "ĠTrinity": 33121, "Ġexploitation": 33122, "ĠSense": 33123, "ĠCMS": 33124, "ĠNoble": 33125, "ĠìĦłíĥĿ": 33126, "Ġswelling": 33127, "electronic": 33128, "]?": 33129, "Ġbrushing": 33130, "Ġliquidity": 33131, "ĠHook": 33132, "ĠConnor": 33133, "ĠAlum": 33134, "Ġgucken": 33135, "suite": 33136, "Ġwiele": 33137, "Ġbarrels": 33138, "ĠRegel": 33139, "ĠMent": 33140, "ĠTrip": 33141, "ĠBrush": 33142, "ĠErik": 33143, "urate": 33144, "ÉĻr": 33145, "ĠCyr": 33146, "ouble": 33147, "ĠBecca": 33148, "Ġpasswords": 33149, "ű": 33150, "borg": 33151, 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"Ġaccumulate": 33384, "Ġmaintains": 33385, "ëĦ": 33386, "ĠFighter": 33387, "íĨł": 33388, "Ġmatin": 33389, "Ġcoupon": 33390, "Ġstunt": 33391, "Ġdebuted": 33392, "å¾ħãģ£ãģ¦": 33393, "Ġprag": 33394, "иваем": 33395, "73": 33396, "Ġexpres": 33397, "Ġìĺ¤ë¹ł": 33398, "ĠпеÑĢÑģон": 33399, "Ġcalculus": 33400, "Ġabrupt": 33401, "ĠInspector": 33402, "ourt": 33403, "æĸĻ": 33404, "źniej": 33405, "intense": 33406, "Ba": 33407, "Ġlounge": 33408, "Ġasthma": 33409, "ĠHiç": 33410, "ª»": 33411, "Ġeditorial": 33412, "Ġseize": 33413, "Ġkır": 33414, "Ġmouve": 33415, "Ġtierra": 33416, "Ġtestosterone": 33417, "Ġrh": 33418, "ĠKingston": 33419, "ELLE": 33420, "ĠRepresentative": 33421, "Ġ1974": 33422, "Ġiba": 33423, "Ts": 33424, "Ġsorta": 33425, "Ġ(?)": 33426, "ĠتÙĪ": 33427, "ĠëĤ´ëł¤": 33428, "Ġbekommt": 33429, "Ġspiritually": 33430, "Ġdistorted": 33431, "Mad": 33432, "Ġreim": 33433, "ánh": 33434, "ĠOttoman": 33435, "ĠRelig": 33436, "ĠEls": 33437, "Ġretained": 33438, "ĠLaughs": 33439, "æĢ»": 33440, "ĠSAS": 33441, "ĠколиÑĩеÑģÑĤво": 33442, "×ķתר": 33443, "Ġinnovate": 33444, "Ġkork": 33445, "ĠÑĢаÑģÑģказÑĭв": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ĠполÑĥÑĩаеÑĤÑģÑı": 33451, "Ġbuns": 33452, "åħ«": 33453, "Ġyüzden": 33454, "Ġsurgeries": 33455, "Ø£ÙĨ": 33456, "Ġbankruptcy": 33457, "welt": 33458, "Ġsiamo": 33459, "Ġdarkest": 33460, "ĠHann": 33461, "gga": 33462, "Ġformas": 33463, "ĠDj": 33464, "named": 33465, "Ġshields": 33466, "ueller": 33467, "ĠFew": 33468, "Ġlace": 33469, "Ġfurious": 33470, "ĠYU": 33471, "Ġsocietal": 33472, "Ġjudgement": 33473, "ĠDos": 33474, "Ġjab": 33475, "laws": 33476, "Ġreinvent": 33477, "ĠKatherine": 33478, "ĠChoi": 33479, "adows": 33480, "Ġrans": 33481, "oden": 33482, "ĠMidwest": 33483, "nın": 33484, "Ġdeport": 33485, "ĠDip": 33486, "ç´ħ": 33487, "Ġatención": 33488, "ĠCourtney": 33489, "ividad": 33490, "ĠÚ©Ûģ": 33491, "Ġefficacy": 33492, "ĠBrooks": 33493, "Ġreferral": 33494, "ĠконÑĨ": 33495, "Ġmalicious": 33496, "Ġkir": 33497, "ĠGoddess": 33498, "Ġfunky": 33499, "Ġinterim": 33500, "ĠKörper": 33501, "Ġìĸ¼ë§": 33502, "kur": 33503, "Ġкли": 33504, "Ġtrucs": 33505, "gesetz": 33506, "Ġzug": 33507, "ĠGlück": 33508, "ĠMinute": 33509, "Ġprestigious": 33510, "Ġniez": 33511, "Ġconcentrations": 33512, "лаÑģÑĤи": 33513, "ĠSis": 33514, "ĠVitamin": 33515, "kov": 33516, "ĠPBS": 33517, "Ġнее": 33518, "Ġretailers": 33519, "Ġconventions": 33520, "ĠSamantha": 33521, "Ġproudly": 33522, "Jordan": 33523, "ĠJASON": 33524, "atk": 33525, "Ġtriste": 33526, "Ġstär": 33527, "Ġreiterate": 33528, "Ġposterior": 33529, "Ġ1973": 33530, "ĠPine": 33531, "ĠJuliet": 33532, "Ġpedir": 33533, "kil": 33534, "Ġoverlapping": 33535, "Ġexclude": 33536, "Ġeconóm": 33537, "Ġaccepts": 33538, "ĠSter": 33539, "決": 33540, "Ġìļ´ëıĻ": 33541, "estab": 33542, "Ġtug": 33543, "arg": 33544, "Ġlivro": 33545, "اص": 33546, "Ġseams": 33547, "Ġburaya": 33548, "Ġello": 33549, "ĠTM": 33550, "ĠPaw": 33551, "ĠIndex": 33552, "Exc": 33553, "Ġinspirational": 33554, "Ġdunk": 33555, "è°ģ": 33556, "akter": 33557, "Ġconditioner": 33558, "ĠSalut": 33559, "ÅĤec": 33560, "Ġìī½": 33561, "ĠÑĥзна": 33562, "ĠRomeo": 33563, "fruit": 33564, "ĠYO": 33565, "Ġchá»ī": 33566, "бÑĥ": 33567, "bons": 33568, "Ġreproductive": 33569, "Ġorada": 33570, "Ġíļ¨": 33571, "Ġtentar": 33572, "Ġmañana": 33573, "ãĤ¬": 33574, "Ġsolvent": 33575, "Jessica": 33576, "ĠLegal": 33577, "Ġtua": 33578, "Ġsic": 33579, "ĠEQ": 33580, "aukee": 33581, "ìĭľëĭ¤": 33582, "ĠÅŀu": 33583, "Ġadhere": 33584, "ĠTul": 33585, "Ġà®Ĩ": 33586, "Ġtextbooks": 33587, "ĠFifth": 33588, "Ġexperi": 33589, "Ġchic": 33590, "Ġheap": 33591, "inely": 33592, "atra": 33593, "Two": 33594, "Ġhelemaal": 33595, "Ġfren": 33596, "æİ¨": 33597, "Ġbisher": 33598, "اش": 33599, "ĠìĦłìĥĿ": 33600, "ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķ×§": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åΰåºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çζ": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åύ": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, "chw": 34655, "Ġtahun": 34656, "ĠErgeb": 34657, "FR": 34658, "Ġìłķë¶Ģ": 34659, "ĠSoldier": 34660, "Ġenlightenment": 34661, "Ġexamining": 34662, "ĠNotre": 34663, "Ġeram": 34664, "ĠSunny": 34665, "Ġlayered": 34666, "ĠDazu": 34667, "rades": 34668, "好åIJĥ": 34669, "ĠнаÑĪей": 34670, "Ġtimber": 34671, "Ġmanners": 34672, "ĠBirmingham": 34673, "Ġminiature": 34674, "ometers": 34675, "Ġfiller": 34676, "ĠRip": 34677, "ĠKomb": 34678, "owner": 34679, "ì¿": 34680, "idian": 34681, "Ġdemás": 34682, "ĠÙĪØª": 34683, "Ġprecautions": 34684, "Ġgoverno": 34685, "zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 34712, "ĠExtremadura": 34713, "Ġdemonstrations": 34714, "ĠMontgomery": 34715, "íķ´ìĦ¤": 34716, "ĠGandhi": 34717, "ãĥĿ": 34718, "ç½®": 34719, "Ġreunion": 34720, "ĠjakiÅĽ": 34721, "ĠZug": 34722, "OUGH": 34723, "lifting": 34724, "Ġà²": 34725, "á¹Ľá¹£": 34726, "eb": 34727, "ĠWOW": 34728, "ĠShiva": 34729, "ometry": 34730, "Ġwildly": 34731, "Ġtended": 34732, "Ġmegap": 34733, "ì²ĺ": 34734, "Ġnause": 34735, "Ġgerek": 34736, "ãĥĭ": 34737, "ĠMarcel": 34738, "Ġneste": 34739, "خر": 34740, "Ġfeh": 34741, "åĨħ": 34742, "suspenseful": 34743, "ĠWrestle": 34744, "ĠPalestinians": 34745, "ĠGORD": 34746, "iyet": 34747, "ĠÑĢади": 34748, "Ġversuchen": 34749, "Ġtransistor": 34750, "ĠÐŁÑĢоÑģÑĤо": 34751, "ĠпонÑĢав": 34752, "Ġrhyme": 34753, "ĠVermont": 34754, "platz": 34755, "è®°": 34756, "ĠÄ°ÅŁte": 34757, "ĠHag": 34758, "ĠÐĺм": 34759, "ĠÑĢаÑģÑģказ": 34760, "Ġmetros": 34761, "ĠInfinity": 34762, "wolf": 34763, "ibal": 34764, "ftig": 34765, "ĠÚĨ": 34766, "Ġíĺ¹ìĭľ": 34767, "Ġoggi": 34768, "Ġdisposit": 34769, "ĠпÑĢил": 34770, "ĠвÑĭпол": 34771, "Ġthôi": 34772, "ĠKENN": 34773, "Ġhanding": 34774, "actus": 34775, "Ġtacos": 34776, "Ġformerly": 34777, "ĠCorinthians": 34778, "ãģ«ãģ¯": 34779, "ÑĨÑĸÑĹ": 34780, "Ġpadre": 34781, "Ġcongregation": 34782, "æij": 34783, "fert": 34784, "Ġsubir": 34785, "aiser": 34786, "qua": 34787, "araoh": 34788, "ĠCurry": 34789, "ĠìķĬëĬĶ": 34790, "елÑİ": 34791, "Ġfuss": 34792, "Ġbooty": 34793, "Ġlows": 34794, "Ġhommes": 34795, "ĠMH": 34796, "ĠDisneyland": 34797, "went": 34798, "Ġresidue": 34799, "Ġbeeping": 34800, "è¼ķ": 34801, "ätta": 34802, "Ġmould": 34803, "ĠProjekt": 34804, "stalk": 34805, "Ġartifact": 34806, "ĠAntrag": 34807, "ĠAMD": 34808, "ĠCrypt": 34809, "Ġë©Ķ": 34810, "ĠFelipe": 34811, "ĠCOB": 34812, "elu": 34813, "Ġselfies": 34814, "ĠSanti": 34815, "chutz": 34816, "ĠУкÑĢаÑĹ": 34817, "gesamt": 34818, "Ġflock": 34819, "jaz": 34820, "plain": 34821, "Ġwrinkles": 34822, "Ġreais": 34823, "Ġpaljon": 34824, "Ġempowerment": 34825, "Ġattendees": 34826, "ppa": 34827, "Ġneden": 34828, "онÑĭ": 34829, "Ġtimeframe": 34830, "ĠCherry": 34831, "Ġidée": 34832, "Ġgag": 34833, "Ġdonkey": 34834, "Ġông": 34835, "ĠHare": 34836, "éļĽ": 34837, "ĠKara": 34838, "Ġacompan": 34839, "places": 34840, "imientos": 34841, "ĠHamm": 34842, "би": 34843, "uben": 34844, "iliyor": 34845, "Ġthirst": 34846, "Ġkry": 34847, "ĠGeorgetown": 34848, "׳×Ķ": 34849, "Ġorch": 34850, "Ġheartbeat": 34851, "Ġtransformations": 34852, "estones": 34853, "ĠKH": 34854, "Ġcartoons": 34855, "Ġanci": 34856, "Ġworthless": 34857, "Ġtailored": 34858, "pu": 34859, "Americans": 34860, "Ġpiles": 34861, "ĠMonkey": 34862, "Ġbasin": 34863, "ĠTemper": 34864, "ĠPaint": 34865, "Ġpunching": 34866, "Ġbaik": 34867, "ĠOakland": 34868, "vre": 34869, "ÅŁallah": 34870, "ydd": 34871, "Ġcasually": 34872, "odu": 34873, "Ġcoded": 34874, "ĠNorwegian": 34875, "ĠVince": 34876, "Ġpremature": 34877, "ĠPromise": 34878, "екÑģÑĤ": 34879, "Ġdevastated": 34880, "ĠPremium": 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"ĠпонимаÑİ": 35112, "Ġadditions": 35113, "Ġasympt": 35114, "ĠменÑĮÑĪе": 35115, "Ġscans": 35116, "Child": 35117, "ĠHide": 35118, "кÑĥÑİ": 35119, "etas": 35120, "Ġdank": 35121, "Ġpleas": 35122, "Ġessays": 35123, "Ġjets": 35124, "åħĴ": 35125, "Ġвед": 35126, "Ġpositives": 35127, "hof": 35128, "-)": 35129, "zzo": 35130, "Ġstarters": 35131, "Ġsmiled": 35132, "Ġ1944": 35133, "quiera": 35134, "Ġrok": 35135, "Ġpuesto": 35136, "Nico": 35137, "Ġsimulations": 35138, "Ġà¶": 35139, "Ġintrigued": 35140, "ĠOverwatch": 35141, "åĸĤ": 35142, "sigh": 35143, "bai": 35144, "Ġë§IJê³ł": 35145, "idé": 35146, "Ġcrabs": 35147, "áºŃp": 35148, "ĠIraqi": 35149, "ìĿ´ë¥¼": 35150, "ÑĤÑı": 35151, "ĠSophia": 35152, "ĠDNS": 35153, "Ġönemli": 35154, "ĠLuo": 35155, "Ŀ¤": 35156, "ĠCounsel": 35157, "ligen": 35158, "анÑĮÑĪе": 35159, "Ġtrumpet": 35160, "Ġdapat": 35161, "ĠJM": 35162, "ĠEVERY": 35163, "Ġå°įä¸įå°į": 35164, "夢": 35165, "ĠLayer": 35166, "Ġcô": 35167, "нал": 35168, "ĠJoo": 35169, "ĠHack": 35170, "Ġsunt": 35171, "ĠLeonard": 35172, "ĠFirebase": 35173, "änger": 35174, "Ġexploding": 35175, "voy": 35176, "Ġì¦IJ": 35177, "ĠÑģеÑĢÑĮ": 35178, "Ġseverity": 35179, "Ġbestimm": 35180, "çµIJæŀľ": 35181, "Ġtiring": 35182, "Ġprocurement": 35183, "Ġdiplomacy": 35184, "Ġdecorative": 35185, "ĠÙĬا": 35186, "Ġpenetration": 35187, "Õ«": 35188, "Ġoutright": 35189, "ENE": 35190, "ĠUni": 35191, "odles": 35192, "Ġzeros": 35193, "Ġdelightful": 35194, "jm": 35195, "Ġdopo": 35196, "没äºĭ": 35197, "Ġpositivity": 35198, "ĠVISTA": 35199, "ĠResource": 35200, "íĥĢë": 35201, "ÑĪие": 35202, "Carl": 35203, "Ġpiping": 35204, "Ġchopping": 35205, "ĠGanze": 35206, "üss": 35207, "ĠAo": 35208, "Ġshattered": 35209, "ĠDetective": 35210, "Ġundoubtedly": 35211, "Ġhalluc": 35212, "Ġench": 35213, "ÑĭÑĩно": 35214, "ÑĥлÑıÑĢ": 35215, "isesti": 35216, "Ġpedals": 35217, "Ġdurum": 35218, "¤íĶ": 35219, "laimer": 35220, "Ġpropre": 35221, "Cu": 35222, "Ġtranslator": 35223, "ĠcaÅĤ": 35224, "Ġ그걸": 35225, "ĠcaÅĤy": 35226, "UA": 35227, "Ġrevised": 35228, "Ġподоб": 35229, "ĠArticle": 35230, "ĠHaiti": 35231, "ĠÃĵ": 35232, "ĠCtrl": 35233, "Ġrozm": 35234, "lait": 35235, "Ġletzte": 35236, "ispering": 35237, "display": 35238, "Ġaluminium": 35239, "Ġpalabras": 35240, "Ġconocer": 35241, "Ġzitten": 35242, "Ġdirig": 35243, "åıªæľī": 35244, "Ġbrainstorm": 35245, "Ġwifi": 35246, "ĠParticip": 35247, "Ġviewpoint": 35248, "ĠQuan": 35249, "Ġhierarch": 35250, "Welcome": 35251, "対": 35252, "Ġoffen": 35253, "ĠRecovery": 35254, "gano": 35255, "Would": 35256, "Ġrepro": 35257, "Ġperceptions": 35258, "Ġdemasi": 35259, "ĠBangladesh": 35260, "ĠIncredible": 35261, "Ġletzt": 35262, "Ġbehaving": 35263, "Ġastonishing": 35264, "ĠâĨ": 35265, "ĠëĤ¨ìŀIJ": 35266, "èµ°äºĨ": 35267, "ãĥĶ": 35268, "ĠGORDON": 35269, "CAR": 35270, "?!\"": 35271, "ĠPrest": 35272, "Ġë§ŀìķĦìļĶ": 35273, "Ġtand": 35274, "Ġlash": 35275, "çĬ": 35276, "ificant": 35277, "Ġintoler": 35278, "ĠгеÑĢо": 35279, "Ġteu": 35280, "aso": 35281, "ĠÑģовеÑĤ": 35282, "Ġtravelers": 35283, "ĠSynd": 35284, "ĠвеÑĢÑģ": 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"ĠmayorÃŃa": 35342, "Ġbureau": 35343, "eterm": 35344, "ĠDID": 35345, "ìĬ·": 35346, "Ġwaving": 35347, "Ġbeb": 35348, "Ġár": 35349, "Ġкв": 35350, "Ġenvoy": 35351, "anut": 35352, "икÑĥ": 35353, "ĠEnvironment": 35354, "ĠAssass": 35355, "ãĤĵãģ§": 35356, "ĠBread": 35357, "ĠТÑĥÑĤ": 35358, "Ġstaircase": 35359, "ĠDisease": 35360, "Ġaucun": 35361, "ĠëĭĪ": 35362, "Ġconfrontation": 35363, "Ġ1941": 35364, "Ġirony": 35365, "Ġworsh": 35366, "ãĤĮãĤĭ": 35367, "Ġfick": 35368, "ĠNaomi": 35369, "Ġbackside": 35370, "ieux": 35371, "Kap": 35372, "Ġvedere": 35373, "Ġlengthy": 35374, "Ġbreaker": 35375, "ĠRolle": 35376, "Ġpredator": 35377, "Ġnossos": 35378, "Ġadvertise": 35379, "è³ĩ": 35380, "ÑĢоде": 35381, "Rednerwechsel": 35382, "reten": 35383, "Ġcollectors": 35384, "ıģımız": 35385, "Ġtrig": 35386, "Ġaxes": 35387, "inters": 35388, "Ġpenalties": 35389, "ĠOsman": 35390, "ĠJenna": 35391, "Ġflakes": 35392, "Ġtrainers": 35393, "Ġstunned": 35394, "ĠScroll": 35395, "ĠPip": 35396, "ĠнаÑģÑĤ": 35397, "ĠnhÃł": 35398, "ĠSmack": 35399, "ẫn": 35400, "ratos": 35401, "ĠÑĢабоÑĤÑĭ": 35402, "Ġucz": 35403, "ĠLemon": 35404, "ĠSind": 35405, "Ġpsychic": 35406, "ĠAbg": 35407, "Ġmammals": 35408, "Ġimmersive": 35409, "Ġbots": 35410, "Ġverschiedene": 35411, "Ġgeral": 35412, "Ġfollower": 35413, "Ġä»ĸ": 35414, "Ġseguridad": 35415, "Ġimmersed": 35416, "feito": 35417, "cross": 35418, "Ġöld": 35419, "íĥĦ": 35420, "Ġãģĵãģ®": 35421, "Ġ×Ķ×Ļ×IJ": 35422, "ĠJian": 35423, "Ġbiliyor": 35424, "area": 35425, "Ġkaf": 35426, "Ġgodt": 35427, "çĽ¸ä¿¡": 35428, "Ġë°©ìĨ¡": 35429, "Ġdetriment": 35430, "æ¥ļ": 35431, "Ñĸл": 35432, "ĠÄijâu": 35433, "Ġchloride": 35434, "øre": 35435, "lei": 35436, "Ġmonte": 35437, "Ġdifférentes": 35438, "à¯ģ.": 35439, "Ġcaregivers": 35440, "Ġinadequ": 35441, "Ġfarewell": 35442, "ĠÑĤипа": 35443, "ontec": 35444, "ĠEph": 35445, "HHH": 35446, "ĠTodos": 35447, "ĠСШÐIJ": 35448, "Ġtrov": 35449, "Ġlige": 35450, "Ġcông": 35451, "ĠCiv": 35452, "Ġcapaz": 35453, "ĠVallahi": 35454, "Ġqueste": 35455, "Ġreplica": 35456, "سب": 35457, "zna": 35458, "ĠÑģлÑĥж": 35459, "ĠPT": 35460, "wave": 35461, "ieni": 35462, "Ġrelied": 35463, "develop": 35464, "Ġdeme": 35465, "ĠAman": 35466, "Ġ[...]": 35467, "Ġcompliments": 35468, "uais": 35469, "ĠíĮ¨": 35470, "Ġsmelling": 35471, "Ġdadurch": 35472, "ÙĪØª": 35473, "Ġoranges": 35474, "Ġлай": 35475, "Ġstabilization": 35476, "åĢį": 35477, "ãĤĮãģŁ": 35478, "楽": 35479, "Ġappliances": 35480, "Ġhm": 35481, "ĥIJë©´": 35482, "odynamics": 35483, "ĠciÄĻ": 35484, "ĠCott": 35485, "MON": 35486, "ĠMang": 35487, "æĶ¯æĮģ": 35488, "Ġallerdings": 35489, "ική": 35490, "shots": 35491, "Ġts": 35492, "ĠGör": 35493, "ĠCHAR": 35494, "Ġ:(": 35495, "Ġwrath": 35496, "Ġfique": 35497, "Ġführen": 35498, "Ġtestament": 35499, "Ġ^^": 35500, "á¹Ľá¹£á¹ĩa": 35501, "ALD": 35502, "Ġtexto": 35503, "ĠDogs": 35504, "Ġsib": 35505, "Ġpathetic": 35506, "ocks": 35507, "Ġradically": 35508, "ĠMORE": 35509, "ĠJAMES": 35510, "Ġingl": 35511, "ĠTechnical": 35512, "Ġporch": 35513, "ĠUT": 35514, "ĠобÑıзаÑĤелÑĮно": 35515, 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"ĠOperations": 36381, "ÑĥÑĶ": 36382, "Ġmoje": 36383, "Ġinstructed": 36384, "ĠëĪĦ구": 36385, "Ġ×Ķ×Ĵ": 36386, "ĠпомоÑīÑĮÑİ": 36387, "Ġsabia": 36388, "ìķĺìĸ´ìļĶ": 36389, "plane": 36390, "pri": 36391, "ĠполноÑģÑĤÑĮÑİ": 36392, "ĠKitty": 36393, "Ġpróprio": 36394, "edere": 36395, "Ġinteresante": 36396, "Ġде": 36397, "Ġcondensed": 36398, "Ġavent": 36399, "TOR": 36400, "Ġgreasy": 36401, "ARK": 36402, "orta": 36403, "AJ": 36404, "Ġdisreg": 36405, "Ġcorrections": 36406, "Ġstero": 36407, "Ġinfluenza": 36408, "Ġdesses": 36409, "Ġballots": 36410, "Ġmeget": 36411, "Ġmafia": 36412, "Ġböl": 36413, "nost": 36414, "ĠÑģÑĤаÑĤÑĮ": 36415, "Ġresponder": 36416, "Ġhinten": 36417, "grav": 36418, "à¸Ńะ": 36419, "ynchron": 36420, "Ġviens": 36421, "Ġsamo": 36422, "Ġdt": 36423, "pannt": 36424, "ĠÅĽwiat": 36425, "ĠзапиÑģ": 36426, "Ġmerged": 36427, "Ġkep": 36428, "Ġmisleading": 36429, "Ġdigamos": 36430, "Ġammon": 36431, "è¾Ľ": 36432, "chet": 36433, "Ġê°Ģìł¸": 36434, "Ġuni": 36435, "ĠëIJĺëĬĶëį°": 36436, "ĠнапÑĢав": 36437, "ĠкоÑĤоÑĢого": 36438, "Ġanimate": 36439, "×ķ×IJ×": 36440, "еÑĢв": 36441, "Ġminced": 36442, "Ġkaum": 36443, "ãģĤãģģ": 36444, "ÏĢε": 36445, "лег": 36446, "existing": 36447, "Ġplataform": 36448, "ĠKRIS": 36449, "ìĽł": 36450, "ĠFamilien": 36451, "ĠLibya": 36452, "Ġbiodiversity": 36453, "Ġidiots": 36454, "irdi": 36455, "Ġszyb": 36456, "ĠRolling": 36457, "ücht": 36458, "ĠÑĥдив": 36459, "ÑģÑĥд": 36460, "Ġrealizar": 36461, "Ġcanned": 36462, "ĠÑĢан": 36463, "Ġmetabolic": 36464, "ĠBeef": 36465, "Ġkilka": 36466, "лÑİÑģ": 36467, "Ġregistry": 36468, "моÑĤÑĢиÑĤе": 36469, "Ġvielä": 36470, "Ġodc": 36471, "Ġcondemned": 36472, "æ©ĭ": 36473, "fal": 36474, "ĠDil": 36475, "woÅĽci": 36476, "Aw": 36477, "Ġstatistically": 36478, "Ġsogen": 36479, "ĠBETH": 36480, "Ġshaving": 36481, "幸": 36482, "ocal": 36483, "ĠFunny": 36484, "Ġpeacefully": 36485, "Ġaddictive": 36486, "ĠInsert": 36487, "lauf": 36488, "Ġexperiencia": 36489, "é¦ĸåħĪ": 36490, "иÑĤелÑı": 36491, "ÃŃgen": 36492, "ágina": 36493, "Ġabdomen": 36494, "íķľëĭ¤": 36495, "icus": 36496, "imana": 36497, "ìį¨": 36498, "arching": 36499, "Ġkonkret": 36500, "ìķĺë": 36501, "ека": 36502, "oufl": 36503, "ivel": 36504, "Ġnude": 36505, "ètres": 36506, "Ġmonsieur": 36507, "Ġclash": 36508, "Ġtherapists": 36509, "Ġcubed": 36510, "Ġretrouver": 36511, "Ġwaveform": 36512, "Ġpotem": 36513, "ĠFormer": 36514, "isión": 36515, "åºľ": 36516, "Ġ×IJ×Ŀ": 36517, "undos": 36518, "ĠMeinung": 36519, "صÙĦ": 36520, "ĠJude": 36521, "ĠnÃ¥r": 36522, "ĠLeonardo": 36523, "ĠCristo": 36524, "ĠGOT": 36525, "ÑģÑĤÑĢÑĥк": 36526, "LAN": 36527, "ĠgÃ¥ng": 36528, "Ġdéb": 36529, "ĠFrankfurt": 36530, "Ġcrappy": 36531, "Ġlil": 36532, "année": 36533, "ĠмеÑģÑĤе": 36534, "RET": 36535, "ĠNer": 36536, "ĠCOSTA": 36537, "Ġjedem": 36538, "Ġcurtains": 36539, "Ġiterations": 36540, "Ġunav": 36541, "Ġplaque": 36542, "orum": 36543, "Ġζ": 36544, "Ġnúmeros": 36545, "Ġdesap": 36546, "²½": 36547, "Ġcompiled": 36548, "Ġrefle": 36549, "Ġrankings": 36550, "Ġrepaired": 36551, "ĠÐĿапÑĢ": 36552, 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36607, "buds": 36608, "Ġfis": 36609, "Ġpytanie": 36610, "Ġinterconnected": 36611, "Ġembaixo": 36612, "ĠSas": 36613, "Ġruh": 36614, "ĠSicht": 36615, "Sur": 36616, "Ġsuperb": 36617, "ĠSabbath": 36618, "ĠDanger": 36619, "kol": 36620, "Ġhou": 36621, "supp": 36622, "ĠNacional": 36623, "Ġsuccession": 36624, "Ġvá": 36625, "ĠMaÃŁnahmen": 36626, "ĠJessie": 36627, "ĠIdaho": 36628, "forest": 36629, "ħĺ": 36630, "Ġ×ŀ×ĵ": 36631, "ĠØ£ÙĬ": 36632, "Ġsweetheart": 36633, "Ġneatly": 36634, "ĠEvangel": 36635, "곡": 36636, "ĠSuite": 36637, "ública": 36638, "ĠÑĥли": 36639, "ĠAnnouncer": 36640, "ligh": 36641, "Ġsensations": 36642, "Ġshelters": 36643, "Ġhart": 36644, "Ġsqueezing": 36645, "ĠRivers": 36646, "ĠCooking": 36647, "ì±ħ": 36648, "personal": 36649, "Ġmanos": 36650, "ÑijÑĤÑģÑı": 36651, "wij": 36652, "Ġgogg": 36653, "ĠMilli": 36654, "ĠFP": 36655, "ünst": 36656, "ĠLS": 36657, "Ġspraying": 36658, "Ġfaux": 36659, "Ġautograph": 36660, "ologic": 36661, "Ġtorment": 36662, "Ġencrypted": 36663, "á»ħ": 36664, 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"×ķ×ŀ×": 36722, "ĠQuinn": 36723, "Ġgauche": 36724, "ĠÙĪØ£": 36725, "ĠÈ": 36726, "çĵľ": 36727, "Ġtechno": 36728, "Ġdispatch": 36729, "ĠaÅŁk": 36730, "Ġeinzel": 36731, "ĠGmail": 36732, "çŀ": 36733, "Ġê°ľìĿ¸": 36734, "ĠÑģемÑĮ": 36735, "Ġjourneys": 36736, "Ġiht": 36737, "Ġfibre": 36738, "Ġdramas": 36739, "ouched": 36740, "Ġrename": 36741, "ĠопеÑĢ": 36742, "Ġpoo": 36743, "ĠDru": 36744, "ĠиÑĤог": 36745, "Ġzast": 36746, "Ġcoz": 36747, "Ġzucch": 36748, "Ġobtaining": 36749, "Ġcommute": 36750, "Ġsubmer": 36751, "ĠVish": 36752, "ĠRabb": 36753, "ogg": 36754, "Ġhut": 36755, "íĸĪìĸ´": 36756, "æ¯Ķå¦Ĥ": 36757, "eremi": 36758, "Ġμα": 36759, "Ġdiskut": 36760, "ĠбÑĥк": 36761, "Ġimpaired": 36762, "depend": 36763, "ĠÙĪØ§": 36764, "ĠÑĢÑĥк": 36765, "ĠбаÑĢ": 36766, "Ġoxidation": 36767, "Ġsituação": 36768, "ÉĻn": 36769, "ução": 36770, "Ġsagte": 36771, "ĠSER": 36772, "ĠCake": 36773, "Ġturmeric": 36774, "ĠKak": 36775, "bung": 36776, "ĠKá¹Ľá¹£á¹ĩa": 36777, "Ġpoisoning": 36778, "Ġslipping": 36779, "ĠSays": 36780, "å°±åı¯ä»¥": 36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, "Ġtampoco": 36838, "Ġphilosophers": 36839, "ĠRecht": 36840, "æĵļ": 36841, "Ġcomentarios": 36842, "yse": 36843, "Ġìľ¤": 36844, "Ġmise": 36845, "ĠGin": 36846, "Ġном": 36847, "ĠFROM": 36848, "liner": 36849, "atif": 36850, "ĠspoÅĤec": 36851, "xa": 36852, "ĠÑĤÑĢÑĥд": 36853, "Ġwag": 36854, "기ìĹIJ": 36855, "ĠMG": 36856, "Ġoffspring": 36857, "ĠUnderstanding": 36858, "åıªæĺ¯": 36859, "ORA": 36860, "Ġwhirring": 36861, "Ġsurrend": 36862, "Ġpoker": 36863, "Ġmonuments": 36864, "ĠâĻ©": 36865, "Ġorganised": 36866, "ĠSozial": 36867, "ĠFactory": 36868, "Ñħа": 36869, "Ġresemble": 36870, "зд": 36871, "Ġexplosions": 36872, "Ġpayroll": 36873, "Ġomn": 36874, "ĠJorge": 36875, "ιÏĥ": 36876, "Ġfracture": 36877, "Ġpersecution": 36878, "Ġdemais": 36879, "ECH": 36880, ",)": 36881, "Ġcriar": 36882, "ĠJOSH": 36883, "Ġdemographics": 36884, "Ġ1600": 36885, "Ġcurrencies": 36886, "ĠTips": 36887, "ĠéĢĻåĢĭ": 36888, "ĠRefer": 36889, "ĠDancing": 36890, "Ġinconsistent": 36891, "Ġdeh": 36892, "Ġimmens": 36893, 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"æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "é̱": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æŃ¦": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 38560, "esters": 38561, "ophren": 38562, "Ġalimentos": 38563, "ĠUz": 38564, "ÖĤ": 38565, "Ġ650": 38566, "ĠпÑĢиеÑħ": 38567, "FI": 38568, "Ġsampai": 38569, "Ġparlé": 38570, "hesion": 38571, "Ġsır": 38572, "Ġapparatus": 38573, "Ġcorrelated": 38574, "ĠPrincipal": 38575, "Ġcorr": 38576, "ĠOfficial": 38577, "иÑĩеÑģкие": 38578, "Ġterminals": 38579, "Should": 38580, "Ġvacun": 38581, "Ġstellt": 38582, "Ġmooi": 38583, "etzung": 38584, "ĠкÑĢа": 38585, "Ġdai": 38586, "Ġпож": 38587, "Team": 38588, "ĠPPE": 38589, "ĠÐŀÑģ": 38590, "ĠLeah": 38591, "ĠIvy": 38592, "yst": 38593, "Ġuhhh": 38594, "Ġnighttime": 38595, "Ġtrendy": 38596, "Ġsecurities": 38597, "Ġcontinents": 38598, "Ġfirsthand": 38599, "ĠVeron": 38600, "ĠëĤ®": 38601, "Ġbrowsing": 38602, "ĠCada": 38603, "tro": 38604, "Ġtramp": 38605, "reib": 38606, "Ġerstmal": 38607, "irler": 38608, "Ġpsic": 38609, "Ġgetir": 38610, "ĠNP": 38611, "Ġdzieci": 38612, "обÑĢаз": 38613, "Ġmagician": 38614, "Ġscrutiny": 38615, "Ġslab": 38616, "ĠOT": 38617, "isty": 38618, "iries": 38619, "orest": 38620, "Ġtasked": 38621, "Ġmorally": 38622, "ìķ¼ì§Ģ": 38623, "ustered": 38624, "Ġfools": 38625, "Ġirrespons": 38626, "Ġeinf": 38627, "Ġviá»ĩc": 38628, "Ġscor": 38629, "Ġpillows": 38630, "ĠGegen": 38631, "Ġtutte": 38632, "Ġquarterly": 38633, "Ġdidnt": 38634, "ĠGym": 38635, "ĠEther": 38636, "ĠØ«": 38637, "лиÑĪком": 38638, "Ġsignaling": 38639, "ĠNode": 38640, "ĠDoncs": 38641, "Ġyah": 38642, "ĠKanal": 38643, "Ġfading": 38644, "etin": 38645, "Ġinfluencers": 38646, "Ġmedals": 38647, "Ġengineered": 38648, "Ġfermented": 38649, "ê²łì§Ģë§Į": 38650, "ĠBeethoven": 38651, "×ŀש": 38652, "inental": 38653, "ĠìķĮ볤": 38654, "ütfen": 38655, "alnya": 38656, "Ġovere": 38657, "Ġdenkt": 38658, "акÑĤеÑĢ": 38659, "Ġâĺ": 38660, "Ġnecesit": 38661, "Ġgenerators": 38662, "grass": 38663, "ĠподÑĥм": 38664, "lieÃŁen": 38665, "Bar": 38666, "ľëıĻ": 38667, "ĠдеÑĤей": 38668, "Ġsucking": 38669, "Ġstencil": 38670, "Ġprimo": 38671, "ĠBreath": 38672, "strom": 38673, "Ġimmensely": 38674, "Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, "Ġcalibration": 38732, "avaÅŁ": 38733, "ansa": 38734, "ikke": 38735, "Ġforesee": 38736, "Ġqualche": 38737, "Ġdeste": 38738, "æ¤": 38739, "ünüz": 38740, "Ġforge": 38741, "Dis": 38742, "esten": 38743, "Ġδια": 38744, "Ġencaps": 38745, "ĠGespr": 38746, "Ġchercher": 38747, "ickets": 38748, "ÑĤоÑĢÑĭ": 38749, "Cr": 38750, "ĠТакже": 38751, "Ġrabbits": 38752, "ĠDot": 38753, "heiten": 38754, "Ġcausal": 38755, "ĠFoster": 38756, "ajÄħc": 38757, "Ġbereit": 38758, "Ġayudar": 38759, "é«Ļ": 38760, "ãģ³": 38761, "song": 38762, "comb": 38763, "Ġfringe": 38764, "Ġcybersecurity": 38765, "Ġ뾨": 38766, "Ġkier": 38767, "Ġbeschäft": 38768, "ĠконÑĨе": 38769, "Ġfacilit": 38770, "ĠNamen": 38771, "Ġbilateral": 38772, "tx": 38773, "ĠWissenschaft": 38774, "Ġnuances": 38775, "Ġripping": 38776, "Ġfy": 38777, "ĠSicherheit": 38778, "ĠGhana": 38779, "olon": 38780, "Ġtopped": 38781, "ĠMorocco": 38782, "Ġradial": 38783, "ĠLEE": 38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, "Ġvalores": 38790, "ê·ľ": 38791, "Hy": 38792, "ĠзадаÑĩ": 38793, "ĠKendall": 38794, "ĠÑħаÑĢ": 38795, "ĠVamp": 38796, "Ġpython": 38797, "Ġmanageable": 38798, "ĠGente": 38799, "oise": 38800, "iciary": 38801, "Ġimposs": 38802, "ĠBunny": 38803, "iesta": 38804, "Andrew": 38805, "Ġsert": 38806, "ĠCec": 38807, "zzarella": 38808, "Ġautomobile": 38809, "ĠTiere": 38810, "allows": 38811, "åĨĨ": 38812, "Ġë°Ģ": 38813, "ĠScorp": 38814, "ĠJelly": 38815, "agara": 38816, "ĠStretch": 38817, "Ġredef": 38818, "Ġexacerb": 38819, "ĠSHA": 38820, "éf": 38821, "orsa": 38822, "Ġflawed": 38823, "ĠNoel": 38824, "?!?": 38825, "Ġprocent": 38826, "Ġmenstru": 38827, "ĠпÑĢоÑĩ": 38828, "Ġinfants": 38829, "ðŁİµ": 38830, "pause": 38831, "ĠRacing": 38832, "Ġ1948": 38833, "Ġsuperintendent": 38834, "idores": 38835, "idy": 38836, "brahim": 38837, "Ġunlucky": 38838, "Ġperk": 38839, "anci": 38840, "Ġë§ĮëĤĺ": 38841, "ĠÐľÐ¾Ñģкв": 38842, "Ġfinans": 38843, "Ġdiferencia": 38844, "łĪìĿ´": 38845, "éħį": 38846, "ORY": 38847, "ĠTac": 38848, "ÛĮا": 38849, "Ġdesem": 38850, "Ġважно": 38851, "ĠJU": 38852, "ĠìŀĪìŀĸìķĦìļĶ": 38853, "ĠÎĿ": 38854, "Ġinformations": 38855, "ĠHEL": 38856, "hst": 38857, "ĠпоговоÑĢ": 38858, "Ġvoiture": 38859, "Ġreus": 38860, "ändig": 38861, "ĠпоÑħож": 38862, "jing": 38863, "Ġdru": 38864, "altra": 38865, "Ġproduits": 38866, "Ġkite": 38867, "Ġeyeball": 38868, "ĠBelt": 38869, "ĠRestaurant": 38870, "Ġgamb": 38871, "Ġporridge": 38872, "itters": 38873, "Ġconverts": 38874, "Ġyardım": 38875, "Ġmáximo": 38876, "wirtschaft": 38877, "ĠíķĺëĤĺë": 38878, "Ġì¤Ģ": 38879, "Ġiceberg": 38880, "Ġvorbei": 38881, "Ġ256": 38882, "ocratic": 38883, "Ġreckless": 38884, "onner": 38885, "Ġmús": 38886, "Ġlogically": 38887, "ĠPrison": 38888, "ĠNetz": 38889, "Ġvacant": 38890, "Ġnimmt": 38891, "ĠHARR": 38892, "Ġзов": 38893, "ĠDee": 38894, "ringe": 38895, "niest": 38896, "ĠRules": 38897, "ìĬ¤ëٽ": 38898, "cussions": 38899, "Ġfloral": 38900, "Ġconstrained": 38901, "Ġdifferentiation": 38902, "ĠQuebec": 38903, "ĠÛģÛĮÚº": 38904, "Ġpública": 38905, "itel": 38906, "Ġaccommodations": 38907, "ĠGrü": 38908, "íľ": 38909, "Ġpickles": 38910, "иÑĩеÑģкиÑħ": 38911, "Ġcommissions": 38912, "ĠBaek": 38913, "ĠçocuÄŁ": 38914, "ĠMedium": 38915, "Ġperiodically": 38916, "Ġwonderfully": 38917, "Ġstaffing": 38918, "ìĽIJë": 38919, "rire": 38920, "fle": 38921, "ĠMcL": 38922, "ĠÑĤеп": 38923, "ĠпеÑĢек": 38924, "нолог": 38925, "Ġíģ¬ê²Į": 38926, "çϼçı¾": 38927, "Ġprosperous": 38928, "ĠSpiritual": 38929, "ĠChick": 38930, "DIA": 38931, "ĠÐŁÑĢивеÑĤ": 38932, "ĠperÃŃ": 38933, "ÑĮÑİÑĤ": 38934, "Ġconsultants": 38935, "ĠEarl": 38936, "ä»Ĭå¹´": 38937, "Ġruining": 38938, "оÑĢе": 38939, "Ġpenser": 38940, "Ġtakiej": 38941, "Ġstrengthened": 38942, "ĠLiquid": 38943, "онеÑĨ": 38944, "аваÑĤÑĮ": 38945, "Ġcamer": 38946, "Ġdisagreement": 38947, "Ġbathing": 38948, "ĠYosh": 38949, "aal": 38950, "prechen": 38951, "RISADAS": 38952, "Ġsuperstar": 38953, "æģŃ": 38954, "лÑıÑĤÑĮ": 38955, "Ġnib": 38956, "ĠTherm": 38957, "ĠDANIEL": 38958, "Ġpaw": 38959, "Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, "Ġendured": 39017, "iada": 39018, "Ġbrewing": 39019, "ìŬë": 39020, "ĠÑģвобод": 39021, "ĠSaints": 39022, "ĠFrançais": 39023, "ĠEconomics": 39024, "Ġdisloc": 39025, "ophobia": 39026, "Camer": 39027, "Ġnegotiated": 39028, "ĠÑģÑĤали": 39029, "ìĬ¤íģ": 39030, "ogie": 39031, "Ġtsunami": 39032, "Ġpeeled": 39033, "Ġmotivations": 39034, "è¨Ń": 39035, "ostat": 39036, "flan": 39037, "ĠDAC": 39038, "Ġkav": 39039, "'RE": 39040, "ĠPearson": 39041, "bbe": 39042, "czenie": 39043, "Ġatenção": 39044, "íĨµëł¹": 39045, "ãģ£ãģ¡": 39046, "ĠÑĥдаÑĢ": 39047, "Ġintroductory": 39048, "ĠIci": 39049, "ëĮĢë": 39050, "akat": 39051, "Ġtrench": 39052, "Ġproceeded": 39053, "ĠCoin": 39054, "Ġderecho": 39055, "ĠRede": 39056, "æ¯Ľ": 39057, "аннÑĭй": 39058, "Ġincarcerated": 39059, "ĠRichmond": 39060, "Rock": 39061, "ĠPav": 39062, "ĠKarma": 39063, "uges": 39064, "Ġconteú": 39065, "ë¹Ħ": 39066, "Ġê·¸ë§Į": 39067, "ĠGone": 39068, "ĠwspóÅĤ": 39069, "ĠRahmen": 39070, "unken": 39071, "Ġì¤ijìļĶíķľ": 39072, "Ġib": 39073, "Ġattaching": 39074, "Hay": 39075, "Ġsuka": 39076, "ìį¹": 39077, "Ġpivotal": 39078, "ĠRespect": 39079, "ÃŃda": 39080, "IB": 39081, "ĠVerantwort": 39082, "wiet": 39083, "Ġforensic": 39084, "ÑĢиÑģÑĤ": 39085, "ĠпÑĢинÑĨипе": 39086, "Ġmarkings": 39087, "Ġkettle": 39088, "ĠOpera": 39089, "ĠDoctors": 39090, "Ġshredded": 39091, "Ġrecuer": 39092, "Ġvigil": 39093, "ĠFail": 39094, "Ġentrev": 39095, "ĠдÑĥÑĪ": 39096, "Ġoutbreaks": 39097, "èµ°åIJ§": 39098, "ĠÏĢο": 39099, "Ġrogue": 39100, "angled": 39101, "Ġyearly": 39102, "ĠCreed": 39103, "Ġwam": 39104, "Ġlotus": 39105, "ê³¼ë": 39106, "ãĢģãĢģ": 39107, "ĠSpit": 39108, "ĠItu": 39109, "Ġstrains": 39110, "Ġstamped": 39111, "Ġplaint": 39112, "Ġpotion": 39113, "Ġconsolidation": 39114, "è©ķ": 39115, "оÑĩкÑĥ": 39116, "Ġvlogging": 39117, "Ġslate": 39118, "ĠAuft": 39119, "ĠIncor": 39120, "ừng": 39121, "§IJ": 39122, "enh": 39123, "ĠheiÃŁ": 39124, "Ġdomest": 39125, "ĠStrom": 39126, "åį³": 39127, "akis": 39128, "Ġfragen": 39129, "Ġfiner": 39130, "ĠSug": 39131, "Ġuphill": 39132, "Ġéén": 39133, "â̦)": 39134, "ĠÑģоп": 39135, "ĠCorey": 39136, "Ġsiebie": 39137, "Ġmuse": 39138, "Ġcloves": 39139, "Ġpous": 39140, "ĠFinanz": 39141, "ĠRoute": 39142, "amat": 39143, "Ġmutually": 39144, "ĠвнÑĥÑĤÑĢи": 39145, "ĠSelena": 39146, "ëĶ": 39147, "ĠGaussian": 39148, "ë¶ĢíĦ°": 39149, "Ġ×ij׼": 39150, "Ġejerc": 39151, "å¾®": 39152, "kea": 39153, "ĠGerry": 39154, "ĠSic": 39155, "大çļĦ": 39156, "Ġ1966": 39157, "iese": 39158, "Ġfossils": 39159, "Ġestad": 39160, "ĠKane": 39161, "ciÄĩ": 39162, "ĠìľłíĬľë": 39163, "Ġпам": 39164, "ĠCruise": 39165, "intérieur": 39166, "Ġbekannt": 39167, "ĠPode": 39168, "Ġdemander": 39169, "Rem": 39170, "Ġinvade": 39171, "Ġdecorating": 39172, "ropic": 39173, "Ġcowboy": 39174, "ĠPhoto": 39175, "opolit": 39176, "Ġì»¬ëŁ¬ë": 39177, "Ġreap": 39178, "Ġhandwriting": 39179, "à¹Ħร": 39180, "Ġëļ": 39181, "Ġبعد": 39182, "ĠMt": 39183, "ÙĢ": 39184, "Ġspaceship": 39185, "Ġnationalism": 39186, "Ġcouncils": 39187, "ĠGriffin": 39188, "ĠAhmed": 39189, "Ġclich": 39190, "ĠOL": 39191, "wl": 39192, "ĠPilot": 39193, "å®®": 39194, "Ġacronym": 39195, "Ġgels": 39196, "Ġelectroly": 39197, "èĵ": 39198, "Ġмной": 39199, "Ġepisod": 39200, "ĠDieses": 39201, "ĠATP": 39202, "Ġediyorum": 39203, "Ġexpresses": 39204, "Ġexhibits": 39205, "Comm": 39206, "ĠкÑĢÑĥп": 39207, "Ġmatar": 39208, "Ġ2025": 39209, "ĠArtem": 39210, "vasive": 39211, "rÃł": 39212, "ĠbeÅŁ": 39213, "é»ĥ": 39214, "Ġlizard": 39215, "Ġfille": 39216, "Ġì§Ī문": 39217, "ĠмоÑī": 39218, "Ġtür": 39219, "Ġculprit": 39220, "Ġwoven": 39221, "ĠANY": 39222, "nim": 39223, "Ġtay": 39224, "Ġpromin": 39225, "Ġacompa": 39226, "Ġidé": 39227, "Ġboiler": 39228, "ĠThemen": 39229, "Ġavenue": 39230, "ĠMud": 39231, "ĠновÑĭе": 39232, "Ġwitnessing": 39233, "Ġlance": 39234, "ĠCHAN": 39235, "ĠBever": 39236, "تÙħ": 39237, "Ġchemotherapy": 39238, "King": 39239, "ĠbÄĻdÄĻ": 39240, "Ġatual": 39241, "Ġtive": 39242, "Ġtalkin": 39243, "Ġquedar": 39244, "ieÃŁ": 39245, "edel": 39246, "Ġìĸ´ìłľ": 39247, "Ġjogar": 39248, "Ġör": 39249, "Ġundertaking": 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48190, "Ġadolescents": 48191, "ĠTisch": 48192, "ĠNeben": 48193, "ĠFlowers": 48194, "ĠEnerg": 48195, "Ġdiret": 48196, "ĠThi": 48197, "ĠPicas": 48198, "æĥľ": 48199, "æĢİä¹Īæł·": 48200, "Ġavete": 48201, "ĠFors": 48202, "ĠChapel": 48203, "Não": 48204, "Et": 48205, "ĠÑģодеÑĢж": 48206, "reno": 48207, "Ġsven": 48208, "ĠdostÄĻp": 48209, "nee": 48210, "ĠSnapdragon": 48211, "ĠIDs": 48212, "ìķĺëĬĶëį°": 48213, "ר×ļ": 48214, "Ġsunflower": 48215, "Ġperpetual": 48216, "ç³ĸ": 48217, "Ġknights": 48218, "Ġgird": 48219, "ĠTold": 48220, "Ġvolcanoes": 48221, "Ġadversary": 48222, "ĠEconomy": 48223, "Ġextrapol": 48224, "Ġbluetooth": 48225, "Ġzooming": 48226, "Ġskys": 48227, "Ġgenial": 48228, "ÃŃculos": 48229, "ambre": 48230, "ĠмеÑĢ": 48231, "Ġteeny": 48232, "Ġstressing": 48233, "ìķĮ": 48234, "ONY": 48235, "Ġtranslucent": 48236, "Ġrounding": 48237, "Ġgrues": 48238, "×Ļ׳×Ķ": 48239, "après": 48240, "Ġprueba": 48241, "Ġpolygon": 48242, "Ġblueberry": 48243, "ĠProgramm": 48244, "Ġtrenches": 48245, "Ġsebagai": 48246, "Ġpalate": 48247, "Ġlaude": 48248, "Ġbehaved": 48249, "Ġlongitudinal": 48250, "ĠModule": 48251, "Ġadmir": 48252, "λι": 48253, "Greg": 48254, "Ġwyst": 48255, "Ġpropagate": 48256, "Ġmolds": 48257, "ĠTub": 48258, "ĠLoud": 48259, "usto": 48260, "Ġunstoppable": 48261, "Ġreinforcing": 48262, "éĿŀ常çļĦ": 48263, "ĠпÑĢоблема": 48264, "Ġpotencial": 48265, "Ġhemp": 48266, "ìŀĶ": 48267, "य": 48268, "Ġoptic": 48269, "Ġerfolgreich": 48270, "ÑģÑĭ": 48271, "олÑĮÑĪе": 48272, "urst": 48273, "ĠPois": 48274, "Ġrespondents": 48275, "Ġnehme": 48276, "ĠExternal": 48277, "olate": 48278, "Hyun": 48279, "Ġquartz": 48280, "Ġmathematician": 48281, "Ġbásicamente": 48282, "Ġail": 48283, "ìłľë¥¼": 48284, "attutto": 48285, "Ġnooit": 48286, "Ġafflict": 48287, "ĠOlga": 48288, "èŃ·": 48289, "ĠнаÑĤ": 48290, "Ġdites": 48291, "Ġrealidade": 48292, "Ġkän": 48293, "Ġuniqueness": 48294, "Ġpadres": 48295, "Ġsubsidi": 48296, "Ġpigeons": 48297, "βα": 48298, "stad": 48299, "Ġderen": 48300, "ĠСлед": 48301, "doo": 48302, 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"rh": 48759, "Ġglamorous": 48760, "ĠVikings": 48761, "ĠOoooh": 48762, "íķĺëĬĶëį°": 48763, "ĠUganda": 48764, "Ġcollapses": 48765, "entry": 48766, "Ġantioxidants": 48767, "ëĤĺë": 48768, "ÑĪаÑı": 48769, "Ġtrivia": 48770, "Ġgäller": 48771, "Ġfungi": 48772, "Ġmilks": 48773, "Ġdicht": 48774, "μη": 48775, "poke": 48776, "ĠвÑĭпÑĥÑģк": 48777, "Ġfeeder": 48778, "ĠAlcohol": 48779, "hower": 48780, "Ġdeserving": 48781, "ĠRebel": 48782, "iosis": 48783, "Ġ103": 48784, "Ġhandout": 48785, "Ġenm": 48786, "Ġlandlords": 48787, "Ġgeology": 48788, "rils": 48789, "Ġcobra": 48790, "ĠVold": 48791, "ĠPanch": 48792, "ĠGREG": 48793, "Ġpross": 48794, "Ġbracelets": 48795, "ĠVega": 48796, "Ġrozum": 48797, "款": 48798, "азд": 48799, "ĠLynd": 48800, "ĠHonors": 48801, "Ġsurrendered": 48802, "Ġlibrarians": 48803, "125": 48804, "ĠÑģиг": 48805, "Ġuniformly": 48806, "ĠEagles": 48807, "ìķĻ": 48808, "иÑĤан": 48809, "andid": 48810, "ĠìłĪëĮĢ": 48811, "Ġض": 48812, "Ġarrests": 48813, "ĠCSV": 48814, "ĠAzerbaijan": 48815, "ortic": 48816, "ĠDX": 48817, "ĠAdventures": 48818, "Ġabus": 48819, "ĠFau": 48820, "Ġschlimm": 48821, "Ġrattling": 48822, "Ġconsumes": 48823, "ĠTolkien": 48824, "Ġresurrected": 48825, "ĠXY": 48826, "íĬ¸ê°Ģ": 48827, "ĠвÑĭÑģÑĤÑĥп": 48828, "ĠAngie": 48829, "żenia": 48830, "Mic": 48831, "ĠSheila": 48832, "achtet": 48833, "Ġoverst": 48834, "Ġlâ": 48835, "Ġineffective": 48836, "æĿ¡": 48837, "æĢİä¹ĪäºĨ": 48838, "å¿Ļ": 48839, "Ġwichtiger": 48840, "Ġvino": 48841, "Ġpum": 48842, "Ġangled": 48843, "ĠPione": 48844, "ĠMỹ": 48845, "ãģĿãĤĮãģ¯": 48846, "woÅĽÄĩ": 48847, "draw": 48848, "ัà¹Ī": 48849, "markets": 48850, "Ġcafes": 48851, "ĠCem": 48852, "âĿ¤": 48853, "ĠSuit": 48854, "MK": 48855, "Ġemphasizes": 48856, "Ġtortilla": 48857, "Ġmejorar": 48858, "ĠSurviv": 48859, "casting": 48860, "Ġeducación": 48861, "ĠGum": 48862, "uely": 48863, "ĠìĹ¬ê¸°ëĬĶ": 48864, "Ġstretchy": 48865, "ença": 48866, "Ġwithhold": 48867, "Ġexiting": 48868, "Ġenthalpy": 48869, "ĠTransit": 48870, "ılmÄ±ÅŁ": 48871, "alies": 48872, "Ġsalvar": 48873, "Ġleaned": 48874, "ĠgroÃŁes": 48875, "Ġfitt": 48876, "аки": 48877, "Sarah": 48878, "Ġhostel": 48879, "Ġfingerna": 48880, "ĠnadziejÄĻ": 48881, "wives": 48882, "Rec": 48883, "Ġspool": 48884, "аÑĤов": 48885, "ĠEnemy": 48886, "Ġfury": 48887, "Ġdetta": 48888, "ĠFay": 48889, "éļ¨": 48890, "ÑıÑİÑĤ": 48891, "Ġaproximadamente": 48892, "Ġsilos": 48893, "Ġmagist": 48894, "Ġcree": 48895, "ĠKrank": 48896, "ĠDOWN": 48897, "Ġstartled": 48898, "Ġreborn": 48899, "ĠUmwelt": 48900, "ĠSuzanne": 48901, "ниÑĨÑĭ": 48902, "outez": 48903, "ĠJAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "Ġparagraphs": 48910, "Ġmeglio": 48911, "Ġisolating": 48912, "Ġaceite": 48913, "ĠHarsh": 48914, "Ġcyst": 48915, "ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "> length: array = array.index_select(dim=axis, index=torch.arange(length)) if array.shape[axis] < length: pad_widths = [(0, 0)] * array.ndim pad_widths[axis] = (0, length - array.shape[axis]) array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) else: if array.shape[axis] > length: array = array.take(indices=range(length), axis=axis) if array.shape[axis] < length: pad_widths = [(0, 0)] * array.ndim pad_widths[axis] = (0, length - array.shape[axis]) array = np.pad(array, pad_widths) return array @lru_cache(maxsize=None) def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: """ load the mel filterbank matrix for projecting STFT into a Mel spectrogram. Allows decoupling librosa dependency; saved using: np.savez_compressed( "mel_filters.npz", mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), ) """ assert n_mels == 80, f"Unsupported n_mels: {n_mels}" with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f: return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS): """ Compute the log-Mel spectrogram of Parameters ---------- audio: Union[str, np.ndarray, torch.Tensor], shape = (*) The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz n_mels: int The number of Mel-frequency filters, only 80 is supported Returns ------- torch.Tensor, shape = (80, n_frames) A Tensor that contains the Mel spectrogram """ if not torch.is_tensor(audio): if isinstance(audio, str): audio = load_audio(audio) audio = torch.from_numpy(audio) window = torch.hann_window(N_FFT).to(audio.device) stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) magnitudes = stft[:, :-1].abs() ** 2 filters = mel_filters(audio.device, n_mels) mel_spec = filters @ magnitudes log_spec = torch.clamp(mel_spec, min=1e-10).log10() log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) log_spec = (log_spec + 4.0) / 4.0 return log_spec ================================================ FILE: musetalk/whisper/whisper/decoding.py ================================================ from dataclasses import dataclass, field from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING import numpy as np import torch import torch.nn.functional as F from torch import Tensor from torch.distributions import Categorical from .audio import CHUNK_LENGTH from .tokenizer import Tokenizer, get_tokenizer from .utils import compression_ratio if TYPE_CHECKING: from .model import Whisper @torch.no_grad() def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]: """ Detect the spoken language in the audio, and return them as list of strings, along with the ids of the most probable language tokens and the probability distribution over all language tokens. This is performed outside the main decode loop in order to not interfere with kv-caching. Returns ------- language_tokens : Tensor, shape = (n_audio,) ids of the most probable language tokens, which appears after the startoftranscript token. language_probs : List[Dict[str, float]], length = n_audio list of dictionaries containing the probability distribution over all languages. """ if tokenizer is None: tokenizer = get_tokenizer(model.is_multilingual) if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence: raise ValueError(f"This model doesn't have language tokens so it can't perform lang id") single = mel.ndim == 2 if single: mel = mel.unsqueeze(0) # skip encoder forward pass if already-encoded audio features were given if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): mel = model.encoder(mel) # forward pass using a single token, startoftranscript n_audio = mel.shape[0] x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] logits = model.logits(x, mel)[:, 0] # collect detected languages; suppress all non-language tokens mask = torch.ones(logits.shape[-1], dtype=torch.bool) mask[list(tokenizer.all_language_tokens)] = False logits[:, mask] = -np.inf language_tokens = logits.argmax(dim=-1) language_token_probs = logits.softmax(dim=-1).cpu() language_probs = [ { c: language_token_probs[i, j].item() for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) } for i in range(n_audio) ] if single: language_tokens = language_tokens[0] language_probs = language_probs[0] return language_tokens, language_probs @dataclass(frozen=True) class DecodingOptions: task: str = "transcribe" # whether to perform X->X "transcribe" or X->English "translate" language: Optional[str] = None # language that the audio is in; uses detected language if None # sampling-related options temperature: float = 0.0 sample_len: Optional[int] = None # maximum number of tokens to sample best_of: Optional[int] = None # number of independent samples to collect, when t > 0 beam_size: Optional[int] = None # number of beams in beam search, when t == 0 patience: Optional[float] = None # patience in beam search (https://arxiv.org/abs/2204.05424) # options for ranking generations (either beams or best-of-N samples) length_penalty: Optional[float] = None # "alpha" in Google NMT, None defaults to length norm # prompt, prefix, and token suppression prompt: Optional[Union[str, List[int]]] = None # text or tokens for the previous context prefix: Optional[Union[str, List[int]]] = None # text or tokens to prefix the current context suppress_blank: bool = True # this will suppress blank outputs # list of tokens ids (or comma-separated token ids) to suppress # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" # timestamp sampling options without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only max_initial_timestamp: Optional[float] = 1.0 # the initial timestamp cannot be later than this # implementation details fp16: bool = True # use fp16 for most of the calculation @dataclass(frozen=True) class DecodingResult: audio_features: Tensor language: str encoder_embeddings: np.ndarray decoder_embeddings: np.ndarray language_probs: Optional[Dict[str, float]] = None tokens: List[int] = field(default_factory=list) text: str = "" avg_logprob: float = np.nan no_speech_prob: float = np.nan temperature: float = np.nan compression_ratio: float = np.nan class Inference: def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: """Perform a forward pass on the decoder and return per-token logits""" raise NotImplementedError def rearrange_kv_cache(self, source_indices) -> None: """Update the key-value cache according to the updated beams""" raise NotImplementedError def cleanup_caching(self) -> None: """Clean up any resources or hooks after decoding is finished""" pass class PyTorchInference(Inference): def __init__(self, model: "Whisper", initial_token_length: int): self.model: "Whisper" = model self.initial_token_length = initial_token_length self.kv_cache = {} self.hooks = [] def logits(self, tokens: Tensor, audio_features: Tensor, include_embeddings=False) -> Tensor: if not self.kv_cache: self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() if tokens.shape[-1] > self.initial_token_length: # only need to use the last token except in the first forward pass tokens = tokens[:, -1:] return_val = self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache, include_embeddings=include_embeddings) return return_val def cleanup_caching(self): for hook in self.hooks: hook.remove() self.kv_cache = {} self.hooks = [] def rearrange_kv_cache(self, source_indices): for module, tensor in self.kv_cache.items(): # update the key/value cache to contain the selected sequences self.kv_cache[module] = tensor[source_indices].detach() class SequenceRanker: def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]: """ Given a list of groups of samples and their cumulative log probabilities, return the indices of the samples in each group to select as the final result """ raise NotImplementedError class MaximumLikelihoodRanker(SequenceRanker): """ Select the sample with the highest log probabilities, penalized using either a simple length normalization or Google NMT paper's length penalty """ def __init__(self, length_penalty: Optional[float]): self.length_penalty = length_penalty def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]): def scores(logprobs, lengths): result = [] for logprob, length in zip(logprobs, lengths): if self.length_penalty is None: penalty = length else: # from the Google NMT paper penalty = ((5 + length) / 6) ** self.length_penalty result.append(logprob / penalty) return result # get the sequence with the highest score lengths = [[len(t) for t in s] for s in tokens] return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] class TokenDecoder: def reset(self): """Initialize any stateful variables for decoding a new sequence""" def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: """Specify how to select the next token, based on the current trace and logits Parameters ---------- tokens : Tensor, shape = (n_batch, current_sequence_length) all tokens in the context so far, including the prefix and sot_sequence tokens logits : Tensor, shape = (n_batch, vocab_size) per-token logits of the probability distribution at the current step sum_logprobs : Tensor, shape = (n_batch) cumulative log probabilities for each sequence Returns ------- tokens : Tensor, shape = (n_batch, current_sequence_length + 1) the tokens, appended with the selected next token completed : bool True if all sequences has reached the end of text """ raise NotImplementedError def finalize( self, tokens: Tensor, sum_logprobs: Tensor ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: """Finalize search and return the final candidate sequences Parameters ---------- tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) all tokens in the context so far, including the prefix and sot_sequence sum_logprobs : Tensor, shape = (n_audio, n_group) cumulative log probabilities for each sequence Returns ------- tokens : Sequence[Sequence[Tensor]], length = n_audio sequence of Tensors containing candidate token sequences, for each audio input sum_logprobs : List[List[float]], length = n_audio sequence of cumulative log probabilities corresponding to the above """ raise NotImplementedError class GreedyDecoder(TokenDecoder): def __init__(self, temperature: float, eot: int): self.temperature = temperature self.eot = eot def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: temperature = self.temperature if temperature == 0: next_tokens = logits.argmax(dim=-1) else: next_tokens = Categorical(logits=logits / temperature).sample() logprobs = F.log_softmax(logits.float(), dim=-1) current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) next_tokens[tokens[:, -1] == self.eot] = self.eot tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) completed = (tokens[:, -1] == self.eot).all() return tokens, completed def finalize(self, tokens: Tensor, sum_logprobs: Tensor): # make sure each sequence has at least one EOT token at the end tokens = F.pad(tokens, (0, 1), value=self.eot) return tokens, sum_logprobs.tolist() class BeamSearchDecoder(TokenDecoder): def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None): self.beam_size = beam_size self.eot = eot self.inference = inference self.patience = patience or 1.0 self.max_candidates: int = round(beam_size * self.patience) self.finished_sequences = None assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})" def reset(self): self.finished_sequences = None def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: if tokens.shape[0] % self.beam_size != 0: raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") n_audio = tokens.shape[0] // self.beam_size if self.finished_sequences is None: # for the first update self.finished_sequences = [{} for _ in range(n_audio)] logprobs = F.log_softmax(logits.float(), dim=-1) next_tokens, source_indices, finished_sequences = [], [], [] for i in range(n_audio): scores, sources, finished = {}, {}, {} # STEP 1: calculate the cumulative log probabilities for possible candidates for j in range(self.beam_size): idx = i * self.beam_size + j prefix = tokens[idx].tolist() for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): new_logprob = (sum_logprobs[idx] + logprob).item() sequence = tuple(prefix + [token.item()]) scores[sequence] = new_logprob sources[sequence] = idx # STEP 2: rank the candidates and keep the top beam_size sequences for each audio saved = 0 for sequence in sorted(scores, key=scores.get, reverse=True): if sequence[-1] == self.eot: finished[sequence] = scores[sequence] else: sum_logprobs[len(next_tokens)] = scores[sequence] next_tokens.append(sequence) source_indices.append(sources[sequence]) saved += 1 if saved == self.beam_size: break finished_sequences.append(finished) tokens = torch.tensor(next_tokens, device=tokens.device) self.inference.rearrange_kv_cache(source_indices) # add newly finished sequences to self.finished_sequences assert len(self.finished_sequences) == len(finished_sequences) for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences): for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): if len(previously_finished) >= self.max_candidates: break # the candidate list is full previously_finished[seq] = newly_finished[seq] # mark as completed if all audio has enough number of samples completed = all( len(sequences) >= self.max_candidates for sequences in self.finished_sequences ) return tokens, completed def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): # collect all finished sequences, including patience, and add unfinished ones if not enough sum_logprobs = sum_logprobs.cpu() for i, sequences in enumerate(self.finished_sequences): if len(sequences) < self.beam_size: # when not enough sequences are finished for j in list(np.argsort(sum_logprobs[i]))[::-1]: sequence = preceding_tokens[i, j].tolist() + [self.eot] sequences[tuple(sequence)] = sum_logprobs[i][j].item() if len(sequences) >= self.beam_size: break tokens: List[List[Tensor]] = [ [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences ] sum_logprobs: List[List[float]] = [ list(sequences.values()) for sequences in self.finished_sequences ] return tokens, sum_logprobs class LogitFilter: def apply(self, logits: Tensor, tokens: Tensor) -> None: """Apply any filtering or masking to logits in-place Parameters ---------- logits : Tensor, shape = (n_batch, vocab_size) per-token logits of the probability distribution at the current step tokens : Tensor, shape = (n_batch, current_sequence_length) all tokens in the context so far, including the prefix and sot_sequence tokens """ raise NotImplementedError class SuppressBlank(LogitFilter): def __init__(self, tokenizer: Tokenizer, sample_begin: int): self.tokenizer = tokenizer self.sample_begin = sample_begin def apply(self, logits: Tensor, tokens: Tensor): if tokens.shape[1] == self.sample_begin: logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf class SuppressTokens(LogitFilter): def __init__(self, suppress_tokens: Sequence[int]): self.suppress_tokens = list(suppress_tokens) def apply(self, logits: Tensor, tokens: Tensor): logits[:, self.suppress_tokens] = -np.inf class ApplyTimestampRules(LogitFilter): def __init__( self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int] ): self.tokenizer = tokenizer self.sample_begin = sample_begin self.max_initial_timestamp_index = max_initial_timestamp_index def apply(self, logits: Tensor, tokens: Tensor): # suppress <|notimestamps|> which is handled by without_timestamps if self.tokenizer.no_timestamps is not None: logits[:, self.tokenizer.no_timestamps] = -np.inf # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly for k in range(tokens.shape[0]): seq = [t for t in tokens[k, self.sample_begin :].tolist()] last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin if last_was_timestamp: if penultimate_was_timestamp: # has to be non-timestamp logits[k, self.tokenizer.timestamp_begin :] = -np.inf else: # cannot be normal text tokens logits[k, : self.tokenizer.eot] = -np.inf # apply the `max_initial_timestamp` option if tokens.shape[1] == self.sample_begin and self.max_initial_timestamp_index is not None: last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index logits[:, last_allowed + 1 :] = -np.inf # if sum of probability over timestamps is above any other token, sample timestamp logprobs = F.log_softmax(logits.float(), dim=-1) for k in range(tokens.shape[0]): timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1) max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() if timestamp_logprob > max_text_token_logprob: logits[k, : self.tokenizer.timestamp_begin] = -np.inf class DecodingTask: inference: Inference sequence_ranker: SequenceRanker decoder: TokenDecoder logit_filters: List[LogitFilter] def __init__(self, model: "Whisper", options: DecodingOptions): self.model = model language = options.language or "en" tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task) self.tokenizer: Tokenizer = tokenizer self.options: DecodingOptions = self._verify_options(options) self.n_group: int = options.beam_size or options.best_of or 1 self.n_ctx: int = model.dims.n_text_ctx self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 self.sot_sequence: Tuple[int] = tokenizer.sot_sequence if self.options.without_timestamps: self.sot_sequence = tokenizer.sot_sequence_including_notimestamps self.initial_tokens: Tuple[int] = self._get_initial_tokens() self.sample_begin: int = len(self.initial_tokens) self.sot_index: int = self.initial_tokens.index(tokenizer.sot) # inference: implements the forward pass through the decoder, including kv caching self.inference = PyTorchInference(model, len(self.initial_tokens)) # sequence ranker: implements how to rank a group of sampled sequences self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) # decoder: implements how to select the next tokens, given the autoregressive distribution if options.beam_size is not None: self.decoder = BeamSearchDecoder( options.beam_size, tokenizer.eot, self.inference, options.patience ) else: self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) # logit filters: applies various rules to suppress or penalize certain tokens self.logit_filters = [] if self.options.suppress_blank: self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) if self.options.suppress_tokens: self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) if not options.without_timestamps: precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds max_initial_timestamp_index = None if options.max_initial_timestamp: max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision) self.logit_filters.append( ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index) ) def _verify_options(self, options: DecodingOptions) -> DecodingOptions: if options.beam_size is not None and options.best_of is not None: raise ValueError("beam_size and best_of can't be given together") if options.temperature == 0: if options.best_of is not None: raise ValueError("best_of with greedy sampling (T=0) is not compatible") if options.patience is not None and options.beam_size is None: raise ValueError("patience requires beam_size to be given") if options.length_penalty is not None and not (0 <= options.length_penalty <= 1): raise ValueError("length_penalty (alpha) should be a value between 0 and 1") return options def _get_initial_tokens(self) -> Tuple[int]: tokens = list(self.sot_sequence) prefix = self.options.prefix prompt = self.options.prompt if prefix: prefix_tokens = ( self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix ) if self.sample_len is not None: max_prefix_len = self.n_ctx // 2 - self.sample_len prefix_tokens = prefix_tokens[-max_prefix_len:] tokens = tokens + prefix_tokens if prompt: prompt_tokens = ( self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt ) tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens return tuple(tokens) def _get_suppress_tokens(self) -> Tuple[int]: suppress_tokens = self.options.suppress_tokens if isinstance(suppress_tokens, str): suppress_tokens = [int(t) for t in suppress_tokens.split(",")] if -1 in suppress_tokens: suppress_tokens = [t for t in suppress_tokens if t >= 0] suppress_tokens.extend(self.tokenizer.non_speech_tokens) elif suppress_tokens is None or len(suppress_tokens) == 0: suppress_tokens = [] # interpret empty string as an empty list else: assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" suppress_tokens.extend( [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] ) if self.tokenizer.no_speech is not None: # no-speech probability is collected separately suppress_tokens.append(self.tokenizer.no_speech) return tuple(sorted(set(suppress_tokens))) def _get_audio_features(self, mel: Tensor, include_embeddings: bool = False): if self.options.fp16: mel = mel.half() if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state): # encoded audio features are given; skip audio encoding audio_features = mel else: result = self.model.encoder(mel, include_embeddings) if include_embeddings: audio_features, embeddings = result else: audio_features = result if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32): return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}") if include_embeddings: return audio_features, embeddings else: return audio_features def _detect_language(self, audio_features: Tensor, tokens: Tensor): languages = [self.options.language] * audio_features.shape[0] lang_probs = None if self.options.language is None or self.options.task == "lang_id": lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer) languages = [max(probs, key=probs.get) for probs in lang_probs] if self.options.language is None: tokens[:, self.sot_index + 1] = lang_tokens # write language tokens return languages, lang_probs def _main_loop(self, audio_features: Tensor, tokens: Tensor): assert audio_features.shape[0] == tokens.shape[0] n_batch = tokens.shape[0] sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) no_speech_probs = [np.nan] * n_batch try: embeddings = [] for i in range(self.sample_len): logits, token_embeddings = self.inference.logits(tokens, audio_features, include_embeddings=True) if i == 0 and self.tokenizer.no_speech is not None: # save no_speech_probs probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() # now we need to consider the logits at the last token only logits = logits[:, -1] token_embeddings = token_embeddings[:, :, -1] # Append embeddings together embeddings.append(token_embeddings) # apply the logit filters, e.g. for suppressing or applying penalty to for logit_filter in self.logit_filters: logit_filter.apply(logits, tokens) # expand the tokens tensor with the selected next tokens tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) if completed or tokens.shape[-1] > self.n_ctx: break finally: if completed: embeddings = embeddings[:-1] embeddings = np.stack(embeddings, 2) self.inference.cleanup_caching() return tokens, sum_logprobs, no_speech_probs, embeddings @torch.no_grad() def run(self, mel: Tensor) -> List[DecodingResult]: self.decoder.reset() tokenizer: Tokenizer = self.tokenizer n_audio: int = mel.shape[0] # encoder forward pass forward_pass: Tuple[Tensor, np.ndarray] = self._get_audio_features(mel, include_embeddings=True) audio_features, encoder_embeddings = forward_pass tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) # detect language if requested, overwriting the language token languages, language_probs = self._detect_language(audio_features, tokens) if self.options.task == "lang_id": return [ DecodingResult(audio_features=features, language=language, language_probs=probs) for features, language, probs in zip(audio_features, languages, language_probs) ] # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling audio_features = audio_features.repeat_interleave(self.n_group, dim=0) tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) # call the main sampling loop tokens, sum_logprobs, no_speech_probs, decoder_embeddings = self._main_loop(audio_features, tokens) # reshape the tensors to have (n_audio, n_group) as the first two dimensions audio_features = audio_features[:: self.n_group] no_speech_probs = no_speech_probs[:: self.n_group] assert audio_features.shape[0] == len(no_speech_probs) == n_audio tokens = tokens.reshape(n_audio, self.n_group, -1) sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) # get the final candidates for each group, and slice between the first sampled token and EOT tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) tokens: List[List[Tensor]] = [ [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens ] # select the top-ranked sample in each group selected = self.sequence_ranker.rank(tokens, sum_logprobs) tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)] fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs) if len(set(map(len, fields))) != 1: raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") return [ DecodingResult( audio_features=features, language=language, tokens=tokens, text=text, avg_logprob=avg_logprob, no_speech_prob=no_speech_prob, temperature=self.options.temperature, compression_ratio=compression_ratio(text), encoder_embeddings=encoder_embeddings, decoder_embeddings=decoder_embeddings ) for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields) ] @torch.no_grad() def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]: """ Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). Parameters ---------- model: Whisper the Whisper model instance mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000) A tensor containing the Mel spectrogram(s) options: DecodingOptions A dataclass that contains all necessary options for decoding 30-second segments Returns ------- result: Union[DecodingResult, List[DecodingResult]] The result(s) of decoding contained in `DecodingResult` dataclass instance(s) """ single = mel.ndim == 2 if single: mel = mel.unsqueeze(0) result = DecodingTask(model, options).run(mel) if single: result = result[0] return result ================================================ FILE: musetalk/whisper/whisper/model.py ================================================ from dataclasses import dataclass from typing import Dict from typing import Iterable, Optional import numpy as np import torch import torch.nn.functional as F from torch import Tensor from torch import nn from .transcribe import transcribe as transcribe_function from .decoding import detect_language as detect_language_function, decode as decode_function @dataclass class ModelDimensions: n_mels: int n_audio_ctx: int n_audio_state: int n_audio_head: int n_audio_layer: int n_vocab: int n_text_ctx: int n_text_state: int n_text_head: int n_text_layer: int class LayerNorm(nn.LayerNorm): def forward(self, x: Tensor) -> Tensor: return super().forward(x.float()).type(x.dtype) class Linear(nn.Linear): def forward(self, x: Tensor) -> Tensor: return F.linear( x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype) ) class Conv1d(nn.Conv1d): def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: return super()._conv_forward( x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) ) def sinusoids(length, channels, max_timescale=10000): """Returns sinusoids for positional embedding""" assert channels % 2 == 0 log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) class MultiHeadAttention(nn.Module): def __init__(self, n_state: int, n_head: int): super().__init__() self.n_head = n_head self.query = Linear(n_state, n_state) self.key = Linear(n_state, n_state, bias=False) self.value = Linear(n_state, n_state) self.out = Linear(n_state, n_state) def forward( self, x: Tensor, xa: Optional[Tensor] = None, mask: Optional[Tensor] = None, kv_cache: Optional[dict] = None, ): q = self.query(x) if kv_cache is None or xa is None: # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; # otherwise, perform key/value projections for self- or cross-attention as usual. k = self.key(x if xa is None else xa) v = self.value(x if xa is None else xa) else: # for cross-attention, calculate keys and values once and reuse in subsequent calls. k = kv_cache.get(self.key, self.key(xa)) v = kv_cache.get(self.value, self.value(xa)) wv = self.qkv_attention(q, k, v, mask) return self.out(wv) def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None): n_batch, n_ctx, n_state = q.shape scale = (n_state // self.n_head) ** -0.25 q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) qk = q @ k if mask is not None: qk = qk + mask[:n_ctx, :n_ctx] w = F.softmax(qk.float(), dim=-1).to(q.dtype) return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2) class ResidualAttentionBlock(nn.Module): def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): super().__init__() self.attn = MultiHeadAttention(n_state, n_head) self.attn_ln = LayerNorm(n_state) self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None n_mlp = n_state * 4 self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)) self.mlp_ln = LayerNorm(n_state) def forward( self, x: Tensor, xa: Optional[Tensor] = None, mask: Optional[Tensor] = None, kv_cache: Optional[dict] = None, ): x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache) if self.cross_attn: x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache) x = x + self.mlp(self.mlp_ln(x)) return x class AudioEncoder(nn.Module): def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): super().__init__() self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] ) self.ln_post = LayerNorm(n_state) def forward(self, x: Tensor, include_embeddings: bool = False): """ x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) the mel spectrogram of the audio include_embeddings: bool whether to include intermediate steps in the output """ x = F.gelu(self.conv1(x)) x = F.gelu(self.conv2(x)) x = x.permute(0, 2, 1) assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" x = (x + self.positional_embedding).to(x.dtype) if include_embeddings: embeddings = [x.cpu().detach().numpy()] for block in self.blocks: x = block(x) if include_embeddings: embeddings.append(x.cpu().detach().numpy()) x = self.ln_post(x) if include_embeddings: embeddings = np.stack(embeddings, axis=1) return x, embeddings else: return x class TextDecoder(nn.Module): def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): super().__init__() self.token_embedding = nn.Embedding(n_vocab, n_state) self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)] ) self.ln = LayerNorm(n_state) mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) self.register_buffer("mask", mask, persistent=False) def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None, include_embeddings: bool = False): """ x : torch.LongTensor, shape = (batch_size, <= n_ctx) the text tokens xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) the encoded audio features to be attended on include_embeddings : bool Whether to include intermediate values in the output to this function """ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]] x = x.to(xa.dtype) if include_embeddings: embeddings = [x.cpu().detach().numpy()] for block in self.blocks: x = block(x, xa, mask=self.mask, kv_cache=kv_cache) if include_embeddings: embeddings.append(x.cpu().detach().numpy()) x = self.ln(x) logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float() if include_embeddings: embeddings = np.stack(embeddings, axis=1) return logits, embeddings else: return logits class Whisper(nn.Module): def __init__(self, dims: ModelDimensions): super().__init__() self.dims = dims self.encoder = AudioEncoder( self.dims.n_mels, self.dims.n_audio_ctx, self.dims.n_audio_state, self.dims.n_audio_head, self.dims.n_audio_layer, ) self.decoder = TextDecoder( self.dims.n_vocab, self.dims.n_text_ctx, self.dims.n_text_state, self.dims.n_text_head, self.dims.n_text_layer, ) def embed_audio(self, mel: torch.Tensor): return self.encoder.forward(mel) def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): return self.decoder.forward(tokens, audio_features) def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]: return self.decoder(tokens, self.encoder(mel)) @property def device(self): return next(self.parameters()).device @property def is_multilingual(self): return self.dims.n_vocab == 51865 def install_kv_cache_hooks(self, cache: Optional[dict] = None): """ The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value tensors calculated for the previous positions. This method returns a dictionary that stores all caches, and the necessary hooks for the key and value projection modules that save the intermediate tensors to be reused during later calculations. Returns ------- cache : Dict[nn.Module, torch.Tensor] A dictionary object mapping the key/value projection modules to its cache hooks : List[RemovableHandle] List of PyTorch RemovableHandle objects to stop the hooks to be called """ cache = {**cache} if cache is not None else {} hooks = [] def save_to_cache(module, _, output): if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]: cache[module] = output # save as-is, for the first token or cross attention else: cache[module] = torch.cat([cache[module], output], dim=1).detach() return cache[module] def install_hooks(layer: nn.Module): if isinstance(layer, MultiHeadAttention): hooks.append(layer.key.register_forward_hook(save_to_cache)) hooks.append(layer.value.register_forward_hook(save_to_cache)) self.decoder.apply(install_hooks) return cache, hooks detect_language = detect_language_function transcribe = transcribe_function decode = decode_function ================================================ FILE: musetalk/whisper/whisper/normalizers/__init__.py ================================================ from .basic import BasicTextNormalizer from .english import EnglishTextNormalizer ================================================ FILE: musetalk/whisper/whisper/normalizers/basic.py ================================================ import re import unicodedata import regex # non-ASCII letters that are not separated by "NFKD" normalization ADDITIONAL_DIACRITICS = { "œ": "oe", "Œ": "OE", "ø": "o", "Ø": "O", "æ": "ae", "Æ": "AE", "ß": "ss", "ẞ": "SS", "đ": "d", "Đ": "D", "ð": "d", "Ð": "D", "þ": "th", "Þ": "th", "ł": "l", "Ł": "L", } def remove_symbols_and_diacritics(s: str, keep=""): """ Replace any other markers, symbols, and punctuations with a space, and drop any diacritics (category 'Mn' and some manual mappings) """ return "".join( c if c in keep else ADDITIONAL_DIACRITICS[c] if c in ADDITIONAL_DIACRITICS else "" if unicodedata.category(c) == "Mn" else " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKD", s) ) def remove_symbols(s: str): """ Replace any other markers, symbols, punctuations with a space, keeping diacritics """ return "".join( " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s) ) class BasicTextNormalizer: def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols self.split_letters = split_letters def __call__(self, s: str): s = s.lower() s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis s = self.clean(s).lower() if self.split_letters: s = " ".join(regex.findall(r"\X", s, regex.U)) s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space return s ================================================ FILE: musetalk/whisper/whisper/normalizers/english.json ================================================ { "accessorise": "accessorize", "accessorised": "accessorized", "accessorises": "accessorizes", "accessorising": "accessorizing", "acclimatisation": "acclimatization", "acclimatise": "acclimatize", "acclimatised": "acclimatized", "acclimatises": "acclimatizes", "acclimatising": "acclimatizing", "accoutrements": "accouterments", "aeon": "eon", "aeons": "eons", "aerogramme": "aerogram", "aerogrammes": "aerograms", "aeroplane": "airplane", "aeroplanes": "airplanes", "aesthete": "esthete", "aesthetes": "esthetes", "aesthetic": "esthetic", "aesthetically": "esthetically", "aesthetics": "esthetics", "aetiology": "etiology", "ageing": "aging", "aggrandisement": "aggrandizement", "agonise": "agonize", "agonised": "agonized", "agonises": "agonizes", "agonising": "agonizing", "agonisingly": "agonizingly", "almanack": "almanac", "almanacks": "almanacs", "aluminium": "aluminum", "amortisable": "amortizable", "amortisation": "amortization", "amortisations": "amortizations", "amortise": "amortize", "amortised": "amortized", "amortises": "amortizes", "amortising": "amortizing", "amphitheatre": "amphitheater", "amphitheatres": "amphitheaters", "anaemia": "anemia", "anaemic": "anemic", "anaesthesia": "anesthesia", "anaesthetic": "anesthetic", "anaesthetics": "anesthetics", "anaesthetise": "anesthetize", "anaesthetised": "anesthetized", "anaesthetises": "anesthetizes", "anaesthetising": "anesthetizing", "anaesthetist": "anesthetist", "anaesthetists": "anesthetists", "anaesthetize": "anesthetize", "anaesthetized": "anesthetized", "anaesthetizes": "anesthetizes", "anaesthetizing": "anesthetizing", "analogue": "analog", "analogues": "analogs", "analyse": "analyze", "analysed": "analyzed", "analyses": "analyzes", "analysing": "analyzing", "anglicise": "anglicize", "anglicised": "anglicized", "anglicises": "anglicizes", "anglicising": "anglicizing", "annualised": "annualized", "antagonise": "antagonize", "antagonised": "antagonized", "antagonises": "antagonizes", "antagonising": "antagonizing", "apologise": "apologize", "apologised": "apologized", "apologises": "apologizes", "apologising": "apologizing", "appal": "appall", "appals": "appalls", "appetiser": "appetizer", "appetisers": "appetizers", "appetising": "appetizing", "appetisingly": "appetizingly", "arbour": "arbor", "arbours": "arbors", "archeological": "archaeological", "archaeologically": "archeologically", "archaeologist": "archeologist", "archaeologists": "archeologists", "archaeology": "archeology", "ardour": "ardor", "armour": "armor", "armoured": "armored", "armourer": "armorer", "armourers": "armorers", "armouries": "armories", "armoury": "armory", "artefact": "artifact", "artefacts": "artifacts", "authorise": "authorize", "authorised": "authorized", "authorises": "authorizes", "authorising": "authorizing", "axe": "ax", "backpedalled": "backpedaled", "backpedalling": "backpedaling", "bannister": "banister", "bannisters": "banisters", "baptise": "baptize", "baptised": "baptized", "baptises": "baptizes", "baptising": "baptizing", "bastardise": "bastardize", "bastardised": "bastardized", "bastardises": "bastardizes", "bastardising": "bastardizing", "battleax": "battleaxe", "baulk": "balk", "baulked": "balked", "baulking": "balking", "baulks": "balks", "bedevilled": "bedeviled", "bedevilling": "bedeviling", "behaviour": "behavior", "behavioural": "behavioral", "behaviourism": "behaviorism", "behaviourist": "behaviorist", "behaviourists": "behaviorists", "behaviours": "behaviors", "behove": "behoove", "behoved": "behooved", "behoves": "behooves", "bejewelled": "bejeweled", "belabour": "belabor", "belaboured": "belabored", "belabouring": "belaboring", "belabours": "belabors", "bevelled": "beveled", "bevvies": "bevies", "bevvy": "bevy", "biassed": "biased", "biassing": "biasing", "bingeing": "binging", "bougainvillaea": "bougainvillea", "bougainvillaeas": "bougainvilleas", "bowdlerise": "bowdlerize", "bowdlerised": "bowdlerized", "bowdlerises": "bowdlerizes", "bowdlerising": "bowdlerizing", "breathalyse": "breathalyze", "breathalysed": "breathalyzed", "breathalyser": "breathalyzer", "breathalysers": "breathalyzers", "breathalyses": "breathalyzes", "breathalysing": "breathalyzing", "brutalise": "brutalize", "brutalised": "brutalized", "brutalises": "brutalizes", "brutalising": "brutalizing", "busses": "buses", "bussing": "busing", "caesarean": "cesarean", "caesareans": "cesareans", "calibre": "caliber", "calibres": "calibers", "calliper": "caliper", "callipers": "calipers", "callisthenics": "calisthenics", "canalise": "canalize", "canalised": "canalized", "canalises": "canalizes", "canalising": "canalizing", "cancelation": "cancellation", "cancelations": "cancellations", "cancelled": "canceled", "cancelling": "canceling", "candour": "candor", "cannibalise": "cannibalize", "cannibalised": "cannibalized", "cannibalises": "cannibalizes", "cannibalising": "cannibalizing", "canonise": "canonize", "canonised": "canonized", "canonises": "canonizes", "canonising": "canonizing", "capitalise": "capitalize", "capitalised": "capitalized", "capitalises": "capitalizes", "capitalising": "capitalizing", "caramelise": "caramelize", "caramelised": "caramelized", "caramelises": "caramelizes", "caramelising": "caramelizing", "carbonise": "carbonize", "carbonised": "carbonized", "carbonises": "carbonizes", "carbonising": "carbonizing", "carolled": "caroled", "carolling": "caroling", "catalogue": "catalog", "catalogued": "cataloged", "catalogues": "catalogs", "cataloguing": "cataloging", "catalyse": "catalyze", "catalysed": "catalyzed", "catalyses": "catalyzes", "catalysing": "catalyzing", "categorise": "categorize", "categorised": "categorized", "categorises": "categorizes", "categorising": "categorizing", "cauterise": "cauterize", "cauterised": "cauterized", "cauterises": "cauterizes", "cauterising": "cauterizing", "cavilled": "caviled", "cavilling": "caviling", "centigramme": "centigram", "centigrammes": "centigrams", "centilitre": "centiliter", "centilitres": "centiliters", "centimetre": "centimeter", "centimetres": "centimeters", "centralise": "centralize", "centralised": "centralized", "centralises": "centralizes", "centralising": "centralizing", "centre": "center", "centred": "centered", "centrefold": "centerfold", "centrefolds": "centerfolds", "centrepiece": "centerpiece", "centrepieces": "centerpieces", "centres": "centers", "channelled": "channeled", "channelling": "channeling", "characterise": "characterize", "characterised": "characterized", "characterises": "characterizes", "characterising": "characterizing", "cheque": "check", "chequebook": "checkbook", "chequebooks": "checkbooks", "chequered": "checkered", "cheques": "checks", "chilli": "chili", "chimaera": "chimera", "chimaeras": "chimeras", "chiselled": "chiseled", "chiselling": "chiseling", "circularise": "circularize", "circularised": "circularized", "circularises": "circularizes", "circularising": "circularizing", "civilise": "civilize", "civilised": "civilized", "civilises": "civilizes", "civilising": "civilizing", "clamour": "clamor", "clamoured": "clamored", "clamouring": "clamoring", "clamours": "clamors", "clangour": "clangor", "clarinettist": "clarinetist", "clarinettists": "clarinetists", "collectivise": "collectivize", "collectivised": "collectivized", "collectivises": "collectivizes", "collectivising": "collectivizing", "colonisation": "colonization", "colonise": "colonize", "colonised": "colonized", "coloniser": "colonizer", "colonisers": "colonizers", "colonises": "colonizes", "colonising": "colonizing", "colour": "color", "colourant": "colorant", "colourants": "colorants", "coloured": "colored", "coloureds": "coloreds", "colourful": "colorful", "colourfully": "colorfully", "colouring": "coloring", "colourize": "colorize", "colourized": "colorized", "colourizes": "colorizes", "colourizing": "colorizing", "colourless": "colorless", "colours": "colors", "commercialise": "commercialize", "commercialised": "commercialized", "commercialises": "commercializes", "commercialising": "commercializing", "compartmentalise": "compartmentalize", "compartmentalised": "compartmentalized", "compartmentalises": "compartmentalizes", "compartmentalising": "compartmentalizing", "computerise": "computerize", "computerised": "computerized", "computerises": "computerizes", "computerising": "computerizing", "conceptualise": "conceptualize", "conceptualised": "conceptualized", "conceptualises": "conceptualizes", "conceptualising": "conceptualizing", "connexion": "connection", "connexions": "connections", "contextualise": "contextualize", "contextualised": "contextualized", "contextualises": "contextualizes", "contextualising": "contextualizing", "cosier": "cozier", "cosies": "cozies", "cosiest": "coziest", "cosily": "cozily", "cosiness": "coziness", "cosy": "cozy", "councillor": "councilor", "councillors": "councilors", "counselled": "counseled", "counselling": "counseling", "counsellor": "counselor", "counsellors": "counselors", "crenelated": "crenellated", "criminalise": "criminalize", "criminalised": "criminalized", "criminalises": "criminalizes", "criminalising": "criminalizing", "criticise": "criticize", "criticised": "criticized", "criticises": "criticizes", "criticising": "criticizing", "crueller": "crueler", "cruellest": "cruelest", "crystallisation": "crystallization", "crystallise": "crystallize", "crystallised": "crystallized", "crystallises": "crystallizes", "crystallising": "crystallizing", "cudgelled": "cudgeled", "cudgelling": "cudgeling", "customise": "customize", "customised": "customized", "customises": "customizes", "customising": "customizing", "cypher": "cipher", "cyphers": "ciphers", "decentralisation": "decentralization", "decentralise": "decentralize", "decentralised": "decentralized", "decentralises": "decentralizes", "decentralising": "decentralizing", "decriminalisation": "decriminalization", "decriminalise": "decriminalize", "decriminalised": "decriminalized", "decriminalises": "decriminalizes", "decriminalising": "decriminalizing", "defence": "defense", "defenceless": "defenseless", "defences": "defenses", "dehumanisation": "dehumanization", "dehumanise": "dehumanize", "dehumanised": "dehumanized", "dehumanises": "dehumanizes", "dehumanising": "dehumanizing", "demeanour": "demeanor", "demilitarisation": "demilitarization", "demilitarise": "demilitarize", "demilitarised": "demilitarized", "demilitarises": "demilitarizes", "demilitarising": "demilitarizing", "demobilisation": "demobilization", "demobilise": "demobilize", "demobilised": "demobilized", "demobilises": "demobilizes", "demobilising": "demobilizing", "democratisation": "democratization", "democratise": "democratize", "democratised": "democratized", "democratises": "democratizes", "democratising": "democratizing", "demonise": "demonize", "demonised": "demonized", "demonises": "demonizes", "demonising": "demonizing", "demoralisation": "demoralization", "demoralise": "demoralize", "demoralised": "demoralized", "demoralises": "demoralizes", "demoralising": "demoralizing", "denationalisation": "denationalization", "denationalise": "denationalize", "denationalised": "denationalized", "denationalises": "denationalizes", "denationalising": "denationalizing", "deodorise": "deodorize", "deodorised": "deodorized", "deodorises": "deodorizes", "deodorising": "deodorizing", "depersonalise": "depersonalize", "depersonalised": "depersonalized", "depersonalises": "depersonalizes", "depersonalising": "depersonalizing", "deputise": "deputize", "deputised": "deputized", "deputises": "deputizes", "deputising": "deputizing", "desensitisation": "desensitization", "desensitise": "desensitize", "desensitised": "desensitized", "desensitises": "desensitizes", "desensitising": "desensitizing", "destabilisation": "destabilization", "destabilise": "destabilize", "destabilised": "destabilized", "destabilises": "destabilizes", "destabilising": "destabilizing", "dialled": "dialed", "dialling": "dialing", "dialogue": "dialog", "dialogues": "dialogs", "diarrhoea": "diarrhea", "digitise": "digitize", "digitised": "digitized", "digitises": "digitizes", "digitising": "digitizing", "disc": "disk", "discolour": "discolor", "discoloured": "discolored", "discolouring": "discoloring", "discolours": "discolors", "discs": "disks", "disembowelled": "disemboweled", "disembowelling": "disemboweling", "disfavour": "disfavor", "dishevelled": "disheveled", "dishonour": "dishonor", "dishonourable": "dishonorable", "dishonourably": "dishonorably", "dishonoured": "dishonored", "dishonouring": "dishonoring", "dishonours": "dishonors", "disorganisation": "disorganization", "disorganised": "disorganized", "distil": "distill", "distils": "distills", "dramatisation": "dramatization", "dramatisations": "dramatizations", "dramatise": "dramatize", "dramatised": "dramatized", "dramatises": "dramatizes", "dramatising": "dramatizing", "draught": "draft", "draughtboard": "draftboard", "draughtboards": "draftboards", "draughtier": "draftier", "draughtiest": "draftiest", "draughts": "drafts", "draughtsman": "draftsman", "draughtsmanship": "draftsmanship", "draughtsmen": "draftsmen", "draughtswoman": "draftswoman", "draughtswomen": "draftswomen", "draughty": "drafty", "drivelled": "driveled", "drivelling": "driveling", "duelled": "dueled", "duelling": "dueling", "economise": "economize", "economised": "economized", "economises": "economizes", "economising": "economizing", "edoema": "edema", "editorialise": "editorialize", "editorialised": "editorialized", "editorialises": "editorializes", "editorialising": "editorializing", "empathise": "empathize", "empathised": "empathized", "empathises": "empathizes", "empathising": "empathizing", "emphasise": "emphasize", "emphasised": "emphasized", "emphasises": "emphasizes", "emphasising": "emphasizing", "enamelled": "enameled", "enamelling": "enameling", "enamoured": "enamored", "encyclopaedia": "encyclopedia", "encyclopaedias": "encyclopedias", "encyclopaedic": "encyclopedic", "endeavour": "endeavor", "endeavoured": "endeavored", "endeavouring": "endeavoring", "endeavours": "endeavors", "energise": "energize", "energised": "energized", "energises": "energizes", "energising": "energizing", "enrol": "enroll", "enrols": "enrolls", "enthral": "enthrall", "enthrals": "enthralls", "epaulette": "epaulet", "epaulettes": "epaulets", "epicentre": "epicenter", "epicentres": "epicenters", "epilogue": "epilog", "epilogues": "epilogs", "epitomise": "epitomize", "epitomised": "epitomized", "epitomises": "epitomizes", "epitomising": "epitomizing", "equalisation": "equalization", "equalise": "equalize", "equalised": "equalized", "equaliser": "equalizer", "equalisers": "equalizers", "equalises": "equalizes", "equalising": "equalizing", "eulogise": "eulogize", "eulogised": "eulogized", "eulogises": "eulogizes", "eulogising": "eulogizing", "evangelise": "evangelize", "evangelised": "evangelized", "evangelises": "evangelizes", "evangelising": "evangelizing", "exorcise": "exorcize", "exorcised": "exorcized", "exorcises": "exorcizes", "exorcising": "exorcizing", "extemporisation": "extemporization", "extemporise": "extemporize", "extemporised": "extemporized", "extemporises": "extemporizes", "extemporising": "extemporizing", "externalisation": "externalization", "externalisations": "externalizations", "externalise": "externalize", "externalised": "externalized", "externalises": "externalizes", "externalising": "externalizing", "factorise": "factorize", "factorised": "factorized", "factorises": "factorizes", "factorising": "factorizing", "faecal": "fecal", "faeces": "feces", "familiarisation": "familiarization", "familiarise": "familiarize", "familiarised": "familiarized", "familiarises": "familiarizes", "familiarising": "familiarizing", "fantasise": "fantasize", "fantasised": "fantasized", "fantasises": "fantasizes", "fantasising": "fantasizing", "favour": "favor", "favourable": "favorable", "favourably": "favorably", "favoured": "favored", "favouring": "favoring", "favourite": "favorite", "favourites": "favorites", "favouritism": "favoritism", "favours": "favors", "feminise": "feminize", "feminised": "feminized", "feminises": "feminizes", "feminising": "feminizing", "fertilisation": "fertilization", "fertilise": "fertilize", "fertilised": "fertilized", "fertiliser": "fertilizer", "fertilisers": "fertilizers", "fertilises": "fertilizes", "fertilising": "fertilizing", "fervour": "fervor", "fibre": "fiber", "fibreglass": "fiberglass", "fibres": "fibers", "fictionalisation": "fictionalization", "fictionalisations": "fictionalizations", "fictionalise": "fictionalize", "fictionalised": "fictionalized", "fictionalises": "fictionalizes", "fictionalising": "fictionalizing", "fillet": "filet", "filleted": "fileted", "filleting": "fileting", "fillets": "filets", "finalisation": "finalization", "finalise": "finalize", "finalised": "finalized", "finalises": "finalizes", "finalising": "finalizing", "flautist": "flutist", "flautists": "flutists", "flavour": "flavor", "flavoured": "flavored", "flavouring": "flavoring", "flavourings": "flavorings", "flavourless": "flavorless", "flavours": "flavors", "flavoursome": "flavorsome", "flyer / flier": "flier / flyer", "foetal": "fetal", "foetid": "fetid", "foetus": "fetus", "foetuses": "fetuses", "formalisation": "formalization", "formalise": "formalize", "formalised": "formalized", "formalises": "formalizes", "formalising": "formalizing", "fossilisation": "fossilization", "fossilise": "fossilize", "fossilised": "fossilized", "fossilises": "fossilizes", "fossilising": "fossilizing", "fraternisation": "fraternization", "fraternise": "fraternize", "fraternised": "fraternized", "fraternises": "fraternizes", "fraternising": "fraternizing", "fulfil": "fulfill", "fulfilment": "fulfillment", "fulfils": "fulfills", "funnelled": "funneled", "funnelling": "funneling", "galvanise": "galvanize", "galvanised": "galvanized", "galvanises": "galvanizes", "galvanising": "galvanizing", "gambolled": "gamboled", "gambolling": "gamboling", "gaol": "jail", "gaolbird": "jailbird", "gaolbirds": "jailbirds", "gaolbreak": "jailbreak", "gaolbreaks": "jailbreaks", "gaoled": "jailed", "gaoler": "jailer", "gaolers": "jailers", "gaoling": "jailing", "gaols": "jails", "gasses": "gases", "gage": "gauge", "gaged": "gauged", "gages": "gauges", "gaging": "gauging", "generalisation": "generalization", "generalisations": "generalizations", "generalise": "generalize", "generalised": "generalized", "generalises": "generalizes", "generalising": "generalizing", "ghettoise": "ghettoize", "ghettoised": "ghettoized", "ghettoises": "ghettoizes", "ghettoising": "ghettoizing", "gipsies": "gypsies", "glamorise": "glamorize", "glamorised": "glamorized", "glamorises": "glamorizes", "glamorising": "glamorizing", "glamor": "glamour", "globalisation": "globalization", "globalise": "globalize", "globalised": "globalized", "globalises": "globalizes", "globalising": "globalizing", "glueing": "gluing", "goitre": "goiter", "goitres": "goiters", "gonorrhoea": "gonorrhea", "gramme": "gram", "grammes": "grams", "gravelled": "graveled", "grey": "gray", "greyed": "grayed", "greying": "graying", "greyish": "grayish", "greyness": "grayness", "greys": "grays", "grovelled": "groveled", "grovelling": "groveling", "groyne": "groin", "groynes": "groins", "gruelling": "grueling", "gruellingly": "gruelingly", "gryphon": "griffin", "gryphons": "griffins", "gynaecological": "gynecological", "gynaecologist": "gynecologist", "gynaecologists": "gynecologists", "gynaecology": "gynecology", "haematological": "hematological", "haematologist": "hematologist", "haematologists": "hematologists", "haematology": "hematology", "haemoglobin": "hemoglobin", "haemophilia": "hemophilia", "haemophiliac": "hemophiliac", "haemophiliacs": "hemophiliacs", "haemorrhage": "hemorrhage", "haemorrhaged": "hemorrhaged", "haemorrhages": "hemorrhages", "haemorrhaging": "hemorrhaging", "haemorrhoids": "hemorrhoids", "harbour": "harbor", "harboured": "harbored", "harbouring": "harboring", "harbours": "harbors", "harmonisation": "harmonization", "harmonise": "harmonize", "harmonised": "harmonized", "harmonises": "harmonizes", "harmonising": "harmonizing", "homoeopath": "homeopath", "homoeopathic": "homeopathic", "homoeopaths": "homeopaths", "homoeopathy": "homeopathy", "homogenise": "homogenize", "homogenised": "homogenized", "homogenises": "homogenizes", "homogenising": "homogenizing", "honour": "honor", "honourable": "honorable", "honourably": "honorably", "honoured": "honored", "honouring": "honoring", "honours": "honors", "hospitalisation": "hospitalization", "hospitalise": "hospitalize", "hospitalised": "hospitalized", "hospitalises": "hospitalizes", "hospitalising": "hospitalizing", "humanise": "humanize", "humanised": "humanized", "humanises": "humanizes", "humanising": "humanizing", "humour": "humor", "humoured": 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"manoeuvrability": "maneuverability", "manoeuvrable": "maneuverable", "manoeuvre": "maneuver", "manoeuvred": "maneuvered", "manoeuvres": "maneuvers", "manoeuvring": "maneuvering", "manoeuvrings": "maneuverings", "marginalisation": "marginalization", "marginalise": "marginalize", "marginalised": "marginalized", "marginalises": "marginalizes", "marginalising": "marginalizing", "marshalled": "marshaled", "marshalling": "marshaling", "marvelled": "marveled", "marvelling": "marveling", "marvellous": "marvelous", "marvellously": "marvelously", "materialisation": "materialization", "materialise": "materialize", "materialised": "materialized", "materialises": "materializes", "materialising": "materializing", "maximisation": "maximization", "maximise": "maximize", "maximised": "maximized", "maximises": "maximizes", "maximising": "maximizing", "meagre": "meager", "mechanisation": "mechanization", "mechanise": "mechanize", "mechanised": "mechanized", "mechanises": "mechanizes", "mechanising": 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"paedophiles": "pedophiles", "paedophilia": "pedophilia", "palaeolithic": "paleolithic", "palaeontologist": "paleontologist", "palaeontologists": "paleontologists", "palaeontology": "paleontology", "panelled": "paneled", "panelling": "paneling", "panellist": "panelist", "panellists": "panelists", "paralyse": "paralyze", "paralysed": "paralyzed", "paralyses": "paralyzes", "paralysing": "paralyzing", "parcelled": "parceled", "parcelling": "parceling", "parlour": "parlor", "parlours": "parlors", "particularise": "particularize", "particularised": "particularized", "particularises": "particularizes", "particularising": "particularizing", "passivisation": "passivization", "passivise": "passivize", "passivised": "passivized", "passivises": "passivizes", "passivising": "passivizing", "pasteurisation": "pasteurization", "pasteurise": "pasteurize", "pasteurised": "pasteurized", "pasteurises": "pasteurizes", "pasteurising": "pasteurizing", "patronise": "patronize", "patronised": "patronized", 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"plough": "plow", "ploughed": "plowed", "ploughing": "plowing", "ploughman": "plowman", "ploughmen": "plowmen", "ploughs": "plows", "ploughshare": "plowshare", "ploughshares": "plowshares", "polarisation": "polarization", "polarise": "polarize", "polarised": "polarized", "polarises": "polarizes", "polarising": "polarizing", "politicisation": "politicization", "politicise": "politicize", "politicised": "politicized", "politicises": "politicizes", "politicising": "politicizing", "popularisation": "popularization", "popularise": "popularize", "popularised": "popularized", "popularises": "popularizes", "popularising": "popularizing", "pouffe": "pouf", "pouffes": "poufs", "practise": "practice", "practised": "practiced", "practises": "practices", "practising": "practicing", "praesidium": "presidium", "praesidiums": "presidiums", "pressurisation": "pressurization", "pressurise": "pressurize", "pressurised": "pressurized", "pressurises": "pressurizes", "pressurising": "pressurizing", 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"signalizes", "signalising": "signalizing", "signalled": "signaled", "signalling": "signaling", "smoulder": "smolder", "smouldered": "smoldered", "smouldering": "smoldering", "smoulders": "smolders", "snivelled": "sniveled", "snivelling": "sniveling", "snorkelled": "snorkeled", "snorkelling": "snorkeling", "snowplough": "snowplow", "snowploughs": "snowplow", "socialisation": "socialization", "socialise": "socialize", "socialised": "socialized", "socialises": "socializes", "socialising": "socializing", "sodomise": "sodomize", "sodomised": "sodomized", "sodomises": "sodomizes", "sodomising": "sodomizing", "solemnise": "solemnize", "solemnised": "solemnized", "solemnises": "solemnizes", "solemnising": "solemnizing", "sombre": "somber", "specialisation": "specialization", "specialisations": "specializations", "specialise": "specialize", "specialised": "specialized", "specialises": "specializes", "specialising": "specializing", "spectre": "specter", "spectres": "specters", "spiralled": "spiraled", "spiralling": "spiraling", "splendour": "splendor", "splendours": "splendors", "squirrelled": "squirreled", "squirrelling": "squirreling", "stabilisation": "stabilization", "stabilise": "stabilize", "stabilised": "stabilized", "stabiliser": "stabilizer", "stabilisers": "stabilizers", "stabilises": "stabilizes", "stabilising": "stabilizing", "standardisation": "standardization", "standardise": "standardize", "standardised": "standardized", "standardises": "standardizes", "standardising": "standardizing", "stencilled": "stenciled", "stencilling": "stenciling", "sterilisation": "sterilization", "sterilisations": "sterilizations", "sterilise": "sterilize", "sterilised": "sterilized", "steriliser": "sterilizer", "sterilisers": "sterilizers", "sterilises": "sterilizes", "sterilising": "sterilizing", "stigmatisation": "stigmatization", "stigmatise": "stigmatize", "stigmatised": "stigmatized", "stigmatises": "stigmatizes", "stigmatising": "stigmatizing", "storey": "story", "storeys": "stories", "subsidisation": "subsidization", "subsidise": "subsidize", "subsidised": "subsidized", "subsidiser": "subsidizer", "subsidisers": "subsidizers", "subsidises": "subsidizes", "subsidising": "subsidizing", "succour": "succor", "succoured": "succored", "succouring": "succoring", "succours": "succors", "sulphate": "sulfate", "sulphates": "sulfates", "sulphide": "sulfide", "sulphides": "sulfides", "sulphur": "sulfur", "sulphurous": "sulfurous", "summarise": "summarize", "summarised": "summarized", "summarises": "summarizes", "summarising": "summarizing", "swivelled": "swiveled", "swivelling": "swiveling", "symbolise": "symbolize", "symbolised": "symbolized", "symbolises": "symbolizes", "symbolising": "symbolizing", "sympathise": "sympathize", "sympathised": "sympathized", "sympathiser": "sympathizer", "sympathisers": "sympathizers", "sympathises": "sympathizes", "sympathising": "sympathizing", "synchronisation": "synchronization", "synchronise": "synchronize", "synchronised": "synchronized", "synchronises": "synchronizes", "synchronising": "synchronizing", "synthesise": "synthesize", "synthesised": "synthesized", "synthesiser": "synthesizer", "synthesisers": "synthesizers", "synthesises": "synthesizes", "synthesising": "synthesizing", "syphon": "siphon", "syphoned": "siphoned", "syphoning": "siphoning", "syphons": "siphons", "systematisation": "systematization", "systematise": "systematize", "systematised": "systematized", "systematises": "systematizes", "systematising": "systematizing", "tantalise": "tantalize", "tantalised": "tantalized", "tantalises": "tantalizes", "tantalising": "tantalizing", "tantalisingly": "tantalizingly", "tasselled": "tasseled", "technicolour": "technicolor", "temporise": "temporize", "temporised": "temporized", "temporises": "temporizes", "temporising": "temporizing", "tenderise": "tenderize", "tenderised": "tenderized", "tenderises": "tenderizes", "tenderising": "tenderizing", "terrorise": "terrorize", "terrorised": "terrorized", "terrorises": "terrorizes", "terrorising": "terrorizing", "theatre": "theater", "theatregoer": "theatergoer", "theatregoers": "theatergoers", "theatres": "theaters", "theorise": "theorize", "theorised": "theorized", "theorises": "theorizes", "theorising": "theorizing", "tonne": "ton", "tonnes": "tons", "towelled": "toweled", "towelling": "toweling", "toxaemia": "toxemia", "tranquillise": "tranquilize", "tranquillised": "tranquilized", "tranquilliser": "tranquilizer", "tranquillisers": "tranquilizers", "tranquillises": "tranquilizes", "tranquillising": "tranquilizing", "tranquillity": "tranquility", "tranquillize": "tranquilize", "tranquillized": "tranquilized", "tranquillizer": "tranquilizer", "tranquillizers": "tranquilizers", "tranquillizes": "tranquilizes", "tranquillizing": "tranquilizing", "tranquilly": "tranquility", "transistorised": "transistorized", "traumatise": "traumatize", "traumatised": "traumatized", "traumatises": "traumatizes", "traumatising": "traumatizing", "travelled": "traveled", "traveller": "traveler", "travellers": "travelers", "travelling": "traveling", "travelog": "travelogue", "travelogs": "travelogues", "trialled": "trialed", "trialling": "trialing", "tricolour": "tricolor", "tricolours": "tricolors", "trivialise": "trivialize", "trivialised": "trivialized", "trivialises": "trivializes", "trivialising": "trivializing", "tumour": "tumor", "tumours": "tumors", "tunnelled": "tunneled", "tunnelling": "tunneling", "tyrannise": "tyrannize", "tyrannised": "tyrannized", "tyrannises": "tyrannizes", "tyrannising": "tyrannizing", "tyre": "tire", "tyres": "tires", "unauthorised": "unauthorized", "uncivilised": "uncivilized", "underutilised": "underutilized", "unequalled": "unequaled", "unfavourable": "unfavorable", "unfavourably": "unfavorably", "unionisation": "unionization", "unionise": "unionize", "unionised": "unionized", "unionises": "unionizes", "unionising": "unionizing", "unorganised": "unorganized", "unravelled": "unraveled", "unravelling": "unraveling", "unrecognisable": "unrecognizable", "unrecognised": "unrecognized", "unrivalled": "unrivaled", "unsavoury": "unsavory", "untrammelled": "untrammeled", "urbanisation": "urbanization", "urbanise": "urbanize", "urbanised": "urbanized", "urbanises": "urbanizes", "urbanising": "urbanizing", "utilisable": "utilizable", "utilisation": "utilization", "utilise": "utilize", "utilised": "utilized", "utilises": "utilizes", "utilising": "utilizing", "valour": "valor", "vandalise": "vandalize", "vandalised": "vandalized", "vandalises": "vandalizes", "vandalising": "vandalizing", "vaporisation": "vaporization", "vaporise": "vaporize", "vaporised": "vaporized", "vaporises": "vaporizes", "vaporising": "vaporizing", "vapour": "vapor", "vapours": "vapors", "verbalise": "verbalize", "verbalised": "verbalized", "verbalises": "verbalizes", "verbalising": "verbalizing", "victimisation": "victimization", "victimise": "victimize", "victimised": "victimized", "victimises": "victimizes", "victimising": "victimizing", "videodisc": "videodisk", "videodiscs": "videodisks", "vigour": "vigor", "visualisation": "visualization", "visualisations": "visualizations", "visualise": "visualize", "visualised": "visualized", "visualises": "visualizes", "visualising": "visualizing", "vocalisation": "vocalization", "vocalisations": "vocalizations", "vocalise": "vocalize", "vocalised": "vocalized", "vocalises": "vocalizes", "vocalising": "vocalizing", "vulcanised": "vulcanized", "vulgarisation": "vulgarization", "vulgarise": "vulgarize", "vulgarised": "vulgarized", "vulgarises": "vulgarizes", "vulgarising": "vulgarizing", "waggon": "wagon", "waggons": "wagons", "watercolour": "watercolor", "watercolours": "watercolors", "weaselled": "weaseled", "weaselling": "weaseling", "westernisation": "westernization", "westernise": "westernize", "westernised": "westernized", "westernises": "westernizes", "westernising": "westernizing", "womanise": "womanize", "womanised": "womanized", "womaniser": "womanizer", "womanisers": "womanizers", "womanises": "womanizes", "womanising": "womanizing", "woollen": "woolen", "woollens": "woolens", "woollies": "woolies", "woolly": "wooly", "worshipped": "worshiped", "worshipping": "worshiping", "worshipper": "worshiper", "yodelled": "yodeled", "yodelling": "yodeling", "yoghourt": "yogurt", "yoghourts": "yogurts", "yoghurt": "yogurt", "yoghurts": "yogurts", "mhm": "hmm", "mm": "hmm", "mmm": "hmm" } ================================================ FILE: musetalk/whisper/whisper/normalizers/english.py ================================================ import json import os import re from fractions import Fraction from typing import Iterator, List, Match, Optional, Union from more_itertools import windowed from .basic import remove_symbols_and_diacritics class EnglishNumberNormalizer: """ Convert any spelled-out numbers into arabic numbers, while handling: - remove any commas - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc. - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars` - spell out `one` and `ones` - interpret successive single-digit numbers as nominal: `one oh one` -> `101` """ def __init__(self): super().__init__() self.zeros = {"o", "oh", "zero"} self.ones = { name: i for i, name in enumerate( [ "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen", "nineteen", ], start=1, ) } self.ones_plural = { "sixes" if name == "six" else name + "s": (value, "s") for name, value in self.ones.items() } self.ones_ordinal = { "zeroth": (0, "th"), "first": (1, "st"), "second": (2, "nd"), "third": (3, "rd"), "fifth": (5, "th"), "twelfth": (12, "th"), **{ name + ("h" if name.endswith("t") else "th"): (value, "th") for name, value in self.ones.items() if value > 3 and value != 5 and value != 12 }, } self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} self.tens = { "twenty": 20, "thirty": 30, "forty": 40, "fifty": 50, "sixty": 60, "seventy": 70, "eighty": 80, "ninety": 90, } self.tens_plural = { name.replace("y", "ies"): (value, "s") for name, value in self.tens.items() } self.tens_ordinal = { name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items() } self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} self.multipliers = { "hundred": 100, "thousand": 1_000, "million": 1_000_000, "billion": 1_000_000_000, "trillion": 1_000_000_000_000, "quadrillion": 1_000_000_000_000_000, "quintillion": 1_000_000_000_000_000_000, "sextillion": 1_000_000_000_000_000_000_000, "septillion": 1_000_000_000_000_000_000_000_000, "octillion": 1_000_000_000_000_000_000_000_000_000, "nonillion": 1_000_000_000_000_000_000_000_000_000_000, "decillion": 1_000_000_000_000_000_000_000_000_000_000_000, } self.multipliers_plural = { name + "s": (value, "s") for name, value in self.multipliers.items() } self.multipliers_ordinal = { name + "th": (value, "th") for name, value in self.multipliers.items() } self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal} self.decimals = {*self.ones, *self.tens, *self.zeros} self.preceding_prefixers = { "minus": "-", "negative": "-", "plus": "+", "positive": "+", } self.following_prefixers = { "pound": "£", "pounds": "£", "euro": "€", "euros": "€", "dollar": "$", "dollars": "$", "cent": "¢", "cents": "¢", } self.prefixes = set( list(self.preceding_prefixers.values()) + list(self.following_prefixers.values()) ) self.suffixers = { "per": {"cent": "%"}, "percent": "%", } self.specials = {"and", "double", "triple", "point"} self.words = set( [ key for mapping in [ self.zeros, self.ones, self.ones_suffixed, self.tens, self.tens_suffixed, self.multipliers, self.multipliers_suffixed, self.preceding_prefixers, self.following_prefixers, self.suffixers, self.specials, ] for key in mapping ] ) self.literal_words = {"one", "ones"} def process_words(self, words: List[str]) -> Iterator[str]: prefix: Optional[str] = None value: Optional[Union[str, int]] = None skip = False def to_fraction(s: str): try: return Fraction(s) except ValueError: return None def output(result: Union[str, int]): nonlocal prefix, value result = str(result) if prefix is not None: result = prefix + result value = None prefix = None return result if len(words) == 0: return for prev, current, next in windowed([None] + words + [None], 3): if skip: skip = False continue next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next) has_prefix = current[0] in self.prefixes current_without_prefix = current[1:] if has_prefix else current if re.match(r"^\d+(\.\d+)?$", current_without_prefix): # arabic numbers (potentially with signs and fractions) f = to_fraction(current_without_prefix) assert f is not None if value is not None: if isinstance(value, str) and value.endswith("."): # concatenate decimals / ip address components value = str(value) + str(current) continue else: yield output(value) prefix = current[0] if has_prefix else prefix if f.denominator == 1: value = f.numerator # store integers as int else: value = current_without_prefix elif current not in self.words: # non-numeric words if value is not None: yield output(value) yield output(current) elif current in self.zeros: value = str(value or "") + "0" elif current in self.ones: ones = self.ones[current] if value is None: value = ones elif isinstance(value, str) or prev in self.ones: if prev in self.tens and ones < 10: # replace the last zero with the digit assert value[-1] == "0" value = value[:-1] + str(ones) else: value = str(value) + str(ones) elif ones < 10: if value % 10 == 0: value += ones else: value = str(value) + str(ones) else: # eleven to nineteen if value % 100 == 0: value += ones else: value = str(value) + str(ones) elif current in self.ones_suffixed: # ordinal or cardinal; yield the number right away ones, suffix = self.ones_suffixed[current] if value is None: yield output(str(ones) + suffix) elif isinstance(value, str) or prev in self.ones: if prev in self.tens and ones < 10: assert value[-1] == "0" yield output(value[:-1] + str(ones) + suffix) else: yield output(str(value) + str(ones) + suffix) elif ones < 10: if value % 10 == 0: yield output(str(value + ones) + suffix) else: yield output(str(value) + str(ones) + suffix) else: # eleven to nineteen if value % 100 == 0: yield output(str(value + ones) + suffix) else: yield output(str(value) + str(ones) + suffix) value = None elif current in self.tens: tens = self.tens[current] if value is None: value = tens elif isinstance(value, str): value = str(value) + str(tens) else: if value % 100 == 0: value += tens else: value = str(value) + str(tens) elif current in self.tens_suffixed: # ordinal or cardinal; yield the number right away tens, suffix = self.tens_suffixed[current] if value is None: yield output(str(tens) + suffix) elif isinstance(value, str): yield output(str(value) + str(tens) + suffix) else: if value % 100 == 0: yield output(str(value + tens) + suffix) else: yield output(str(value) + str(tens) + suffix) elif current in self.multipliers: multiplier = self.multipliers[current] if value is None: value = multiplier elif isinstance(value, str) or value == 0: f = to_fraction(value) p = f * multiplier if f is not None else None if f is not None and p.denominator == 1: value = p.numerator else: yield output(value) value = multiplier else: before = value // 1000 * 1000 residual = value % 1000 value = before + residual * multiplier elif current in self.multipliers_suffixed: multiplier, suffix = self.multipliers_suffixed[current] if value is None: yield output(str(multiplier) + suffix) elif isinstance(value, str): f = to_fraction(value) p = f * multiplier if f is not None else None if f is not None and p.denominator == 1: yield output(str(p.numerator) + suffix) else: yield output(value) yield output(str(multiplier) + suffix) else: # int before = value // 1000 * 1000 residual = value % 1000 value = before + residual * multiplier yield output(str(value) + suffix) value = None elif current in self.preceding_prefixers: # apply prefix (positive, minus, etc.) if it precedes a number if value is not None: yield output(value) if next in self.words or next_is_numeric: prefix = self.preceding_prefixers[current] else: yield output(current) elif current in self.following_prefixers: # apply prefix (dollars, cents, etc.) only after a number if value is not None: prefix = self.following_prefixers[current] yield output(value) else: yield output(current) elif current in self.suffixers: # apply suffix symbols (percent -> '%') if value is not None: suffix = self.suffixers[current] if isinstance(suffix, dict): if next in suffix: yield output(str(value) + suffix[next]) skip = True else: yield output(value) yield output(current) else: yield output(str(value) + suffix) else: yield output(current) elif current in self.specials: if next not in self.words and not next_is_numeric: # apply special handling only if the next word can be numeric if value is not None: yield output(value) yield output(current) elif current == "and": # ignore "and" after hundreds, thousands, etc. if prev not in self.multipliers: if value is not None: yield output(value) yield output(current) elif current == "double" or current == "triple": if next in self.ones or next in self.zeros: repeats = 2 if current == "double" else 3 ones = self.ones.get(next, 0) value = str(value or "") + str(ones) * repeats skip = True else: if value is not None: yield output(value) yield output(current) elif current == "point": if next in self.decimals or next_is_numeric: value = str(value or "") + "." else: # should all have been covered at this point raise ValueError(f"Unexpected token: {current}") else: # all should have been covered at this point raise ValueError(f"Unexpected token: {current}") if value is not None: yield output(value) def preprocess(self, s: str): # replace " and a half" with " point five" results = [] segments = re.split(r"\band\s+a\s+half\b", s) for i, segment in enumerate(segments): if len(segment.strip()) == 0: continue if i == len(segments) - 1: results.append(segment) else: results.append(segment) last_word = segment.rsplit(maxsplit=2)[-1] if last_word in self.decimals or last_word in self.multipliers: results.append("point five") else: results.append("and a half") s = " ".join(results) # put a space at number/letter boundary s = re.sub(r"([a-z])([0-9])", r"\1 \2", s) s = re.sub(r"([0-9])([a-z])", r"\1 \2", s) # but remove spaces which could be a suffix s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s) return s def postprocess(self, s: str): def combine_cents(m: Match): try: currency = m.group(1) integer = m.group(2) cents = int(m.group(3)) return f"{currency}{integer}.