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 .
Copy ../nerfstream /nerfstream
WORKDIR /nerfstream
CMD ["python3", "app.py"]
================================================
FILE: LICENSE
================================================
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Copyright [livetalking@lipku]
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================================================
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`
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FILE: musetalk/whisper/whisper/assets/multilingual/special_tokens_map.json
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FILE: musetalk/whisper/whisper/assets/multilingual/tokenizer_config.json
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FILE: musetalk/whisper/whisper/assets/multilingual/vocab.json
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13075, "Ġikke": 13076, "ĠTour": 13077, "ĠDJ": 13078, "otte": 13079, "ĠJahren": 13080, "Ġpreparation": 13081, "ĠдÑĥм": 13082, "Ġ800": 13083, "cop": 13084, "iking": 13085, "Ġ문": 13086, "ĠнÑĥ": 13087, "ĠлеÑĤ": 13088, "åIJĮ": 13089, "ĠIde": 13090, "Ġì¡°ê¸Ī": 13091, "Ġlaughter": 13092, "Ġmolecules": 13093, "ĠRest": 13094, "Ġobserved": 13095, "dzie": 13096, "Ġadvertising": 13097, "erto": 13098, "Ġmoins": 13099, "ĠMIT": 13100, "Ġexcit": 13101, "Ġtum": 13102, "Ġtyl": 13103, "Ġinvested": 13104, "Ġpharm": 13105, "Ġunexpected": 13106, "Ġphi": 13107, "otype": 13108, "weise": 13109, "Ġgeç": 13110, "jourd": 13111, "Ġhorses": 13112, "nÄħ": 13113, "=\"": 13114, "ĠSM": 13115, "Ġfib": 13116, "Ġclips": 13117, "çķ¶": 13118, "å¦Ĥæŀľ": 13119, "Ġregime": 13120, "Ġrotate": 13121, "rou": 13122, "nik": 13123, "Ġarmor": 13124, "ðŁĺ": 13125, "еÑĢа": 13126, "度": 13127, "ĠOch": 13128, "Ġrichtig": 13129, "üzel": 13130, "aneously": 13131, "mek": 13132, "éĮ¯": 13133, "ĠXiao": 13134, "Ġexisted": 13135, "worth": 13136, 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17978, "ĠIls": 17979, "Ġunfold": 17980, "¶Ģë": 17981, "Ġprofits": 17982, "Ġwarming": 17983, "ĠGang": 17984, "Ġnetworking": 17985, "Ġmega": 17986, "Ġthoroughly": 17987, "lements": 17988, "ĠHm": 17989, "Ġdeciding": 17990, "Ġemotionally": 17991, "Ġexhausted": 17992, "ĠÐŁÐ¾ÑĤ": 17993, "cido": 17994, "ĠHTML": 17995, "Ġcopyright": 17996, "Ġmelody": 17997, "yim": 17998, "Ġanders": 17999, "oshop": 18000, "Ġë³¼": 18001, "Ġathlete": 18002, "ĠGE": 18003, "Ġfrequent": 18004, "Ġdesires": 18005, "Ġneeding": 18006, "ĠYun": 18007, "Ġrifle": 18008, "Ġlover": 18009, "'T": 18010, "Ġdense": 18011, "Ġtão": 18012, "Ġnotified": 18013, "Ġidi": 18014, "ìĹŃ": 18015, "íĨ": 18016, "Ġinteracting": 18017, "Ġrapport": 18018, "еÑĢи": 18019, "ski": 18020, "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, 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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": 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"Ġbenchmark": 18927, "ваÑĤ": 18928, "amine": 18929, "ãģ¡ãĤĥ": 18930, "Ġanh": 18931, "Ġcontinent": 18932, "ĠFDA": 18933, "ì¡°": 18934, "Ġêtes": 18935, "×Ļ×IJ": 18936, "å¼Ģ": 18937, "Ġbloody": 18938, "ĠNine": 18939, "ielt": 18940, "emand": 18941, "Ġë³´ê³ł": 18942, "Ġtidak": 18943, "ĠScient": 18944, "plex": 18945, "osten": 18946, "Ġanimated": 18947, "assa": 18948, "Ġderived": 18949, "ĠиÑģÑĤоÑĢ": 18950, "ĠMig": 18951, "ìħĺ": 18952, "Ġros": 18953, "plus": 18954, "osaur": 18955, "Ġ^": 18956, "Ġintensive": 18957, "Ġglobally": 18958, "Ġdiferen": 18959, "ìĿ´ê³ł": 18960, "ä½łçļĦ": 18961, "Äħd": 18962, "Ġdés": 18963, "Ġpresentations": 18964, "ĠCro": 18965, "Ġesses": 18966, "ĠBetween": 18967, "Pa": 18968, "Ġnaw": 18969, "à¸Ńà¸ĩ": 18970, "Ġbreed": 18971, "ichte": 18972, "ĠÐŀни": 18973, "ĠBuilding": 18974, "Ġconform": 18975, "MO": 18976, "ĠÐĸ": 18977, "ĠKid": 18978, "nas": 18979, "ĠDue": 18980, "rés": 18981, "Ġdiox": 18982, "ĠBin": 18983, "Ġtaxi": 18984, "Ġsap": 18985, "ĠHub": 18986, "çĤºä»Ģ麼": 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"аÑĤелÑĮно": 20279, "ĠÐŀна": 20280, "Ġperceive": 20281, "Ġê²Ģ": 20282, "Ġê°Ģìŀ¥": 20283, "ĠиÑģк": 20284, "ĠMatter": 20285, "ĠBluetooth": 20286, "Ġpearl": 20287, "Ġarise": 20288, "Ġmonument": 20289, "Ġименно": 20290, "agi": 20291, "ÙĦÙĬ": 20292, "Ġrho": 20293, "Ġsmarter": 20294, "Ġconj": 20295, "ока": 20296, "Ġkeen": 20297, "ĠTreat": 20298, "клÑİÑĩ": 20299, "Ġpacket": 20300, "elsius": 20301, "ĠAlab": 20302, "ини": 20303, "Ġpsi": 20304, "Ġenjoyable": 20305, "ĠEllen": 20306, "Ġвм": 20307, "Ġeliminated": 20308, "ĠRow": 20309, "Ġzombie": 20310, "ĠKu": 20311, "Ġphrases": 20312, "Ġgren": 20313, "uter": 20314, "Ġdirekt": 20315, "×ĸ": 20316, "enen": 20317, "usa": 20318, "ĠÑģлов": 20319, "İ": 20320, "ĠGh": 20321, "Ġcorrid": 20322, "Ġqueer": 20323, "ĠLinda": 20324, "Ġona": 20325, "Ġobligation": 20326, "dar": 20327, "Ġص": 20328, "emment": 20329, "acies": 20330, "Ġscrewed": 20331, "Ġnak": 20332, "Ġayud": 20333, "ä¸Ķ": 20334, "ár": 20335, "lez": 20336, "Ġdrown": 20337, "ĠMedicine": 20338, "Ġlabs": 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"ĠÄį": 22392, "ceptions": 22393, "cca": 22394, "izable": 22395, "mand": 22396, "Ġbreakthrough": 22397, "Ġtablespoon": 22398, "ĠSEC": 22399, "ikh": 22400, "ĠSão": 22401, "Ġпло": 22402, "amen": 22403, "Ġprac": 22404, "Ġdarling": 22405, "Ġtaller": 22406, "Ġrendering": 22407, "Ġìļ°ë¦¬ê°Ģ": 22408, "ĠÏĦηÏĤ": 22409, "Ġmã": 22410, "Ġesos": 22411, "uerdo": 22412, "ĠÑģÑĩиÑĤ": 22413, "aller": 22414, "ìĹĪìĸ´ìļĶ": 22415, "Ġmillones": 22416, "lerin": 22417, "Ġpegar": 22418, "onne": 22419, "Ġenrollment": 22420, "Ġliegt": 22421, "Ġboa": 22422, "wiÄĻ": 22423, "bsp": 22424, "Ġcycling": 22425, "ĠBernie": 22426, "Ġ1989": 22427, "ĠдалÑĮ": 22428, "ĠDakota": 22429, "ĠÑģвÑıз": 22430, "ĠCP": 22431, "Ġstare": 22432, "íĤ¤": 22433, "Ġprosperity": 22434, "Ġarrangements": 22435, "Ġarriving": 22436, "mä": 22437, "Ġkayak": 22438, "ipt": 22439, "Ġpardon": 22440, "Ġrelat": 22441, "Ġverste": 22442, "ĠFig": 22443, "Ġfoil": 22444, "ĠTalking": 22445, "peare": 22446, "Ġnoi": 22447, "ĠпÑĢиÑĪ": 22448, "Ġhockey": 22449, 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"pi": 22630, "gle": 22631, "Ġchorus": 22632, "Ġgly": 22633, "Ġreinforce": 22634, "Ġmuff": 22635, "ĠShen": 22636, "ĠHola": 22637, "Ñĥг": 22638, "videmment": 22639, "vial": 22640, "acious": 22641, "laimed": 22642, "ĠRico": 22643, "Ġvegg": 22644, "Ġillustration": 22645, "ĠButter": 22646, "owad": 22647, "Ġeux": 22648, "Ġenfants": 22649, "ĠLeader": 22650, "ĠVillage": 22651, "etically": 22652, "ÙĨÙĬ": 22653, "Ġstew": 22654, "Ġsurprises": 22655, "Ġcue": 22656, "ĠGrandma": 22657, "ĠCelsius": 22658, "ĠRicht": 22659, "enc": 22660, "Ġpetition": 22661, "Ġherb": 22662, "Ġwicked": 22663, "Ġschle": 22664, "ocaly": 22665, "Ġtransf": 22666, "Ġtokens": 22667, "ĠGray": 22668, "ĠBBC": 22669, "IK": 22670, "Ġ1500": 22671, "zn": 22672, "ĠNev": 22673, "Ġkoy": 22674, "Ġzar": 22675, "Ġbullshit": 22676, "ĠColombia": 22677, "ulative": 22678, "Ġwidespread": 22679, "yect": 22680, "kit": 22681, "Ġempresa": 22682, "Ġnour": 22683, "Ġburns": 22684, "atin": 22685, "aired": 22686, "Ġrevolutionary": 22687, "ĠгодÑĥ": 22688, "ĠLogan": 22689, "Ġ1996": 22690, "ĠGraham": 22691, "reb": 22692, "ĠNHS": 22693, "æľĽ": 22694, "Ġcostumes": 