{cents:02d}" except ValueError: return m.string def extract_cents(m: Match): try: return f"¢{int(m.group(1))}" except ValueError: return m.string # apply currency postprocessing; "$2 and ¢7" -> "$2.07" s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s) s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s) # write "one(s)" instead of "1(s)", just for the readability s = re.sub(r"\b1(s?)\b", r"one\1", s) return s def __call__(self, s: str): s = self.preprocess(s) s = " ".join(word for word in self.process_words(s.split()) if word is not None) s = self.postprocess(s) return s class EnglishSpellingNormalizer: """ Applies British-American spelling mappings as listed in [1]. [1] https://www.tysto.com/uk-us-spelling-list.html """ def __init__(self): mapping_path = os.path.join(os.path.dirname(__file__), "english.json") self.mapping = json.load(open(mapping_path)) def __call__(self, s: str): return " ".join(self.mapping.get(word, word) for word in s.split()) class EnglishTextNormalizer: def __init__(self): self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b" self.replacers = { # common contractions r"\bwon't\b": "will not", r"\bcan't\b": "can not", r"\blet's\b": "let us", r"\bain't\b": "aint", r"\by'all\b": "you all", r"\bwanna\b": "want to", r"\bgotta\b": "got to", r"\bgonna\b": "going to", r"\bi'ma\b": "i am going to", r"\bimma\b": "i am going to", r"\bwoulda\b": "would have", r"\bcoulda\b": "could have", r"\bshoulda\b": "should have", r"\bma'am\b": "madam", # contractions in titles/prefixes r"\bmr\b": "mister ", r"\bmrs\b": "missus ", r"\bst\b": "saint ", r"\bdr\b": "doctor ", r"\bprof\b": "professor ", r"\bcapt\b": "captain ", r"\bgov\b": "governor ", r"\bald\b": "alderman ", r"\bgen\b": "general ", r"\bsen\b": "senator ", r"\brep\b": "representative ", r"\bpres\b": "president ", r"\brev\b": "reverend ", r"\bhon\b": "honorable ", r"\basst\b": "assistant ", r"\bassoc\b": "associate ", r"\blt\b": "lieutenant ", r"\bcol\b": "colonel ", r"\bjr\b": "junior ", r"\bsr\b": "senior ", r"\besq\b": "esquire ", # prefect tenses, ideally it should be any past participles, but it's harder.. r"'d been\b": " had been", r"'s been\b": " has been", r"'d gone\b": " had gone", r"'s gone\b": " has gone", r"'d done\b": " had done", # "'s done" is ambiguous r"'s got\b": " has got", # general contractions r"n't\b": " not", r"'re\b": " are", r"'s\b": " is", r"'d\b": " would", r"'ll\b": " will", r"'t\b": " not", r"'ve\b": " have", r"'m\b": " am", } self.standardize_numbers = EnglishNumberNormalizer() self.standardize_spellings = EnglishSpellingNormalizer() def __call__(self, s: str): s = s.lower() s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis s = re.sub(self.ignore_patterns, "", s) s = re.sub(r"\s+'", "'", s) # standardize when there's a space before an apostrophe for pattern, replacement in self.replacers.items(): s = re.sub(pattern, replacement, s) s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep some symbols for numerics s = self.standardize_numbers(s) s = self.standardize_spellings(s) # now remove prefix/suffix symbols that are not preceded/followed by numbers s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) s = re.sub(r"([^0-9])%", r"\1 ", s) s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space return s ================================================ FILE: musetalk/whisper/whisper/tokenizer.py ================================================ import os from dataclasses import dataclass from functools import lru_cache from typing import List, Optional, Tuple, Union import numpy as np import torch from transformers import GPT2TokenizerFast LANGUAGES = { "en": "english", "zh": "chinese", "de": "german", "es": "spanish", "ru": "russian", "ko": "korean", "fr": "french", "ja": "japanese", "pt": "portuguese", "tr": "turkish", "pl": "polish", "ca": "catalan", "nl": "dutch", "ar": "arabic", "sv": "swedish", "it": "italian", "id": "indonesian", "hi": "hindi", "fi": "finnish", "vi": "vietnamese", "iw": "hebrew", "uk": "ukrainian", "el": "greek", "ms": "malay", "cs": "czech", "ro": "romanian", "da": "danish", "hu": "hungarian", "ta": "tamil", "no": "norwegian", "th": "thai", "ur": "urdu", "hr": "croatian", "bg": "bulgarian", "lt": "lithuanian", "la": "latin", "mi": "maori", "ml": "malayalam", "cy": "welsh", "sk": "slovak", "te": "telugu", "fa": "persian", "lv": "latvian", "bn": "bengali", "sr": "serbian", "az": "azerbaijani", "sl": "slovenian", "kn": "kannada", "et": "estonian", "mk": "macedonian", "br": "breton", "eu": "basque", "is": "icelandic", "hy": "armenian", "ne": "nepali", "mn": "mongolian", "bs": "bosnian", "kk": "kazakh", "sq": "albanian", "sw": "swahili", "gl": "galician", "mr": "marathi", "pa": "punjabi", "si": "sinhala", "km": "khmer", "sn": "shona", "yo": "yoruba", "so": "somali", "af": "afrikaans", "oc": "occitan", "ka": "georgian", "be": "belarusian", "tg": "tajik", "sd": "sindhi", "gu": "gujarati", "am": "amharic", "yi": "yiddish", "lo": "lao", "uz": "uzbek", "fo": "faroese", "ht": "haitian creole", "ps": "pashto", "tk": "turkmen", "nn": "nynorsk", "mt": "maltese", "sa": "sanskrit", "lb": "luxembourgish", "my": "myanmar", "bo": "tibetan", "tl": "tagalog", "mg": "malagasy", "as": "assamese", "tt": "tatar", "haw": "hawaiian", "ln": "lingala", "ha": "hausa", "ba": "bashkir", "jw": "javanese", "su": "sundanese", } # language code lookup by name, with a few language aliases TO_LANGUAGE_CODE = { **{language: code for code, language in LANGUAGES.items()}, "burmese": "my", "valencian": "ca", "flemish": "nl", "haitian": "ht", "letzeburgesch": "lb", "pushto": "ps", "panjabi": "pa", "moldavian": "ro", "moldovan": "ro", "sinhalese": "si", "castilian": "es", } @dataclass(frozen=True) class Tokenizer: """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens""" tokenizer: "GPT2TokenizerFast" language: Optional[str] sot_sequence: Tuple[int] def encode(self, text, **kwargs): return self.tokenizer.encode(text, **kwargs) def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs): return self.tokenizer.decode(token_ids, **kwargs) def decode_with_timestamps(self, tokens) -> str: """ Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". """ outputs = [[]] for token in tokens: if token >= self.timestamp_begin: timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" outputs.append(timestamp) outputs.append([]) else: outputs[-1].append(token) outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] return "".join(outputs) @property @lru_cache() def eot(self) -> int: return self.tokenizer.eos_token_id @property @lru_cache() def sot(self) -> int: return self._get_single_token_id("<|startoftranscript|>") @property @lru_cache() def sot_lm(self) -> int: return self._get_single_token_id("<|startoflm|>") @property @lru_cache() def sot_prev(self) -> int: return self._get_single_token_id("<|startofprev|>") @property @lru_cache() def no_speech(self) -> int: return self._get_single_token_id("<|nospeech|>") @property @lru_cache() def no_timestamps(self) -> int: return self._get_single_token_id("<|notimestamps|>") @property @lru_cache() def timestamp_begin(self) -> int: return self.tokenizer.all_special_ids[-1] + 1 @property @lru_cache() def language_token(self) -> int: """Returns the token id corresponding to the value of the `language` field""" if self.language is None: raise ValueError(f"This tokenizer does not have language token configured") additional_tokens = dict( zip( self.tokenizer.additional_special_tokens, self.tokenizer.additional_special_tokens_ids, ) ) candidate = f"<|{self.language}|>" if candidate in additional_tokens: return additional_tokens[candidate] raise KeyError(f"Language {self.language} not found in tokenizer.") @property @lru_cache() def all_language_tokens(self) -> Tuple[int]: result = [] for token, token_id in zip( self.tokenizer.additional_special_tokens, self.tokenizer.additional_special_tokens_ids, ): if token.strip("<|>") in LANGUAGES: result.append(token_id) return tuple(result) @property @lru_cache() def all_language_codes(self) -> Tuple[str]: return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) @property @lru_cache() def sot_sequence_including_notimestamps(self) -> Tuple[int]: return tuple(list(self.sot_sequence) + [self.no_timestamps]) @property @lru_cache() def non_speech_tokens(self) -> Tuple[int]: """ Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. - ♪♪♪ - ( SPEAKING FOREIGN LANGUAGE ) - [DAVID] Hey there, keeping basic punctuations like commas, periods, question marks, exclamation points, etc. """ symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』") symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() # symbols that may be a single token or multiple tokens depending on the tokenizer. # In case they're multiple tokens, suppress the first token, which is safe because: # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. miscellaneous = set("♩♪♫♬♭♮♯") assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} for symbol in symbols + list(miscellaneous): for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]: if len(tokens) == 1 or symbol in miscellaneous: result.add(tokens[0]) return tuple(sorted(result)) def _get_single_token_id(self, text) -> int: tokens = self.tokenizer.encode(text) assert len(tokens) == 1, f"{text} is not encoded as a single token" return tokens[0] @lru_cache(maxsize=None) def build_tokenizer(name: str = "gpt2"): os.environ["TOKENIZERS_PARALLELISM"] = "false" path = os.path.join(os.path.dirname(__file__), "assets", name) tokenizer = GPT2TokenizerFast.from_pretrained(path) specials = [ "<|startoftranscript|>", *[f"<|{lang}|>" for lang in LANGUAGES.keys()], "<|translate|>", "<|transcribe|>", "<|startoflm|>", "<|startofprev|>", "<|nospeech|>", "<|notimestamps|>", ] tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) return tokenizer @lru_cache(maxsize=None) def get_tokenizer( multilingual: bool, *, task: Optional[str] = None, # Literal["transcribe", "translate", None] language: Optional[str] = None, ) -> Tokenizer: if language is not None: language = language.lower() if language not in LANGUAGES: if language in TO_LANGUAGE_CODE: language = TO_LANGUAGE_CODE[language] else: raise ValueError(f"Unsupported language: {language}") if multilingual: tokenizer_name = "multilingual" task = task or "transcribe" language = language or "en" else: tokenizer_name = "gpt2" task = None language = None tokenizer = build_tokenizer(name=tokenizer_name) all_special_ids: List[int] = tokenizer.all_special_ids sot: int = all_special_ids[1] translate: int = all_special_ids[-6] transcribe: int = all_special_ids[-5] langs = tuple(LANGUAGES.keys()) sot_sequence = [sot] if language is not None: sot_sequence.append(sot + 1 + langs.index(language)) if task is not None: sot_sequence.append(transcribe if task == "transcribe" else translate) return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence)) ================================================ FILE: musetalk/whisper/whisper/transcribe.py ================================================ import argparse import os import warnings from typing import List, Optional, Tuple, Union, TYPE_CHECKING import numpy as np import torch import tqdm from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram from .decoding import DecodingOptions, DecodingResult from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt if TYPE_CHECKING: from .model import Whisper def transcribe( model: "Whisper", audio: Union[str, np.ndarray, torch.Tensor], *, verbose: Optional[bool] = None, temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), compression_ratio_threshold: Optional[float] = 2.4, logprob_threshold: Optional[float] = -1.0, no_speech_threshold: Optional[float] = 0.6, condition_on_previous_text: bool = True, force_extraction: bool = False, **decode_options, ): """ Transcribe an audio file using Whisper Parameters ---------- model: Whisper The Whisper model instance audio: Union[str, np.ndarray, torch.Tensor] The path to the audio file to open, or the audio waveform verbose: bool Whether to display the text being decoded to the console. If True, displays all the details, If False, displays minimal details. If None, does not display anything temperature: Union[float, Tuple[float, ...]] Temperature for sampling. It can be a tuple of temperatures, which will be successfully used upon failures according to either `compression_ratio_threshold` or `logprob_threshold`. compression_ratio_threshold: float If the gzip compression ratio is above this value, treat as failed logprob_threshold: float If the average log probability over sampled tokens is below this value, treat as failed no_speech_threshold: float If the no_speech probability is higher than this value AND the average log probability over sampled tokens is below `logprob_threshold`, consider the segment as silent condition_on_previous_text: bool if True, the previous output of the model is provided as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. decode_options: dict Keyword arguments to construct `DecodingOptions` instances Returns ------- A dictionary containing the resulting text ("text") and segment-level details ("segments"), and the spoken language ("language"), which is detected when `decode_options["language"]` is None. """ dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32 if model.device == torch.device("cpu"): if torch.cuda.is_available(): warnings.warn("Performing inference on CPU when CUDA is available") if dtype == torch.float16: warnings.warn("FP16 is not supported on CPU; using FP32 instead") dtype = torch.float32 if hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): warnings.warn("Performing inference on CPU when MPS is available") if dtype == torch.float32: decode_options["fp16"] = False mel = log_mel_spectrogram(audio) all_segments = [] def add_segment( *, start: float, end: float, encoder_embeddings ): all_segments.append( { "start": start, "end": end, "encoder_embeddings":encoder_embeddings, } ) # show the progress bar when verbose is False (otherwise the transcribed text will be printed) num_frames = mel.shape[-1] seek = 0 previous_seek_value = seek sample_skip = 3000 # with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar: while seek < num_frames: # seek是开始的帧数 end_seek = min(seek + sample_skip, num_frames) segment = pad_or_trim(mel[:,seek:seek+sample_skip], N_FRAMES).to(model.device).to(dtype) single = segment.ndim == 2 if single: segment = segment.unsqueeze(0) if dtype == torch.float16: segment = segment.half() audio_features, embeddings = model.encoder(segment, include_embeddings = True) encoder_embeddings = embeddings #print(f"encoder_embeddings shape {encoder_embeddings.shape}") add_segment( start=seek, end=end_seek, #text_tokens=tokens, #result=result, encoder_embeddings=encoder_embeddings, ) seek+=sample_skip return dict(segments=all_segments) def cli(): from . import available_models parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe") parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use") parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default") parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "mps", help="device to use for PyTorch inference") parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages") parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')") parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection") parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling") parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature") parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero") parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search") parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default") parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations") parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.") parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop") parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default") parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below") parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed") parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed") parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence") parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") args = parser.parse_args().__dict__ model_name: str = args.pop("model") model_dir: str = args.pop("model_dir") output_dir: str = args.pop("output_dir") device: str = args.pop("device") os.makedirs(output_dir, exist_ok=True) if model_name.endswith(".en") and args["language"] not in {"en", "English"}: if args["language"] is not None: warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.") args["language"] = "en" temperature = args.pop("temperature") temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") if temperature_increment_on_fallback is not None: temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback)) else: temperature = [temperature] threads = args.pop("threads") if threads > 0: torch.set_num_threads(threads) from . import load_model model = load_model(model_name, device=device, download_root=model_dir) for audio_path in args.pop("audio"): result = transcribe(model, audio_path, temperature=temperature, **args) audio_basename = os.path.basename(audio_path) # save TXT with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt: write_txt(result["segments"], file=txt) # save VTT with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt: write_vtt(result["segments"], file=vtt) # save SRT with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt: write_srt(result["segments"], file=srt) if __name__ == '__main__': cli() ================================================ FILE: musetalk/whisper/whisper/utils.py ================================================ import zlib from typing import Iterator, TextIO def exact_div(x, y): assert x % y == 0 return x // y def str2bool(string): str2val = {"True": True, "False": False} if string in str2val: return str2val[string] else: raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") def optional_int(string): return None if string == "None" else int(string) def optional_float(string): return None if string == "None" else float(string) def compression_ratio(text) -> float: return len(text) / len(zlib.compress(text.encode("utf-8"))) def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'): assert seconds >= 0, "non-negative timestamp expected" milliseconds = round(seconds * 1000.0) hours = milliseconds // 3_600_000 milliseconds -= hours * 3_600_000 minutes = milliseconds // 60_000 milliseconds -= minutes * 60_000 seconds = milliseconds // 1_000 milliseconds -= seconds * 1_000 hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" def write_txt(transcript: Iterator[dict], file: TextIO): for segment in transcript: print(segment['text'].strip(), file=file, flush=True) def write_vtt(transcript: Iterator[dict], file: TextIO): print("WEBVTT\n", file=file) for segment in transcript: print( f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" f"{segment['text'].strip().replace('-->', '->')}\n", file=file, flush=True, ) def write_srt(transcript: Iterator[dict], file: TextIO): """ Write a transcript to a file in SRT format. Example usage: from pathlib import Path from whisper.utils import write_srt result = transcribe(model, audio_path, temperature=temperature, **args) # save SRT audio_basename = Path(audio_path).stem with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt: write_srt(result["segments"], file=srt) """ for i, segment in enumerate(transcript, start=1): # write srt lines print( f"{i}\n" f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" f"{segment['text'].strip().replace('-->', '->')}\n", file=file, flush=True, ) ================================================ FILE: requirements.txt ================================================ torch-ema ninja trimesh opencv-python tensorboardX numpy pandas tqdm matplotlib PyMCubes rich dearpygui packaging scipy scikit-learn face_alignment python_speech_features numba resampy #pyaudio soundfile==0.12.1 einops configargparse lpips==0.1.3 imageio-ffmpeg transformers==4.46.2 edge_tts flask flask_sockets opencv-python-headless aiortc aiohttp_cors ffmpeg-python omegaconf diffusers accelerate librosa openai websockets==12.0 gradio_client azure-cognitiveservices-speech ================================================ FILE: ttsreal.py ================================================ ############################################################################### # Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking # email: lipku@foxmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### from __future__ import annotations import time import numpy as np import soundfile as sf import resampy import asyncio import edge_tts import os import hmac import hashlib import base64 import json import uuid from typing import Iterator import requests import queue from queue import Queue from io import BytesIO import copy,websockets,gzip import azure.cognitiveservices.speech as speechsdk from threading import Thread, Event from enum import Enum from typing import TYPE_CHECKING if TYPE_CHECKING: from basereal import BaseReal from logger import logger class State(Enum): RUNNING=0 PAUSE=1 class BaseTTS: def __init__(self, opt, parent:BaseReal): 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.input_stream = BytesIO() self.msgqueue = Queue() self.state = State.RUNNING def flush_talk(self): self.msgqueue.queue.clear() self.state = State.PAUSE def put_msg_txt(self,msg:str,datainfo:dict={}): if len(msg)>0: self.msgqueue.put((msg,datainfo)) def render(self,quit_event): process_thread = Thread(target=self.process_tts, args=(quit_event,)) process_thread.start() def process_tts(self,quit_event): while not quit_event.is_set(): try: msg:tuple[str, dict] = self.msgqueue.get(block=True, timeout=1) self.state=State.RUNNING except queue.Empty: continue self.txt_to_audio(msg) logger.info('ttsreal thread stop') def txt_to_audio(self,msg:tuple[str, dict]): pass ########################################################################################### class EdgeTTS(BaseTTS): def txt_to_audio(self,msg:tuple[str, dict]): voicename = self.opt.REF_FILE #"zh-CN-YunxiaNeural" text,textevent = msg t = time.time() asyncio.new_event_loop().run_until_complete(self.__main(voicename,text)) logger.info(f'-------edge tts time:{time.time()-t:.4f}s') if self.input_stream.getbuffer().nbytes<=0: #edgetts err logger.error('edgetts err!!!!!') return self.input_stream.seek(0) stream = self.__create_bytes_stream(self.input_stream) streamlen = stream.shape[0] idx=0 while streamlen >= self.chunk and self.state==State.RUNNING: eventpoint={} streamlen -= self.chunk if idx==0: eventpoint={'status':'start','text':text} eventpoint.update(**textevent) #eventpoint={'status':'start','text':text,'msgevent':textevent} elif streamlen0: #skip last frame(not 20ms) # self.queue.put(stream[idx:]) self.input_stream.seek(0) self.input_stream.truncate() def __create_bytes_stream(self,byte_stream): #byte_stream=BytesIO(buffer) stream, sample_rate = sf.read(byte_stream) # [T*sample_rate,] float64 logger.info(f'[INFO]tts audio stream {sample_rate}: {stream.shape}') stream = stream.astype(np.float32) if stream.ndim > 1: logger.info(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: logger.info(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 async def __main(self,voicename: str, text: str): try: communicate = edge_tts.Communicate(text, voicename) #with open(OUTPUT_FILE, "wb") as file: first = True async for chunk in communicate.stream(): if first: first = False if chunk["type"] == "audio" and self.state==State.RUNNING: #self.push_audio(chunk["data"]) self.input_stream.write(chunk["data"]) #file.write(chunk["data"]) elif chunk["type"] == "WordBoundary": pass except Exception as e: logger.exception('edgetts') ########################################################################################### class FishTTS(BaseTTS): def txt_to_audio(self,msg:tuple[str, dict]): text,textevent = msg self.stream_tts( self.fish_speech( text, self.opt.REF_FILE, self.opt.REF_TEXT, "zh", #en args.language, self.opt.TTS_SERVER, #"http://127.0.0.1:5000", #args.server_url, ), msg ) def fish_speech(self, text, reffile, reftext,language, server_url) -> Iterator[bytes]: start = time.perf_counter() req={ 'text':text, 'reference_id':reffile, 'format':'wav', 'streaming':True, 'use_memory_cache':'on' } try: res = requests.post( f"{server_url}/v1/tts", json=req, stream=True, headers={ "content-type": "application/json", }, ) end = time.perf_counter() logger.info(f"fish_speech Time to make POST: {end-start}s") if res.status_code != 200: logger.error("Error:%s", res.text) return first = True for chunk in res.iter_content(chunk_size=17640): # 1764 44100*20ms*2 #print('chunk len:',len(chunk)) if first: end = time.perf_counter() logger.info(f"fish_speech Time to first chunk: {end-start}s") first = False if chunk and self.state==State.RUNNING: yield chunk #print("gpt_sovits response.elapsed:", res.elapsed) except Exception as e: logger.exception('fishtts') def stream_tts(self,audio_stream,msg:tuple[str, dict]): text,textevent = msg first = True for chunk in audio_stream: if chunk is not None and len(chunk)>0: stream = np.frombuffer(chunk, dtype=np.int16).astype(np.float32) / 32767 stream = resampy.resample(x=stream, sr_orig=44100, 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: eventpoint={} if first: eventpoint={'status':'start','text':text} eventpoint.update(**textevent) #eventpoint={'status':'start','text':text,'msgevent':textevent} first = False self.parent.put_audio_frame(stream[idx:idx+self.chunk],eventpoint) streamlen -= self.chunk idx += self.chunk eventpoint={'status':'end','text':text} eventpoint.update(**textevent) #eventpoint={'status':'end','text':text,'msgevent':textevent} self.parent.put_audio_frame(np.zeros(self.chunk,np.float32),eventpoint) ########################################################################################### class SovitsTTS(BaseTTS): def txt_to_audio(self,msg:tuple[str, dict]): text,textevent = msg self.stream_tts( self.gpt_sovits( text=text, reffile=self.opt.REF_FILE, reftext=self.opt.REF_TEXT, language="zh", #en args.language, server_url=self.opt.TTS_SERVER, #"http://127.0.0.1:5000", #args.server_url, ), msg ) def gpt_sovits(self, text, reffile, reftext,language, server_url) -> Iterator[bytes]: start = time.perf_counter() req={ 'text':text, 'text_lang':language, 'ref_audio_path':reffile, 'prompt_text':reftext, 'prompt_lang':language, 'media_type':'ogg', 'streaming_mode':True } # req["text"] = text # req["text_language"] = language # req["character"] = character # req["emotion"] = emotion # #req["stream_chunk_size"] = stream_chunk_size # you can reduce it to get faster response, but degrade quality # req["streaming_mode"] = True try: res = requests.post( f"{server_url}/tts", json=req, stream=True, ) end = time.perf_counter() logger.info(f"gpt_sovits Time to make POST: {end-start}s") if res.status_code != 200: logger.error("Error:%s", res.text) return first = True for chunk in res.iter_content(chunk_size=None): #12800 1280 32K*20ms*2 logger.info('chunk len:%d',len(chunk)) if first: end = time.perf_counter() logger.info(f"gpt_sovits Time to first chunk: {end-start}s") first = False if chunk and self.state==State.RUNNING: yield chunk #print("gpt_sovits response.elapsed:", res.elapsed) except Exception as e: logger.exception('sovits') def __create_bytes_stream(self,byte_stream): #byte_stream=BytesIO(buffer) stream, sample_rate = sf.read(byte_stream) # [T*sample_rate,] float64 logger.info(f'[INFO]tts audio stream {sample_rate}: {stream.shape}') stream = stream.astype(np.float32) if stream.ndim > 1: logger.info(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: logger.info(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 stream_tts(self,audio_stream,msg:tuple[str, dict]): text,textevent = msg first = True for chunk in audio_stream: if chunk is not None and len(chunk)>0: #stream = np.frombuffer(chunk, dtype=np.int16).astype(np.float32) / 32767 #stream = resampy.resample(x=stream, sr_orig=32000, sr_new=self.sample_rate) byte_stream=BytesIO(chunk) stream = self.__create_bytes_stream(byte_stream) streamlen = stream.shape[0] idx=0 while streamlen >= self.chunk: eventpoint={} if first: eventpoint={'status':'start','text':text} eventpoint.update(**textevent) first = False self.parent.put_audio_frame(stream[idx:idx+self.chunk],eventpoint) streamlen -= self.chunk idx += self.chunk eventpoint={'status':'end','text':text} eventpoint.update(**textevent) self.parent.put_audio_frame(np.zeros(self.chunk,np.float32),eventpoint) ########################################################################################### class CosyVoiceTTS(BaseTTS): def txt_to_audio(self,msg:tuple[str, dict]): text,textevent = msg self.stream_tts( self.cosy_voice( text, self.opt.REF_FILE, self.opt.REF_TEXT, "zh", #en args.language, self.opt.TTS_SERVER, #"http://127.0.0.1:5000", #args.server_url, ), msg ) def cosy_voice(self, text, reffile, reftext,language, server_url) -> Iterator[bytes]: start = time.perf_counter() payload = { 'tts_text': text, 'prompt_text': reftext } try: files = [('prompt_wav', ('prompt_wav', open(reffile, 'rb'), 'application/octet-stream'))] res = requests.request("GET", f"{server_url}/inference_zero_shot", data=payload, files=files, stream=True) end = time.perf_counter() logger.info(f"cosy_voice Time to make POST: {end-start}s") if res.status_code != 200: logger.error("Error:%s", res.text) return first = True for chunk in res.iter_content(chunk_size=9600): # 960 24K*20ms*2 if first: end = time.perf_counter() logger.info(f"cosy_voice Time to first chunk: {end-start}s") first = False if chunk and self.state==State.RUNNING: yield chunk except Exception as e: logger.exception('cosyvoice') def stream_tts(self,audio_stream,msg:tuple[str, dict]): text,textevent = msg first = True for chunk in audio_stream: if chunk is not None and len(chunk)>0: stream = np.frombuffer(chunk, 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: eventpoint={} if first: eventpoint={'status':'start','text':text} eventpoint.update(**textevent) first = False self.parent.put_audio_frame(stream[idx:idx+self.chunk],eventpoint) streamlen -= self.chunk idx += self.chunk eventpoint={'status':'end','text':text} eventpoint.update(**textevent) self.parent.put_audio_frame(np.zeros(self.chunk,np.float32),eventpoint) ########################################################################################### _PROTOCOL = "https://" _HOST = "tts.cloud.tencent.com" _PATH = "/stream" _ACTION = "TextToStreamAudio" class TencentTTS(BaseTTS): def __init__(self, opt, parent): super().__init__(opt,parent) self.appid = os.getenv("TENCENT_APPID") self.secret_key = os.getenv("TENCENT_SECRET_KEY") self.secret_id = os.getenv("TENCENT_SECRET_ID") self.voice_type = int(opt.REF_FILE) self.codec = "pcm" self.sample_rate = 16000 self.volume = 0 self.speed = 0 def __gen_signature(self, params): sort_dict = sorted(params.keys()) sign_str = "POST" + _HOST + _PATH + "?" for key in sort_dict: sign_str = sign_str + key + "=" + str(params[key]) + '&' sign_str = sign_str[:-1] hmacstr = hmac.new(self.secret_key.encode('utf-8'), sign_str.encode('utf-8'), hashlib.sha1).digest() s = base64.b64encode(hmacstr) s = s.decode('utf-8') return s def __gen_params(self, session_id, text): params = dict() params['Action'] = _ACTION params['AppId'] = int(self.appid) params['SecretId'] = self.secret_id params['ModelType'] = 1 params['VoiceType'] = self.voice_type params['Codec'] = self.codec params['SampleRate'] = self.sample_rate params['Speed'] = self.speed params['Volume'] = self.volume params['SessionId'] = session_id params['Text'] = text timestamp = int(time.time()) params['Timestamp'] = timestamp params['Expired'] = timestamp + 24 * 60 * 60 return params def txt_to_audio(self,msg:tuple[str, dict]): text,textevent = msg self.stream_tts( self.tencent_voice( text, self.opt.REF_FILE, self.opt.REF_TEXT, "zh", #en args.language, self.opt.TTS_SERVER, #"http://127.0.0.1:5000", #args.server_url, ), msg ) def tencent_voice(self, text, reffile, reftext,language, server_url) -> Iterator[bytes]: start = time.perf_counter() session_id = str(uuid.uuid1()) params = self.__gen_params(session_id, text) signature = self.__gen_signature(params) headers = { "Content-Type": "application/json", "Authorization": str(signature) } url = _PROTOCOL + _HOST + _PATH try: res = requests.post(url, headers=headers, data=json.dumps(params), stream=True) end = time.perf_counter() logger.info(f"tencent Time to make POST: {end-start}s") first = True for chunk in res.iter_content(chunk_size=6400): # 640 16K*20ms*2 #logger.info('chunk len:%d',len(chunk)) if first: try: rsp = json.loads(chunk) #response["Code"] = rsp["Response"]["Error"]["Code"] #response["Message"] = rsp["Response"]["Error"]["Message"] logger.error("tencent tts:%s",rsp["Response"]["Error"]["Message"]) return except: end = time.perf_counter() logger.info(f"tencent Time to first chunk: {end-start}s") first = False if chunk and self.state==State.RUNNING: yield chunk except Exception as e: logger.exception('tencent') def stream_tts(self,audio_stream,msg:tuple[str, dict]): text,textevent = msg first = True last_stream = np.array([],dtype=np.float32) for chunk in audio_stream: if chunk is not None and len(chunk)>0: stream = np.frombuffer(chunk, dtype=np.int16).astype(np.float32) / 32767 stream = np.concatenate((last_stream,stream)) #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: eventpoint={} if first: eventpoint={'status':'start','text':text} eventpoint.update(**textevent) first = False self.parent.put_audio_frame(stream[idx:idx+self.chunk],eventpoint) streamlen -= self.chunk idx += self.chunk last_stream = stream[idx:] #get the remain stream eventpoint={'status':'end','text':text} eventpoint.update(**textevent) self.parent.put_audio_frame(np.zeros(self.chunk,np.float32),eventpoint) ########################################################################################### class DoubaoTTS(BaseTTS): def __init__(self, opt, parent): super().__init__(opt, parent) # 从配置中读取火山引擎参数 self.appid = os.getenv("DOUBAO_APPID") self.token = os.getenv("DOUBAO_TOKEN") _cluster = 'volcano_tts' _host = "openspeech.bytedance.com" self.api_url = f"wss://{_host}/api/v1/tts/ws_binary" self.request_json = { "app": { "appid": self.appid, "token": "access_token", "cluster": _cluster }, "user": { "uid": "xxx" }, "audio": { "voice_type": "xxx", "encoding": "pcm", "rate": 16000, "speed_ratio": 1.0, "volume_ratio": 1.0, "pitch_ratio": 1.0, }, "request": { "reqid": "xxx", "text": "字节跳动语音合成。", "text_type": "plain", "operation": "xxx" } } async def doubao_voice(self, text): # -> Iterator[bytes]: start = time.perf_counter() voice_type = self.opt.REF_FILE try: # 创建请求对象 default_header = bytearray(b'\x11\x10\x11\x00') submit_request_json = copy.deepcopy(self.request_json) submit_request_json["user"]["uid"] = self.parent.sessionid submit_request_json["audio"]["voice_type"] = voice_type submit_request_json["request"]["text"] = text submit_request_json["request"]["reqid"] = str(uuid.uuid4()) submit_request_json["request"]["operation"] = "submit" payload_bytes = str.encode(json.dumps(submit_request_json)) payload_bytes = gzip.compress(payload_bytes) # if no compression, comment this line full_client_request = bytearray(default_header) full_client_request.extend((len(payload_bytes)).to_bytes(4, 'big')) # payload size(4 bytes) full_client_request.extend(payload_bytes) # payload header = {"Authorization": f"Bearer; {self.token}"} first = True async with websockets.connect(self.api_url, extra_headers=header, ping_interval=None) as ws: await ws.send(full_client_request) while True: res = await ws.recv() header_size = res[0] & 0x0f message_type = res[1] >> 4 message_type_specific_flags = res[1] & 0x0f payload = res[header_size*4:] if message_type == 0xb: # audio-only server response if message_type_specific_flags == 0: # no sequence number as ACK #print(" Payload size: 0") continue else: if first: end = time.perf_counter() logger.info(f"doubao tts Time to first chunk: {end-start}s") first = False sequence_number = int.from_bytes(payload[:4], "big", signed=True) payload_size = int.from_bytes(payload[4:8], "big", signed=False) payload = payload[8:] yield payload if sequence_number < 0: break else: break except Exception as e: logger.exception('doubao') # # 检查响应状态码 # if response.status_code == 200: # # 处理响应数据 # audio_data = base64.b64decode(response.json().get('data')) # yield audio_data # else: # logger.error(f"请求失败,状态码: {response.status_code}") # return def txt_to_audio(self, msg:tuple[str, dict]): text, textevent = msg asyncio.new_event_loop().run_until_complete( self.stream_tts( self.doubao_voice(text), msg ) ) async def stream_tts(self, audio_stream, msg:tuple[str, dict]): text, textevent = msg first = True last_stream = np.array([],dtype=np.float32) async for chunk in audio_stream: if chunk is not None and len(chunk) > 0: stream = np.frombuffer(chunk, dtype=np.int16).astype(np.float32) / 32767 stream = np.concatenate((last_stream,stream)) #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: eventpoint = {} if first: eventpoint={'status':'start','text':text} eventpoint.update(**textevent) first = False self.parent.put_audio_frame(stream[idx:idx + self.chunk], eventpoint) streamlen -= self.chunk idx += self.chunk last_stream = stream[idx:] #get the remain stream eventpoint={'status':'end','text':text} eventpoint.update(**textevent) self.parent.put_audio_frame(np.zeros(self.chunk, np.float32), eventpoint) ########################################################################################### class IndexTTS2(BaseTTS): def __init__(self, opt, parent): super().