22695, "Ġnawet": 22696, "Ġlovers": 22697, "ĠLucy": 22698, "ĠIndigenous": 22699, "íķĺ기": 22700, "Ġimmunity": 22701, "¥´ë": 22702, "uito": 22703, "Ġexcessive": 22704, "Ġdonations": 22705, "Ġ×Ķר": 22706, "Ġ첫": 22707, "éīĦ": 22708, "Ġdrying": 22709, "melon": 22710, "Ġsurveys": 22711, "Ġ무ìĬ¨": 22712, "風": 22713, "aaa": 22714, "Ġprobe": 22715, "ancial": 22716, "Ġlouder": 22717, "Ġhotels": 22718, "Ã¼ÄŁ": 22719, "agner": 22720, "Ġorigins": 22721, "Ġë§Īì§Ģë§ī": 22722, "Ġ**": 22723, "Ġstrangers": 22724, "ĠHaus": 22725, "comed": 22726, "Ġanthrop": 22727, "Ġuso": 22728, "ĠìķĦì§ģ": 22729, "ĠYuan": 22730, "ĠíķĦìļĶ": 22731, "pler": 22732, "ressive": 22733, "Ġspraw": 22734, "ĠStew": 22735, "Ġ1994": 22736, "Ġelders": 22737, "Ġmeinen": 22738, "Ġjunt": 22739, "Ġacoust": 22740, "ĠWohn": 22741, "Ġbananas": 22742, "Ġprojection": 22743, "ĠStick": 22744, "legt": 22745, "speed": 22746, "ĠcÅ©ng": 22747, "ĠWort": 22748, "ĠBaltimore": 22749, "ĠÑĨел": 22750, "Ġdunno": 22751, "å¼·": 22752, "?,": 22753, "ãĥīãĥ³": 22754, "ĠLocal": 22755, "osto": 22756, "ÐŃ": 22757, "ода": 22758, "ĠPortuguese": 22759, "Ġtheirs": 22760, "Ġdém": 22761, "åı¦": 22762, "Ġdrauf": 22763, "ĠBuddhist": 22764, "erta": 22765, "Ge": 22766, "Ġcarrot": 22767, "ĠWonderful": 22768, "Ġsoak": 22769, "Ġchairman": 22770, "ggi": 22771, "ICA": 22772, "fried": 22773, "Ġflick": 22774, "ĠThroughout": 22775, "Ġìļ°ë": 22776, "Ġcough": 22777, "Ġfluffy": 22778, "school": 22779, "Ġripped": 22780, "--------": 22781, "ĠZukunft": 22782, "Ġнеб": 22783, "Ġsto": 22784, "ĠBO": 22785, "pent": 22786, "ĠLawrence": 22787, "ÏīÏĤ": 22788, "sticks": 22789, "ĠEins": 22790, "ĠÑĢÑĭ": 22791, "ĠStrong": 22792, "Ġcaramel": 22793, "Ġspite": 22794, "azar": 22795, "éĥ½æĺ¯": 22796, "Ġcritically": 22797, "Ġobra": 22798, "owitz": 22799, "ĠZone": 22800, "ĠÑĢек": 22801, "Ġsug": 22802, "arded": 22803, "Ġgì": 22804, "ffentlich": 22805, "anche": 22806, "ØŁ": 22807, "astically": 22808, "ìĿ¼ë": 22809, "лав": 22810, "Ġsimplest": 22811, "ĠFriend": 22812, "Ġquello": 22813, "Ġambition": 22814, "Ġabbiamo": 22815, "åºķ": 22816, "ĠÑĦоÑĢм": 22817, "ĠEssa": 22818, "Ġeducators": 22819, "Ġstatistical": 22820, "éĢĻéĤĬ": 22821, "Ġchanger": 22822, "Ġatau": 22823, "étais": 22824, "ĠShakespeare": 22825, "ëIJĺ": 22826, "Ġtriggers": 22827, "Ġrealiz": 22828, "Ġcelui": 22829, "wheel": 22830, "Ġloyalty": 22831, "Ġscreams": 22832, "kehr": 22833, "ĠMega": 22834, "east": 22835, "Ġtops": 22836, "ĠTotally": 22837, "ountain": 22838, "lord": 22839, "Ġviolation": 22840, "ĠGA": 22841, "Ġnicer": 22842, "ĠFresh": 22843, "ĠMelissa": 22844, "function": 22845, "Ġrape": 22846, "Ġexceptions": 22847, "Ġsilicon": 22848, "Ġliberty": 22849, "Ġhouseholds": 22850, "ãģįãģ¾ãģĻ": 22851, "ĠCA": 22852, "ĠÐŀб": 22853, "Ġlib": 22854, "ŀĮ": 22855, "cific": 22856, "Ġtropical": 22857, "Ġinvestigating": 22858, "HD": 22859, "Ġadapter": 22860, "ĠPitt": 22861, "ancia": 22862, "ĠShell": 22863, 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27928, "ĠAIDS": 27929, "ëIJľ": 27930, "Ġfreel": 27931, "ĠAlzheimer": 27932, "ĠÅļ": 27933, "Her": 27934, "Ġtornado": 27935, "agens": 27936, "ĠìŀĪìĸ´ìĦľ": 27937, "ĠTransform": 27938, "Ġprocesso": 27939, "Ġmillise": 27940, "Ġprofessionally": 27941, "Ġmemb": 27942, "ocation": 27943, "Ġstyling": 27944, "ĠобÑıз": 27945, "ĠOperation": 27946, "Ġwygl": 27947, "ĠRan": 27948, "ĠçļĦ": 27949, "ĠKin": 27950, "á»±c": 27951, "ĠBAR": 27952, "Ġpaperwork": 27953, "Ġtule": 27954, "Ġqueria": 27955, "Ġcomply": 27956, "ĠHair": 27957, "×Ļ׼": 27958, "ĠпÑĢоÑģÑĤ": 27959, "Ġmutation": 27960, "Ġreprés": 27961, "Ġoctopus": 27962, "Ġimportantes": 27963, "Ġdeserved": 27964, "etr": 27965, "Ġdisasters": 27966, "lında": 27967, "iqué": 27968, "ĠDeshalb": 27969, "soo": 27970, "ossip": 27971, "Ġrelieved": 27972, "ĠCollins": 27973, "Ġwaterproof": 27974, "ĠYuk": 27975, "Ġcopying": 27976, "Ġbütün": 27977, "ĠHeute": 27978, "ĠEntre": 27979, "Ġresidual": 27980, "Ġcolonies": 27981, "Ġénorm": 27982, "ĠErin": 27983, "Ġstan": 27984, "Ġtremendously": 27985, "Ġcaptures": 27986, "ĠSai": 27987, "âce": 27988, "ĠmiaÅĤ": 27989, "Ġ87": 27990, "Ġlogging": 27991, "Ġinserted": 27992, "Ġinherently": 27993, "ìĿij": 27994, "lave": 27995, "ниÑĩ": 27996, "Ġfemmes": 27997, "Ġdép": 27998, "uks": 27999, "acia": 28000, "ĠWade": 28001, "Ġjij": 28002, "ĠVincent": 28003, "ĠIceland": 28004, "hem": 28005, "Ġapology": 28006, "ĠPeg": 28007, "Ġglued": 28008, "Ġcompanions": 28009, "ĠLiver": 28010, "Ġcriticized": 28011, "leading": 28012, "Ġsäga": 28013, "æ¼Ĥ": 28014, "Ġsquid": 28015, "Ġnarratives": 28016, "Ġtaka": 28017, "nez": 28018, "weit": 28019, "Ġtripod": 28020, "Ġexplic": 28021, "Ġspinal": 28022, "Ġapproximation": 28023, "Ġpagar": 28024, "ĠCalvin": 28025, "ĠведÑĮ": 28026, "Ġlac": 28027, "Ġproactive": 28028, "ĠTrain": 28029, "orf": 28030, "Ġsten": 28031, "Ġgrapes": 28032, "Ġmeus": 28033, "Ġautomat": 28034, "Ġbiased": 28035, "Ġchaîne": 28036, "coal": 28037, "Ġrencont": 28038, "ĠKum": 28039, "Ġfestivals": 28040, "Ġstartups": 28041, "Ġaka": 28042, "ãģ¹": 28043, "Ġcylind": 28044, "sna": 28045, "CRI": 28046, "Ġresultado": 28047, "Ġmilestone": 28048, "ĠÏħ": 28049, "Ġteleport": 28050, "zych": 28051, "62": 28052, "åħ³": 28053, "ĠFear": 28054, "Ġnucleus": 28055, "Ġshines": 28056, "hov": 28057, "ĠPartners": 28058, "ĠKas": 28059, "Ġnadie": 28060, "Ġalerts": 28061, "ĠBILL": 28062, "strong": 28063, "ĠNate": 28064, "ĠDenmark": 28065, "ĠCav": 28066, "OST": 28067, "hält": 28068, "ĠìķĦëĭĮ": 28069, "anyon": 28070, "Ġencourages": 28071, "ĠпоÑģÑĤав": 28072, "ĠHuang": 28073, "ãģĬé¡ĺãģĦ": 28074, "STA": 28075, "Ġpaints": 28076, "ãģĻãģĶ": 28077, "Ġschedules": 28078, "Ġcheated": 28079, "Ġapprox": 28080, "Ġï·": 28081, "Ġ».": 28082, "Ġsmiles": 28083, "isure": 28084, "Ġnered": 28085, "arden": 28086, "Ġcurt": 28087, "ĠëĮ": 28088, "ĠRoth": 28089, "Ġpuisque": 28090, "ĠGET": 28091, "ĠVeget": 28092, "Ġproduz": 28093, "ĠBelgium": 28094, "ĠCampus": 28095, "ר×Ļ×Ŀ": 28096, "icut": 28097, "ĠÑģним": 28098, "Ġréuss": 28099, "Ġslippery": 28100, "ĠEw": 28101, "ų": 28102, "ĠLegends": 28103, "ĠTiffany": 28104, "ализ": 28105, "ĠпеÑĢев": 28106, "ĠогÑĢом": 28107, "Ġcros": 28108, "ĠCE": 28109, "Bu": 28110, "Ġensures": 28111, "Ġgrandchildren": 28112, "Ġacuerdo": 28113, "Ġprisoner": 28114, "Ġthirsty": 28115, "bane": 28116, "Ġë¹ł": 28117, "Ġúltima": 28118, "ĠLaunch": 28119, "nity": 28120, "Ġcombustion": 28121, "Ġunicorn": 28122, "Ġfamille": 28123, "Ġlowering": 28124, "ĠYing": 28125, "building": 28126, "Ġduo": 28127, "ĠMéxico": 28128, "astian": 28129, "Ġ먹ìĿĦ": 28130, "ĠRalph": 28131, "Ġrewrite": 28132, "Ġglam": 28133, "ifique": 28134, "Er": 28135, "ĠRunning": 28136, "онов": 28137, "Ġmeanings": 28138, "Ġchewy": 28139, "ĠLeslie": 28140, "Ġfinest": 28141, "Ġhahaha": 28142, "ĠSTEP": 28143, "Ġloneliness": 28144, "rians": 28145, "Ġquestioned": 28146, "Ġesque": 28147, "Ġsinking": 28148, "Ġpeso": 28149, "ĠWrong": 28150, "asmine": 28151, "Ġdefinitive": 28152, "Ġbuys": 28153, "Ġcruc": 28154, "cool": 28155, "ĠëłĪ": 28156, "Ġpó": 28157, "Ġutilized": 28158, 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"ĠOutside": 28218, "Ġharus": 28219, "æºĸ": 28220, "одаÑĢ": 28221, "Ġvisitor": 28222, "çŃĶ": 28223, "Ġstrengthening": 28224, "Ġ92": 28225, "vio": 28226, "Ġ리": 28227, "Ġgreedy": 28228, "Ġpoquito": 28229, "uder": 28230, "ĠKopf": 28231, "Ġëĭ¤ìĿĮìĹIJ": 28232, "Ġseis": 28233, "ático": 28234, "Ġtrusting": 28235, "ÃŃp": 28236, "ĠEmm": 28237, "leen": 28238, "ĠاÙĦÙĨ": 28239, "Ġrecruitment": 28240, "ĠFilip": 28241, "ĠÙĥÙĦ": 28242, "Clint": 28243, "ĠвеÑģ": 28244, "auft": 28245, "Ġdominate": 28246, "Ġresto": 28247, "Ġkra": 28248, "ái": 28249, "ĠCait": 28250, "rows": 28251, "Ġcountryside": 28252, "Ġ1945": 28253, "аÑĨиÑİ": 28254, "Ġди": 28255, "Ġkernel": 28256, "lov": 28257, "Ġcalculating": 28258, "دا": 28259, "ĠWalt": 28260, "Ġempowering": 28261, "Ġchassis": 28262, "linear": 28263, "гÑĥ": 28264, "Ġnova": 28265, "Ġuy": 28266, "Ġ69": 28267, "Ġencompass": 28268, "trl": 28269, "Ġcomputational": 28270, "Ġworms": 28271, "Ġnhiá»ģu": 28272, "Ġastronauts": 28273, "Ġves": 28274, "Ġsytu": 28275, "Ġdemanded": 28276, "Ġcs": 28277, "ĠMol": 28278, "Ġ`": 28279, "Ġchant": 28280, "Ġthereby": 28281, "Ġpenis": 28282, "Ġemoc": 28283, "wyn": 28284, "Ñĥже": 28285, "Ġtread": 28286, "óle": 28287, "Ġdeepest": 28288, "Ġmache": 28289, "ĠVent": 28290, "ĠAmsterdam": 28291, "ãĥĽ": 28292, "Ġrebel": 28293, "Ġ61": 28294, "ĠвкÑĥÑģ": 28295, "uffs": 28296, "ĠdoÄŁru": 28297, "ĠNapole": 28298, "ήÏĥ": 28299, "Ġworkouts": 28300, "ĠGlad": 28301, "неÑģ": 28302, "Ġtensions": 28303, "ĠShift": 28304, "ĠGuer": 28305, "íĮIJ": 28306, "Ġì¹ľêµ¬": 28307, "Ðĸ": 28308, "Ġimplant": 28309, "êu": 28310, "ê¸Ģ": 28311, "Ġauthorized": 28312, "CER": 28313, "ĠRV": 28314, "Ġhil": 28315, "lev": 28316, "cimento": 28317, "ĠUFO": 28318, "ìĥĪ": 28319, "è¨Ĥ": 28320, "wor": 28321, "Ġdances": 28322, "ĠPixel": 28323, "çľĭä¸Ģä¸ĭ": 28324, "Ġtrotzdem": 28325, "Ġobten": 28326, "ĠAlfred": 28327, "Ġcostly": 28328, "ĠStanley": 28329, "Ġterrorists": 28330, "ĠWid": 28331, "ħëĭĪëĭ¤": 28332, "Ġleicht": 28333, "ìĿ´ìĬ¤": 28334, "Ġdobrze": 28335, "Ġhesit": 28336, "Ġerzäh": 28337, "Ġeinige": 28338, "Ġhebt": 28339, "Ñģе": 28340, "Ġunpredict": 28341, "Cómo": 28342, "remos": 28343, "ĠThankfully": 28344, "Ġpurse": 28345, "chs": 28346, "ancer": 28347, "ulos": 28348, "stud": 28349, "æľīæ²Ĵæľī": 28350, "Ġneurolog": 28351, "ĠAncient": 28352, "Out": 28353, "awsze": 28354, "Ġoppose": 28355, "Ġantibodies": 28356, "ĠSomehow": 28357, "ropolitan": 28358, "ktor": 28359, "ĠÑģÑĤоÑĢонÑĭ": 28360, "Ġrockets": 28361, "Ġdisable": 28362, "Ġcatastroph": 28363, "´ìŀ": 28364, "Ġcyn": 28365, "ĠдÑĢÑĥзÑĮÑı": 28366, "Ġinstructors": 28367, "emaal": 28368, "Ġetwa": 28369, "Ġyuan": 28370, "ĠGround": 28371, "Ġpremiere": 28372, "Ñĩив": 28373, "Ġsaint": 28374, "yba": 28375, "Ġkok": 28376, "Ġcontractors": 28377, "Ġê°ģ": 28378, "Ġ×IJ׾": 28379, "Ġheadline": 28380, "Ġcompletamente": 28381, "Ġinexpensive": 28382, "Ġviu": 28383, "ĠGrande": 28384, "Ġbleed": 28385, "물": 28386, "Ġ73": 28387, "ĠtodavÃŃa": 28388, "ĠRush": 28389, "ĠElder": 28390, "ê°ĢëĬĶ": 28391, "ĠRou": 28392, "ĠженÑī": 28393, "ĠMira": 28394, "Ġdeine": 28395, "Ġkarma": 28396, "Ġumm": 28397, "Ġentsche": 28398, "ĠHolocaust": 28399, "Ġdiscoveries": 28400, "aments": 28401, "Ġraison": 28402, "Ġburgers": 28403, "Back": 28404, "Ġgdy": 28405, "ĠAG": 28406, "ĠDaw": 28407, "ìķł": 28408, "headed": 28409, "ĠClar": 28410, "Inst": 28411, "ĠLieutenant": 28412, "ĠAfD": 28413, "ĠCes": 28414, "Ġpersonalized": 28415, "Ġinterfaces": 28416, "à¸Īะ": 28417, "ĠÑĢеж": 28418, "Ġsuic": 28419, "Ġstarving": 28420, "Ġoxide": 28421, "Ġdecorated": 28422, "ĠDU": 28423, "ĠìĺĪìģĺ": 28424, "Ġquo": 28425, "Ġdistortion": 28426, "段": 28427, "Ġ먹ìĸ´ë": 28428, "Ġstakes": 28429, "æĺİçϽ": 28430, "Ġsyntax": 28431, "Ġbiết": 28432, "thy": 28433, "icie": 28434, "Ġbrasile": 28435, "isis": 28436, "RC": 28437, "Ġshook": 28438, "Ġdepths": 28439, "ĠCosta": 28440, "Ġvocals": 28441, "Ġcoaster": 28442, "Ġfalou": 28443, "ettle": 28444, "Ġkennen": 28445, "Ġderive": 28446, "Ġaids": 28447, "ĠÐĿик": 28448, "Ġentwic": 28449, "Ġvertically": 28450, "ĠÍ": 28451, "ĠSUV": 28452, "Ġfireworks": 28453, "Ġspecifics": 28454, "交": 28455, "Ġinsisted": 28456, "Ġdeshalb": 28457, "ĠGonz": 28458, "love": 28459, "ĠMilitary": 28460, "ĠPierre": 28461, "ĠâĪ": 28462, "ĠWhose": 28463, "Ġperfume": 28464, "ĠÏĢε": 28465, "Ġlowered": 28466, "Ġcrosses": 28467, "Ġtranslates": 28468, "Ġarriba": 28469, "ÃŃdo": 28470, "ĠLev": 28471, "åħ§": 28472, "ĠCiao": 28473, "Ġscholarships": 28474, "Ġgestures": 28475, "ĠÑĢезÑĥлÑĮÑĤаÑĤ": 28476, "Ġquestão": 28477, "ĠColonel": 28478, "ĠBott": 28479, "رÙģ": 28480, "NING": 28481, "ĠWatching": 28482, "ĠPurple": 28483, "ÑģÑĤÑĢан": 28484, "Ġexecutives": 28485, "ĠKris": 28486, "orneys": 28487, "еннÑĭй": 28488, "Ġcoated": 28489, "Ä©": 28490, "Ġparked": 28491, "ĠÑģвеÑĤ": 28492, "!!!!!": 28493, "ĠFloyd": 28494, "ısı": 28495, "ziÄĩ": 28496, "Ġmotivate": 28497, "ĠElon": 28498, "lean": 28499, "Ĩĵ": 28500, "Ġip": 28501, "Ġniż": 28502, "ĠExperience": 28503, "ĠTina": 28504, "ĠKollege": 28505, "ĠAmbassador": 28506, "inya": 28507, "Ġtheft": 28508, "Ġheures": 28509, "ĠMyst": 28510, "Ġmaison": 28511, "leb": 28512, "Ġbowls": 28513, "ĠBürger": 28514, "ĠRoosevelt": 28515, "RP": 28516, "ê°ĢìļĶ": 28517, "ĠDelicious": 28518, "erdings": 28519, "ĠAssociate": 28520, "ousse": 28521, "ĠCort": 28522, "ĠRepeat": 28523, "ĠGlory": 28524, "Ġcontag": 28525, "à¹Ģล": 28526, "ĠParad": 28527, "ĠKerry": 28528, "Ġê¿": 28529, "ĠWave": 28530, "å¿ħ": 28531, "Ġgateway": 28532, "çIJĥ": 28533, "!ãĢį": 28534, "Ġtranscend": 28535, "Ġdamages": 28536, "Ġtails": 28537, "Ġgravitational": 28538, "ĠShield": 28539, "Ġprimitive": 28540, "Ġcarriers": 28541, "ĠHuawei": 28542, "ÙĤد": 28543, "Ġfeliz": 28544, "ĠMia": 28545, "åĥķ": 28546, "ĠпÑĢÑıмо": 28547, "ĠпÑĢоиÑģÑħодиÑĤ": 28548, "ĠMurphy": 28549, "ĠActiv": 28550, "ãĥĥãĤ¯": 28551, "Ġdiscomfort": 28552, "×ij×Ķ": 28553, "ĠKell": 28554, "ĠCentury": 28555, "Ġspaghetti": 28556, "ĠDurch": 28557, "Ġcierto": 28558, "ĠEmpress": 28559, "Ġguts": 28560, "neg": 28561, "ĠдоÑģÑĤаÑĤоÑĩно": 28562, "Ġvoluntary": 28563, "失": 28564, "Ġsquirrel": 28565, "欢": 28566, "ãģ¡ãĤī": 28567, "ĠMaz": 28568, "´ìĭ¬": 28569, "Ġви": 28570, "ãĤ§": 28571, "ĠÑĤакиÑħ": 28572, "ĠSharon": 28573, "Ġenthusiastic": 28574, "irement": 28575, "Ġíŀĺëĵ¤": 28576, "Ġpotrze": 28577, "Ġinitiated": 28578, "ãĥ§": 28579, "ĠÅĽrod": 28580, "ĠìĿ´ë¦Ħ": 28581, "Ġremake": 28582, "Ġculmin": 28583, "Ġconfuse": 28584, "miyor": 28585, "urar": 28586, "CTOR": 28587, "Ġbunny": 28588, "Ġ大": 28589, "ä¸įèĥ½": 28590, "elp": 28591, "Ġvampire": 28592, "Ġillumin": 28593, "ĠHend": 28594, "ĠкаÑĩе": 28595, "ĠSalv": 28596, "Ġканал": 28597, "Ġporta": 28598, "Ġasshole": 28599, "Ġsupporter": 28600, "Ġskeptical": 28601, "Ġknead": 28602, "Ġìĺ¬": 28603, "eza": 28604, "Ġquê": 28605, "ĠDH": 28606, "Ġrodz": 28607, "owners": 28608, "Ġplots": 28609, "Ġdelays": 28610, "Ġbelonged": 28611, "Ġahh": 28612, "Ġcarved": 28613, "Ġrisen": 28614, "Ġorden": 28615, "phony": 28616, "issy": 28617, "!!!!!!!!": 28618, "ĠolduÄŁunu": 28619, "Ġroses": 28620, "Ġintrins": 28621, "ĠAngst": 28622, "Ġfinalement": 28623, "ì§Ŀ": 28624, "SOUND": 28625, "Ġindul": 28626, "°Į": 28627, "Ġ×ķ×Ķ": 28628, "chy": 28629, "акÑģим": 28630, "Ġnggak": 28631, "Ġliz": 28632, "Ġelectoral": 28633, "ĠShawn": 28634, "ricia": 28635, "Ġarsen": 28636, "ĠPep": 28637, "Ġ2030": 28638, "Ġtrophy": 28639, "Ġsmoother": 28640, "Ġerre": 28641, "Ġcrashes": 28642, "Ġschne": 28643, "Ġasi": 