__init__(opt, parent) # IndexTTS2 配置参数 self.server_url = opt.TTS_SERVER # Gradio服务器地址,如 "http://127.0.0.1:7860/" self.ref_audio_path = opt.REF_FILE # 参考音频文件路径 self.max_tokens = getattr(opt, 'MAX_TOKENS', 120) # 最大token数 # 初始化Gradio客户端 try: from gradio_client import Client, handle_file self.client = Client(self.server_url) self.handle_file = handle_file logger.info(f"IndexTTS2 Gradio客户端初始化成功: {self.server_url}") except ImportError: logger.error("IndexTTS2 需要安装 gradio_client: pip install gradio_client") raise except Exception as e: logger.error(f"IndexTTS2 Gradio客户端初始化失败: {e}") raise def txt_to_audio(self, msg): text, textevent = msg try: # 先进行文本分割 segments = self.split_text(text) if not segments: logger.error("IndexTTS2 文本分割失败") return logger.info(f"IndexTTS2 文本分割为 {len(segments)} 个片段") # 循环生成每个片段的音频 for i, segment_text in enumerate(segments): if self.state != State.RUNNING: break logger.info(f"IndexTTS2 正在生成第 {i+1}/{len(segments)} 段音频...") audio_file = self.indextts2_generate(segment_text) if audio_file: # 为每个片段创建事件信息 segment_msg = (segment_text, textevent) self.file_to_stream(audio_file, segment_msg, is_first=(i==0), is_last=(i==len(segments)-1)) else: logger.error(f"IndexTTS2 第 {i+1} 段音频生成失败") except Exception as e: logger.exception(f"IndexTTS2 txt_to_audio 错误: {e}") def split_text(self, text): """使用 IndexTTS2 API 分割文本""" try: logger.info(f"IndexTTS2 开始分割文本,长度: {len(text)}") # 调用文本分割 API result = self.client.predict( text=text, max_text_tokens_per_segment=self.max_tokens, api_name="/on_input_text_change" ) # 解析分割结果 if 'value' in result and 'data' in result['value']: data = result['value']['data'] logger.info(f"IndexTTS2 共分割为 {len(data)} 个片段") segments = [] for i, item in enumerate(data): 序号 = item[0] + 1 分句内容 = item[1] token数 = item[2] logger.info(f"片段 {序号}: {len(分句内容)} 字符, {token数} tokens") segments.append(分句内容) return segments else: logger.error(f"IndexTTS2 文本分割结果格式异常: {result}") return [text] # 如果分割失败,返回原文本 except Exception as e: logger.exception(f"IndexTTS2 文本分割失败: {e}") return [text] # 如果分割失败,返回原文本 def indextts2_generate(self, text): """调用 IndexTTS2 Gradio API 生成语音""" start = time.perf_counter() try: # 调用 gen_single API result = self.client.predict( emo_control_method="Same as the voice reference", prompt=self.handle_file(self.ref_audio_path), text=text, emo_ref_path=self.handle_file(self.ref_audio_path), emo_weight=0.8, vec1=0.5, vec2=0, vec3=0, vec4=0, vec5=0, vec6=0, vec7=0, vec8=0, emo_text="", emo_random=False, max_text_tokens_per_segment=self.max_tokens, param_16=True, param_17=0.8, param_18=30, param_19=0.8, param_20=0, param_21=3, param_22=10, param_23=1500, api_name="/gen_single" ) end = time.perf_counter() logger.info(f"IndexTTS2 片段生成完成,耗时: {end-start:.2f}s") # 返回生成的音频文件路径 if 'value' in result: audio_file = result['value'] return audio_file else: logger.error(f"IndexTTS2 结果格式异常: {result}") return None except Exception as e: logger.exception(f"IndexTTS2 API调用失败: {e}") return None def file_to_stream(self, audio_file, msg, is_first=False, is_last=False): """将音频文件转换为音频流""" text, textevent = msg try: # 读取音频文件 stream, sample_rate = sf.read(audio_file) logger.info(f'IndexTTS2 音频文件 {sample_rate}Hz: {stream.shape}') # 转换为float32 stream = stream.astype(np.float32) # 如果是多声道,只取第一个声道 if stream.ndim > 1: logger.info(f'IndexTTS2 音频有 {stream.shape[1]} 个声道,只使用第一个') stream = stream[:, 0] # 重采样到目标采样率 if sample_rate != self.sample_rate and stream.shape[0] > 0: logger.info(f'IndexTTS2 重采样: {sample_rate}Hz -> {self.sample_rate}Hz') stream = resampy.resample(x=stream, sr_orig=sample_rate, sr_new=self.sample_rate) # 分块发送音频流 streamlen = stream.shape[0] idx = 0 first_chunk = True while streamlen >= self.chunk and self.state == State.RUNNING: eventpoint = None # 只在第一个片段的第一个chunk发送start事件 if is_first and first_chunk: eventpoint = {'status': 'start', 'text': text, 'msgevent': textevent} first_chunk = False self.parent.put_audio_frame(stream[idx:idx + self.chunk], eventpoint) idx += self.chunk streamlen -= self.chunk # 只在最后一个片段发送end事件 if is_last: eventpoint = {'status': 'end', 'text': text, 'msgevent': textevent} self.parent.put_audio_frame(np.zeros(self.chunk, np.float32), eventpoint) # 清理临时文件 try: if os.path.exists(audio_file): os.remove(audio_file) logger.info(f"IndexTTS2 已删除临时文件: {audio_file}") except Exception as e: logger.warning(f"IndexTTS2 删除临时文件失败: {e}") except Exception as e: logger.exception(f"IndexTTS2 音频流处理失败: {e}") ########################################################################################### class XTTS(BaseTTS): def __init__(self, opt, parent): super().__init__(opt,parent) self.speaker = self.get_speaker(opt.REF_FILE, opt.TTS_SERVER) def txt_to_audio(self,msg:tuple[str, dict]): text,textevent = msg self.stream_tts( self.xtts( text, self.speaker, "zh-cn", #en args.language, self.opt.TTS_SERVER, #"http://localhost:9000", #args.server_url, "20" #args.stream_chunk_size ), msg ) def get_speaker(self,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(self,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 try: res = requests.post( f"{server_url}/tts_stream", json=speaker, stream=True, ) end = time.perf_counter() logger.info(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=None): #24K*20ms*2 if first: end = time.perf_counter() logger.info(f"xtts Time to first chunk: {end-start}s") first = False if chunk: yield chunk except Exception as e: print(e) def stream_tts(self,audio_stream,msg:tuple[str, dict]): text,textevent = msg first = True last_stream = np.array([],dtype=np.float32) for chunk in audio_stream: if chunk is not None and len(chunk)>0: stream = np.frombuffer(chunk, dtype=np.int16).astype(np.float32) / 32767 stream = resampy.resample(x=stream, sr_orig=24000, sr_new=self.sample_rate) stream = np.concatenate((last_stream,stream)) #byte_stream=BytesIO(buffer) #stream = self.__create_bytes_stream(byte_stream) streamlen = stream.shape[0] idx=0 while streamlen >= self.chunk: eventpoint={} if first: eventpoint={'status':'start','text':text} eventpoint.update(**textevent) first = False self.parent.put_audio_frame(stream[idx:idx+self.chunk],eventpoint) streamlen -= self.chunk idx += self.chunk last_stream = stream[idx:] #get the remain stream eventpoint={'status':'end','text':text} eventpoint.update(**textevent) self.parent.put_audio_frame(np.zeros(self.chunk,np.float32),eventpoint) ########################################################################################### class AzureTTS(BaseTTS): CHUNK_SIZE = 640 # 16kHz, 20ms, 16-bit Mono PCM size def __init__(self, opt, parent): super().__init__(opt,parent) self.audio_buffer = b'' voicename = self.opt.REF_FILE # 比如"zh-CN-XiaoxiaoMultilingualNeural" speech_key = os.getenv("AZURE_SPEECH_KEY") tts_region = os.getenv("AZURE_TTS_REGION") speech_endpoint = f"wss://{tts_region}.tts.speech.microsoft.com/cognitiveservices/websocket/v2" speech_config = speechsdk.SpeechConfig(subscription=speech_key,endpoint=speech_endpoint) speech_config.speech_synthesis_voice_name = voicename speech_config.set_speech_synthesis_output_format(speechsdk.SpeechSynthesisOutputFormat.Raw16Khz16BitMonoPcm) # 获取内存中流形式的结果 self.speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=None) self.speech_synthesizer.synthesizing.connect(self._on_synthesizing) def txt_to_audio(self,msg:tuple[str, dict]): msg_text: str = msg[0] result=self.speech_synthesizer.speak_text(msg_text) # 延迟指标 fb_latency = int(result.properties.get_property( speechsdk.PropertyId.SpeechServiceResponse_SynthesisFirstByteLatencyMs )) fin_latency = int(result.properties.get_property( speechsdk.PropertyId.SpeechServiceResponse_SynthesisFinishLatencyMs )) logger.info(f"azure音频生成相关:首字节延迟: {fb_latency} ms, 完成延迟: {fin_latency} ms, result_id: {result.result_id}") # === 回调 === def _on_synthesizing(self, evt: speechsdk.SpeechSynthesisEventArgs): if evt.result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted: logger.info("SynthesizingAudioCompleted") elif evt.result.reason == speechsdk.ResultReason.Canceled: cancellation_details = evt.result.cancellation_details logger.info(f"Speech synthesis canceled: {cancellation_details.reason}") if cancellation_details.reason == speechsdk.CancellationReason.Error: if cancellation_details.error_details: logger.info(f"Error details: {cancellation_details.error_details}") if self.state != State.RUNNING: self.audio_buffer = b'' return # evt.result.audio_data 是刚到的一小段原始 PCM self.audio_buffer += evt.result.audio_data while len(self.audio_buffer) >= self.CHUNK_SIZE: chunk = self.audio_buffer[:self.CHUNK_SIZE] self.audio_buffer = self.audio_buffer[self.CHUNK_SIZE:] frame = (np.frombuffer(chunk, dtype=np.int16) .astype(np.float32) / 32767.0) self.parent.put_audio_frame(frame) ================================================ FILE: ultralight/audio2feature.py ================================================ from transformers import Wav2Vec2Processor, HubertModel import torch import numpy as np class Audio2Feature(): def __init__(self): self.device = 'cuda' if torch.cuda.is_available() else 'cpu' self.processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft") self.model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft").to(self.device) @torch.no_grad() def get_hubert_from_16k_speech(self, speech): if speech.ndim == 2: speech = speech[:, 0] # [T, 2] ==> [T,] input_values_all = self.processor(speech, return_tensors="pt", sampling_rate=16000).input_values # [1, T] input_values_all = input_values_all.to(self.device) kernel = 400 stride = 320 clip_length = stride * 1000 num_iter = input_values_all.shape[1] // clip_length expected_T = (input_values_all.shape[1] - (kernel-stride)) // stride res_lst = [] for i in range(num_iter): if i == 0: start_idx = 0 end_idx = clip_length - stride + kernel else: start_idx = clip_length * i end_idx = start_idx + (clip_length - stride + kernel) input_values = input_values_all[:, start_idx: end_idx] hidden_states = self.model.forward(input_values).last_hidden_state # [B=1, T=pts//320, hid=1024] res_lst.append(hidden_states[0]) if num_iter > 0: input_values = input_values_all[:, clip_length * num_iter:] else: input_values = input_values_all if input_values.shape[1] >= kernel: # if the last batch is shorter than kernel_size, skip it hidden_states = self.model(input_values).last_hidden_state # [B=1, T=pts//320, hid=1024] res_lst.append(hidden_states[0]) ret = torch.cat(res_lst, dim=0).cpu() # [T, 1024] assert abs(ret.shape[0] - expected_T) <= 1 if ret.shape[0] < expected_T: ret = torch.nn.functional.pad(ret, (0,0,0,expected_T-ret.shape[0])) else: ret = ret[:expected_T] return ret def get_sliced_feature(self, feature_array, vid_idx, audio_feat_length=[8,8], fps=25): """ Get sliced features based on a given index :param feature_array: :param start_idx: the start index of the feature :param audio_feat_length: :return: """ length = len(feature_array) selected_feature = [] selected_idx = [] center_idx = int(vid_idx*50/fps) left_idx = center_idx-audio_feat_length[0]*2 right_idx = center_idx + (audio_feat_length[1])*2 for idx in range(left_idx,right_idx): idx = max(0, idx) idx = min(length-1, idx) x = feature_array[idx] selected_feature.append(x) selected_idx.append(idx) selected_feature = np.concatenate(selected_feature, axis=0) selected_feature = selected_feature.reshape(-1, 1024) return selected_feature,selected_idx def feature2chunks(self,feature_array,fps,batch_size,audio_feat_length = [8,8],start=0): whisper_chunks = [] whisper_idx_multiplier = 50./fps i = 0 #print(f"video in {fps} FPS, audio idx in 50FPS") for _ in range(batch_size): # start_idx = int(i * whisper_idx_multiplier) # if start_idx>=len(feature_array): # break selected_feature,selected_idx = self.get_sliced_feature(feature_array= feature_array,vid_idx = i+start,audio_feat_length=audio_feat_length,fps=fps) #print(f"i:{i},selected_idx {selected_idx}") whisper_chunks.append(selected_feature) i += 1 return whisper_chunks ================================================ FILE: ultralight/face_detect_utils/base_module.py ================================================ #!/usr/bin/env python3 # -*- coding:utf-8 -*- import torch from torch.nn import Module, Sequential, Conv2d, BatchNorm2d, ReLU import math import torch.nn as nn import torch.nn.functional as F from typing import Optional, List, Tuple def Conv_Block(in_channel, out_channel, kernel_size, stride, padding, group=1, has_bn=True, is_linear=False): return Sequential( Conv2d(in_channel, out_channel, kernel_size, stride, padding=padding, groups=group, bias=False), BatchNorm2d(out_channel) if has_bn else Sequential(), ReLU(inplace=True) if not is_linear else Sequential() ) class InvertedResidual(Module): def __init__(self, in_channel, out_channel, stride, use_res_connect, expand_ratio): super(InvertedResidual, self).__init__() self.stride = stride assert stride in [1, 2] exp_channel = in_channel * expand_ratio self.use_res_connect = use_res_connect self.inv_res = Sequential( Conv_Block(in_channel=in_channel, out_channel=exp_channel, kernel_size=1, stride=1, padding=0), Conv_Block(in_channel=exp_channel, out_channel=exp_channel, kernel_size=3, stride=stride, padding=1, group=exp_channel), Conv_Block(in_channel=exp_channel, out_channel=out_channel, kernel_size=1, stride=1, padding=0, is_linear=True) ) def forward(self, x): if self.use_res_connect: return x + self.inv_res(x) else: return self.inv_res(x) class GhostModule(Module): def __init__(self, in_channel, out_channel, is_linear=False): super(GhostModule, self).__init__() self.out_channel = out_channel init_channel = math.ceil(out_channel / 2) new_channel = init_channel self.primary_conv = Conv_Block(in_channel, init_channel, 1, 1, 0, is_linear=is_linear) self.cheap_operation = Conv_Block(init_channel, new_channel, 3, 1, 1, group=init_channel, is_linear=is_linear) def forward(self, x): x1 = self.primary_conv(x) x2 = self.cheap_operation(x1) out = torch.cat([x1, x2], dim=1) return out[:, :self.out_channel, :, :] class GhostBottleneck(Module): def __init__(self, in_channel, hidden_channel, out_channel, stride): super(GhostBottleneck, self).__init__() assert stride in [1, 2] self.ghost_conv = Sequential( # GhostModule GhostModule(in_channel, hidden_channel, is_linear=False), # DepthwiseConv-linear Conv_Block(hidden_channel, hidden_channel, 3, stride, 1, group=hidden_channel, is_linear=True) if stride == 2 else Sequential(), # GhostModule-linear GhostModule(hidden_channel, out_channel, is_linear=True) ) if stride == 1 and in_channel == out_channel: self.shortcut = Sequential() else: self.shortcut = Sequential( Conv_Block(in_channel, in_channel, 3, stride, 1, group=in_channel, is_linear=True), Conv_Block(in_channel, out_channel, 1, 1, 0, is_linear=True) ) def forward(self, x): return self.ghost_conv(x) + self.shortcut(x) class GhostOneModule(Module): def __init__(self, in_channel, out_channel, is_linear=False, inference_mode=False, num_conv_branches=1): super(GhostOneModule, self).__init__() self.out_channel = out_channel half_outchannel = math.ceil(out_channel / 2) self.inference_mode = inference_mode self.num_conv_branches = num_conv_branches self.primary_conv = MobileOneBlock(in_channels=in_channel, out_channels=half_outchannel, kernel_size=1, stride=1, padding=0, groups=1, inference_mode=self.inference_mode, use_se=False, num_conv_branches=self.num_conv_branches, is_linear=is_linear) self.cheap_operation = MobileOneBlock(in_channels=half_outchannel, out_channels=half_outchannel, kernel_size=3, stride=1, padding=1, groups=half_outchannel, inference_mode=self.inference_mode, use_se=False, num_conv_branches=self.num_conv_branches, is_linear=is_linear) def forward(self, x): x1 = self.primary_conv(x) x2 = self.cheap_operation(x1) out = torch.cat([x1, x2], dim=1) return out class GhostOneBottleneck(Module): def __init__(self, in_channel, hidden_channel, out_channel, stride, inference_mode=False, num_conv_branches=1): super(GhostOneBottleneck, self).__init__() assert stride in [1, 2] self.inference_mode = inference_mode self.num_conv_branches = num_conv_branches self.ghost_conv = Sequential( # GhostModule GhostOneModule(in_channel, hidden_channel, is_linear=False, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches), # DepthwiseConv-linear MobileOneBlock(in_channels=hidden_channel, out_channels=hidden_channel, kernel_size=3, stride=stride, padding=1, groups=hidden_channel, inference_mode=self.inference_mode, use_se=False, num_conv_branches=self.num_conv_branches, is_linear=True) if stride == 2 else Sequential(), # GhostModule-linear GhostOneModule(hidden_channel, out_channel, is_linear=True, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) ) def forward(self, x): return self.ghost_conv(x) class SEBlock(nn.Module): """ Squeeze and Excite module. Pytorch implementation of `Squeeze-and-Excitation Networks` - https://arxiv.org/pdf/1709.01507.pdf """ def __init__(self, in_channels: int, rd_ratio: float = 0.0625) -> None: """ Construct a Squeeze and Excite Module. :param in_channels: Number of input channels. :param rd_ratio: Input channel reduction ratio. """ super(SEBlock, self).__init__() self.reduce = nn.Conv2d(in_channels=in_channels, out_channels=int(in_channels * rd_ratio), kernel_size=1, stride=1, bias=True) self.expand = nn.Conv2d(in_channels=int(in_channels * rd_ratio), out_channels=in_channels, kernel_size=1, stride=1, bias=True) def forward(self, inputs: torch.Tensor) -> torch.Tensor: """ Apply forward pass. """ b, c, h, w = inputs.size() x = F.avg_pool2d(inputs, kernel_size=[h, w]) x = self.reduce(x) x = F.relu(x) x = self.expand(x) x = torch.sigmoid(x) x = x.view(-1, c, 1, 1) return inputs * x class MobileOneBlock(nn.Module): """ MobileOne building block. This block has a multi-branched architecture at train-time and plain-CNN style architecture at inference time For more details, please refer to our paper: `An Improved One millisecond Mobile Backbone` - https://arxiv.org/pdf/2206.04040.pdf """ def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, inference_mode: bool = False, use_se: bool = False, num_conv_branches: int = 1, is_linear: bool = False) -> None: """ Construct a MobileOneBlock module. :param in_channels: Number of channels in the input. :param out_channels: Number of channels produced by the block. :param kernel_size: Size of the convolution kernel. :param stride: Stride size. :param padding: Zero-padding size. :param dilation: Kernel dilation factor. :param groups: Group number. :param inference_mode: If True, instantiates model in inference mode. :param use_se: Whether to use SE-ReLU activations. :param num_conv_branches: Number of linear conv branches. """ super(MobileOneBlock, self).__init__() self.inference_mode = inference_mode self.groups = groups self.stride = stride self.kernel_size = kernel_size self.in_channels = in_channels self.out_channels = out_channels self.num_conv_branches = num_conv_branches # Check if SE-ReLU is requested if use_se: self.se = SEBlock(out_channels) else: self.se = nn.Identity() if is_linear: self.activation = nn.Identity() else: self.activation = nn.ReLU() if inference_mode: self.reparam_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True) else: # Re-parameterizable skip connection self.rbr_skip = nn.BatchNorm2d(num_features=in_channels) \ if out_channels == in_channels and stride == 1 else None # Re-parameterizable conv branches rbr_conv = list() for _ in range(self.num_conv_branches): rbr_conv.append(self._conv_bn(kernel_size=kernel_size, padding=padding)) self.rbr_conv = nn.ModuleList(rbr_conv) # Re-parameterizable scale branch self.rbr_scale = None if kernel_size > 1: self.rbr_scale = self._conv_bn(kernel_size=1, padding=0) def forward(self, x: torch.Tensor) -> torch.Tensor: """ Apply forward pass. """ # Inference mode forward pass. if self.inference_mode: return self.activation(self.se(self.reparam_conv(x))) # Multi-branched train-time forward pass. # Skip branch output identity_out = 0 if self.rbr_skip is not None: identity_out = self.rbr_skip(x) # Scale branch output scale_out = 0 if self.rbr_scale is not None: scale_out = self.rbr_scale(x) # Other branches out = scale_out + identity_out for ix in range(self.num_conv_branches): out += self.rbr_conv[ix](x) return self.activation(self.se(out)) def reparameterize(self): """ Following works like `RepVGG: Making VGG-style ConvNets Great Again` - https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched architecture used at training time to obtain a plain CNN-like structure for inference. """ if self.inference_mode: return kernel, bias = self._get_kernel_bias() self.reparam_conv = nn.Conv2d(in_channels=self.rbr_conv[0].conv.in_channels, out_channels=self.rbr_conv[0].conv.out_channels, kernel_size=self.rbr_conv[0].conv.kernel_size, stride=self.rbr_conv[0].conv.stride, padding=self.rbr_conv[0].conv.padding, dilation=self.rbr_conv[0].conv.dilation, groups=self.rbr_conv[0].conv.groups, bias=True) self.reparam_conv.weight.data = kernel self.reparam_conv.bias.data = bias # Delete un-used branches for para in self.parameters(): para.detach_() self.__delattr__('rbr_conv') self.__delattr__('rbr_scale') if hasattr(self, 'rbr_skip'): self.__delattr__('rbr_skip') self.inference_mode = True def _get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]: """ Method to obtain re-parameterized kernel and bias. Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83 :return: Tuple of (kernel, bias) after fusing branches. """ # get weights and bias of scale branch kernel_scale = 0 bias_scale = 0 if self.rbr_scale is not None: kernel_scale, bias_scale = self._fuse_bn_tensor(self.rbr_scale) # Pad scale branch kernel to match conv branch kernel size. pad = self.kernel_size // 2 kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad]) # get weights and bias of skip branch kernel_identity = 0 bias_identity = 0 if self.rbr_skip is not None: kernel_identity, bias_identity = self._fuse_bn_tensor(self.rbr_skip) # get weights and bias of conv branches kernel_conv = 0 bias_conv = 0 for ix in range(self.num_conv_branches): _kernel, _bias = self._fuse_bn_tensor(self.rbr_conv[ix]) kernel_conv += _kernel bias_conv += _bias kernel_final = kernel_conv + kernel_scale + kernel_identity bias_final = bias_conv + bias_scale + bias_identity return kernel_final, bias_final def _fuse_bn_tensor(self, branch) -> Tuple[torch.Tensor, torch.Tensor]: """ Method to fuse batchnorm layer with preceeding conv layer. Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95 :param branch: :return: Tuple of (kernel, bias) after fusing batchnorm. """ if isinstance(branch, nn.Sequential): kernel = branch.conv.weight running_mean = branch.bn.running_mean running_var = branch.bn.running_var gamma = branch.bn.weight beta = branch.bn.bias eps = branch.bn.eps else: assert isinstance(branch, nn.BatchNorm2d) if not hasattr(self, 'id_tensor'): input_dim = self.in_channels // self.groups kernel_value = torch.zeros((self.in_channels, input_dim, self.kernel_size, self.kernel_size), dtype=branch.weight.dtype, device=branch.weight.device) for i in range(self.in_channels): kernel_value[i, i % input_dim, self.kernel_size // 2, self.kernel_size // 2] = 1 self.id_tensor = kernel_value kernel = self.id_tensor running_mean = branch.running_mean running_var = branch.running_var gamma = branch.weight beta = branch.bias eps = branch.eps std = (running_var + eps).sqrt() t = (gamma / std).reshape(-1, 1, 1, 1) return kernel * t, beta - running_mean * gamma / std def _conv_bn(self, kernel_size: int, padding: int) -> nn.Sequential: """ Helper method to construct conv-batchnorm layers. :param kernel_size: Size of the convolution kernel. :param padding: Zero-padding size. :return: Conv-BN module. """ mod_list = nn.Sequential() mod_list.add_module('conv', nn.Conv2d(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=kernel_size, stride=self.stride, padding=padding, groups=self.groups, bias=False)) mod_list.add_module('bn', nn.BatchNorm2d(num_features=self.out_channels)) return mod_list ================================================ FILE: ultralight/face_detect_utils/detect_face.py ================================================ import cv2 import time import argparse import numpy as np class SCRFD(): def __init__(self, onnxmodel, confThreshold=0.5, nmsThreshold=0.5): self.inpWidth = 640 self.inpHeight = 640 self.confThreshold = confThreshold self.nmsThreshold = nmsThreshold self.net = cv2.dnn.readNet(onnxmodel) self.keep_ratio = True self.fmc = 3 self._feat_stride_fpn = [8, 16, 32] self._num_anchors = 2 def resize_image(self, srcimg): padh, padw, newh, neww = 0, 0, self.inpHeight, self.inpWidth if self.keep_ratio and srcimg.shape[0] != srcimg.shape[1]: hw_scale = srcimg.shape[0] / srcimg.shape[1] if hw_scale > 1: newh, neww = self.inpHeight, int(self.inpWidth / hw_scale) img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA) padw = int((self.inpWidth - neww) * 0.5) img = cv2.copyMakeBorder(img, 0, 0, padw, self.inpWidth - neww - padw, cv2.BORDER_CONSTANT, value=0) # add border else: newh, neww = int(self.inpHeight * hw_scale) + 1, self.inpWidth img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA) padh = int((self.inpHeight - newh) * 0.5) img = cv2.copyMakeBorder(img, padh, self.inpHeight - newh - padh, 0, 0, cv2.BORDER_CONSTANT, value=0) else: img = cv2.resize(srcimg, (self.inpWidth, self.inpHeight), interpolation=cv2.INTER_AREA) return img, newh, neww, padh, padw 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 = x1.clamp(min=0, max=max_shape[1]) y1 = y1.clamp(min=0, max=max_shape[0]) x2 = x2.clamp(min=0, max=max_shape[1]) y2 = y2.clamp(min=0, max=max_shape[0]) return np.stack([x1, y1, x2, y2], axis=-1) def distance2kps(self, points, distance, max_shape=None): preds = [] for i in range(0, distance.shape[1], 2): px = points[:, i % 2] + distance[:, i] py = points[:, i % 2 + 1] + distance[:, i + 1] if max_shape is not None: px = px.clamp(min=0, max=max_shape[1]) py = py.clamp(min=0, max=max_shape[0]) preds.append(px) preds.append(py) return np.stack(preds, axis=-1) def detect(self, srcimg): t1 = time.time() img, newh, neww, padh, padw = self.resize_image(srcimg) blob = cv2.dnn.blobFromImage(img, 1.0 / 128, (self.inpWidth, self.inpHeight), (127.5, 127.5, 127.5), swapRB=True) # Sets the input to the network self.net.setInput(blob) # Runs the forward pass to get output of the output layers outs = self.net.forward(self.net.getUnconnectedOutLayersNames()) # inference output scores_list, bboxes_list, kpss_list = [], [], [] for idx, stride in enumerate(self._feat_stride_fpn): scores = outs[idx][0] bbox_preds = outs[idx + self.fmc * 1][0] * stride kps_preds = outs[idx + self.fmc * 2][0] * stride height = blob.shape[2] // stride width = blob.shape[3] // stride anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32) anchor_centers = (anchor_centers * stride).reshape((-1, 2)) if self._num_anchors > 1: anchor_centers = np.stack([anchor_centers] * self._num_anchors, axis=1).reshape((-1, 2)) pos_inds = np.where(scores >= self.confThreshold)[0] bboxes = self.distance2bbox(anchor_centers, bbox_preds) pos_scores = scores[pos_inds] pos_bboxes = bboxes[pos_inds] scores_list.append(pos_scores) bboxes_list.append(pos_bboxes) kpss = self.distance2kps(anchor_centers, kps_preds) # kpss = kps_preds kpss = kpss.reshape((kpss.shape[0], -1, 2)) pos_kpss = kpss[pos_inds] kpss_list.append(pos_kpss) scores = np.vstack(scores_list).ravel() # bboxes = np.vstack(bboxes_list) / det_scale # kpss = np.vstack(kpss_list) / det_scale bboxes = np.vstack(bboxes_list) kpss = np.vstack(kpss_list) bboxes[:, 2:4] = bboxes[:, 2:4] - bboxes[:, 0:2] ratioh, ratiow = srcimg.shape[0] / newh, srcimg.shape[1] / neww bboxes[:, 0] = (bboxes[:, 0] - padw) * ratiow bboxes[:, 1] = (bboxes[:, 1] - padh) * ratioh bboxes[:, 2] = bboxes[:, 2] * ratiow bboxes[:, 3] = bboxes[:, 3] * ratioh kpss[:, :, 0] = (kpss[:, :, 0] - padw) * ratiow kpss[:, :, 1] = (kpss[:, :, 1] - padh) * ratioh indices = cv2.dnn.NMSBoxes(bboxes.tolist(), scores.tolist(), self.confThreshold, self.nmsThreshold) return bboxes, indices, kpss ================================================ FILE: ultralight/face_detect_utils/get_landmark.py ================================================ import argparse from os import wait3 import numpy as np import cv2 import math import torch import torchvision from .detect_face import SCRFD # from models.pfld_lite import PFLDInference # from models.pfld import PFLDInference from .pfld_mobileone import PFLD_GhostOne as PFLDInference def face_det(img, model): cropped_imgs = [] boxes_list = [] center_list = [] alpha_list = [] height, width = img.shape[:2] bboxes, indices, kps = model.detect(img) for i in indices: x1, y1, x2, y2 = int(bboxes[i, 0]), int(bboxes[i, 1]), int(bboxes[i, 0] + bboxes[i, 2]), int(bboxes[i, 1] + bboxes[i, 3]) p1 = kps[i,0] p2 = kps[i,1] w = x2 - x1 h = y2 - y1 cx = (x2+x1)//2 cy = (y2+y1)//2 wh = np.asarray([w,h]) boxsize = int(np.max(wh)*1.05) size = boxsize xy = np.asarray((cx - size // 2, cy - size//2), dtype=np.int32) x1, y1 = xy x2, y2 = xy + size height, width, _ = img.shape dx = max(0, -x1) dy = max(0, -y1) x1 = max(0, x1) y1 = max(0, y1) edx = max(0, x2 - width) edy = max(0, y2 - height) x2 = min(width, x2) y2 = min(height, y2) cropped = img[y1:y2, x1:x2] if (dx > 0 or dy > 0 or edx >0 or edy > 0): cropped = cv2.copyMakeBorder(cropped, dy, edy, dx, edx, cv2.BORDER_CONSTANT, 0) y1 = y1-dy x1 = x1-dx center = (int((x2-x1)//2), int((y2-y1)//2)) boxes_list.append([x1,y1,x2,y2]) center_list.append(center) alpha = math.atan2(p2[1]-p1[1], p2[0]-p1[0]) * 180 / math.pi rot_mat = cv2.getRotationMatrix2D(center, alpha, 1) # img_rotated_by_alpha = cv2.warpAffine(cropped, rot_mat, # (cropped.shape[1], cropped.shape[0])) # cropped_imgs.append(img_rotated_by_alpha) cropped_imgs.append(cropped) alpha_list.append(alpha) break return cropped_imgs, boxes_list, center_list, alpha_list class Landmark: def __init__(self): with open('./face_detect_utils/mean_face.txt', 'r') as f_mean_face: mean_face = f_mean_face.read() self.mean_face = np.asarray(mean_face.split(' '), dtype=np.float32) self.det_net = SCRFD('./face_detect_utils/scrfd_2.5g_kps.onnx', confThreshold=0.1, nmsThreshold=0.5) checkpoint = torch.load('./face_detect_utils/checkpoint_epoch_335.pth.tar') self.pfld_backbone = PFLDInference().cuda() self.pfld_backbone.load_state_dict(checkpoint['pfld_backbone']) self.pfld_backbone.eval() def detect(self, img_path): img = cv2.imread(img_path) img_ori = img.copy() h,w = img_ori.shape[:2] cropped_imgs, boxes_list, center_list, alpha_list = face_det(img, self.det_net) cropped = cropped_imgs[0] # cv2.imshow("cropped", cropped) h,w = cropped.shape[:2] x1, y1, x2, y2 = boxes_list[0] transform = torchvision.transforms.Compose( [torchvision.transforms.ToTensor()]) input = cv2.resize(cropped, (192, 192)) input = np.asarray(input, dtype=np.float32) / 255.0 input = input.transpose(2,0,1) input = torch.from_numpy(input)[None] input = input.cuda() # print(input) # asd # input = transform(input).unsqueeze(0).cuda() landmarks = self.pfld_backbone(input) pre_landmark = landmarks[0] pre_landmark = pre_landmark.cpu().detach().numpy() pre_landmark = pre_landmark + self.mean_face pre_landmark = pre_landmark.reshape(-1, 2) pre_landmark[:,0] *= w pre_landmark[:,1] *= h pre_landmark = pre_landmark.astype(np.int32) return pre_landmark, x1, y1 ================================================ FILE: ultralight/face_detect_utils/mean_face.txt ================================================ 0.07823661 0.22561455 0.07775262 0.28360514 0.07767719 0.34125846 0.07962388 0.39897107 0.0852785 0.45675877 0.0948296 0.51397081 0.10821601 0.57026014 0.12654839 0.624922 0.15092454 0.67696214 0.18117697 0.72501614 0.21636663 0.76926954 0.25593645 0.80971635 0.29881339 0.84644004 0.34358275 0.88036131 0.39073567 0.9098104 0.44371907 0.92960952 0.50159897 0.93640387 0.55961423 0.92988122 0.61295041 0.91019805 0.66039048 0.88071415 0.70537286 0.84665546 0.74849443 0.80988414 0.78839783 0.7694265 0.82393673 0.72515046 0.85446429 0.67704921 0.87904849 0.62493643 0.89749134 0.57016489 0.91091031 0.51374694 0.92042848 0.45636828 0.92597962 0.39841539 0.9278124 0.34047619 0.92761708 0.28261676 0.9269087 0.22448013 0.18333619 0.14799449 0.23011323 0.10008151 0.29151757 0.09043833 0.35420108 0.09683785 0.41239033 0.11419665 0.40433308 0.15312312 0.34988543 0.14151342 0.29250138 0.13487604 0.23594365 0.13835329 0.81760045 0.14719518 0.77032645 0.09917382 0.70833021 0.08949374 0.64518572 0.09615324 0.58674485 0.11388598 0.5950631 0.15290701 0.64974596 0.14094804 0.70746059 0.13400588 0.76445238 0.13742621 0.49899366 0.44126486 0.49926835 0.36584839 0.49954545 0.29154656 0.4997629 0.21590981 0.44175713 0.23971188 0.41748147 0.40751618 0.38250881 0.46847228 0.41250806 0.50733037 0.44348765 0.49384309 0.49968962 0.52071968 0.55587692 0.49389505 0.58745985 0.50735921 0.61725701 0.46843178 0.58200092 0.40750888 0.5582543 0.23969454 0.24420926 0.24232664 0.27853367 0.22262796 0.32139181 0.21649367 0.36352965 0.22694906 0.39399409 0.2539187 0.35570468 0.2607508 0.31553149 0.26475557 0.27670887 0.25815946 0.32108413 0.2401999 0.32039199 0.24018672 0.28731028 0.24066018 0.35345833 0.23970116 0.75291827 0.24135213 0.71813078 0.22180503 0.67499031 0.21574654 0.63271 0.2262944 0.60232031 0.25348639 0.64072191 0.26024382 0.68106141 0.26409874 0.72013163 0.25732273 0.67941506 0.23945947 0.67611672 0.23945686 0.64289896 0.23914819 0.70935429 0.23975898 0.3542491 0.64762846 0.39788583 0.61425828 0.45907503 0.59267412 0.49816209 0.60023844 0.53725534 0.5924438 0.59894311 0.61360978 0.64317025 0.6471479 0.61072743 0.69408156 0.5629294 0.72856417 0.49846957 0.74046476 0.43417159 0.72868171 0.38664602 0.69431213 0.37477622 0.64829514 0.41275304 0.63913694 0.49825266 0.63758627 0.5843274 0.63877947 0.62280633 0.64783215 0.58603199 0.66379551 0.49850656 0.6787626 0.4114343 0.66389504 ================================================ FILE: ultralight/face_detect_utils/pfld_mobileone.py ================================================ #!/usr/bin/env python3 # -*- coding:utf-8 -*- import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import Module, AvgPool2d, Linear from .base_module import MobileOneBlock, GhostOneBottleneck, Conv_Block class PFLD_GhostOne(Module): def __init__(self, width_factor=0.5, input_size=192, landmark_number=110, inference_mode=False): super(PFLD_GhostOne, self).