28644, "ĠMaÃŁ": 28645, "Ñĥли": 28646, "ÑĩеÑģки": 28647, "ieves": 28648, "REAM": 28649, "Ġstirring": 28650, "ãĥĢ": 28651, "usta": 28652, "Ġinver": 28653, "sight": 28654, "ordu": 28655, "oor": 28656, "ĠÄĥn": 28657, "Ġpermitted": 28658, "ÑĢÑĮ": 28659, "Ġchalk": 28660, "ãĤĪãģĹ": 28661, "Ġtattoos": 28662, "ĠRelations": 28663, "ĠHoy": 28664, "ksam": 28665, "Ġdentist": 28666, "Ġ미êµŃ": 28667, "Ġsofa": 28668, "ĠÑĶ": 28669, "Ġforme": 28670, "ÙĤØ©": 28671, "Ġë²ł": 28672, "Ġembraced": 28673, "mil": 28674, "Ġsunglasses": 28675, "Ġê°Ķ": 28676, "Ġseamless": 28677, "Ġbeep": 28678, "ächst": 28679, "Ġsweets": 28680, "Ġsemaine": 28681, "Ġirrelevant": 28682, "Ġdesenvol": 28683, "ÏģÏī": 28684, "ĠпÑĢоизвод": 28685, "angs": 28686, "Ġaroma": 28687, "Ġpools": 28688, "Ġgiá»Ŀ": 28689, "ĠUg": 28690, "Ġclimbed": 28691, "Ġtrending": 28692, "Ġseperti": 28693, "ĠBarr": 28694, "ĠpÅĤ": 28695, "ĠOriginally": 28696, "ĠÚ¯": 28697, "utto": 28698, "Ĭ¸ë": 28699, "ĠкоÑĤоÑĢÑĭÑħ": 28700, "ĠзаÑħ": 28701, "Ġeigenen": 28702, "Ġmurderer": 28703, "ername": 28704, "Åŀ": 28705, "Ġannouncing": 28706, "ĠPlatform": 28707, "Ġexplanations": 28708, "Ġpresente": 28709, "ĠNasıl": 28710, "Ġorphan": 28711, "ĠFortnite": 28712, "rospect": 28713, "eredith": 28714, "ĠìĹĨìĸ´": 28715, "ĠNIH": 28716, "wagen": 28717, "Ġremed": 28718, "§Ģë": 28719, "mont": 28720, "ĠJeffrey": 28721, "prom": 28722, "Ġfünf": 28723, "Ġназад": 28724, "Ġcucumber": 28725, "ĠSummit": 28726, "åĪĿ": 28727, "§¤": 28728, "ÐĿÐIJЯ": 28729, "ĠJet": 28730, "Ġcambio": 28731, "ÑĥйÑĤе": 28732, "Ġcubic": 28733, "Ġdisproportion": 28734, "erez": 28735, "Ġmadness": 28736, "çĹĽ": 28737, "Ġtint": 28738, "Ġfueron": 28739, "Ġky": 28740, "Ġbipart": 28741, "ãģ¾ãģĽ": 28742, "Sam": 28743, "Ġë½": 28744, "Ġriv": 28745, "ĠTank": 28746, "ĠëĨĵ": 28747, "Ġrendered": 28748, "ÅĽlÄĻ": 28749, "conds": 28750, "Ġdisruption": 28751, "Ġinconven": 28752, "Ġquiser": 28753, "Ġdenial": 28754, "Ġgalaxies": 28755, "Ġsovereign": 28756, "Ġpolsk": 28757, "ÏģÏİ": 28758, "Ġmex": 28759, "Ġcaracter": 28760, "ĠLego": 28761, "anden": 28762, ".'\"": 28763, "ĠíĶĮë": 28764, "Ġcompressor": 28765, "ĠMovie": 28766, "Ġapplicants": 28767, "ziehen": 28768, "Ġvegetation": 28769, "Ġbelle": 28770, "ĠGOOD": 28771, "ĠBau": 28772, "Ġresent": 28773, "sex": 28774, "amentos": 28775, "Ġ×Ķ×ĸ×Ķ": 28776, "Ġoverload": 28777, "Ġsilicone": 28778, "еÑģÑĤно": 28779, "Ġdenken": 28780, "Ġdefinit": 28781, "ĠWasn": 28782, "Ġaltered": 28783, "ĠSoo": 28784, "ĠWing": 28785, "indre": 28786, "ĠNPC": 28787, "ÏģÎŃ": 28788, "ĠTwenty": 28789, "ĠLiebe": 28790, "Ġhomelessness": 28791, "oulder": 28792, "ĠÐĺÑĤак": 28793, "ÑģкаÑı": 28794, "Ġcuatro": 28795, "ĠHarvey": 28796, "Ġphilan": 28797, "ĠBeet": 28798, "Ġpolicing": 28799, "ĠAlexand": 28800, "Ġмолод": 28801, "Ġmüs": 28802, "Ġhizo": 28803, "ë³´ëĭ¤": 28804, "Ġпозвол": 28805, "ĠпÑĭÑĤ": 28806, "оÑĩемÑĥ": 28807, "Ġíĥľ": 28808, "Ġcryptocurrency": 28809, "Ġloro": 28810, "Ġsummation": 28811, "Ġbakalım": 28812, "Ġneuros": 28813, "Ø¥": 28814, "Ġможем": 28815, "Ġüst": 28816, "Ġpreliminary": 28817, "Ġhorns": 28818, "ĠTI": 28819, "ÙĥÙĦ": 28820, "YO": 28821, "Ġhinge": 28822, "Ġrepairs": 28823, "Ġbonding": 28824, "Ġbize": 28825, "ĠÑĪÑĤ": 28826, "Ġmotive": 28827, "ĠNigeria": 28828, "120": 28829, "block": 28830, "Ġaviation": 28831, "ĠKommun": 28832, "Ġоказ": 28833, "Ġtenha": 28834, "Ġeducating": 28835, "Ġstaat": 28836, "æ¶Ī": 28837, "ĠÑģколÑĮко": 28838, "Ġfrightened": 28839, "Ġseeks": 28840, "ÑĢÑĥÑĪ": 28841, "quent": 28842, "ĠNou": 28843, "Ġprat": 28844, "ĠShot": 28845, "Work": 28846, "karang": 28847, "ĠLightning": 28848, "nolds": 28849, "rolled": 28850, "glass": 28851, "Ġcredibility": 28852, "ITY": 28853, "Ġatmospheric": 28854, "Ġhavia": 28855, "ändern": 28856, "cheers": 28857, "These": 28858, "ĠCell": 28859, "Ġmagnes": 28860, "ĠBravo": 28861, "season": 28862, "ĠÅŁeyler": 28863, "ðŁİ": 28864, "white": 28865, "ĠMB": 28866, "Ġstacked": 28867, "Ġ74": 28868, "Ġдавай": 28869, "Ġpave": 28870, "ĠоÑħ": 28871, "Ġdataset": 28872, "Ġretour": 28873, "Ġmaturity": 28874, "Ġquase": 28875, "Ġ93": 28876, "ĠSym": 28877, "Ġbriefing": 28878, "Ġculturally": 28879, "Ġì·¨": 28880, "inhas": 28881, "Ġmadam": 28882, "Ġajudar": 28883, "ĠTibet": 28884, "Ġleaks": 28885, "cile": 28886, "Ġtheaters": 28887, "ìĺ¨": 28888, "ãĥĸ": 28889, "72": 28890, "ĠWash": 28891, "ĠQuality": 28892, "ĠIvan": 28893, "ĠBent": 28894, "igator": 28895, "ĠGeschichte": 28896, "Ġreactive": 28897, "Ġ1900": 28898, "æ¡Ī": 28899, "Ġcontradict": 28900, "Ġziemlich": 28901, "Ġcohort": 28902, "á»§": 28903, "Ġpestic": 28904, "Ġoraz": 28905, "Ġtellement": 28906, "é¾": 28907, "ĠNowadays": 28908, "crew": 28909, "Steve": 28910, "Ġfictional": 28911, "Ġilk": 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30423, "Ġcerteza": 30424, "Ġexempt": 30425, "ìĬ¹": 30426, "selling": 30427, "ĠTask": 30428, "hoon": 30429, "ĠCoc": 30430, "ĠParks": 30431, "Ġrepetition": 30432, "ĠÑĤÑĥда": 30433, "Ġensl": 30434, "ĠdeÄŁiÅŁ": 30435, "ĠOrlando": 30436, "ĠMainten": 30437, "æŃ¢": 30438, "ocument": 30439, "ĠHC": 30440, "Ġscooter": 30441, "ĠнапиÑģ": 30442, "Ġtighter": 30443, "Ġtease": 30444, "Ġremoves": 30445, "Ġkijken": 30446, "ĠÑģÑĥÑīеÑģÑĤв": 30447, "Ġthé": 30448, "ĠвÑĭглÑıд": 30449, "Ġrelieve": 30450, "Ġmitä": 30451, "Ġstationary": 30452, "öff": 30453, "pable": 30454, "Ġarter": 30455, "Ġdéf": 30456, "rative": 30457, "Ġconect": 30458, "Ġsaddle": 30459, "ĠDiane": 30460, "Ġcommemor": 30461, "fendim": 30462, "SÃŃ": 30463, "Ġíģ´ë": 30464, "Ġmange": 30465, "atte": 30466, "Ġarrogant": 30467, "Ġrobotic": 30468, "ĠgiÃł": 30469, "æĺ¯çļĦ": 30470, "Ġneighbourhood": 30471, "isson": 30472, "Ġдвиж": 30473, "ĠRI": 30474, "ĠNorman": 30475, "brand": 30476, "amation": 30477, "Ġrazor": 30478, "Ġmurders": 30479, "ĠÑĤÑĥ": 30480, "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": 30536, "temps": 30537, "Ġconfigured": 30538, "ĠCompared": 30539, "neh": 30540, "icating": 30541, "Ġnickel": 30542, "ÙĪÙĤ": 30543, "ÙĥÙĪÙĨ": 30544, "opes": 30545, "Ġformulas": 30546, "ĠÐķÑģÑĤÑĮ": 30547, "Ġpobl": 30548, "ĠPJ": 30549, "ĠLud": 30550, "ä»ĬåĽŀ": 30551, "ĠBrid": 30552, "ĠHog": 30553, "ĠBris": 30554, "Jen": 30555, "Ġshading": 30556, "ĠYas": 30557, "Ġdisturbed": 30558, "Ġrecommending": 30559, "Ġcé": 30560, "ĠHOW": 30561, "ìĹĪìĸ´": 30562, "Ġreversed": 30563, "ĠInterestingly": 30564, "ioxid": 30565, "åħŃ": 30566, "Ġìĺ¤ì¼ĢìĿ´": 30567, "ếu": 30568, "xx": 30569, "Ġouais": 30570, "ĠYouTubers": 30571, "ĠRosa": 30572, "ĠHaupt": 30573, "jadi": 30574, "Ġvlogs": 30575, "Ġcultura": 30576, "ĠLeadership": 30577, "ĠHep": 30578, "Ġillum": 30579, "´ëıĻ": 30580, "Ġcustomized": 30581, "Ġmarca": 30582, "Ġquatro": 30583, "Ġнаг": 30584, "ĠSpaceX": 30585, "ĠEigen": 30586, "asting": 30587, "ĠolduÄŁu": 30588, "Ġforts": 30589, "ãģī": 30590, "riment": 30591, "iencia": 30592, "Ġtenir": 30593, "roffen": 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, "Ġmilitar": 30653, "Ġsensing": 30654, "Ġpojaw": 30655, "Ġancora": 30656, "Ġabundant": 30657, "Ġhairst": 30658, "ãģĤãĤĮ": 30659, "Ġtwee": 30660, "Ġnächste": 30661, "ĠMöglichkeit": 30662, "Ġhoo": 30663, "ufficient": 30664, "Ġfantast": 30665, "Ġedible": 30666, "Ġëĸ¨ìĸ´ì": 30667, "ìĽĥ": 30668, "Ġvein": 30669, "ucci": 30670, "Ġdevotion": 30671, "Ġconcealer": 30672, "income": 