__init__() self.inference_mode = inference_mode self.num_conv_branches = 6 self.conv1 = MobileOneBlock(in_channels=3, out_channels=int(64 * width_factor), kernel_size=3, stride=2, padding=1, groups=1, inference_mode=self.inference_mode, use_se=False, num_conv_branches=self.num_conv_branches, is_linear=False) self.conv2 = MobileOneBlock(in_channels=int(64 * width_factor), out_channels=int(64 * width_factor), kernel_size=3, stride=1, padding=1, groups=int(64 * width_factor), inference_mode=self.inference_mode, use_se=False, num_conv_branches=self.num_conv_branches, is_linear=False) # def _make_bottlenecks(self): # modules = OrderedDict() # stage_name = "Bottlenecks" # # First module is the only one with t=1 # bottleneck1 = self._make_stage(inplanes=self.c[0], outplanes=self.c[1], n=self.n[1], stride=self.s[1], t=1, # stage=0) # modules[stage_name + "_0"] = bottleneck1 # # add more LinearBottleneck depending on number of repeats # for i in range(1, len(self.c) - 1): # name = stage_name + "_{}".format(i) # module = self._make_stage(inplanes=self.c[i], outplanes=self.c[i + 1], n=self.n[i + 1], # stride=self.s[i + 1], # t=self.t, stage=i) # modules[name] = module # return nn.Sequential(modules) self.conv3_1 = GhostOneBottleneck(int(64 * width_factor), int(96 * width_factor), int(80 * width_factor), stride=2, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv3_2 = GhostOneBottleneck(int(80 * width_factor), int(120 * width_factor), int(80 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv3_3 = GhostOneBottleneck(int(80 * width_factor), int(120 * width_factor), int(80 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv4_1 = GhostOneBottleneck(int(80 * width_factor), int(200 * width_factor), int(96 * width_factor), stride=2, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv4_2 = GhostOneBottleneck(int(96 * width_factor), int(240 * width_factor), int(96 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv4_3 = GhostOneBottleneck(int(96 * width_factor), int(240 * width_factor), int(96 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv5_1 = GhostOneBottleneck(int(96 * width_factor), int(336 * width_factor), int(144 * width_factor), stride=2, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv5_2 = GhostOneBottleneck(int(144 * width_factor), int(504 * width_factor), int(144 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv5_3 = GhostOneBottleneck(int(144 * width_factor), int(504 * width_factor), int(144 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv5_4 = GhostOneBottleneck(int(144 * width_factor), int(504 * width_factor), int(144 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv6 = GhostOneBottleneck(int(144 * width_factor), int(216 * width_factor), int(16 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv7 = MobileOneBlock(in_channels=int(16 * width_factor), out_channels=int(32 * width_factor), kernel_size=3, stride=1, padding=1, groups=1, inference_mode=self.inference_mode, use_se=False, num_conv_branches=self.num_conv_branches, is_linear=False) self.conv8 = Conv_Block(int(32 * width_factor), int(128 * width_factor), input_size // 16, 1, 0, has_bn=False) self.avg_pool1 = AvgPool2d(input_size // 2) self.avg_pool2 = AvgPool2d(input_size // 4) self.avg_pool3 = AvgPool2d(input_size // 8) self.avg_pool4 = AvgPool2d(input_size // 16) self.conv_out = nn.Conv2d(int(512*width_factor), landmark_number*2, 1, 1, 0) # 这个大小需要改 self.localization = nn.Sequential( nn.Conv2d(1, 8, kernel_size=7), nn.MaxPool2d(2, stride=2), nn.ReLU(True), nn.Conv2d(8, 10, kernel_size=5), nn.MaxPool2d(2, stride=2), nn.ReLU(True) ) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x1 = self.avg_pool1(x) # x1 = x1.view(x1.size(0), -1) x = self.conv3_1(x) x = self.conv3_2(x) x = self.conv3_3(x) x2 = self.avg_pool2(x) # x2 = x2.view(x2.size(0), -1) x = self.conv4_1(x) x = self.conv4_2(x) x = self.conv4_3(x) x3 = self.avg_pool3(x) # x3 = x3.view(x3.size(0), -1) x = self.conv5_1(x) x = self.conv5_2(x) x = self.conv5_3(x) x = self.conv5_4(x) x4 = self.avg_pool4(x) # x4 = x4.view(x4.size(0), -1) x = self.conv6(x) x = self.conv7(x) x5 = self.conv8(x) # x5 = x5.view(x5.size(0), -1) multi_scale = torch.cat([x1, x2, x3, x4, x5], 1) landmarks = self.conv_out(multi_scale) landmarks = landmarks.view(landmarks.size(0), -1) return landmarks class PFLD_GhostOne_WithSTN(Module): def __init__(self, width_factor=0.5, input_size=112, landmark_number=110, inference_mode=False): super(PFLD_GhostOne, self).__init__() self.inference_mode = inference_mode self.num_conv_branches = 6 self.conv1 = MobileOneBlock(in_channels=3, out_channels=int(64 * width_factor), kernel_size=3, stride=2, padding=1, groups=1, inference_mode=self.inference_mode, use_se=False, num_conv_branches=self.num_conv_branches, is_linear=False) self.conv2 = MobileOneBlock(in_channels=int(64 * width_factor), out_channels=int(64 * width_factor), kernel_size=3, stride=1, padding=1, groups=int(64 * width_factor), inference_mode=self.inference_mode, use_se=False, num_conv_branches=self.num_conv_branches, is_linear=False) # def _make_bottlenecks(self): # modules = OrderedDict() # stage_name = "Bottlenecks" # # First module is the only one with t=1 # bottleneck1 = self._make_stage(inplanes=self.c[0], outplanes=self.c[1], n=self.n[1], stride=self.s[1], t=1, # stage=0) # modules[stage_name + "_0"] = bottleneck1 # # add more LinearBottleneck depending on number of repeats # for i in range(1, len(self.c) - 1): # name = stage_name + "_{}".format(i) # module = self._make_stage(inplanes=self.c[i], outplanes=self.c[i + 1], n=self.n[i + 1], # stride=self.s[i + 1], # t=self.t, stage=i) # modules[name] = module # return nn.Sequential(modules) self.conv3_1 = GhostOneBottleneck(int(64 * width_factor), int(96 * width_factor), int(80 * width_factor), stride=2, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv3_2 = GhostOneBottleneck(int(80 * width_factor), int(120 * width_factor), int(80 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv3_3 = GhostOneBottleneck(int(80 * width_factor), int(120 * width_factor), int(80 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv4_1 = GhostOneBottleneck(int(80 * width_factor), int(200 * width_factor), int(96 * width_factor), stride=2, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv4_2 = GhostOneBottleneck(int(96 * width_factor), int(240 * width_factor), int(96 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv4_3 = GhostOneBottleneck(int(96 * width_factor), int(240 * width_factor), int(96 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv5_1 = GhostOneBottleneck(int(96 * width_factor), int(336 * width_factor), int(144 * width_factor), stride=2, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv5_2 = GhostOneBottleneck(int(144 * width_factor), int(504 * width_factor), int(144 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv5_3 = GhostOneBottleneck(int(144 * width_factor), int(504 * width_factor), int(144 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv5_4 = GhostOneBottleneck(int(144 * width_factor), int(504 * width_factor), int(144 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv6 = GhostOneBottleneck(int(144 * width_factor), int(216 * width_factor), int(16 * width_factor), stride=1, inference_mode=self.inference_mode, num_conv_branches=self.num_conv_branches) self.conv7 = MobileOneBlock(in_channels=int(16 * width_factor), out_channels=int(32 * width_factor), kernel_size=3, stride=1, padding=1, groups=1, inference_mode=self.inference_mode, use_se=False, num_conv_branches=self.num_conv_branches, is_linear=False) self.conv8 = Conv_Block(int(32 * width_factor), int(128 * width_factor), input_size // 16, 1, 0, has_bn=False) self.avg_pool1 = AvgPool2d(input_size // 2) self.avg_pool2 = AvgPool2d(input_size // 4) self.avg_pool3 = AvgPool2d(input_size // 8) self.avg_pool4 = AvgPool2d(input_size // 16) self.conv_out = nn.Conv2d(int(512*width_factor), landmark_number*2, 1, 1, 0) # 这个大小需要改 def forward(self, x): x = self.conv1(x) x = self.conv2(x) x1 = self.avg_pool1(x) # x1 = x1.view(x1.size(0), -1) x = self.conv3_1(x) x = self.conv3_2(x) x = self.conv3_3(x) x2 = self.avg_pool2(x) # x2 = x2.view(x2.size(0), -1) x = self.conv4_1(x) x = self.conv4_2(x) x = self.conv4_3(x) x3 = self.avg_pool3(x) # x3 = x3.view(x3.size(0), -1) x = self.conv5_1(x) x = self.conv5_2(x) x = self.conv5_3(x) x = self.conv5_4(x) x4 = self.avg_pool4(x) # x4 = x4.view(x4.size(0), -1) x = self.conv6(x) x = self.conv7(x) x5 = self.conv8(x) # x5 = x5.view(x5.size(0), -1) multi_scale = torch.cat([x1, x2, x3, x4, x5], 1) landmarks = self.conv_out(multi_scale) landmarks = landmarks.view(landmarks.size(0), -1) return landmarks class AuxiliaryNet(Module): def __init__(self, width_factor=1): super(AuxiliaryNet, self).__init__() self.conv1 = Conv_Block(int(64 * width_factor), int(64 * width_factor), 1, 1, 0) self.conv2 = Conv_Block(int(80 * width_factor), int(64 * width_factor), 1, 1, 0) self.conv3 = Conv_Block(int(96 * width_factor), int(64 * width_factor), 1, 1, 0) self.conv4 = Conv_Block(int(144 * width_factor), int(64 * width_factor), 1, 1, 0) self.merge1 = Conv_Block(int(64 * width_factor), int(64 * width_factor), 3, 1, 1) self.merge2 = Conv_Block(int(64 * width_factor), int(64 * width_factor), 3, 1, 1) self.merge3 = Conv_Block(int(64 * width_factor), int(64 * width_factor), 3, 1, 1) self.conv_out = Conv_Block(int(64 * width_factor), 1, 1, 1, 0) def forward(self, out1, out2, out3, out4): output1 = self.conv1(out1) output2 = self.conv2(out2) output3 = self.conv3(out3) output4 = self.conv4(out4) up4 = F.interpolate(output4, size=[output3.size(2), output3.size(3)], mode="nearest") output3 = output3 + up4 output3 = self.merge3(output3) up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode="nearest") output2 = output2 + up3 output2 = self.merge2(output2) up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode="nearest") output1 = output1 + up2 output1 = self.merge1(output1) output1 = self.conv_out(output1) return output1 if __name__ == "__main__": import time import onnx import numpy as np from thop import profile INPUT_SIZE = 256 net = PFLD_GhostOne(0.5, INPUT_SIZE, 110, True) torch_in = torch.zeros([1, 3, INPUT_SIZE, INPUT_SIZE]) flops, params = profile(net, (torch_in,)) print(flops) for i in range(11): t1 = time.time() _ = net(torch_in) t2 = time.time() print(t2-t1) def check_onnx(torch_out, torch_in): onnx_model = onnx.load(onnx_path) onnx.checker.check_model(onnx_model) import onnxruntime ort_session = onnxruntime.InferenceSession(onnx_path) ort_inputs = {ort_session.get_inputs()[0].name: torch_in.cpu().numpy()} ort_outs = ort_session.run(None, ort_inputs) np.testing.assert_allclose(torch_out[0].cpu().numpy(), ort_outs[0][0], rtol=1e-03, atol=1e-05) print("Exported model has been tested with ONNXRuntime, and the result looks good!") source_file = './1.pth' onnx_path = './pfld_mobileone_256.onnx' torch.save(net.state_dict(), source_file) input_size = 256 print("=====> load pytorch checkpoint...") # checkpoint = torch.load(source_file, map_location=torch.device('cpu')) dummy_input = torch.randn(1, 3, input_size, input_size) # input_names = ["input"] # output_names = ["output"] # net.load_state_dict(checkpoint) torch_in = torch.zeros([1,3,input_size,input_size]) with torch.no_grad(): torch_out = net(torch_in) print(torch_out) torch.onnx.export(net, torch_in, onnx_path, input_names=['input'], output_names=['output'], # example_outputs=torch_out, opset_version=11, export_params=True) ================================================ FILE: ultralight/genavatar-bak.py ================================================ import argparse import os import cv2 import torch import numpy as np import torch.nn as nn from torch import optim from tqdm import tqdm from torch.utils.data import DataLoader from unet import Model import pickle # from unet2 import Model # from unet_att import Model import time def osmakedirs(path_list): for path in path_list: os.makedirs(path) if not os.path.exists(path) else None parser = argparse.ArgumentParser(description='Train', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--dataset', type=str, default="") #parser.add_argument('--save_path', type=str, default="") # end with .mp4 please parser.add_argument('--checkpoint', type=str, default="") parser.add_argument('--avatar_id', default='ultralight_avatar1', type=str) args = parser.parse_args() checkpoint = args.checkpoint dataset_dir = args.dataset img_dir = os.path.join(dataset_dir, "full_body_img/") lms_dir = os.path.join(dataset_dir, "landmarks/") avatar_path = f"./results/avatars/{args.avatar_id}" full_imgs_path = f"{avatar_path}/full_imgs" face_imgs_path = f"{avatar_path}/face_imgs" coords_path = f"{avatar_path}/coords.pkl" pth_path = f"{avatar_path}/ultralight.pth" osmakedirs([avatar_path,full_imgs_path,face_imgs_path]) len_img = len(os.listdir(img_dir)) - 1 exm_img = cv2.imread(img_dir+"0.jpg") h, w = exm_img.shape[:2] step_stride = 0 img_idx = 0 coord_list = [] net = Model(6, 'hubert').cuda() net.load_state_dict(torch.load(checkpoint)) net.eval() for i in range(len_img): if img_idx>len_img - 1: step_stride = -1 if img_idx<1: step_stride = 1 img_idx += step_stride img_path = img_dir + str(img_idx)+'.jpg' lms_path = lms_dir + str(img_idx)+'.lms' img = cv2.imread(img_path) lms_list = [] with open(lms_path, "r") as f: lines = f.read().splitlines() for line in lines: arr = line.split(" ") arr = np.array(arr, dtype=np.float32) lms_list.append(arr) lms = np.array(lms_list, dtype=np.int32) xmin = lms[1][0] ymin = lms[52][1] xmax = lms[31][0] width = xmax - xmin ymax = ymin + width crop_img = img[ymin:ymax, xmin:xmax] h, w = crop_img.shape[:2] crop_img = cv2.resize(crop_img, (168, 168), cv2.INTER_AREA) crop_img_ori = crop_img.copy() img_real_ex = crop_img[4:164, 4:164].copy() img_real_ex_ori = img_real_ex.copy() img_masked = cv2.rectangle(img_real_ex_ori,(5,5,150,145),(0,0,0),-1) img_masked = img_masked.transpose(2,0,1).astype(np.float32) img_real_ex = img_real_ex.transpose(2,0,1).astype(np.float32) img_real_ex_T = torch.from_numpy(img_real_ex / 255.0) img_masked_T = torch.from_numpy(img_masked / 255.0) img_concat_T = torch.cat([img_real_ex_T, img_masked_T], axis=0)[None] audio_feat = torch.zeros(1, 32, 32, 32) #print('audio_feat:',audio_feat.shape) audio_feat = audio_feat.cuda() img_concat_T = img_concat_T.cuda() #print('img_concat_T:',img_concat_T.shape) with torch.no_grad(): pred = net(img_concat_T, audio_feat)[0] pred = pred.cpu().numpy().transpose(1,2,0)*255 pred = np.array(pred, dtype=np.uint8) crop_img_ori[4:164, 4:164] = pred crop_img_ori = cv2.resize(crop_img_ori, (w, h)) img[ymin:ymax, xmin:xmax] = crop_img_ori cv2.putText(img, "LiveTalking", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (128,128,128), 1) cv2.imwrite(f"{full_imgs_path}/{img_idx:08d}.png", img) cv2.imwrite(f"{face_imgs_path}/{img_idx:08d}.png", crop_img) coord_list.append((xmin, ymin, xmin+w, ymin+h)) with open(coords_path, 'wb') as f: pickle.dump(coord_list, f) os.system(f"cp {checkpoint} {pth_path}") # ffmpeg -i test_video.mp4 -i test_audio.pcm -c:v libx264 -c:a aac result_test.mp4 ================================================ FILE: ultralight/genavatar.py ================================================ import argparse import os import cv2 import torch import numpy as np import torch.nn as nn from torch import optim from tqdm import tqdm import pickle from glob import glob from face_detect_utils.get_landmark import Landmark # from unet2 import Model # from unet_att import Model import time def osmakedirs(path_list): for path in path_list: os.makedirs(path) if not os.path.exists(path) else None parser = argparse.ArgumentParser(description='Train', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--video_path', default='', type=str) parser.add_argument('--img_size', default=168, type=int) parser.add_argument('--checkpoint', type=str, default="") parser.add_argument('--avatar_id', default='ultralight_avatar1', type=str) args = parser.parse_args() def video2imgs(vid_path, save_path, ext = '.png',cut_frame = 10000000): print(f"即将使用OpenCV将视频: {vid_path} 转换为图片") 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 print("视频转换完成") def read_imgs(img_list): frames = [] print('读取图片到内存...') for img_path in tqdm(img_list): frame = cv2.imread(img_path) frames.append(frame) return frames # ffmpeg -i test_video.mp4 -i test_audio.pcm -c:v libx264 -c:a aac result_test.mp4 if __name__ == "__main__": avatar_path = f"./results/avatars/{args.avatar_id}" full_imgs_path = f"{avatar_path}/full_imgs" face_imgs_path = f"{avatar_path}/face_imgs" coords_path = f"{avatar_path}/coords.pkl" pth_path = f"{avatar_path}/ultralight.pth" osmakedirs([avatar_path,full_imgs_path,face_imgs_path]) print(args) video2imgs(args.video_path, full_imgs_path, ext = 'png') input_img_list = sorted(glob(os.path.join(full_imgs_path, '*.[jpJP][pnPN]*[gG]'))) #frames = read_imgs(input_img_list) #face_det_results = face_detect(frames) coord_list = [] idx = 0 print(f"开始人脸检测") landmark = Landmark() target_size = args.img_size for i in tqdm(range(len(input_img_list))): img = cv2.imread(input_img_list[i]) lms, x1, y1 = landmark.detect(input_img_list[i]) xmin = lms[1][0]+x1 ymin = lms[52][1]+y1 xmax = lms[31][0]+x1 width = xmax - xmin ymax = ymin + width crop_img = img[ymin:ymax, xmin:xmax] h, w = crop_img.shape[:2] crop_img = cv2.resize(crop_img, (target_size, target_size), cv2.INTER_AREA) # cv2.imwrite(f"{full_imgs_path}/{idx:08d}.png", img) cv2.imwrite(f"{face_imgs_path}/{idx:08d}.png", crop_img) coord_list.append((xmin, ymin, xmin+w, ymin+h)) idx = idx + 1 print(f"共检测到{idx}张人脸") print(f"写入数据到坐标文件:{coords_path}") with open(coords_path, 'wb') as f: pickle.dump(coord_list, f) os.system(f"cp {args.checkpoint} {pth_path}") ================================================ FILE: ultralight/unet.py ================================================ import time import math import torch import torch.nn as nn import torch.nn.functional as F class InvertedResidual(nn.Module): def __init__(self, inp, oup, stride, use_res_connect, expand_ratio=6): super(InvertedResidual, self).__init__() self.stride = stride assert stride in [1, 2] self.use_res_connect = use_res_connect self.conv = nn.Sequential( nn.Conv2d(inp, inp * expand_ratio, 1, 1, 0, bias=False), nn.BatchNorm2d(inp * expand_ratio), nn.ReLU(inplace=True), nn.Conv2d(inp * expand_ratio, inp * expand_ratio, 3, stride, 1, groups=inp * expand_ratio, bias=False), nn.BatchNorm2d(inp * expand_ratio), nn.ReLU(inplace=True), nn.Conv2d(inp * expand_ratio, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), ) def forward(self, x): if self.use_res_connect: return x + self.conv(x) else: return self.conv(x) class DoubleConvDW(nn.Module): def __init__(self, in_channels, out_channels, stride=2): super(DoubleConvDW, self).__init__() self.double_conv = nn.Sequential( InvertedResidual(in_channels, out_channels, stride=stride, use_res_connect=False, expand_ratio=2), InvertedResidual(out_channels, out_channels, stride=1, use_res_connect=True, expand_ratio=2) ) def forward(self, x): return self.double_conv(x) class InConvDw(nn.Module): def __init__(self, in_channels, out_channels): super(InConvDw, self).__init__() self.inconv = nn.Sequential( InvertedResidual(in_channels, out_channels, stride=1, use_res_connect=False, expand_ratio=2) ) def forward(self, x): return self.inconv(x) class Down(nn.Module): def __init__(self, in_channels, out_channels): super(Down, self).__init__() self.maxpool_conv = nn.Sequential( DoubleConvDW(in_channels, out_channels, stride=2) ) def forward(self, x): return self.maxpool_conv(x) class Up(nn.Module): def __init__(self, in_channels, out_channels): super(Up, self).__init__() self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) self.conv = DoubleConvDW(in_channels, out_channels, stride=1) def forward(self, x1, x2): x1 = self.up(x1) diffY = x2.shape[2] - x1.shape[2] diffX = x2.shape[3] - x1.shape[3] x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2]) x = torch.cat([x1, x2], axis=1) return self.conv(x) class OutConv(nn.Module): def __init__(self, in_channels, out_channels): super(OutConv, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) def forward(self, x): return self.conv(x) class AudioConvWenet(nn.Module): def __init__(self): super(AudioConvWenet, self).__init__() # ch = [16, 32, 64, 128, 256] # if you want to run this model on a mobile device, use this. ch = [32, 64, 128, 256, 512] self.conv1 = InvertedResidual(ch[2], ch[3], stride=1, use_res_connect=False, expand_ratio=2) self.conv2 = InvertedResidual(ch[3], ch[3], stride=1, use_res_connect=True, expand_ratio=2) self.conv3 = nn.Conv2d(ch[3], ch[3], kernel_size=3, padding=1, stride=(1,2)) self.bn3 = nn.BatchNorm2d(ch[3]) self.conv4 = InvertedResidual(ch[3], ch[3], stride=1, use_res_connect=True, expand_ratio=2) self.conv5 = nn.Conv2d(ch[3], ch[4], kernel_size=3, padding=3, stride=2) self.bn5 = nn.BatchNorm2d(ch[4]) self.relu = nn.ReLU() self.conv6 = InvertedResidual(ch[4], ch[4], stride=1, use_res_connect=True, expand_ratio=2) self.conv7 = InvertedResidual(ch[4], ch[4], stride=1, use_res_connect=True, expand_ratio=2) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = self.relu(self.bn3(self.conv3(x))) x = self.conv4(x) x = self.relu(self.bn5(self.conv5(x))) x = self.conv6(x) x = self.conv7(x) return x class AudioConvHubert(nn.Module): def __init__(self): super(AudioConvHubert, self).__init__() # ch = [16, 32, 64, 128, 256] # if you want to run this model on a mobile device, use this. ch = [32, 64, 128, 256, 512] self.conv1 = InvertedResidual(16, ch[1], stride=1, use_res_connect=False, expand_ratio=2) self.conv2 = InvertedResidual(ch[1], ch[2], stride=1, use_res_connect=False, expand_ratio=2) self.conv3 = nn.Conv2d(ch[2], ch[3], kernel_size=3, padding=1, stride=(2,2)) self.bn3 = nn.BatchNorm2d(ch[3]) self.conv4 = InvertedResidual(ch[3], ch[3], stride=1, use_res_connect=True, expand_ratio=2) self.conv5 = nn.Conv2d(ch[3], ch[4], kernel_size=3, padding=3, stride=2) self.bn5 = nn.BatchNorm2d(ch[4]) self.relu = nn.ReLU() self.conv6 = InvertedResidual(ch[4], ch[4], stride=1, use_res_connect=True, expand_ratio=2) self.conv7 = InvertedResidual(ch[4], ch[4], stride=1, use_res_connect=True, expand_ratio=2) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = self.relu(self.bn3(self.conv3(x))) x = self.conv4(x) x = self.relu(self.bn5(self.conv5(x))) x = self.conv6(x) x = self.conv7(x) return x class Model(nn.Module): def __init__(self,n_channels=6, mode='wenet'): super(Model, self).__init__() self.n_channels = n_channels #BGR # ch = [16, 32, 64, 128, 256] # if you want to run this model on a mobile device, use this. ch = [32, 64, 128, 256, 512] if mode=='hubert': self.audio_model = AudioConvHubert() if mode=='wenet': self.audio_model = AudioConvWenet() self.fuse_conv = nn.Sequential( DoubleConvDW(ch[4]*2, ch[4], stride=1), DoubleConvDW(ch[4], ch[3], stride=1) ) self.inc = InConvDw(n_channels, ch[0]) self.down1 = Down(ch[0], ch[1]) self.down2 = Down(ch[1], ch[2]) self.down3 = Down(ch[2], ch[3]) self.down4 = Down(ch[3], ch[4]) self.up1 = Up(ch[4], ch[3]//2) self.up2 = Up(ch[3], ch[2]//2) self.up3 = Up(ch[2], ch[1]//2) self.up4 = Up(ch[1], ch[0]) self.outc = OutConv(ch[0], 3) def forward(self, x, audio_feat): x1 = self.inc(x) x2 = self.down1(x1) x3 = self.down2(x2) x4 = self.down3(x3) x5 = self.down4(x4) audio_feat = self.audio_model(audio_feat) x5 = torch.cat([x5, audio_feat], axis=1) x5 = self.fuse_conv(x5) x = self.up1(x5, x4) x = self.up2(x, x3) x = self.up3(x, x2) x = self.up4(x, x1) out = self.outc(x) out = F.sigmoid(out) return out if __name__ == '__main__': import time import copy import onnx import numpy as np onnx_path = "./unet.onnx" from thop import profile, clever_format def reparameterize_model(model: torch.nn.Module) -> torch.nn.Module: """ Method returns a model where a multi-branched structure used in training is re-parameterized into a single branch for inference. :param model: MobileOne model in train mode. :return: MobileOne model in inference mode. """ # Avoid editing original graph model = copy.deepcopy(model) for module in model.modules(): if hasattr(module, 'reparameterize'): module.reparameterize() return model device = torch.device("cuda") def check_onnx(torch_out, torch_in, audio): onnx_model = onnx.load(onnx_path) onnx.checker.check_model(onnx_model) import onnxruntime providers = ["CUDAExecutionProvider"] ort_session = onnxruntime.InferenceSession(onnx_path, providers=providers) print(ort_session.get_providers()) ort_inputs = {ort_session.get_inputs()[0].name: torch_in.cpu().numpy(), ort_session.get_inputs()[1].name: audio.cpu().numpy()} ort_outs = ort_session.run(None, ort_inputs) np.testing.assert_allclose(torch_out[0].cpu().numpy(), ort_outs[0][0], rtol=1e-03, atol=1e-05) print("Exported model has been tested with ONNXRuntime, and the result looks good!") net = Model(6).eval().to(device) img = torch.zeros([1, 6, 160, 160]).to(device) audio = torch.zeros([1, 16, 32, 32]).to(device) # net = reparameterize_model(net) flops, params = profile(net, (img,audio)) macs, params = clever_format([flops, params], "%3f") print(macs, params) # dynamic_axes= {'input':[2, 3], 'output':[2, 3]} input_dict = {"input": img, "audio": audio} with torch.no_grad(): torch_out = net(img, audio) print(torch_out.shape) torch.onnx.export(net, (img, audio), onnx_path, input_names=['input', "audio"], output_names=['output'], # dynamic_axes=dynamic_axes, # example_outputs=torch_out, opset_version=11, export_params=True) check_onnx(torch_out, img, audio) # img = torch.zeros([1, 6, 160, 160]).to(device) # audio = torch.zeros([1, 16, 32, 32]).to(device) # with torch.no_grad(): # for i in range(100000): # t1 = time.time() # out = net(img, audio) # t2 = time.time() # # print(out.shape) # print('time cost::', t2-t1) # torch.save(net.state_dict(), '1.pth') ================================================ FILE: wav2lip/audio.py ================================================ import librosa import librosa.filters import numpy as np # import tensorflow as tf from scipy import signal from scipy.io import wavfile from .hparams import hparams as hp def load_wav(path, sr): return librosa.core.load(path, sr=sr)[0] def save_wav(wav, path, sr): wav *= 32767 / max(0.01, np.max(np.abs(wav))) #proposed by @dsmiller wavfile.write(path, sr, wav.astype(np.int16)) def save_wavenet_wav(wav, path, sr): librosa.output.write_wav(path, wav, sr=sr) def preemphasis(wav, k, preemphasize=True): if preemphasize: return signal.lfilter([1, -k], [1], wav) return wav def inv_preemphasis(wav, k, inv_preemphasize=True): if inv_preemphasize: return signal.lfilter([1], [1, -k], wav) return wav def get_hop_size(): hop_size = hp.hop_size if hop_size is None: assert hp.frame_shift_ms is not None hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate) return hop_size def linearspectrogram(wav): D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) S = _amp_to_db(np.abs(D)) - hp.ref_level_db if hp.signal_normalization: return _normalize(S) return S def melspectrogram(wav): D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db if hp.signal_normalization: return _normalize(S) return S def _lws_processor(): import lws return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech") def _stft(y): if hp.use_lws: return _lws_processor(hp).stft(y).T else: return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size) ########################################################## #Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) def num_frames(length, fsize, fshift): """Compute number of time frames of spectrogram """ pad = (fsize - fshift) if length % fshift == 0: M = (length + pad * 2 - fsize) // fshift + 1 else: M = (length + pad * 2 - fsize) // fshift + 2 return M def pad_lr(x, fsize, fshift): """Compute left and right padding """ M = num_frames(len(x), fsize, fshift) pad = (fsize - fshift) T = len(x) + 2 * pad r = (M - 1) * fshift + fsize - T return pad, pad + r ########################################################## #Librosa correct padding def librosa_pad_lr(x, fsize, fshift): return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] # Conversions _mel_basis = None def _linear_to_mel(spectogram): global _mel_basis if _mel_basis is None: _mel_basis = _build_mel_basis() return np.dot(_mel_basis, spectogram) def _build_mel_basis(): assert hp.fmax <= hp.sample_rate // 2 return librosa.filters.mel(sr=float(hp.sample_rate), n_fft=hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin, fmax=hp.fmax) def _amp_to_db(x): min_level = np.exp(hp.min_level_db / 20 * np.log(10)) return 20 * np.log10(np.maximum(min_level, x)) def _db_to_amp(x): return np.power(10.0, (x) * 0.05) def _normalize(S): if hp.allow_clipping_in_normalization: if hp.symmetric_mels: return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value, -hp.max_abs_value, hp.max_abs_value) else: return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value) assert S.max() <= 0 and S.min() - hp.min_level_db >= 0 if hp.symmetric_mels: return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value else: return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)) def _denormalize(D): if hp.allow_clipping_in_normalization: if hp.symmetric_mels: return (((np.clip(D, -hp.max_abs_value, hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db) else: return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) if hp.symmetric_mels: return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db) else: return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) ================================================ FILE: wav2lip/face_detection/README.md ================================================ 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. ================================================ FILE: wav2lip/face_detection/__init__.py ================================================ # -*- coding: utf-8 -*- __author__ = """Adrian Bulat""" __email__ = 'adrian.bulat@nottingham.ac.uk' __version__ = '1.0.1' from .api import FaceAlignment, LandmarksType, NetworkSize ================================================ FILE: wav2lip/face_detection/api.py ================================================ 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 ROOT = os.path.dirname(os.path.abspath(__file__)) 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 # 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 ================================================ FILE: wav2lip/face_detection/detection/__init__.py ================================================ from .core import FaceDetector ================================================ FILE: wav2lip/face_detection/detection/core.py ================================================ 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 ================================================ FILE: wav2lip/face_detection/detection/sfd/__init__.py ================================================ from .sfd_detector import SFDDetector as FaceDetector ================================================ FILE: wav2lip/face_detection/detection/sfd/bbox.py ================================================ 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 ================================================ FILE: wav2lip/face_detection/detection/sfd/detect.py ================================================ 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) 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[:, 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]) ================================================ FILE: wav2lip/face_detection/detection/sfd/net_s3fd.py ================================================ 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] ================================================ FILE: wav2lip/face_detection/detection/sfd/sfd_detector.py ================================================ 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 ================================================ FILE: wav2lip/face_detection/models.py ================================================ 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 ================================================ FILE: wav2lip/face_detection/utils.py ================================================ 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 ================================================ FILE: wav2lip/genavatar.py ================================================ from os import listdir, path import numpy as np import scipy, cv2, os, sys, argparse import json, subprocess, random, string from tqdm import tqdm from glob import glob import torch import pickle import face_detection parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models') parser.add_argument('--img_size', default=96, type=int) parser.add_argument('--avatar_id', default='wav2lip_avatar1', type=str) parser.add_argument('--video_path', default='', type=str) parser.add_argument('--nosmooth', default=False, action='store_true', help='Prevent smoothing face detections over a short temporal window') parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0], help='Padding (top, bottom, left, right). Please adjust to include chin at least') parser.add_argument('--face_det_batch_size', type=int, help='Batch size for face detection', default=16) args = parser.parse_args() device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} for inference.'.format(device)) def osmakedirs(path_list): for path in path_list: os.makedirs(path) if not os.path.exists(path) else None 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.putText(frame, "LiveTalking", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (128,128,128), 1) 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_smoothened_boxes(boxes, T): for i in range(len(boxes)): if i + T > len(boxes): window = boxes[len(boxes) - T:] else: window = boxes[i : i + T] boxes[i] = np.mean(window, axis=0) return boxes def face_detect(images): detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, device=device) batch_size = args.face_det_batch_size while 1: predictions = [] try: for i in tqdm(range(0, len(images), batch_size)): predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size]))) except RuntimeError: if batch_size == 1: raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument') batch_size //= 2 print('Recovering from OOM error; New batch size: {}'.format(batch_size)) continue break results = [] pady1, pady2, padx1, padx2 = args.pads for rect, image in zip(predictions, images): if rect is None: cv2.imwrite('temp/faulty_frame.jpg', image) # check this frame where the face was not detected. raise ValueError('Face not detected! Ensure the video contains a face in all the frames.') y1 = max(0, rect[1] - pady1) y2 = min(image.shape[0], rect[3] + pady2) x1 = max(0, rect[0] - padx1) x2 = min(image.shape[1], rect[2] + padx2) results.append([x1, y1, x2, y2]) boxes = np.array(results) if not args.nosmooth: boxes = get_smoothened_boxes(boxes, T=5) results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)] del detector return results if __name__ == "__main__": avatar_path = f"./results/avatars/{args.avatar_id}" full_imgs_path = f"{avatar_path}/full_imgs" face_imgs_path = f"{avatar_path}/face_imgs" coords_path = f"{avatar_path}/coords.pkl" osmakedirs([avatar_path,full_imgs_path,face_imgs_path]) print(args) #if os.path.isfile(args.video_path): video2imgs(args.video_path, full_imgs_path, ext = 'png') input_img_list = sorted(glob(os.path.join(full_imgs_path, '*.[jpJP][pnPN]*[gG]'))) frames = read_imgs(input_img_list) face_det_results = face_detect(frames) coord_list = [] idx = 0 for frame,coords in face_det_results: #x1, y1, x2, y2 = bbox resized_crop_frame = cv2.resize(frame,(args.img_size, args.img_size)) #,interpolation = cv2.INTER_LANCZOS4) cv2.imwrite(f"{face_imgs_path}/{idx:08d}.png", resized_crop_frame) coord_list.append(coords) idx = idx + 1 with open(coords_path, 'wb') as f: pickle.dump(coord_list, f) ================================================ FILE: wav2lip/hparams.py ================================================ from glob import glob import os def get_image_list(data_root, split): filelist = [] with open('filelists/{}.txt'.format(split)) as f: for line in f: line = line.strip() if ' ' in line: line = line.split()[0] filelist.append(os.path.join(data_root, line)) return filelist class HParams: def __init__(self, **kwargs): self.data = {} for key, value in kwargs.items(): self.data[key] = value def __getattr__(self, key): if key not in self.data: raise AttributeError("'HParams' object has no attribute %s" % key) return self.data[key] def set_hparam(self, key, value): self.data[key] = value # Default hyperparameters hparams = HParams( num_mels=80, # Number of mel-spectrogram channels and local conditioning dimensionality # network rescale=True, # Whether to rescale audio prior to preprocessing rescaling_max=0.9, # Rescaling value # Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction # It"s preferred to set True to use with https://github.com/r9y9/wavenet_vocoder # Does not work if n_ffit is not multiple of hop_size!! use_lws=False, n_fft=800, # Extra window size is filled with 0 paddings to match this parameter hop_size=200, # For 16000Hz, 200 = 12.5 ms (0.0125 * sample_rate) win_size=800, # For 16000Hz, 800 = 50 ms (If None, win_size = n_fft) (0.05 * sample_rate) sample_rate=16000, # 16000Hz (corresponding to librispeech) (sox --i ) frame_shift_ms=None, # Can replace hop_size parameter. (Recommended: 12.5) # Mel and Linear spectrograms normalization/scaling and clipping signal_normalization=True, # Whether to normalize mel spectrograms to some predefined range (following below parameters) allow_clipping_in_normalization=True, # Only relevant if mel_normalization = True symmetric_mels=True, # Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2, # faster and cleaner convergence) max_abs_value=4., # max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not # be too big to avoid gradient explosion, # not too small for fast convergence) # Contribution by @begeekmyfriend # Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude # levels. Also allows for better G&L phase reconstruction) preemphasize=True, # whether to apply filter preemphasis=0.97, # filter coefficient. # Limits min_level_db=-100, ref_level_db=20, fmin=55, # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To # test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) fmax=7600, # To be increased/reduced depending on data. ###################### Our training parameters ################################# img_size=96, fps=25, batch_size=16, initial_learning_rate=1e-4, nepochs=200000000000000000, ### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs num_workers=16, checkpoint_interval=3000, eval_interval=3000, save_optimizer_state=True, syncnet_wt=0.0, # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence. syncnet_batch_size=64, syncnet_lr=1e-4, syncnet_eval_interval=10000, syncnet_checkpoint_interval=10000, disc_wt=0.07, disc_initial_learning_rate=1e-4, ) def hparams_debug_string(): values = hparams.values() hp = [" %s: %s" % (name, values[name]) for name in sorted(values) if name != "sentences"] return "Hyperparameters:\n" + "\n".join(hp) ================================================ FILE: web/asr/index.html ================================================ 语音识别
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选择录音模式:
  

选择asr模型模式:
     

逆文本标准化(ITN):
  

热词设置(一行一个关键字,空格隔开权重,如"阿里巴巴 20"):

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请点击开始
================================================ FILE: web/asr/main.js ================================================ /** * Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights * Reserved. MIT License (https://opensource.org/licenses/MIT) */ /* 2022-2023 by zhaoming,mali aihealthx.com */ // 连接; 定义socket连接类对象与语音对象 var wsconnecter = new WebSocketConnectMethod({msgHandle:getJsonMessage,stateHandle:getConnState}); var audioBlob; // 录音; 定义录音对象,wav格式 var rec = Recorder({ type:"pcm", bitRate:16, sampleRate:16000, onProcess:recProcess }); var sampleBuf=new Int16Array(); // 定义按钮响应事件 var btnStart = document.getElementById('btnStart'); btnStart.onclick = record; var btnStop = document.getElementById('btnStop'); btnStop.onclick = stop; btnStop.disabled = true; btnStart.disabled = true; btnConnect= document.getElementById('btnConnect'); btnConnect.onclick = start; var awsslink= document.getElementById('wsslink'); var rec_text=""; // for online rec asr result var offline_text=""; // for offline rec asr result var info_div = document.getElementById('info_div'); var upfile = document.getElementById('upfile'); var isfilemode=false; // if it is in file mode var file_ext=""; var file_sample_rate=16000; //for wav file sample rate var file_data_array; // array to save file data var totalsend=0; // var now_ipaddress=window.location.href; // now_ipaddress=now_ipaddress.replace("https://","wss://"); // now_ipaddress=now_ipaddress.replace("static/index.html",""); // var localport=window.location.port; // now_ipaddress=now_ipaddress.replace(localport,"10095"); // document.getElementById('wssip').value=now_ipaddress; addresschange(); function addresschange() { var Uri = document.getElementById('wssip').value; document.getElementById('info_wslink').innerHTML="点此处手工授权(IOS手机)"; Uri=Uri.replace(/wss/g,"https"); console.log("addresschange uri=",Uri); awsslink.onclick=function(){ window.open(Uri, '_blank'); } } upfile.onclick=function() { btnStart.disabled = true; btnStop.disabled = true; btnConnect.disabled=false; } // from https://github.com/xiangyuecn/Recorder/tree/master var readWavInfo=function(bytes){ //读取wav文件头,统一成44字节的头 if(bytes.byteLength<44){ return null; }; var wavView=bytes; var eq=function(p,s){ for(var i=0;i=chunk_size){ sendBuf=sampleBuf.slice(0,chunk_size); totalsend=totalsend+sampleBuf.length; sampleBuf=sampleBuf.slice(chunk_size,sampleBuf.length); wsconnecter.wsSend(sendBuf); } stop(); } function on_recoder_mode_change() { var item = null; var obj = document.getElementsByName("recoder_mode"); for (var i = 0; i < obj.length; i++) { //遍历Radio if (obj[i].checked) { item = obj[i].value; break; } } if(item=="mic") { document.getElementById("mic_mode_div").style.display = 'block'; document.getElementById("rec_mode_div").style.display = 'none'; btnStart.disabled = true; btnStop.disabled = true; btnConnect.disabled=false; isfilemode=false; } else { document.getElementById("mic_mode_div").style.display = 'none'; document.getElementById("rec_mode_div").style.display = 'block'; btnStart.disabled = true; btnStop.disabled = true; btnConnect.disabled=true; isfilemode=true; info_div.innerHTML='请点击选择文件'; } } function getHotwords(){ var obj = document.getElementById("varHot"); if(typeof(obj) == 'undefined' || obj==null || obj.value.length<=0){ return null; } let val = obj.value.toString(); console.log("hotwords="+val); let items = val.split(/[(\r\n)\r\n]+/); //split by \r\n var jsonresult = {}; const regexNum = /^[0-9]*$/; // test number for (item of items) { let result = item.split(" "); if(result.length>=2 && regexNum.test(result[result.length-1])) { var wordstr=""; for(var i=0;i new Promise((resolve) => setTimeout(resolve, delay)) async function is_speaking() { const response = await fetch('/is_speaking', { body: JSON.stringify({ sessionid: parseInt(parent.document.getElementById('sessionid').value), }), headers: { 'Content-Type': 'application/json' }, method: 'POST' }); const data = await response.json(); console.log('is_speaking res:',data) return data.data } async function waitSpeakingEnd() { rec.stop() //关闭录音 for(let i=0;i<10;i++) { //等待数字人开始讲话,最长等待10s bspeak = await is_speaking() if(bspeak) { break } await sleep(1000) } while(true) { //等待数字人讲话结束 bspeak = await is_speaking() if(!bspeak) { break } await sleep(1000) } await sleep(2000) rec.start() } // 语音识别结果; 对jsonMsg数据解析,将识别结果附加到编辑框中 function getJsonMessage( jsonMsg ) { //console.log(jsonMsg); console.log( "message: " + JSON.parse(jsonMsg.data)['text'] ); var rectxt=""+JSON.parse(jsonMsg.data)['text']; var asrmodel=JSON.parse(jsonMsg.data)['mode']; var is_final=JSON.parse(jsonMsg.data)['is_final']; var timestamp=JSON.parse(jsonMsg.data)['timestamp']; if(asrmodel=="2pass-offline" || asrmodel=="offline") { offline_text=offline_text+rectxt.replace(/ +/g,"")+'\n'; //handleWithTimestamp(rectxt,timestamp); //rectxt; //.replace(/ +/g,""); rec_text=offline_text; fetch('/human', { body: JSON.stringify({ text: rectxt.replace(/ +/g,""), type: 'chat', sessionid:parseInt(parent.document.getElementById('sessionid').value), }), headers: { 'Content-Type': 'application/json' }, method: 'POST' }); waitSpeakingEnd(); } else { rec_text=rec_text+rectxt; //.replace(/ +/g,""); } var varArea=document.getElementById('varArea'); varArea.value=rec_text; console.log( "offline_text: " + asrmodel+","+offline_text); console.log( "rec_text: " + rec_text); if (isfilemode==true && is_final==true){ console.log("call stop ws!"); play_file(); wsconnecter.wsStop(); info_div.innerHTML="请点击连接"; btnStart.disabled = true; btnStop.disabled = true; btnConnect.disabled=false; } } // 连接状态响应 function getConnState( connState ) { if ( connState === 0 ) { //on open info_div.innerHTML='连接成功!请点击开始'; if (isfilemode==true){ info_div.innerHTML='请耐心等待,大文件等待时间更长'; start_file_send(); } else { btnStart.disabled = false; btnStop.disabled = true; btnConnect.disabled=true; } } else if ( connState === 1 ) { //stop(); } else if ( connState === 2 ) { stop(); console.log( 'connecttion error' ); alert("连接地址"+document.getElementById('wssip').value+"失败,请检查asr地址和端口。或试试界面上手动授权,再连接。"); btnStart.disabled = true; btnStop.disabled = true; btnConnect.disabled=false; info_div.innerHTML='请点击连接'; } } function record() { rec.open( function(){ rec.start(); console.log("开始"); btnStart.disabled = true; btnStop.disabled = false; btnConnect.disabled=true; }); } // 识别启动、停止、清空操作 function start() { // 清除显示 clear(); //控件状态更新 console.log("isfilemode"+isfilemode); //启动连接 var ret=wsconnecter.wsStart(); // 1 is ok, 0 is error if(ret==1){ info_div.innerHTML="正在连接asr服务器,请等待..."; isRec = true; btnStart.disabled = true; btnStop.disabled = true; btnConnect.disabled=true; return 1; } else { info_div.innerHTML="请点击开始"; btnStart.disabled = true; btnStop.disabled = true; btnConnect.disabled=false; return 0; } } function stop() { var chunk_size = new Array( 5, 10, 5 ); var request = { "chunk_size": chunk_size, "wav_name": "h5", "is_speaking": false, "chunk_interval":10, "mode":getAsrMode(), }; console.log(request); if(sampleBuf.length>0){ wsconnecter.wsSend(sampleBuf); console.log("sampleBuf.length"+sampleBuf.length); sampleBuf=new Int16Array(); } wsconnecter.wsSend( JSON.stringify(request) ); // 控件状态更新 isRec = false; info_div.innerHTML="发送完数据,请等候,正在识别..."; if(isfilemode==false){ btnStop.disabled = true; btnStart.disabled = true; btnConnect.disabled=true; //wait 3s for asr result setTimeout(function(){ console.log("call stop ws!"); wsconnecter.wsStop(); btnConnect.disabled=false; info_div.innerHTML="请点击连接";}, 3000 ); rec.stop(function(blob,duration){ console.log(blob); var audioBlob = Recorder.pcm2wav(data = {sampleRate:16000, bitRate:16, blob:blob}, function(theblob,duration){ console.log(theblob); var audio_record = document.getElementById('audio_record'); audio_record.src = (window.URL||webkitURL).createObjectURL(theblob); audio_record.controls=true; //audio_record.play(); } ,function(msg){ console.log(msg); } ); },function(errMsg){ console.log("errMsg: " + errMsg); }); } // 停止连接 } function clear() { var varArea=document.getElementById('varArea'); varArea.value=""; rec_text=""; offline_text=""; } function recProcess( buffer, powerLevel, bufferDuration, bufferSampleRate,newBufferIdx,asyncEnd ) { if ( isRec === true ) { var data_48k = buffer[buffer.length-1]; var array_48k = new Array(data_48k); var data_16k=Recorder.SampleData(array_48k,bufferSampleRate,16000).data; sampleBuf = Int16Array.from([...sampleBuf, ...data_16k]); var chunk_size=960; // for asr chunk_size [5, 10, 5] info_div.innerHTML=""+bufferDuration/1000+"s"; while(sampleBuf.length>=chunk_size){ sendBuf=sampleBuf.slice(0,chunk_size); sampleBuf=sampleBuf.slice(chunk_size,sampleBuf.length); wsconnecter.wsSend(sendBuf); } } } function getUseITN() { var obj = document.getElementsByName("use_itn"); for (var i = 0; i < obj.length; i++) { if (obj[i].checked) { return obj[i].value === "true"; } } return false; } ================================================ FILE: web/asr/pcm.js ================================================ /* pcm编码器+编码引擎 https://github.com/xiangyuecn/Recorder 编码原理:本编码器输出的pcm格式数据其实就是Recorder中的buffers原始数据(经过了重新采样),16位时为LE小端模式(Little Endian),并未经过任何编码处理 编码的代码和wav.js区别不大,pcm加上一个44字节wav头即成wav文件;所以要播放pcm就很简单了,直接转成wav文件来播放,已提供转换函数 Recorder.pcm2wav */ (function(){ "use strict"; Recorder.prototype.enc_pcm={ stable:true ,testmsg:"pcm为未封装的原始音频数据,pcm数据文件无法直接播放;支持位数8位、16位(填在比特率里面),采样率取值无限制" }; Recorder.prototype.pcm=function(res,True,False){ var This=this,set=This.set ,size=res.length ,bitRate=set.bitRate==8?8:16; var buffer=new ArrayBuffer(size*(bitRate/8)); var data=new DataView(buffer); var offset=0; // 写入采样数据 if(bitRate==8) { for(var i=0;i>8)+128; data.setInt8(offset,val,true); }; }else{ for (var i=0;i=pcmSampleRate时不会进行任何处理,小于时会进行重新采样 prevChunkInfo:{} 可选,上次调用时的返回值,用于连续转换,本次调用将从上次结束位置开始进行处理。或可自行定义一个ChunkInfo从pcmDatas指定的位置开始进行转换 option:{ 可选,配置项 frameSize:123456 帧大小,每帧的PCM Int16的数量,采样率转换后的pcm长度为frameSize的整数倍,用于连续转换。目前仅在mp3格式时才有用,frameSize取值为1152,这样编码出来的mp3时长和pcm的时长完全一致,否则会因为mp3最后一帧录音不够填满时添加填充数据导致mp3的时长变长。 frameType:"" 帧类型,一般为rec.set.type,提供此参数时无需提供frameSize,会自动使用最佳的值给frameSize赋值,目前仅支持mp3=1152(MPEG1 Layer3的每帧采采样数),其他类型=1。 以上两个参数用于连续转换时使用,最多使用一个,不提供时不进行帧的特殊处理,提供时必须同时提供prevChunkInfo才有作用。最后一段数据处理时无需提供帧大小以便输出最后一丁点残留数据。 } 返回ChunkInfo:{ //可定义,从指定位置开始转换到结尾 index:0 pcmDatas已处理到的索引 offset:0.0 已处理到的index对应的pcm中的偏移的下一个位置 //仅作为返回值 frameNext:null||[Int16,...] 下一帧的部分数据,frameSize设置了的时候才可能会有 sampleRate:16000 结果的采样率,<=newSampleRate data:[Int16,...] 转换后的PCM结果;如果是连续转换,并且pcmDatas中并没有新数据时,data的长度可能为0 } */ Recorder.SampleData=function(pcmDatas,pcmSampleRate,newSampleRate,prevChunkInfo,option){ prevChunkInfo||(prevChunkInfo={}); var index=prevChunkInfo.index||0; var offset=prevChunkInfo.offset||0; var frameNext=prevChunkInfo.frameNext||[]; option||(option={}); var frameSize=option.frameSize||1; if(option.frameType){ frameSize=option.frameType=="mp3"?1152:1; }; var nLen=pcmDatas.length; if(index>nLen+1){ CLog("SampleData似乎传入了未重置chunk "+index+">"+nLen,3); }; var size=0; for(var i=index;i1){//新采样低于录音采样,进行抽样 size=Math.floor(size/step); }else{//新采样高于录音采样不处理,省去了插值处理 step=1; newSampleRate=pcmSampleRate; }; size+=frameNext.length; var res=new Int16Array(size); var idx=0; //添加上一次不够一帧的剩余数据 for(var i=0;i0){ var u8Pos=(res.length-frameNextSize)*2; frameNext=new Int16Array(res.buffer.slice(u8Pos)); res=new Int16Array(res.buffer.slice(0,u8Pos)); }; return { index:index ,offset:offset ,frameNext:frameNext ,sampleRate:newSampleRate ,data:res }; }; /*计算音量百分比的一个方法 pcmAbsSum: pcm Int16所有采样的绝对值的和 pcmLength: pcm长度 返回值:0-100,主要当做百分比用 注意:这个不是分贝,因此没用volume当做名称*/ Recorder.PowerLevel=function(pcmAbsSum,pcmLength){ /*计算音量 https://blog.csdn.net/jody1989/article/details/73480259 更高灵敏度算法: 限定最大感应值10000 线性曲线:低音量不友好 power/10000*100 对数曲线:低音量友好,但需限定最低感应值 (1+Math.log10(power/10000))*100 */ var power=(pcmAbsSum/pcmLength) || 0;//NaN var level; if(power<1251){//1250的结果10%,更小的音量采用线性取值 level=Math.round(power/1250*10); }else{ level=Math.round(Math.min(100,Math.max(0,(1+Math.log(power/10000)/Math.log(10))*100))); }; return level; }; /*计算音量,单位dBFS(满刻度相对电平) maxSample: 为16位pcm采样的绝对值中最大的一个(计算峰值音量),或者为pcm中所有采样的绝对值的平局值 返回值:-100~0 (最大值0dB,最小值-100代替-∞) */ Recorder.PowerDBFS=function(maxSample){ var val=Math.max(0.1, maxSample||0),Pref=0x7FFF; val=Math.min(val,Pref); //https://www.logiclocmusic.com/can-you-tell-the-decibel/ //https://blog.csdn.net/qq_17256689/article/details/120442510 val=20*Math.log(val/Pref)/Math.log(10); return Math.max(-100,Math.round(val)); }; //带时间的日志输出,可设为一个空函数来屏蔽日志输出 //CLog(msg,errOrLogMsg, logMsg...) err为数字时代表日志类型1:error 2:log默认 3:warn,否则当做内容输出,第一个参数不能是对象因为要拼接时间,后面可以接无数个输出参数 Recorder.CLog=function(msg,err){ var now=new Date(); var t=("0"+now.getMinutes()).substr(-2) +":"+("0"+now.getSeconds()).substr(-2) +"."+("00"+now.getMilliseconds()).substr(-3); var recID=this&&this.envIn&&this.envCheck&&this.id; var arr=["["+t+" "+RecTxt+(recID?":"+recID:"")+"]"+msg]; var a=arguments,console=window.console||{}; var i=2,fn=console.log; if(typeof(err)=="number"){ fn=err==1?console.error:err==3?console.warn:fn; }else{ i=1; }; for(;i1?arr:""); }else{ fn.apply(console,arr); }; }; var CLog=function(){ Recorder.CLog.apply(this,arguments); }; var IsLoser=true;try{IsLoser=!console.log.apply;}catch(e){}; var ID=0; function initFn(set){ this.id=++ID; //如果开启了流量统计,这里将发送一个图片请求 Traffic(); var o={ type:"mp3" //输出类型:mp3,wav,wav输出文件尺寸超大不推荐使用,但mp3编码支持会导致js文件超大,如果不需支持mp3可以使js文件大幅减小 ,bitRate:16 //比特率 wav:16或8位,MP3:8kbps 1k/s,8kbps 2k/s 录音文件很小 ,sampleRate:16000 //采样率,wav格式大小=sampleRate*时间;mp3此项对低比特率有影响,高比特率几乎无影响。 //wav任意值,mp3取值范围:48000, 44100, 32000, 24000, 22050, 16000, 12000, 11025, 8000 //采样率参考https://www.cnblogs.com/devin87/p/mp3-recorder.html ,onProcess:NOOP //fn(buffers,powerLevel,bufferDuration,bufferSampleRate,newBufferIdx,asyncEnd) buffers=[[Int16,...],...]:缓冲的PCM数据,为从开始录音到现在的所有pcm片段;powerLevel:当前缓冲的音量级别0-100,bufferDuration:已缓冲时长,bufferSampleRate:缓冲使用的采样率(当type支持边录边转码(Worker)时,此采样率和设置的采样率相同,否则不一定相同);newBufferIdx:本次回调新增的buffer起始索引;asyncEnd:fn() 如果onProcess是异步的(返回值为true时),处理完成时需要调用此回调,如果不是异步的请忽略此参数,此方法回调时必须是真异步(不能真异步时需用setTimeout包裹)。onProcess返回值:如果返回true代表开启异步模式,在某些大量运算的场合异步是必须的,必须在异步处理完成时调用asyncEnd(不能真异步时需用setTimeout包裹),在onProcess执行后新增的buffer会全部替换成空数组,因此本回调开头应立即将newBufferIdx到本次回调结尾位置的buffer全部保存到另外一个数组内,处理完成后写回buffers中本次回调的结尾位置。 //*******高级设置****** //,sourceStream:MediaStream Object //可选直接提供一个媒体流,从这个流中录制、实时处理音频数据(当前Recorder实例独享此流);不提供时为普通的麦克风录音,由getUserMedia提供音频流(所有Recorder实例共享同一个流) //比如:audio、video标签dom节点的captureStream方法(实验特性,不同浏览器支持程度不高)返回的流;WebRTC中的remote流;自己创建的流等 //注意:流内必须至少存在一条音轨(Audio Track),比如audio标签必须等待到可以开始播放后才会有音轨,否则open会失败 //,audioTrackSet:{ deviceId:"",groupId:"", autoGainControl:true, echoCancellation:true, noiseSuppression:true } //普通麦克风录音时getUserMedia方法的audio配置参数,比如指定设备id,回声消除、降噪开关;注意:提供的任何配置值都不一定会生效 //由于麦克风是全局共享的,所以新配置后需要close掉以前的再重新open //更多参考: https://developer.mozilla.org/en-US/docs/Web/API/MediaTrackConstraints //,disableEnvInFix:false 内部参数,禁用设备卡顿时音频输入丢失补偿功能 //,takeoffEncodeChunk:NOOP //fn(chunkBytes) chunkBytes=[Uint8,...]:实时编码环境下接管编码器输出,当编码器实时编码出一块有效的二进制音频数据时实时回调此方法;参数为二进制的Uint8Array,就是编码出来的音频数据片段,所有的chunkBytes拼接在一起即为完整音频。本实现的想法最初由QQ2543775048提出 //当提供此回调方法时,将接管编码器的数据输出,编码器内部将放弃存储生成的音频数据;环境要求比较苛刻:如果当前环境不支持实时编码处理,将在open时直接走fail逻辑 //因此提供此回调后调用stop方法将无法获得有效的音频数据,因为编码器内没有音频数据,因此stop时返回的blob将是一个字节长度为0的blob //目前只有mp3格式实现了实时编码,在支持实时处理的环境中将会实时的将编码出来的mp3片段通过此方法回调,所有的chunkBytes拼接到一起即为完整的mp3,此种拼接的结果比mock方法实时生成的音质更加,因为天然避免了首尾的静默 //目前除mp3外其他格式不可以提供此回调,提供了将在open时直接走fail逻辑 }; for(var k in set){ o[k]=set[k]; }; this.set=o; this._S=9;//stop同步锁,stop可以阻止open过程中还未运行的start this.Sync={O:9,C:9};//和Recorder.Sync一致,只不过这个是非全局的,仅用来简化代码逻辑,无实际作用 }; //同步锁,控制对Stream的竞争;用于close时中断异步的open;一个对象open如果变化了都要阻止close,Stream的控制权交个新的对象 Recorder.Sync={/*open*/O:9,/*close*/C:9}; Recorder.prototype=initFn.prototype={ CLog:CLog //流相关的数据存储在哪个对象里面;如果提供了sourceStream,数据直接存储在当前对象中,否则存储在全局 ,_streamStore:function(){ if(this.set.sourceStream){ return this; }else{ return Recorder; } } //打开录音资源True(),False(msg,isUserNotAllow),需要调用close。注意:此方法是异步的;一般使用时打开,用完立即关闭;可重复调用,可用来测试是否能录音 ,open:function(True,False){ var This=this,streamStore=This._streamStore(); True=True||NOOP; var failCall=function(errMsg,isUserNotAllow){ isUserNotAllow=!!isUserNotAllow; This.CLog("录音open失败:"+errMsg+",isUserNotAllow:"+isUserNotAllow,1); False&&False(errMsg,isUserNotAllow); }; var ok=function(){ This.CLog("open ok id:"+This.id); True(); This._SO=0;//解除stop对open中的start调用的阻止 }; //同步锁 var Lock=streamStore.Sync; var lockOpen=++Lock.O,lockClose=Lock.C; This._O=This._O_=lockOpen;//记住当前的open,如果变化了要阻止close,这里假定了新对象已取代当前对象并且不再使用 This._SO=This._S;//记住open过程中的stop,中途任何stop调用后都不能继续open中的start var lockFail=function(){ //允许多次open,但不允许任何一次close,或者自身已经调用了关闭 if(lockClose!=Lock.C || !This._O){ var err="open被取消"; if(lockOpen==Lock.O){ //无新的open,已经调用了close进行取消,此处应让上次的close明确生效 This.close(); }else{ err="open被中断"; }; failCall(err); return true; }; }; //环境配置检查 var checkMsg=This.envCheck({envName:"H5",canProcess:true}); if(checkMsg){ failCall("不能录音:"+checkMsg); return; }; //***********已直接提供了音频流************ if(This.set.sourceStream){ if(!Recorder.GetContext()){ failCall("不支持此浏览器从流中获取录音"); return; }; Disconnect(streamStore);//可能已open过,直接先尝试断开 This.Stream=This.set.sourceStream; This.Stream._call={}; try{ Connect(streamStore); }catch(e){ failCall("从流中打开录音失败:"+e.message); return; } ok(); return; }; //***********打开麦克风得到全局的音频流************ var codeFail=function(code,msg){ try{//跨域的优先检测一下 window.top.a; }catch(e){ failCall('无权录音(跨域,请尝试给iframe添加麦克风访问策略,如allow="camera;microphone")'); return; }; if(/Permission|Allow/i.test(code)){ failCall("用户拒绝了录音权限",true); }else if(window.isSecureContext===false){ failCall("浏览器禁止不安全页面录音,可开启https解决"); }else if(/Found/i.test(code)){//可能是非安全环境导致的没有设备 failCall(msg+",无可用麦克风"); }else{ failCall(msg); }; }; //如果已打开并且有效就不要再打开了 if(Recorder.IsOpen()){ ok(); return; }; if(!Recorder.Support()){ codeFail("","此浏览器不支持录音"); return; }; //请求权限,如果从未授权,一般浏览器会弹出权限请求弹框 var f1=function(stream){ //https://github.com/xiangyuecn/Recorder/issues/14 获取到的track.readyState!="live",刚刚回调时可能是正常的,但过一下可能就被关掉了,原因不明。延迟一下保证真异步。对正常浏览器不影响 setTimeout(function(){ stream._call={}; var oldStream=Recorder.Stream; if(oldStream){ Disconnect(); //直接断开已存在的,旧的Connect未完成会自动终止 stream._call=oldStream._call; }; Recorder.Stream=stream; if(lockFail())return; if(Recorder.IsOpen()){ if(oldStream)This.CLog("发现同时多次调用open",1); Connect(streamStore,1); ok(); }else{ failCall("录音功能无效:无音频流"); }; },100); }; var f2=function(e){ var code=e.name||e.message||e.code+":"+e; This.CLog("请求录音权限错误",1,e); codeFail(code,"无法录音:"+code); }; var trackSet={ noiseSuppression:false //默认禁用降噪,原声录制,免得移动端表现怪异(包括系统播放声音变小) ,echoCancellation:false //回声消除 }; var trackSet2=This.set.audioTrackSet; for(var k in trackSet2)trackSet[k]=trackSet2[k]; trackSet.sampleRate=Recorder.Ctx.sampleRate;//必须指明采样率,不然手机上MediaRecorder采样率16k try{ var pro=Recorder.Scope[getUserMediaTxt]({audio:trackSet},f1,f2); }catch(e){//不能设置trackSet就算了 This.CLog(getUserMediaTxt,3,e); pro=Recorder.Scope[getUserMediaTxt]({audio:true},f1,f2); }; if(pro&&pro.then){ pro.then(f1)[CatchTxt](f2); //fix 关键字,保证catch压缩时保持字符串形式 }; } //关闭释放录音资源 ,close:function(call){ call=call||NOOP; var This=this,streamStore=This._streamStore(); This._stop(); var Lock=streamStore.Sync; This._O=0; if(This._O_!=Lock.O){ //唯一资源Stream的控制权已交给新对象,这里不能关闭。此处在每次都弹权限的浏览器内可能存在泄漏,新对象被拒绝权限可能不会调用close,忽略这种不处理 This.CLog("close被忽略(因为同时open了多个rec,只有最后一个会真正close)",3); call(); return; }; Lock.C++;//获得控制权 Disconnect(streamStore); This.CLog("close"); call(); } /*模拟一段录音数据,后面可以调用stop进行编码,需提供pcm数据[1,2,3...],pcm的采样率*/ ,mock:function(pcmData,pcmSampleRate){ var This=this; This._stop();//清理掉已有的资源 This.isMock=1; This.mockEnvInfo=null; This.buffers=[pcmData]; This.recSize=pcmData.length; This[srcSampleRateTxt]=pcmSampleRate; return This; } ,envCheck:function(envInfo){//平台环境下的可用性检查,任何时候都可以调用检查,返回errMsg:""正常,"失败原因" //envInfo={envName:"H5",canProcess:true} var errMsg,This=this,set=This.set; //检测CPU的数字字节序,TypedArray字节序是个迷,直接拒绝罕见的大端模式,因为找不到这种CPU进行测试 var tag="CPU_BE"; if(!errMsg && !Recorder[tag] && window.Int8Array && !new Int8Array(new Int32Array([1]).buffer)[0]){ Traffic(tag); //如果开启了流量统计,这里将发送一个图片请求 errMsg="不支持"+tag+"架构"; }; //编码器检查环境下配置是否可用 if(!errMsg){ var type=set.type; if(This[type+"_envCheck"]){//编码器已实现环境检查 errMsg=This[type+"_envCheck"](envInfo,set); }else{//未实现检查的手动检查配置是否有效 if(set.takeoffEncodeChunk){ errMsg=type+"类型"+(This[type]?"":"(未加载编码器)")+"不支持设置takeoffEncodeChunk"; }; }; }; return errMsg||""; } ,envStart:function(mockEnvInfo,sampleRate){//平台环境相关的start调用 var This=this,set=This.set; This.isMock=mockEnvInfo?1:0;//非H5环境需要启用mock,并提供envCheck需要的环境信息 This.mockEnvInfo=mockEnvInfo; This.buffers=[];//数据缓冲 This.recSize=0;//数据大小 This.envInLast=0;//envIn接收到最后录音内容的时间 This.envInFirst=0;//envIn接收到的首个录音内容的录制时间 This.envInFix=0;//补偿的总时间 This.envInFixTs=[];//补偿计数列表 //engineCtx需要提前确定最终的采样率 var setSr=set[sampleRateTxt]; if(setSr>sampleRate){ set[sampleRateTxt]=sampleRate; }else{ setSr=0 } This[srcSampleRateTxt]=sampleRate; This.CLog(srcSampleRateTxt+": "+sampleRate+" set."+sampleRateTxt+": "+set[sampleRateTxt]+(setSr?" 忽略"+setSr:""), setSr?3:0); This.engineCtx=0; //此类型有边录边转码(Worker)支持 if(This[set.type+"_start"]){ var engineCtx=This.engineCtx=This[set.type+"_start"](set); if(engineCtx){ engineCtx.pcmDatas=[]; engineCtx.pcmSize=0; }; }; } ,envResume:function(){//和平台环境无关的恢复录音 //重新开始计数 this.envInFixTs=[]; } ,envIn:function(pcm,sum){//和平台环境无关的pcm[Int16]输入 var This=this,set=This.set,engineCtx=This.engineCtx; var bufferSampleRate=This[srcSampleRateTxt]; var size=pcm.length; var powerLevel=Recorder.PowerLevel(sum,size); var buffers=This.buffers; var bufferFirstIdx=buffers.length;//之前的buffer都是经过onProcess处理好的,不允许再修改 buffers.push(pcm); //有engineCtx时会被覆盖,这里保存一份 var buffersThis=buffers; var bufferFirstIdxThis=bufferFirstIdx; //卡顿丢失补偿:因为设备很卡的时候导致H5接收到的数据量不够造成播放时候变速,结果比实际的时长要短,此处保证了不会变短,但不能修复丢失的音频数据造成音质变差。当前算法采用输入时间侦测下一帧是否需要添加补偿帧,需要(6次输入||超过1秒)以上才会开始侦测,如果滑动窗口内丢失超过1/3就会进行补偿 var now=Date.now(); var pcmTime=Math.round(size/bufferSampleRate*1000); This.envInLast=now; if(This.buffers.length==1){//记下首个录音数据的录制时间 This.envInFirst=now-pcmTime; }; var envInFixTs=This.envInFixTs; envInFixTs.splice(0,0,{t:now,d:pcmTime}); //保留3秒的计数滑动窗口,另外超过3秒的停顿不补偿 var tsInStart=now,tsPcm=0; for(var i=0;i3000){ envInFixTs.length=i; break; }; tsInStart=o.t; tsPcm+=o.d; }; //达到需要的数据量,开始侦测是否需要补偿 var tsInPrev=envInFixTs[1]; var tsIn=now-tsInStart; var lost=tsIn-tsPcm; if( lost>tsIn/3 && (tsInPrev&&tsIn>1000 || envInFixTs.length>=6) ){ //丢失过多,开始执行补偿 var addTime=now-tsInPrev.t-pcmTime;//距离上次输入丢失这么多ms if(addTime>pcmTime/5){//丢失超过本帧的1/5 var fixOpen=!set.disableEnvInFix; This.CLog("["+now+"]"+(fixOpen?"":"未")+"补偿"+addTime+"ms",3); This.envInFix+=addTime; //用静默进行补偿 if(fixOpen){ var addPcm=new Int16Array(addTime*bufferSampleRate/1000); size+=addPcm.length; buffers.push(addPcm); }; }; }; var sizeOld=This.recSize,addSize=size; var bufferSize=sizeOld+addSize; This.recSize=bufferSize;//此值在onProcess后需要修正,可能新数据被修改 //此类型有边录边转码(Worker)支持,开启实时转码 if(engineCtx){ //转换成set的采样率 var chunkInfo=Recorder.SampleData(buffers,bufferSampleRate,set[sampleRateTxt],engineCtx.chunkInfo); engineCtx.chunkInfo=chunkInfo; sizeOld=engineCtx.pcmSize; addSize=chunkInfo.data.length; bufferSize=sizeOld+addSize; engineCtx.pcmSize=bufferSize;//此值在onProcess后需要修正,可能新数据被修改 buffers=engineCtx.pcmDatas; bufferFirstIdx=buffers.length; buffers.push(chunkInfo.data); bufferSampleRate=chunkInfo[sampleRateTxt]; }; var duration=Math.round(bufferSize/bufferSampleRate*1000); var bufferNextIdx=buffers.length; var bufferNextIdxThis=buffersThis.length; //允许异步处理buffer数据 var asyncEnd=function(){ //重新计算size,异步的早已减去添加的,同步的需去掉本次添加的然后重新计算 var num=asyncBegin?0:-addSize; var hasClear=buffers[0]==null; for(var i=bufferFirstIdx;i10 && This.envInFirst-now>1000){ //1秒后开始onProcess性能监测 This.CLog(procTxt+"低性能,耗时"+slowT+"ms",3); }; if(asyncBegin===true){ //开启了异步模式,onProcess已接管buffers新数据,立即清空,避免出现未处理的数据 var hasClear=0; for(var i=bufferFirstIdx;i"+res.length+" 花:"+(Date.now()-t1)+"ms"); setTimeout(function(){ t1=Date.now(); This[set.type](res,function(blob){ ok(blob,duration); },function(msg){ err(msg); }); }); } }; if(window[RecTxt]){ CLog("重复引入"+RecTxt,3); window[RecTxt].Destroy(); }; window[RecTxt]=Recorder; //=======从WebM字节流中提取pcm数据,提取成功返回Float32Array,失败返回null||-1===== var WebM_Extract=function(inBytes, scope){ if(!scope.pos){ scope.pos=[0]; scope.tracks={}; scope.bytes=[]; }; var tracks=scope.tracks, position=[scope.pos[0]]; var endPos=function(){ scope.pos[0]=position[0] }; var sBL=scope.bytes.length; var bytes=new Uint8Array(sBL+inBytes.length); bytes.set(scope.bytes); bytes.set(inBytes,sBL); scope.bytes=bytes; //先读取文件头和Track信息 if(!scope._ht){ readMatroskaVInt(bytes, position);//EBML Header readMatroskaBlock(bytes, position);//跳过EBML Header内容 if(!BytesEq(readMatroskaVInt(bytes, position), [0x18,0x53,0x80,0x67])){ return;//未识别到Segment } readMatroskaVInt(bytes, position);//跳过Segment长度值 while(position[0]1){//多声道,提取一个声道 var arr2=[]; for(var i=0;i=arr.length)return; var b0=arr[i],b2=("0000000"+b0.toString(2)).substr(-8); var m=/^(0*1)(\d*)$/.exec(b2); if(!m)return; var len=m[1].length, val=[]; if(i+len>arr.length)return; for(var i2=0;i2arr.length)return; for(var i2=0;i2>8)+128; data.setInt8(offset,val,true); }; }else{ for (var i=0;i