30673, "Ġrecycled": 30674, "ĠìĬ¤íĥĢ": 30675, "Ġpontos": 30676, "Ġdessus": 30677, "Ġvérit": 30678, "Ġreflections": 30679, "ĠAA": 30680, "Ġtakeaway": 30681, "bare": 30682, "ĠContact": 30683, "eil": 30684, "ĠHear": 30685, "Ġmirac": 30686, "ĠGerilim": 30687, "ĠÑģамÑĭй": 30688, "Ġvivo": 30689, "Ġkilograms": 30690, "ĠCrim": 30691, "ût": 30692, "78": 30693, "Ġsincerely": 30694, "raz": 30695, "Ġë³µ": 30696, "Ġarriv": 30697, "Ġconception": 30698, "ĠPersian": 30699, "Ġsjäl": 30700, "Ġstarring": 30701, "ĠìķĦ무": 30702, "ĠForever": 30703, "еÑģÑĤÑĮ": 30704, "Ġveil": 30705, "Ġsubtit": 30706, "odka": 30707, "ĠоÑĤноÑĪ": 30708, "Ġcooks": 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, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "Ġİyi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "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, 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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, 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"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, 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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": 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33031, "Ġmaze": 33032, "Ġfluids": 33033, "ĠProducer": 33034, "ĠKrsna": 33035, "好åķ¦": 33036, "laf": 33037, "Ġ×IJ×ķ": 33038, "Ġmiesz": 33039, "ĠXing": 33040, "ointed": 33041, "sein": 33042, "ĠFuk": 33043, "ĠDepression": 33044, "ĠDuty": 33045, "ĠPanther": 33046, "Ġsund": 33047, "Ġrefere": 33048, "Ġexclusion": 33049, "Ġnaval": 33050, "ĠWinston": 33051, "Ġslogan": 33052, "Ġhypothetical": 33053, "Ġelevate": 33054, "ëł¹": 33055, "Ġcabeça": 33056, "ĠGesund": 33057, "meter": 33058, "ĠìķĦëĭĪë©´": 33059, "Ġcloudy": 33060, "â̦?": 33061, "ĠSchritt": 33062, "ĠJS": 33063, "ìį": 33064, "ĠSprings": 33065, "ĠBatter": 33066, "·°": 33067, "Ġtailor": 33068, "ĠPTSD": 33069, "ĠGent": 33070, "ĠbaÄŁ": 33071, "Ġspatula": 33072, "Ġcray": 33073, "ĠLegisl": 33074, "Ġsú": 33075, "Ġleve": 33076, "าม": 33077, "Ġerad": 33078, "Ġdong": 33079, "Ġderm": 33080, "ĠBanks": 33081, "icho": 33082, "åħĪçĶŁ": 33083, "ĠFranz": 33084, "ravel": 33085, "éģĶ": 33086, "оло": 33087, "Ġflute": 33088, "ĠEk": 33089, "Ġjoyful": 33090, "Ġchased": 33091, "ĠLarge": 33092, "Over": 33093, "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, "Ġvendo": 33152, "ĠClaus": 33153, "ĠFaz": 33154, "indest": 33155, "Ġdeceased": 33156, "Ġcomparisons": 33157, "ĠLCD": 33158, "ĠPork": 33159, "Ġeventual": 33160, "Ġpatreon": 33161, "Ġinability": 33162, "Ġextinction": 33163, "Ġì¢ĭìķĦíķĺëĬĶ": 33164, "ĠÑģоÑģ": 33165, "aju": 33166, "Ġ×ij×IJ×": 33167, "Ġsofort": 33168, "Ġdestined": 33169, "ĠRin": 33170, "Ġmouths": 33171, "ĠNatürlich": 33172, "Ġpreserving": 33173, "Ġlimp": 33174, "黨": 33175, "ocused": 33176, "инг": 33177, "Ġexposing": 33178, "Ġξ": 33179, "ëį": 33180, "laugh": 33181, "Ġhiss": 33182, "ãģłãģĭãĤī": 33183, "Ġindie": 33184, "Ġdetal": 33185, "ÑĢавÑģÑĤв": 33186, "Ġtrên": 33187, "æķ°": 33188, "Ġogni": 33189, "Ġsimplemente": 33190, "Ġ1978": 33191, "Ġgoo": 33192, "Ġ1967": 33193, "Ġgenug": 33194, "hö": 33195, "Ġhistó": 33196, "å®Ł": 33197, "Ġlobster": 33198, "cendo": 33199, "Ġteil": 33200, "Ġallevi": 33201, "0000": 33202, "OLD": 33203, "Ġpesos": 33204, "Ġbonuses": 33205, "Ġami": 33206, "Ġrevival": 33207, "ĠHorse": 33208, "Ġsack": 33209, "Talk": 33210, "Ġmulher": 33211, "ĠпоÑģÑĤоÑıн": 33212, "ĠHood": 33213, "Huh": 33214, "Ġë¶ģ": 33215, "Ġhyung": 33216, "ĠMeeting": 33217, "Ġimporta": 33218, "Ġì°¾ìķĦ": 33219, "ĠVern": 33220, "Ġstripped": 33221, "Ġrefuses": 33222, "Ġqualifications": 33223, "opl": 33224, "ĢëıĦ": 33225, "ixÃŃ": 33226, "Ġdiab": 33227, "itime": 33228, "flows": 33229, "Ġinac": 33230, "ĠGong": 33231, "Ġmeaningless": 33232, "Ġcourageous": 33233, "Ġmicrobi": 33234, "azy": 33235, "hist": 33236, "Ġvolunteering": 33237, "VIE": 33238, "Ġviolated": 33239, "Ġsympathy": 33240, "ĠEdit": 33241, "好åĥı": 33242, "electric": 33243, "product": 33244, "Ġpandemia": 33245, "Ġgeometric": 33246, "ĠConvers": 33247, "gre": 33248, "Ġglut": 33249, "isted": 33250, "ĠاÙĦÙĥ": 33251, "ĠChain": 33252, "ĠPresent": 33253, "ĠYin": 33254, "ĠÑģог": 33255, "ĠVlog": 33256, "Ġìĸ´ë¨¸": 33257, "Ġdonn": 33258, "Ġhitch": 33259, "ucking": 33260, "ãģĬãģĦ": 33261, "wald": 33262, "risk": 33263, "Ġhari": 33264, "ĠKens": 33265, "ĠIdol": 33266, "Ġвнимание": 33267, "Ġtodd": 33268, "Ġsmashed": 33269, "Ġinvari": 33270, "ĠконÑĤÑĢ": 33271, "Ġautistic": 33272, "ìŀ¥ëĭĺ": 33273, "Res": 33274, "дÑĭ": 33275, "chau": 33276, "Ġselv": 33277, "Ġhätten": 33278, "ि": 33279, "Ġexpects": 33280, "Ïģη": 33281, "Ġaçık": 33282, "ĠHTTP": 33283, "leÅŁ": 33284, "Ġsweeping": 33285, "ĠBeta": 33286, "Ġcounterparts": 33287, "abile": 33288, "ĠSims": 33289, "Cs": 33290, "Ġrepar": 33291, "squ": 33292, "Ġprovincial": 33293, "Ġshareholders": 33294, "Ġrunter": 33295, "Ġgedacht": 33296, "ĠTeen": 33297, "Ġgrands": 33298, "çĶ¢": 33299, "agles": 33300, "Ġrocky": 33301, "vens": 33302, "Ġrivals": 33303, "unal": 33304, "Ġreacts": 33305, "ë©": 33306, "Ġmercury": 33307, "ĠLuigi": 33308, "Ġог": 33309, "ĠJUST": 33310, "Ġlod": 33311, "Ġcortex": 33312, "wig": 33313, "Ġlakh": 33314, "ì¤ijìĹIJ": 33315, "ĠVic": 33316, "ĠMund": 33317, "Ġmapped": 33318, "ĠDell": 33319, "ĠDruck": 33320, "Ġlifes": 33321, "алÑĮное": 33322, "ividual": 33323, "adım": 33324, "Ġatrav": 33325, "ĠFlug": 33326, "ĠKlein": 33327, "ê±°ìķ¼": 33328, "หà¸Ļ": 33329, "Ġappli": 33330, "ா?": 33331, "üyorum": 33332, "ĠинÑĤеÑĢеÑģно": 33333, "Ġdisinfect": 33334, ">-": 33335, "Ġchampagne": 33336, "Ġkla": 33337, "opers": 33338, "Trans": 33339, "ĠDesert": 33340, "Ġcultivate": 33341, "ĠFucking": 33342, "idelity": 33343, "ĠÑĤан": 33344, "Ġincub": 33345, "Ġtemu": 33346, "Ġlearner": 33347, "founder": 33348, "ĠSyl": 33349, "ãĤĢ": 33350, "Ġfato": 33351, "zier": 33352, "ĠìĹĨìĿ´": 33353, "Ġì΍": 33354, "Ġpsycho": 33355, "ĠÑĤелеÑĦ": 33356, "Ġregarde": 33357, "Ġrepresentations": 33358, "Ġlitigation": 33359, "Ġspann": 33360, "ults": 33361, "bior": 33362, "è¦ĭãģ¦": 33363, "ä¸įå¤ļ": 33364, "ĠSurvey": 33365, "ĠLEDs": 33366, "Ġträ": 33367, "Ġlên": 33368, "Ġantioxid": 33369, "еÑĢом": 33370, "Ġinduction": 33371, "Ġfooled": 33372, "ätzlich": 33373, "ĠговоÑĢÑıÑĤ": 33374, "ĠFact": 33375, "umbai": 33376, "Ġwiggle": 33377, "NOUN": 33378, "Ġdévelopp": 33379, "ĠClaro": 33380, "Ġì¸": 33381, "ë¬": 33382, "ãģªãĤĵãģł": 33383, "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": 34881, "ĠParam": 34882, "ĠÃĸyle": 34883, "umuz": 34884, "PO": 34885, "rators": 34886, "Ġlamps": 34887, "Ġterritorial": 34888, "Ġbackbone": 34889, "listed": 34890, "DY": 34891, "ĠاÙĦر": 34892, "Ġpursued": 34893, "ĠCommons": 34894, "Ġ곡": 34895, "locks": 34896, "edor": 34897, "Ġconceived": 34898, "gere": 34899, "Ġdisappearing": 34900, "ĠSull": 34901, "ĠìŰë": 34902, "Ġhoffe": 34903, "Ġdetox": 34904, "íĶĮ": 34905, "Ġretir": 34906, "ĠëģĿëĤ": 34907, "Ġpergunta": 34908, "ĠBOY": 34909, "ç²¾": 34910, "Ġpenn": 34911, "æĿ¥äºĨ": 34912, "hés": 34913, "hon": 34914, "Ġcatastrophic": 34915, "Ġaust": 34916, "Ġtorso": 34917, "Ġìĸ´ëĬIJ": 34918, "ĠìĤ¬ëŀĮëĵ¤ìĿ´": 34919, "Ġmarvelous": 34920, "ĠHarley": 34921, "achine": 34922, "Ġtiế": 34923, "itto": 34924, "ĠIÃŃm": 34925, "ylon": 34926, "Ġshutdown": 34927, ".''": 34928, "Ġapologies": 34929, "ĠCommunication": 34930, "ĠговоÑĢÑİ": 34931, "ãģĤãĥ¼": 34932, "âĦ¢": 34933, "ÃŃveis": 34934, "acun": 34935, "Ġretaining": 34936, "Ġcontradiction": 34937, "ĠADAM": 34938, "COM": 34939, "Bryan": 34940, "ĠMonsieur": 34941, "Ġadapting": 34942, "ШÐIJ": 34943, "ĠScr": 34944, "ändert": 34945, "Ġplaus": 34946, "ä»Ĭ天çļĦ": 34947, "Ġonset": 34948, "Ġassistants": 34949, "Ġvalves": 34950, "Ġscatter": 34951, "ĠRust": 34952, "awia": 34953, "Ġreadiness": 34954, "Ġpais": 34955, "Ġbible": 34956, "Ġambiente": 34957, "ĠамеÑĢик": 34958, "Ġuncond": 34959, "Ġkalk": 34960, "åĬ¨": 34961, "Ġmoc": 34962, "unn": 34963, "Ġactu": 34964, "Ġhumming": 34965, "issimo": 34966, "ĠPatrol": 34967, "gow": 34968, "ãĥ¤": 34969, "ĠTHEY": 34970, "ĠBoden": 34971, "ĠBie": 34972, "Ġreel": 34973, "ĠÑĥÑģлов": 34974, "Ġendeavor": 34975, "ĠPeriod": 34976, "ustomed": 34977, "mals": 34978, "alon": 34979, "Box": 34980, "ĠÏĥαÏĤ": 34981, "Ġomdat": 34982, "Ġaltre": 34983, "ĠHeh": 34984, "kad": 34985, "Ġprotector": 34986, "Ġdominance": 34987, "odynamic": 34988, "Ġcommunicated": 34989, "kö": 34990, "Ġpredecessor": 34991, "ĠLuk": 34992, "ĠFlower": 34993, "Ġãģ©": 34994, "poque": 34995, "ÑĤиÑĢов": 34996, "Ġretrospect": 34997, "Ġdecisive": 34998, "Ġexempel": 34999, "{\\": 35000, "ĠRück": 35001, "rite": 35002, "ĠZeus": 35003, "Ġcalorie": 35004, "Ġattractions": 35005, "ĠHinter": 35006, "Ġuhm": 35007, "ĠíĮIJ": 35008, "Ġrulers": 35009, "Ġdiscouraged": 35010, "Ġacontecer": 35011, "Ġaccents": 35012, "ĠOptim": 35013, "ĠAlg": 35014, "kids": 35015, "2021": 35016, "ĠLindsay": 35017, "Ġfilmmakers": 35018, "prowad": 35019, "Ġterug": 35020, "ëĭ´": 35021, "ĠSommer": 35022, "2018": 35023, "Ġborrowing": 35024, "ĠTransfer": 35025, "ноп": 35026, "arias": 35027, "Ġheadphone": 35028, "ì¼ľ": 35029, "Ġtranslating": 35030, "Ġaufge": 35031, "à®ªà®Ł": 35032, "weis": 35033, "avant": 35034, "paid": 35035, "baby": 35036, "Ġtoughest": 35037, "Ġrepeats": 35038, "ĠTeresa": 35039, "Lord": 35040, "Ġacabar": 35041, "ĠRide": 35042, "dir": 35043, "Ġleng": 35044, "Ġdwa": 35045, "Ġheadaches": 35046, "Ġnữa": 35047, "ĠнаÑģÑĤоÑıÑī": 35048, "Ġboils": 35049, "Ġlonging": 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"ĠBryce": 35109, "ĠÑıкÑĸ": 35110, "Ġpearls": 35111, "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": 35285, "Fonda": 35286, "adı": 35287, "Ġtranscription": 35288, "Ġtitanium": 35289, "Ġtwists": 35290, "Ġgearbox": 35291, "ensation": 35292, "fat": 35293, "Coll": 35294, "ĠCommonwealth": 35295, "zon": 35296, "ĠPolizei": 35297, "ĠAPPLAUSE": 35298, "fry": 35299, "ĠJuda": 35300, "esteem": 35301, "Ġsock": 35302, "ĠJugend": 35303, "ĠкÑģÑĤаÑĤи": 35304, "ĠDro": 35305, "Ġprochaine": 35306, "ãĥ¼ãĥ«": 35307, "Ġliksom": 35308, "ĠEnergie": 35309, "ĠMarina": 35310, "Ġ230": 35311, "Ġê°ĢìĦľ": 35312, "umping": 35313, "Ġlone": 35314, "ç´ļ": 35315, "Ġfonts": 35316, "Ġbusinessman": 35317, "Ġply": 35318, "Ġdoe": 35319, "grid": 35320, "ĠMilwaukee": 35321, "ĠEden": 35322, "!\".": 35323, "ĠÛĮÛģ": 35324, "ogens": 35325, "Ġteaser": 35326, "Ġquién": 35327, "Ġincentiv": 35328, "govern": 35329, "Ġchildcare": 35330, "Ġsneakers": 35331, "Ġimprisoned": 35332, "®": 35333, "иÑĤеÑģÑĮ": 35334, "anbul": 35335, "Ġregain": 35336, "Ġtranquil": 35337, "Redner": 35338, "鼨": 35339, "IFA": 35340, "Ġideological": 35341, "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, "Ġrenewal": 35516, "Ġaesthetics": 35517, "ikum": 35518, "Ġbeverage": 35519, "dern": 35520, "Ġpredictive": 35521, "Ġchuy": 35522, "ĠRegarding": 35523, "ĠForward": 35524, "ĠÙĪÙĦ": 35525, "Ġcontextual": 35526, "Ġdwarf": 35527, "Ġprehe": 35528, "Ġgoverned": 35529, "ħĦ": 35530, "Ġtrabalhar": 35531, "Ġnegócio": 35532, "ĠболÑĮÑĪой": 35533, "еÑĩаÑĤ": 35534, "ĠдÑĥÑħ": 35535, "Ġfloods": 35536, "Ġbowling": 35537, "ĠOB": 35538, "ĠHär": 35539, "Ġgrading": 35540, "주ëĬĶ": 35541, "Ġgars": 35542, "dling": 35543, "Ġrak": 35544, "ëĪ": 35545, "creat": 35546, "ĠÑīе": 35547, "Ġneighbours": 35548, "food": 35549, "Query": 35550, "Ġheroin": 35551, "iceps": 35552, "ĠKinda": 35553, "NET": 35554, "Ġmari": 35555, "Ġimitate": 35556, "Ġachter": 35557, "Ġsettlements": 35558, "rare": 35559, "cciones": 35560, "Ġëĵľ": 35561, "Ġfik": 35562, "itung": 35563, "ĠмакÑģим": 35564, "Ġelf": 35565, "Ġdalla": 35566, "ĠPolsce": 35567, "ĠPul": 35568, "ЧÑĤо": 35569, "ĠMorgen": 35570, "ØŃÙħ": 35571, "Ġsupremacy": 35572, "Ġkys": 35573, "ĠHurricane": 35574, "ĠGTA": 35575, "ĠFeh": 35576, "Ġfinalmente": 35577, "mund": 35578, "ĠKrie": 35579, "époque": 35580, "ĠTucker": 35581, "ITT": 35582, "Ġlur": 35583, "Ġdipping": 35584, "äv": 35585, "Ġeerste": 35586, "ĠFlint": 35587, "bildung": 35588, "ูà¹ī": 35589, "Ġtoim": 35590, "Ġpracy": 35591, "Ġtransforms": 35592, "Ġspeeding": 35593, "Ġpresenter": 35594, "Ġfellows": 35595, "filled": 35596, "ieza": 35597, "Ġadvising": 35598, "ĠInterview": 35599, "игÑĢ": 35600, "wehr": 35601, "ĠDante": 35602, "pture": 35603, "Ī문": 35604, "¯¸ë": 35605, "IJIJ": 35606, "ĠCounter": 35607, "Ġcrist": 35608, "Ġì§ľ": 35609, "Ġjeune": 35610, "ĠÑģÑĤÑĢаÑĪ": 35611, "ĠmieÄĩ": 35612, "Ġtutor": 35613, "Ġmasala": 35614, "Ġpowdered": 35615, "Ġnau": 35616, "ĠFrederick": 35617, "Ġbilling": 35618, "ĠEisen": 35619, "ĠдобÑĢ": 35620, "Ġmest": 35621, "æ½": 35622, "Ġsnipp": 35623, "Ġmono": 35624, "ĠAlo": 35625, "ĠMercy": 35626, "érience": 35627, "Ġcasualties": 35628, "ĠANNOUNCER": 35629, "ä»İ": 35630, "Ġtocar": 35631, "Ġbacterial": 35632, "Ho": 35633, "Ġstreak": 35634, "ĠJENN": 35635, "Ġplast": 35636, "Ñģлед": 35637, "Ġreapp": 35638, "Ġpaycheck": 35639, "Ġminers": 35640, "habt": 35641, "ĠJap": 35642, "нÑĥÑĤ": 35643, "Ġredemption": 35644, "Ġquir": 35645, "hnlich": 35646, "Ġaccumulation": 35647, "Ġshove": 35648, "Ġadrenaline": 35649, "Make": 35650, "ĠHern": 35651, "ossing": 35652, "ĠVil": 35653, "ubby": 35654, "hertz": 35655, "breaks": 35656, "Ġspur": 35657, "ĠDaha": 35658, "USTIN": 35659, "Ġcontinuer": 35660, "ĠSaul": 35661, "ãģ®ãģ¯": 35662, "ĠíıŃ": 35663, "ĠëIJĺë©´": 35664, "Ġë§IJìĶĢ": 35665, "Ġож": 35666, "Ġsuspects": 35667, "Ġlaquelle": 35668, "ĠMuchas": 35669, "Ġvöllig": 35670, "ulen": 35671, "Ġimpres": 35672, "Ġlobb": 35673, "enee": 35674, "Ġнаж": 35675, "Ta": 35676, "Ġréalité": 35677, "ĠRex": 35678, "Ġharvesting": 35679, "Ġestr": 35680, "æ¶": 35681, "ospace": 35682, "OSS": 35683, "Ġdisturbance": 35684, "assic": 35685, "ĠIsab": 35686, 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36604, "Young": 36605, "Ġdependencies": 36606, "ĠBradley": 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, "Ġestre": 36665, "ç¹¼": 36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×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, "Ġmeist": 36894, "Ġimpatient": 36895, "Ġbehaves": 36896, "æĿ¾": 36897, "ĠëĤ´ìļ©": 36898, "Ġbackstory": 36899, "Ġagreeing": 36900, "ĠÅģ": 36901, "ihin": 36902, "Ġtemperatura": 36903, "ĠBackground": 36904, "Ġnutzen": 36905, "Ġëħ¹": 36906, "ĠMänner": 36907, "Ġcollaborations": 36908, "ĠKos": 36909, "éģİåİ»": 36910, "Ġnightmares": 36911, "ëĵ±": 36912, "ĠQueensland": 36913, "Ġassociates": 36914, "ĠKok": 36915, "Ġfactorial": 36916, "ĠHyung": 36917, "Ġê·¸ëĭ¤ìĿĮ": 36918, "Ġfilho": 36919, "Ġelét": 36920, "Ġíĸīë³µ": 36921, "°±": 36922, "Ġgefunden": 36923, "Ġsemicondu": 36924, "Ġcounselors": 