WebSocket Test

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================================================ FILE: web/client.js ================================================ var pc = null; function negotiate() { pc.addTransceiver('video', { direction: 'recvonly' }); pc.addTransceiver('audio', { direction: 'recvonly' }); return pc.createOffer().then((offer) => { return pc.setLocalDescription(offer); }).then(() => { // wait for ICE gathering to complete return new Promise((resolve) => { if (pc.iceGatheringState === 'complete') { resolve(); } else { const checkState = () => { if (pc.iceGatheringState === 'complete') { pc.removeEventListener('icegatheringstatechange', checkState); resolve(); } }; pc.addEventListener('icegatheringstatechange', checkState); } }); }).then(() => { var offer = pc.localDescription; return fetch('/offer', { body: JSON.stringify({ sdp: offer.sdp, type: offer.type, }), headers: { 'Content-Type': 'application/json' }, method: 'POST' }); }).then((response) => { return response.json(); }).then((answer) => { document.getElementById('sessionid').value = answer.sessionid return pc.setRemoteDescription(answer); }).catch((e) => { alert(e); }); } function start() { var config = { sdpSemantics: 'unified-plan' }; if (document.getElementById('use-stun').checked) { config.iceServers = [{ urls: ['stun:stun.l.google.com:19302'] }]; } pc = new RTCPeerConnection(config); // connect audio / video pc.addEventListener('track', (evt) => { if (evt.track.kind == 'video') { document.getElementById('video').srcObject = evt.streams[0]; } else { document.getElementById('audio').srcObject = evt.streams[0]; } }); document.getElementById('start').style.display = 'none'; negotiate(); document.getElementById('stop').style.display = 'inline-block'; } function stop() { document.getElementById('stop').style.display = 'none'; // close peer connection setTimeout(() => { pc.close(); }, 500); } window.onunload = function(event) { // 在这里执行你想要的操作 setTimeout(() => { pc.close(); }, 500); }; window.onbeforeunload = function (e) { setTimeout(() => { pc.close(); }, 500); e = e || window.event // 兼容IE8和Firefox 4之前的版本 if (e) { e.returnValue = '关闭提示' } // Chrome, Safari, Firefox 4+, Opera 12+ , IE 9+ return '关闭提示' } ================================================ FILE: web/dashboard.html ================================================ livetalking数字人交互平台

livetalking数字人交互平台

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系统: 欢迎使用livetalking,请点击"开始连接"按钮开始对话。
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================================================ FILE: web/echo.html ================================================

WebSocket Test

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================================================ FILE: web/echoapi.html ================================================

WebSocket Test

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================================================ FILE: web/rtcpush.html ================================================ WebRTC webcam

input text

Media

================================================ FILE: web/rtcpushapi-asr.html ================================================ WebRTC webcam

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Media

================================================ FILE: web/rtcpushapi.html ================================================ WebRTC webcam

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Media

================================================ FILE: web/rtcpushchat.html ================================================ WebRTC webcam

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Media

================================================ FILE: web/srs.sdk.js ================================================ // // Copyright (c) 2013-2021 Winlin // // SPDX-License-Identifier: MIT // 'use strict'; function SrsError(name, message) { this.name = name; this.message = message; this.stack = (new Error()).stack; } SrsError.prototype = Object.create(Error.prototype); SrsError.prototype.constructor = SrsError; // Depends on adapter-7.4.0.min.js from https://github.com/webrtc/adapter // Async-awat-prmise based SRS RTC Publisher. function SrsRtcPublisherAsync() { var self = {}; // https://developer.mozilla.org/en-US/docs/Web/API/MediaDevices/getUserMedia self.constraints = { audio: true, video: { width: {ideal: 320, max: 576} } }; // @see https://github.com/rtcdn/rtcdn-draft // @url The WebRTC url to play with, for example: // webrtc://r.ossrs.net/live/livestream // or specifies the API port: // webrtc://r.ossrs.net:11985/live/livestream // or autostart the publish: // webrtc://r.ossrs.net/live/livestream?autostart=true // or change the app from live to myapp: // webrtc://r.ossrs.net:11985/myapp/livestream // or change the stream from livestream to mystream: // webrtc://r.ossrs.net:11985/live/mystream // or set the api server to myapi.domain.com: // webrtc://myapi.domain.com/live/livestream // or set the candidate(eip) of answer: // webrtc://r.ossrs.net/live/livestream?candidate=39.107.238.185 // or force to access https API: // webrtc://r.ossrs.net/live/livestream?schema=https // or use plaintext, without SRTP: // webrtc://r.ossrs.net/live/livestream?encrypt=false // or any other information, will pass-by in the query: // webrtc://r.ossrs.net/live/livestream?vhost=xxx // webrtc://r.ossrs.net/live/livestream?token=xxx self.publish = async function (url) { var conf = self.__internal.prepareUrl(url); self.pc.addTransceiver("audio", {direction: "sendonly"}); self.pc.addTransceiver("video", {direction: "sendonly"}); //self.pc.addTransceiver("video", {direction: "sendonly"}); //self.pc.addTransceiver("audio", {direction: "sendonly"}); if (!navigator.mediaDevices && window.location.protocol === 'http:' && window.location.hostname !== 'localhost') { throw new SrsError('HttpsRequiredError', `Please use HTTPS or localhost to publish, read https://github.com/ossrs/srs/issues/2762#issuecomment-983147576`); } var stream = await navigator.mediaDevices.getUserMedia(self.constraints); // @see https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection/addStream#Migrating_to_addTrack stream.getTracks().forEach(function (track) { self.pc.addTrack(track); // Notify about local track when stream is ok. self.ontrack && self.ontrack({track: track}); }); var offer = await self.pc.createOffer(); await self.pc.setLocalDescription(offer); var session = await new Promise(function (resolve, reject) { // @see https://github.com/rtcdn/rtcdn-draft var data = { api: conf.apiUrl, tid: conf.tid, streamurl: conf.streamUrl, clientip: null, sdp: offer.sdp }; console.log("Generated offer: ", data); const xhr = new XMLHttpRequest(); xhr.onload = function() { if (xhr.readyState !== xhr.DONE) return; if (xhr.status !== 200 && xhr.status !== 201) return reject(xhr); const data = JSON.parse(xhr.responseText); console.log("Got answer: ", data); return data.code ? reject(xhr) : resolve(data); } xhr.open('POST', conf.apiUrl, true); xhr.setRequestHeader('Content-type', 'application/json'); xhr.send(JSON.stringify(data)); }); await self.pc.setRemoteDescription( new RTCSessionDescription({type: 'answer', sdp: session.sdp}) ); session.simulator = conf.schema + '//' + conf.urlObject.server + ':' + conf.port + '/rtc/v1/nack/'; return session; }; // Close the publisher. self.close = function () { self.pc && self.pc.close(); self.pc = null; }; // The callback when got local stream. // @see https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection/addStream#Migrating_to_addTrack self.ontrack = function (event) { // Add track to stream of SDK. self.stream.addTrack(event.track); }; // Internal APIs. self.__internal = { defaultPath: '/rtc/v1/publish/', prepareUrl: function (webrtcUrl) { var urlObject = self.__internal.parse(webrtcUrl); // If user specifies the schema, use it as API schema. var schema = urlObject.user_query.schema; schema = schema ? schema + ':' : window.location.protocol; var port = urlObject.port || 1985; if (schema === 'https:') { port = urlObject.port || 443; } // @see https://github.com/rtcdn/rtcdn-draft var api = urlObject.user_query.play || self.__internal.defaultPath; if (api.lastIndexOf('/') !== api.length - 1) { api += '/'; } var apiUrl = schema + '//' + urlObject.server + ':' + port + api; for (var key in urlObject.user_query) { if (key !== 'api' && key !== 'play') { apiUrl += '&' + key + '=' + urlObject.user_query[key]; } } // Replace /rtc/v1/play/&k=v to /rtc/v1/play/?k=v apiUrl = apiUrl.replace(api + '&', api + '?'); var streamUrl = urlObject.url; return { apiUrl: apiUrl, streamUrl: streamUrl, schema: schema, urlObject: urlObject, port: port, tid: Number(parseInt(new Date().getTime()*Math.random()*100)).toString(16).slice(0, 7) }; }, parse: function (url) { // @see: http://stackoverflow.com/questions/10469575/how-to-use-location-object-to-parse-url-without-redirecting-the-page-in-javascri var a = document.createElement("a"); a.href = url.replace("rtmp://", "http://") .replace("webrtc://", "http://") .replace("rtc://", "http://"); var vhost = a.hostname; var app = a.pathname.substring(1, a.pathname.lastIndexOf("/")); var stream = a.pathname.slice(a.pathname.lastIndexOf("/") + 1); // parse the vhost in the params of app, that srs supports. app = app.replace("...vhost...", "?vhost="); if (app.indexOf("?") >= 0) { var params = app.slice(app.indexOf("?")); app = app.slice(0, app.indexOf("?")); if (params.indexOf("vhost=") > 0) { vhost = params.slice(params.indexOf("vhost=") + "vhost=".length); if (vhost.indexOf("&") > 0) { vhost = vhost.slice(0, vhost.indexOf("&")); } } } // when vhost equals to server, and server is ip, // the vhost is __defaultVhost__ if (a.hostname === vhost) { var re = /^(\d+)\.(\d+)\.(\d+)\.(\d+)$/; if (re.test(a.hostname)) { vhost = "__defaultVhost__"; } } // parse the schema var schema = "rtmp"; if (url.indexOf("://") > 0) { schema = url.slice(0, url.indexOf("://")); } var port = a.port; if (!port) { // Finger out by webrtc url, if contains http or https port, to overwrite default 1985. if (schema === 'webrtc' && url.indexOf(`webrtc://${a.host}:`) === 0) { port = (url.indexOf(`webrtc://${a.host}:80`) === 0) ? 80 : 443; } // Guess by schema. if (schema === 'http') { port = 80; } else if (schema === 'https') { port = 443; } else if (schema === 'rtmp') { port = 1935; } } var ret = { url: url, schema: schema, server: a.hostname, port: port, vhost: vhost, app: app, stream: stream }; self.__internal.fill_query(a.search, ret); // For webrtc API, we use 443 if page is https, or schema specified it. if (!ret.port) { if (schema === 'webrtc' || schema === 'rtc') { if (ret.user_query.schema === 'https') { ret.port = 443; } else if (window.location.href.indexOf('https://') === 0) { ret.port = 443; } else { // For WebRTC, SRS use 1985 as default API port. ret.port = 1985; } } } return ret; }, fill_query: function (query_string, obj) { // pure user query object. obj.user_query = {}; if (query_string.length === 0) { return; } // split again for angularjs. if (query_string.indexOf("?") >= 0) { query_string = query_string.split("?")[1]; } var queries = query_string.split("&"); for (var i = 0; i < queries.length; i++) { var elem = queries[i]; var query = elem.split("="); obj[query[0]] = query[1]; obj.user_query[query[0]] = query[1]; } // alias domain for vhost. if (obj.domain) { obj.vhost = obj.domain; } } }; self.pc = new RTCPeerConnection(null); // To keep api consistent between player and publisher. // @see https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection/addStream#Migrating_to_addTrack // @see https://webrtc.org/getting-started/media-devices self.stream = new MediaStream(); return self; } // Depends on adapter-7.4.0.min.js from https://github.com/webrtc/adapter // Async-await-promise based SRS RTC Player. function SrsRtcPlayerAsync() { var self = {}; // @see https://github.com/rtcdn/rtcdn-draft // @url The WebRTC url to play with, for example: // webrtc://r.ossrs.net/live/livestream // or specifies the API port: // webrtc://r.ossrs.net:11985/live/livestream // webrtc://r.ossrs.net:80/live/livestream // or autostart the play: // webrtc://r.ossrs.net/live/livestream?autostart=true // or change the app from live to myapp: // webrtc://r.ossrs.net:11985/myapp/livestream // or change the stream from livestream to mystream: // webrtc://r.ossrs.net:11985/live/mystream // or set the api server to myapi.domain.com: // webrtc://myapi.domain.com/live/livestream // or set the candidate(eip) of answer: // webrtc://r.ossrs.net/live/livestream?candidate=39.107.238.185 // or force to access https API: // webrtc://r.ossrs.net/live/livestream?schema=https // or use plaintext, without SRTP: // webrtc://r.ossrs.net/live/livestream?encrypt=false // or any other information, will pass-by in the query: // webrtc://r.ossrs.net/live/livestream?vhost=xxx // webrtc://r.ossrs.net/live/livestream?token=xxx self.play = async function(url) { var conf = self.__internal.prepareUrl(url); self.pc.addTransceiver("audio", {direction: "recvonly"}); self.pc.addTransceiver("video", {direction: "recvonly"}); //self.pc.addTransceiver("video", {direction: "recvonly"}); //self.pc.addTransceiver("audio", {direction: "recvonly"}); var offer = await self.pc.createOffer(); await self.pc.setLocalDescription(offer); var session = await new Promise(function(resolve, reject) { // @see https://github.com/rtcdn/rtcdn-draft var data = { api: conf.apiUrl, tid: conf.tid, streamurl: conf.streamUrl, clientip: null, sdp: offer.sdp }; console.log("Generated offer: ", data); const xhr = new XMLHttpRequest(); xhr.onload = function() { if (xhr.readyState !== xhr.DONE) return; if (xhr.status !== 200 && xhr.status !== 201) return reject(xhr); const data = JSON.parse(xhr.responseText); console.log("Got answer: ", data); return data.code ? reject(xhr) : resolve(data); } xhr.open('POST', conf.apiUrl, true); xhr.setRequestHeader('Content-type', 'application/json'); xhr.send(JSON.stringify(data)); }); await self.pc.setRemoteDescription( new RTCSessionDescription({type: 'answer', sdp: session.sdp}) ); session.simulator = conf.schema + '//' + conf.urlObject.server + ':' + conf.port + '/rtc/v1/nack/'; return session; }; // Close the player. self.close = function() { self.pc && self.pc.close(); self.pc = null; }; // The callback when got remote track. // Note that the onaddstream is deprecated, @see https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection/onaddstream self.ontrack = function (event) { // https://webrtc.org/getting-started/remote-streams self.stream.addTrack(event.track); }; // Internal APIs. self.__internal = { defaultPath: '/rtc/v1/play/', prepareUrl: function (webrtcUrl) { var urlObject = self.__internal.parse(webrtcUrl); // If user specifies the schema, use it as API schema. var schema = urlObject.user_query.schema; schema = schema ? schema + ':' : window.location.protocol; var port = urlObject.port || 1985; if (schema === 'https:') { port = urlObject.port || 443; } // @see https://github.com/rtcdn/rtcdn-draft var api = urlObject.user_query.play || self.__internal.defaultPath; if (api.lastIndexOf('/') !== api.length - 1) { api += '/'; } var apiUrl = schema + '//' + urlObject.server + ':' + port + api; for (var key in urlObject.user_query) { if (key !== 'api' && key !== 'play') { apiUrl += '&' + key + '=' + urlObject.user_query[key]; } } // Replace /rtc/v1/play/&k=v to /rtc/v1/play/?k=v apiUrl = apiUrl.replace(api + '&', api + '?'); var streamUrl = urlObject.url; return { apiUrl: apiUrl, streamUrl: streamUrl, schema: schema, urlObject: urlObject, port: port, tid: Number(parseInt(new Date().getTime()*Math.random()*100)).toString(16).slice(0, 7) }; }, parse: function (url) { // @see: http://stackoverflow.com/questions/10469575/how-to-use-location-object-to-parse-url-without-redirecting-the-page-in-javascri var a = document.createElement("a"); a.href = url.replace("rtmp://", "http://") .replace("webrtc://", "http://") .replace("rtc://", "http://"); var vhost = a.hostname; var app = a.pathname.substring(1, a.pathname.lastIndexOf("/")); var stream = a.pathname.slice(a.pathname.lastIndexOf("/") + 1); // parse the vhost in the params of app, that srs supports. app = app.replace("...vhost...", "?vhost="); if (app.indexOf("?") >= 0) { var params = app.slice(app.indexOf("?")); app = app.slice(0, app.indexOf("?")); if (params.indexOf("vhost=") > 0) { vhost = params.slice(params.indexOf("vhost=") + "vhost=".length); if (vhost.indexOf("&") > 0) { vhost = vhost.slice(0, vhost.indexOf("&")); } } } // when vhost equals to server, and server is ip, // the vhost is __defaultVhost__ if (a.hostname === vhost) { var re = /^(\d+)\.(\d+)\.(\d+)\.(\d+)$/; if (re.test(a.hostname)) { vhost = "__defaultVhost__"; } } // parse the schema var schema = "rtmp"; if (url.indexOf("://") > 0) { schema = url.slice(0, url.indexOf("://")); } var port = a.port; if (!port) { // Finger out by webrtc url, if contains http or https port, to overwrite default 1985. if (schema === 'webrtc' && url.indexOf(`webrtc://${a.host}:`) === 0) { port = (url.indexOf(`webrtc://${a.host}:80`) === 0) ? 80 : 443; } // Guess by schema. if (schema === 'http') { port = 80; } else if (schema === 'https') { port = 443; } else if (schema === 'rtmp') { port = 1935; } } var ret = { url: url, schema: schema, server: a.hostname, port: port, vhost: vhost, app: app, stream: stream }; self.__internal.fill_query(a.search, ret); // For webrtc API, we use 443 if page is https, or schema specified it. if (!ret.port) { if (schema === 'webrtc' || schema === 'rtc') { if (ret.user_query.schema === 'https') { ret.port = 443; } else if (window.location.href.indexOf('https://') === 0) { ret.port = 443; } else { // For WebRTC, SRS use 1985 as default API port. ret.port = 1985; } } } return ret; }, fill_query: function (query_string, obj) { // pure user query object. obj.user_query = {}; if (query_string.length === 0) { return; } // split again for angularjs. if (query_string.indexOf("?") >= 0) { query_string = query_string.split("?")[1]; } var queries = query_string.split("&"); for (var i = 0; i < queries.length; i++) { var elem = queries[i]; var query = elem.split("="); obj[query[0]] = query[1]; obj.user_query[query[0]] = query[1]; } // alias domain for vhost. if (obj.domain) { obj.vhost = obj.domain; } } }; self.pc = new RTCPeerConnection(null); // Create a stream to add track to the stream, @see https://webrtc.org/getting-started/remote-streams self.stream = new MediaStream(); // https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection/ontrack self.pc.ontrack = function(event) { if (self.ontrack) { self.ontrack(event); } }; return self; } // Depends on adapter-7.4.0.min.js from https://github.com/webrtc/adapter // Async-awat-prmise based SRS RTC Publisher by WHIP. function SrsRtcWhipWhepAsync() { var self = {}; // https://developer.mozilla.org/en-US/docs/Web/API/MediaDevices/getUserMedia self.constraints = { audio: true, video: { width: {ideal: 320, max: 576} } }; // See https://datatracker.ietf.org/doc/draft-ietf-wish-whip/ // @url The WebRTC url to publish with, for example: // http://localhost:1985/rtc/v1/whip/?app=live&stream=livestream // @options The options to control playing, supports: // videoOnly: boolean, whether only play video, default to false. // audioOnly: boolean, whether only play audio, default to false. self.publish = async function (url, options) { if (url.indexOf('/whip/') === -1) throw new Error(`invalid WHIP url ${url}`); if (options?.videoOnly && options?.audioOnly) throw new Error(`The videoOnly and audioOnly in options can't be true at the same time`); if (!options?.videoOnly) { self.pc.addTransceiver("audio", {direction: "sendonly"}); } else { self.constraints.audio = false; } if (!options?.audioOnly) { self.pc.addTransceiver("video", {direction: "sendonly"}); } else { self.constraints.video = false; } if (!navigator.mediaDevices && window.location.protocol === 'http:' && window.location.hostname !== 'localhost') { throw new SrsError('HttpsRequiredError', `Please use HTTPS or localhost to publish, read https://github.com/ossrs/srs/issues/2762#issuecomment-983147576`); } var stream = await navigator.mediaDevices.getUserMedia(self.constraints); // @see https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection/addStream#Migrating_to_addTrack stream.getTracks().forEach(function (track) { self.pc.addTrack(track); // Notify about local track when stream is ok. self.ontrack && self.ontrack({track: track}); }); var offer = await self.pc.createOffer(); await self.pc.setLocalDescription(offer); const answer = await new Promise(function (resolve, reject) { console.log(`Generated offer: ${offer.sdp}`); const xhr = new XMLHttpRequest(); xhr.onload = function() { if (xhr.readyState !== xhr.DONE) return; if (xhr.status !== 200 && xhr.status !== 201) return reject(xhr); const data = xhr.responseText; console.log("Got answer: ", data); return data.code ? reject(xhr) : resolve(data); } xhr.open('POST', url, true); xhr.setRequestHeader('Content-type', 'application/sdp'); xhr.send(offer.sdp); }); await self.pc.setRemoteDescription( new RTCSessionDescription({type: 'answer', sdp: answer}) ); return self.__internal.parseId(url, offer.sdp, answer); }; // See https://datatracker.ietf.org/doc/draft-ietf-wish-whip/ // @url The WebRTC url to play with, for example: // http://localhost:1985/rtc/v1/whep/?app=live&stream=livestream // @options The options to control playing, supports: // videoOnly: boolean, whether only play video, default to false. // audioOnly: boolean, whether only play audio, default to false. self.play = async function(url, options) { if (url.indexOf('/whip-play/') === -1 && url.indexOf('/whep/') === -1) throw new Error(`invalid WHEP url ${url}`); if (options?.videoOnly && options?.audioOnly) throw new Error(`The videoOnly and audioOnly in options can't be true at the same time`); if (!options?.videoOnly) self.pc.addTransceiver("audio", {direction: "recvonly"}); if (!options?.audioOnly) self.pc.addTransceiver("video", {direction: "recvonly"}); var offer = await self.pc.createOffer(); await self.pc.setLocalDescription(offer); const answer = await new Promise(function(resolve, reject) { console.log(`Generated offer: ${offer.sdp}`); const xhr = new XMLHttpRequest(); xhr.onload = function() { if (xhr.readyState !== xhr.DONE) return; if (xhr.status !== 200 && xhr.status !== 201) return reject(xhr); const data = xhr.responseText; console.log("Got answer: ", data); return data.code ? reject(xhr) : resolve(data); } xhr.open('POST', url, true); xhr.setRequestHeader('Content-type', 'application/sdp'); xhr.send(offer.sdp); }); await self.pc.setRemoteDescription( new RTCSessionDescription({type: 'answer', sdp: answer}) ); return self.__internal.parseId(url, offer.sdp, answer); }; // Close the publisher. self.close = function () { self.pc && self.pc.close(); self.pc = null; }; // The callback when got local stream. // @see https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection/addStream#Migrating_to_addTrack self.ontrack = function (event) { // Add track to stream of SDK. self.stream.addTrack(event.track); }; self.pc = new RTCPeerConnection(null); // To keep api consistent between player and publisher. // @see https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection/addStream#Migrating_to_addTrack // @see https://webrtc.org/getting-started/media-devices self.stream = new MediaStream(); // Internal APIs. self.__internal = { parseId: (url, offer, answer) => { let sessionid = offer.substr(offer.indexOf('a=ice-ufrag:') + 'a=ice-ufrag:'.length); sessionid = sessionid.substr(0, sessionid.indexOf('\n') - 1) + ':'; sessionid += answer.substr(answer.indexOf('a=ice-ufrag:') + 'a=ice-ufrag:'.length); sessionid = sessionid.substr(0, sessionid.indexOf('\n')); const a = document.createElement("a"); a.href = url; return { sessionid: sessionid, // Should be ice-ufrag of answer:offer. simulator: a.protocol + '//' + a.host + '/rtc/v1/nack/', }; }, }; // https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection/ontrack self.pc.ontrack = function(event) { if (self.ontrack) { self.ontrack(event); } }; return self; } // Format the codec of RTCRtpSender, kind(audio/video) is optional filter. // https://developer.mozilla.org/en-US/docs/Web/Media/Formats/WebRTC_codecs#getting_the_supported_codecs function SrsRtcFormatSenders(senders, kind) { var codecs = []; senders.forEach(function (sender) { var params = sender.getParameters(); params && params.codecs && params.codecs.forEach(function(c) { if (kind && sender.track.kind !== kind) { return; } if (c.mimeType.indexOf('/red') > 0 || c.mimeType.indexOf('/rtx') > 0 || c.mimeType.indexOf('/fec') > 0) { return; } var s = ''; s += c.mimeType.replace('audio/', '').replace('video/', ''); s += ', ' + c.clockRate + 'HZ'; if (sender.track.kind === "audio") { s += ', channels: ' + c.channels; } s += ', pt: ' + c.payloadType; codecs.push(s); }); }); return codecs.join(", "); } ================================================ FILE: web/webrtc.html ================================================ WebRTC webcam

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================================================ FILE: web/webrtcapi-asr.html ================================================ WebRTC webcam

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================================================ FILE: web/webrtcapi-custom.html ================================================ WebRTC webcam

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================================================ FILE: web/webrtcapi.html ================================================ WebRTC webcam

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================================================ FILE: web/webrtcchat.html ================================================ WebRTC webcam

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================================================ FILE: web/whep.js ================================================ var pc = null; function negotiate() { var host = window.location.hostname pc.addTransceiver('video', { direction: 'recvonly' }); pc.addTransceiver('audio', { direction: 'recvonly' }); return pc.createOffer().then((offer) => { return pc.setLocalDescription(offer); }).then(() => { // wait for ICE gathering to complete return new Promise((resolve) => { if (pc.iceGatheringState === 'complete') { resolve(); } else { const checkState = () => { if (pc.iceGatheringState === 'complete') { pc.removeEventListener('icegatheringstatechange', checkState); resolve(); } }; pc.addEventListener('icegatheringstatechange', checkState); } }); }).then(() => { var offer = pc.localDescription; return fetch("http://"+host+":1985/rtc/v1/whep/?app=live&stream=livestream", { body: offer.sdp, headers: { 'Content-Type': 'application/sdp' }, method: 'POST' }); }).then((response) => { console.log(response) return response.data; }).then((answer) => { return pc.setRemoteDescription({sdp:answer,type:'answer'}); }).catch((e) => { alert(e); }); } function start() { var config = { sdpSemantics: 'unified-plan' }; if (document.getElementById('use-stun').checked) { config.iceServers = [{ urls: ['stun:stun.l.google.com:19302'] }]; } pc = new RTCPeerConnection(config); // connect audio / video pc.addEventListener('track', (evt) => { if (evt.track.kind == 'video') { document.getElementById('video').srcObject = evt.streams[0]; } else { document.getElementById('audio').srcObject = evt.streams[0]; } }); document.getElementById('start').style.display = 'none'; negotiate(); document.getElementById('stop').style.display = 'inline-block'; } function stop() { document.getElementById('stop').style.display = 'none'; // close peer connection setTimeout(() => { pc.close(); }, 500); } ================================================ FILE: webrtc.py ================================================ ############################################################################### # Copyright (C) 2024 LiveTalking@lipku https://github.com/lipku/LiveTalking # email: lipku@foxmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### import asyncio import json import logging import threading import time from typing import Tuple, Dict, Optional, Set, Union from av.frame import Frame from av.packet import Packet from av import AudioFrame import fractions import numpy as np AUDIO_PTIME = 0.020 # 20ms audio packetization VIDEO_CLOCK_RATE = 90000 VIDEO_PTIME = 0.040 #1 / 25 # 30fps VIDEO_TIME_BASE = fractions.Fraction(1, VIDEO_CLOCK_RATE) SAMPLE_RATE = 16000 AUDIO_TIME_BASE = fractions.Fraction(1, SAMPLE_RATE) #from aiortc.contrib.media import MediaPlayer, MediaRelay #from aiortc.rtcrtpsender import RTCRtpSender from aiortc import ( MediaStreamTrack, ) logging.basicConfig() logger = logging.getLogger(__name__) from logger import logger as mylogger class PlayerStreamTrack(MediaStreamTrack): """ A video track that returns an animated flag. """ def __init__(self, player, kind): super().__init__() # don't forget this! self.kind = kind self._player = player self._queue = asyncio.Queue(maxsize=100) self.timelist = [] #记录最近包的时间戳 self.current_frame_count = 0 if self.kind == 'video': self.framecount = 0 self.lasttime = time.perf_counter() self.totaltime = 0 _start: float _timestamp: int async def next_timestamp(self) -> Tuple[int, fractions.Fraction]: if self.readyState != "live": raise Exception if self.kind == 'video': if hasattr(self, "_timestamp"): #self._timestamp = (time.time()-self._start) * VIDEO_CLOCK_RATE self._timestamp += int(VIDEO_PTIME * VIDEO_CLOCK_RATE) self.current_frame_count += 1 wait = self._start + self.current_frame_count * VIDEO_PTIME - time.time() # wait = self.timelist[0] + len(self.timelist)*VIDEO_PTIME - time.time() if wait>0: await asyncio.sleep(wait) # if len(self.timelist)>=100: # self.timelist.pop(0) # self.timelist.append(time.time()) else: self._start = time.time() self._timestamp = 0 self.timelist.append(self._start) mylogger.info('video start:%f',self._start) return self._timestamp, VIDEO_TIME_BASE else: #audio if hasattr(self, "_timestamp"): #self._timestamp = (time.time()-self._start) * SAMPLE_RATE self._timestamp += int(AUDIO_PTIME * SAMPLE_RATE) self.current_frame_count += 1 wait = self._start + self.current_frame_count * AUDIO_PTIME - time.time() # wait = self.timelist[0] + len(self.timelist)*AUDIO_PTIME - time.time() if wait>0: await asyncio.sleep(wait) # if len(self.timelist)>=200: # self.timelist.pop(0) # self.timelist.pop(0) # self.timelist.append(time.time()) else: self._start = time.time() self._timestamp = 0 self.timelist.append(self._start) mylogger.info('audio start:%f',self._start) return self._timestamp, AUDIO_TIME_BASE async def recv(self) -> Union[Frame, Packet]: # frame = self.frames[self.counter % 30] self._player._start(self) # if self.kind == 'video': # frame = await self._queue.get() # else: #audio # if hasattr(self, "_timestamp"): # wait = self._start + self._timestamp / SAMPLE_RATE + AUDIO_PTIME - time.time() # if wait>0: # await asyncio.sleep(wait) # if self._queue.qsize()<1: # #frame = AudioFrame(format='s16', layout='mono', samples=320) # audio = np.zeros((1, 320), dtype=np.int16) # frame = AudioFrame.from_ndarray(audio, layout='mono', format='s16') # frame.sample_rate=16000 # else: # frame = await self._queue.get() # else: # frame = await self._queue.get() frame,eventpoint = await self._queue.get() pts, time_base = await self.next_timestamp() frame.pts = pts frame.time_base = time_base if eventpoint and self._player is not None: self._player.notify(eventpoint) if frame is None: self.stop() raise Exception if self.kind == 'video': self.totaltime += (time.perf_counter() - self.lasttime) self.framecount += 1 self.lasttime = time.perf_counter() if self.framecount==100: mylogger.info(f"------actual avg final fps:{self.framecount/self.totaltime:.4f}") self.framecount = 0 self.totaltime=0 return frame def stop(self): super().stop() # Drain & delete remaining frames while not self._queue.empty(): item = self._queue.get_nowait() del item if self._player is not None: self._player._stop(self) self._player = None def player_worker_thread( quit_event, loop, container, audio_track, video_track ): container.render(quit_event,loop,audio_track,video_track) class HumanPlayer: def __init__( self, nerfreal, format=None, options=None, timeout=None, loop=False, decode=True ): self.__thread: Optional[threading.Thread] = None self.__thread_quit: Optional[threading.Event] = None # examine streams self.__started: Set[PlayerStreamTrack] = set() self.__audio: Optional[PlayerStreamTrack] = None self.__video: Optional[PlayerStreamTrack] = None self.__audio = PlayerStreamTrack(self, kind="audio") self.__video = PlayerStreamTrack(self, kind="video") self.__container = nerfreal def notify(self,eventpoint): if self.__container is not None: self.__container.notify(eventpoint) @property def audio(self) -> MediaStreamTrack: """ A :class:`aiortc.MediaStreamTrack` instance if the file contains audio. """ return self.__audio @property def video(self) -> MediaStreamTrack: """ A :class:`aiortc.MediaStreamTrack` instance if the file contains video. """ return self.__video def _start(self, track: PlayerStreamTrack) -> None: self.__started.add(track) if self.__thread is None: self.__log_debug("Starting worker thread") self.__thread_quit = threading.Event() self.__thread = threading.Thread( name="media-player", target=player_worker_thread, args=( self.__thread_quit, asyncio.get_event_loop(), self.__container, self.__audio, self.__video ), ) self.__thread.start() def _stop(self, track: PlayerStreamTrack) -> None: self.__started.discard(track) if not self.__started and self.__thread is not None: self.__log_debug("Stopping worker thread") self.__thread_quit.set() self.__thread.join() self.__thread = None if not self.__started and self.__container is not None: #self.__container.close() self.__container = None def __log_debug(self, msg: str, *args) -> None: mylogger.debug(f"HumanPlayer {msg}", *args)