36925, "ĠUpper": 36926, "ĠAub": 36927, "ickers": 36928, "Ver": 36929, "Ġnorthwest": 36930, "ĠMaintenant": 36931, "ĠLakes": 36932, "аÑıв": 36933, "inté": 36934, "ì°½": 36935, "Ġгаз": 36936, "Ġgiorn": 36937, "Ġdigitally": 36938, "ĠCircuit": 36939, "ì¼Ģ": 36940, "ãĤĬãģ¾ãģĹãģŁ": 36941, "Ġcheerful": 36942, "ĠPeterson": 36943, "ĠDanish": 36944, "ativos": 36945, "Ġliken": 36946, "Ġharbor": 36947, "алиÑģÑĤ": 36948, "xe": 36949, "Ġcurls": 36950, "ĠRhod": 36951, "End": 36952, "ĠET": 36953, "Ġacquaint": 36954, "ĠKelvin": 36955, "Ġtrif": 36956, "ĠAway": 36957, "ìŀIJëĬĶ": 36958, "vs": 36959, "Ġpágina": 36960, "Ġinlet": 36961, "ĠSantos": 36962, "Ġìļ°ìĻĢ": 36963, "Ġyapıyorsun": 36964, "theme": 36965, "Ġsouff": 36966, "Ġinjected": 36967, "Ġpóźniej": 36968, "iverso": 36969, "amped": 36970, "Ġdaher": 36971, "Ġdagger": 36972, "ĠлÑİбим": 36973, "Ġtummy": 36974, "Ġenlightened": 36975, "cents": 36976, "ĠDah": 36977, "Ġcuest": 36978, "ä¾Ĩ說": 36979, "ILY": 36980, "Ġ×ijר": 36981, "Ġbanging": 36982, "ĠEmil": 36983, "ĠCler": 36984, "ĠBorder": 36985, "ижÑĥ": 36986, "Ġpresenters": 36987, "ĠSTUD": 36988, "coins": 36989, "ĠíĻį": 36990, "Ġperks": 36991, "Ġparap": 36992, "Ġcertaines": 36993, "ĠLore": 36994, "öst": 36995, "ĠMARTIN": 36996, "Ġbios": 36997, "Ġwhereby": 36998, "verts": 36999, "ĠMiranda": 37000, "Ġstip": 37001, "澤": 37002, "andez": 37003, "׼׾": 37004, "ujin": 37005, "Ġê¾": 37006, "Ġallergies": 37007, "plate": 37008, "Ġyapıl": 37009, "Ġundertake": 37010, "ĠëĤĺê°Ģ": 37011, "Part": 37012, "Ġkızım": 37013, "hguru": 37014, "ãģĤãģ¨": 37015, "ĠJohns": 37016, "Ġeyelashes": 37017, "Ġdrained": 37018, "ĠstÃ¥r": 37019, "ãģĤãĤĬãģ¾ãģĻ": 37020, "ĠJade": 37021, "Ġcalend": 37022, "film": 37023, "Ġmesa": 37024, "Ġludzie": 37025, "Ġattracts": 37026, "Ġjuices": 37027, "Ġкил": 37028, "Ġnieuwe": 37029, "Ġmencion": 37030, "Ġignition": 37031, "Ġbladder": 37032, "andaag": 37033, "ĠExtension": 37034, "íĤ¨": 37035, "feed": 37036, "ĠÙĪÙĩ": 37037, "Ġspun": 37038, "Ġtät": 37039, "оÑĢоÑĤ": 37040, "tyard": 37041, "ronics": 37042, "ĠHuge": 37043, "Ñĥжд": 37044, "string": 37045, "Ġunjust": 37046, "Ġprawn": 37047, "Ġfrosting": 37048, "Ġdisappearance": 37049, "iosa": 37050, "Ġcardi": 37051, "ĠPriest": 37052, "ĠcientÃŃfic": 37053, "åĵªè£¡": 37054, "ĠÐĴаÑģ": 37055, "Ġë¶Ģíĥģ": 37056, "Ġthieves": 37057, "Ġphysique": 37058, "ĠEugene": 37059, "Ġблиз": 37060, "Ġmonopoly": 37061, "Ġbiography": 37062, "ĠhoÅŁ": 37063, "Ġtö": 37064, "mac": 37065, "Ġshocks": 37066, "ìĦ¸ë": 37067, "hit": 37068, "Ġsnug": 37069, "Ġincl": 37070, "Ġdedic": 37071, "Ġultras": 37072, "ĠизвеÑģÑĤ": 37073, "Ġutilization": 37074, "ĠÑģовеÑĢÑĪенно": 37075, "Ġservi": 37076, "stag": 37077, "180": 37078, "Ġsewer": 37079, "ĠChoice": 37080, "Ġdischarged": 37081, "ĠJD": 37082, "олеÑĤ": 37083, "ĠкваÑĢÑĤи": 37084, "Ġtelescop": 37085, "ĠJeÅĽli": 37086, "ĠNana": 37087, "cale": 37088, "ĠÑĤон": 37089, "mmm": 37090, "äºĨåIJ§": 37091, "Ġgehabt": 37092, "ëĤł": 37093, "æĬķ": 37094, "à¸Ļà¸Ļ": 37095, "Ġether": 37096, "Ġzen": 37097, "Ġresearched": 37098, "ĠCzyli": 37099, "å®Įåħ¨": 37100, "workers": 37101, "Ġ경찰": 37102, "Ġsheriff": 37103, "allo": 37104, "Ġtipos": 37105, "Ġprosecution": 37106, "Ġfrogs": 37107, "Ġfalt": 37108, "jd": 37109, "ĠíĮĶ": 37110, "Ġfiltered": 37111, "ĠOft": 37112, "Ġìį": 37113, "Ġdisfr": 37114, "ĠMustang": 37115, "Ġwoah": 37116, "ĠREALLY": 37117, "Ġмогли": 37118, "Ġentrada": 37119, "ĠигÑĢа": 37120, "Ġmixes": 37121, "ĠавÑĤомоб": 37122, "ÐĻ": 37123, "Ġshin": 37124, "Ġparanormal": 37125, "Ġsomeplace": 37126, "Ġdishon": 37127, "etaan": 37128, "Ġfuerte": 37129, "Ù¹": 37130, "Ġdoom": 37131, "ìĪľ": 37132, "Ġexistential": 37133, "Ġbuld": 37134, "ĠSDK": 37135, "ĠпÑĢавда": 37136, "Ġturnover": 37137, "ĠìĹ¬ê¸°ìĹIJ": 37138, "Ġह": 37139, "Ġmodeled": 37140, "Ġbugün": 37141, "Ġexperimentation": 37142, "Ġmornings": 37143, "Ġmedo": 37144, "Stevie": 37145, "Ġplayable": 37146, "Ġairlines": 37147, "gments": 37148, "Ġ기ë¶Ħ": 37149, "ĠTomb": 37150, "ĠMVP": 37151, "AUDIENCE": 37152, "Ġcheckout": 37153, "Ġpasst": 37154, "Ġbeispiel": 37155, "ĠLinks": 37156, "heavy": 37157, "Ġquestionable": 37158, "Ġìĵ°ë": 37159, "Ġsill": 37160, "Ġmanipulated": 37161, "ĠLoren": 37162, "Ġìľ¼": 37163, "Ġverge": 37164, "ák": 37165, "IES": 37166, "Ġsabot": 37167, "ĠCustomer": 37168, "ależy": 37169, "Ġnominee": 37170, "ĠGad": 37171, "Ġnouvelles": 37172, "ĠSPE": 37173, "istling": 37174, 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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, 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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": 39250, "ĠStrength": 39251, "Ġmilhões": 39252, "ĠWine": 39253, "ĠMolt": 39254, "讲": 39255, "ãģijãĤĮ": 39256, "Ġundermine": 39257, "ĠArchives": 39258, "vana": 39259, "mercial": 39260, "MC": 39261, "Ġcaste": 39262, "пÑĢ": 39263, "Ġlegislators": 39264, "ulators": 39265, "ênio": 39266, "Ġëį°ë": 39267, "ĠÑħоÑĤиÑĤе": 39268, "Ġнек": 39269, "Ġsurn": 39270, "Ġconsci": 39271, "ĠPOW": 39272, "Ġculinary": 39273, "ĠKAT": 39274, "ĠFolks": 39275, "Ñĭваем": 39276, "Ġвок": 39277, "ãģijãĤĭ": 39278, "service": 39279, "pts": 39280, "Ġпобед": 39281, "æĺ¯åķĬ": 39282, "Ġtents": 39283, "Ġnord": 39284, "STE": 39285, "Ġrepublican": 39286, "Ġwyk": 39287, "Ġminions": 39288, "èĻķ": 39289, "Ġmemang": 39290, "jest": 39291, "Ġcomparative": 39292, "Ġtyle": 39293, "carbon": 39294, "bedingt": 39295, "ksen": 39296, "Ġnegativity": 39297, "Ġsjälv": 39298, "Ġdú": 39299, "æīĢæľī": 39300, "Ġrecalled": 39301, "cra": 39302, "ĠTada": 39303, "ĠÑĢÑĥки": 39304, "ĠопÑĢедел": 39305, "Ġprocrast": 39306, "Ġjogos": 39307, "ĠOo": 39308, "ĠHearts": 39309, "Ġéch": 39310, "ĠksiÄħż": 39311, "Ġcoarse": 39312, "ĠTube": 39313, "ĠGreens": 39314, "Ġén": 39315, "Ġdumbbell": 39316, "ĠÑĤи": 39317, "Ġquerer": 39318, "اØŃ": 39319, "Ïĥει": 39320, "ĠпÑĢавилÑĮно": 39321, "Ġпап": 39322, "Ġcompra": 39323, "Ġtér": 39324, "ĠAntes": 39325, "Ġoptimum": 39326, "Ġbiscuit": 39327, "κι": 39328, "aczego": 39329, "Ġìĭľê°ĦìĿ´": 39330, "ĠMarines": 39331, "vero": 39332, "Ġvaccinations": 39333, "Ġpetty": 39334, "riters": 39335, "Ġал": 39336, "country": 39337, "Ġcounters": 39338, "Ġattendant": 39339, "ĠHui": 39340, "ãģ¨ãģĦãģĨãģĵãģ¨ãģ§": 39341, "cka": 39342, "ÑģÑĤвеннÑĭй": 39343, "guy": 39344, "Ġtricked": 39345, "ĠRED": 39346, "Ġthrilling": 39347, "ÏĢοι": 39348, "Ġpiggy": 39349, "Ġanunci": 39350, "ORTER": 39351, "ĠValue": 39352, "Ġrond": 39353, "ĠADA": 39354, "Ġposer": 39355, "hores": 39356, "ĠRoland": 39357, "ĵ¯": 39358, "Ġnoir": 39359, "Ġש×IJ×": 39360, "ë°ľ": 39361, "iemand": 39362, "ĠпоÑĤеÑĢ": 39363, "ê³³": 39364, "Ġê±±": 39365, "Ġformatting": 39366, "ĠLed": 39367, "è§Ģçľ¾": 39368, "Ġkillers": 39369, "ĠÄijấy": 39370, "Ġhaar": 39371, "again": 39372, "!": 39373, "Ġsomethin": 39374, "Ġcoughing": 39375, "Ġnave": 39376, "Ġprospective": 39377, "ĠHK": 39378, "ĠRescue": 39379, "maybe": 39380, "gger": 39381, "ĠÑĢабоÑĤÑĥ": 39382, "×ķ׾×Ŀ": 39383, "tails": 39384, "íķĺíķĺ": 39385, "Ġeyelid": 39386, "Ġcustomization": 39387, "avilion": 39388, "Ġprochain": 39389, "Ġglaze": 39390, "æĥħæ³ģ": 39391, "Sim": 39392, "ĠопаÑģ": 39393, "Ġmosquitoes": 39394, "Ġfent": 39395, "Ġcapacities": 39396, "Ġapostles": 39397, "Ġaltura": 39398, "Ġ묻": 39399, "Ġseront": 39400, "ĠAnytime": 39401, "¥´ëĬĶ": 39402, "Ġcosplay": 39403, "Ġspac": 39404, "Ġsamen": 39405, "ãĥĦ": 39406, "ucc": 39407, "ières": 39408, "Ġsibling": 39409, "ĠCock": 39410, "Ġëıħ": 39411, "ĠпÑĢедÑģÑĤавлÑı": 39412, "Ġinstallment": 39413, "Ġdije": 39414, "ĠMCU": 39415, "ĠEH": 39416, "ĠNing": 39417, "Ġprepares": 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"Ġdécidé": 43206, "ĠcÅĵur": 43207, "Ġentwickelt": 43208, "ĠHQ": 43209, "KK": 43210, "odar": 43211, "Ġhone": 43212, "Ġconfiance": 43213, "Ġissuing": 43214, "Ġdiagnost": 43215, "ĠìŀĦ": 43216, "ĠкÑĢÑĥÑĤ": 43217, "ĠкаÑģ": 43218, "Ġþ": 43219, "Ġrestrictive": 43220, "ĠCastro": 43221, "ĠuÄŁ": 43222, "Ġempre": 43223, "ĠMoo": 43224, "ĠFigure": 43225, "phonetic": 43226, "Prof": 43227, "ĠпÑĢе": 43228, "Ġtilted": 43229, "ĠNegative": 43230, "ĠLimited": 43231, "meno": 43232, "lamation": 43233, "Ġtrustees": 43234, "Ġintensely": 43235, "Ġaçıl": 43236, "ĠUsed": 43237, "Ġzul": 43238, "Ġappreciative": 43239, "Ġtinc": 43240, "Ġconquest": 43241, "ĠعÙĨد": 43242, "Ġsuicidal": 43243, "Ġmulheres": 43244, "Ġdetach": 43245, "Ġkamera": 43246, "ĠAirPods": 43247, "INDISTINCT": 43248, "глий": 43249, "ĠëĥĦ": 43250, "Ġwrestle": 43251, "æ´Ĺ": 43252, "Ġfirearm": 43253, "Ġlire": 43254, "pra": 43255, "Ġjewels": 43256, "ĠCornell": 43257, "Ġíķłê²ĮìļĶ": 43258, "Ġsucker": 43259, "Ġnombreux": 43260, "ĠFerm": 43261, "ìĽIJìĿ´": 43262, "ĠPis": 43263, "ĠизÑĥÑĩ": 43264, "Ġmiten": 43265, "Ġcev": 43266, "ĠURLs": 43267, "ĠCAS": 43268, "Ġåı¯ä»¥": 43269, "finden": 43270, "Ġbravery": 43271, "ĠÑģлово": 43272, "Ġnenhuma": 43273, "Ġencuentra": 43274, "ĠShirley": 43275, "Ġpercept": 43276, "frames": 43277, "ĠRover": 43278, "ĠAlberta": 43279, "occ": 43280, "ĠëĿ¼ê³ł": 43281, "Ġsúper": 43282, "Ġpresume": 43283, "Ġgland": 43284, "Ġpacing": 43285, "Ġneurot": 43286, "Ġsno": 43287, "Ġplotted": 43288, "ĠpaÅĦstwa": 43289, "ĠOwner": 43290, "ĠDefence": 43291, "ridges": 43292, "Ġwallpaper": 43293, "onian": 43294, "Bro": 43295, "ĠAriana": 43296, "缴æİ¥": 43297, "kry": 43298, "Ġnarration": 43299, "Ġcriança": 43300, "ĠAlrighty": 43301, "ĠìĿ½": 43302, "Ġìĵ°ê³ł": 43303, "Ġliberated": 43304, "Ġexceeds": 43305, "Ġdominating": 43306, "Ġbakın": 43307, "lk": 43308, "Ġslapped": 43309, "ÐĹд": 43310, "umental": 43311, "gettable": 43312, "ĠRoz": 43313, "ĠGul": 43314, "ouvert": 43315, "Ġsmashing": 43316, "azuje": 43317, "Sir": 43318, "Ġgrated": 43319, "ä½łæľī": 43320, "ATT": 43321, "Ġarticulated": 43322, "Ġstora": 43323, "Ġextrater": 43324, "á»ī": 43325, "ÏĥÏī": 43326, "wir": 43327, "ĠMete": 43328, "Imp": 43329, "Ġhoor": 43330, "phase": 43331, "ĠÑĩÑĥд": 43332, "ĠбÑĢаÑĤ": 43333, "Ġidag": 43334, "Ġcinq": 43335, "Ġaparecer": 43336, "ĠICE": 43337, "åĪĹ": 43338, "Ġquieter": 43339, "Ġfalsch": 43340, "adic": 43341, "ĠплÑİÑģ": 43342, "ĠMenu": 43343, "uxe": 43344, "ĠTôi": 43345, "ĠMIL": 43346, "ĠHaj": 43347, "verbs": 43348, "Ġtubing": 43349, "Ġmachst": 43350, "Ġdall": 43351, "Ter": 43352, "Ġgelen": 43353, "Ġcucumbers": 43354, "Ġwidgets": 43355, "Ġdevrait": 43356, "Ġmike": 43357, "Ġintra": 43358, "íķŃ": 43359, "ĠÃħ": 43360, "ĠHund": 43361, "æ§ĭ": 43362, "quarter": 43363, "Ġew": 43364, "Ġkeluar": 43365, "Ġmats": 43366, "ĠTrick": 43367, "ĠInfinite": 43368, "ŀ¨": 43369, "Ġpeac": 43370, "ĠProte": 43371, "à¥Ī": 43372, "Ġ1700": 43373, "ĠRais": 43374, "à¹Ĭ": 43375, "ählt": 43376, "ifica": 43377, "aimer": 43378, "aÄĩ": 43379, "Ġakl": 43380, "ĠVolvo": 43381, "ĠTyson": 43382, "ĠRong": 43383, "irsin": 43384, "ĠâĻ¥": 43385, "Ġparody": 43386, "national": 43387, "pod": 43388, "ayd": 43389, "ambled": 43390, "Ġgovernmental": 43391, "Ġconfort": 43392, "icides": 43393, "Ġnasze": 43394, "ĠShepherd": 43395, "ĠKontakt": 43396, "Ġdisproportionately": 43397, "ĠклÑİÑĩ": 43398, "ĠtÃŃtulo": 43399, "Ġsina": 43400, "Ġcompositions": 43401, "ĠPF": 43402, "Ġverkl": 43403, "Ġsuivre": 43404, "Ġasta": 43405, "Ġstakeholder": 43406, "Ġsamma": 43407, "ĠBLACK": 43408, "Ġnodig": 43409, "Ġleva": 43410, "Ġjuegos": 43411, "Ġernst": 43412, "Ġbottoms": 43413, "ĠSignal": 43414, "Ġpollut": 43415, "Ġdura": 43416, "Musik": 43417, "Ġкомна": 43418, "ĠвÑģей": 43419, "alter": 43420, "ĠStef": 43421, "ĠBigQuery": 43422, "ĠVerantwortung": 43423, "Ġëĭ¹ìŰ": 43424, "Ġquizz": 43425, "ĠLetter": 43426, "ĠInvestment": 43427, "ÑĪÑĤ": 43428, "IJëį°": 43429, "Ġencoding": 43430, "Ġtänker": 43431, "ĠKw": 43432, "annie": 43433, "åĭĿ": 43434, "110": 43435, "Ġzwy": 43436, "Ġì§§": 43437, "Ġdaw": 43438, "estä": 43439, "Ġdeceive": 43440, "ĠLänder": 43441, "isko": 43442, "Ġpodstaw": 43443, "ĠPharaoh": 43444, "쳤": 43445, "éĻIJ": 43446, "últ": 43447, "Ġtyö": 43448, "Ġmusimy": 43449, "質": 43450, "Ġpc": 43451, "ĠNT": 43452, "ĠCostco": 43453, "Ġå°ı": 43454, "ĠÏĥοÏħ": 43455, "Ġunin": 43456, "rounds": 43457, "Ġreminders": 43458, "Ġpuisqu": 43459, "Ġkrijgen": 43460, "Ġworkflows": 43461, "neten": 43462, "ĠëIJĺì§Ģ": 43463, "Ġsleek": 43464, "Ġcoworkers": 43465, "amientos": 43466, "Ġwitches": 43467, "baar": 43468, "eties": 43469, "Ġunnatural": 43470, "ĠSick": 43471, "ĠEfendi": 43472, "ãĥ³ãĥĢãĥĽ": 43473, "jcie": 43474, "Ġchamado": 43475, "ìĺĢìĬµëĭĪëĭ¤": 43476, "ĠprzedsiÄĻbior": 43477, "Ġbookstore": 43478, "Ġìŀłê¹IJ": 43479, "ĠSepar": 43480, "angi": 43481, "Evet": 43482, "Ġemergencies": 43483, "ĠXML": 43484, "нд": 43485, "¥´ë©´": 43486, "Ġê¿Ī": 43487, "Ġëĵ¤ê³ł": 43488, "Ġsut": 43489, "ĠWiz": 43490, "å±ķ": 43491, "Ġdynamically": 43492, "operation": 43493, "dot": 43494, "Ġinefficient": 43495, "clears": 43496, "Ġmundane": 43497, "ĠVeronica": 43498, "èĮ¶": 43499, "رت": 43500, "pose": 43501, "pai": 43502, "Ġnylon": 43503, "Ġaumentar": 43504, "ĠalltsÃ¥": 43505, "vak": 43506, "Ġcapacidad": 43507, "ĠWrestling": 43508, "Ġfertile": 43509, "Ġmég": 43510, "ĠNano": 43511, "аÑĤели": 43512, "Ġìĸ´ì©": 43513, "Ġtoca": 43514, "ĠEg": 43515, "âģ": 43516, "Ġì³": 43517, "luent": 43518, "Ġsolem": 43519, "Ġcinemat": 43520, "ĠQuel": 43521, "Ġorbits": 43522, "ĠHarm": 43523, "ricanes": 43524, "Ġblurred": 43525, "å¦Ĥä½ķ": 43526, "ĠاÙĦذÙĬ": 43527, "Ġjin": 43528, "Ġgrenades": 43529, "Ġatroc": 43530, "Ġwherein": 43531, "Ġreplen": 43532, "ĠComics": 43533, "edaan": 43534, "Ġdenim": 43535, "Ġembarrassment": 43536, "ĠGomez": 43537, "ĠBusan": 43538, "ivities": 43539, "Ġsaliva": 43540, "Ġmerk": 43541, "Ġilgili": 43542, "ĠкÑĢÑĥг": 43543, "Ġoccupational": 43544, "ĠSahib": 43545, "Sta": 43546, "Ġadviser": 43547, "ĠTruly": 43548, "ĠYEAH": 43549, "ĠìŀĪëĬĶëį°ìļĶ": 43550, "zew": 43551, "baren": 43552, "Ġstol": 43553, "Ġbelongings": 43554, "ĠResearchers": 43555, "Ġefendim": 43556, "ÏħÏĩ": 43557, "ÅĤÄħcz": 43558, "ĠUng": 43559, "ĠJub": 43560, "Ġcerebral": 43561, "á»ĩu": 43562, "Ġצר": 43563, "ĠподаÑĢ": 43564, "Ġmarched": 43565, "Ġawaken": 43566, "Ġako": 43567, "Ġacept": 43568, "Ġinitiation": 43569, "è¯ī": 43570, "lot": 43571, "ĠwÅĤas": 43572, "ĠMongol": 43573, "utral": 43574, "Ġtentang": 43575, "Ġinversion": 43576, "ĠìĿ´íĽĦ": 43577, "Ġlok": 43578, "ÅĤbym": 43579, "RS": 43580, "Ġstos": 43581, "Ġinteracts": 43582, "ĠCalendar": 43583, "Ġvanish": 43584, "Ġphysiology": 43585, "Ġlinearly": 43586, "ĠJY": 43587, "ÄŁan": 43588, "funded": 43589, "iziert": 43590, "Ġzmian": 43591, "ĠGrill": 43592, "Ġunbelievably": 43593, "otechnology": 43594, "ĠCars": 43595, "ĠÙĨÛģ": 43596, "ĠFolge": 43597, "ĠBeverly": 43598, "äischen": 43599, "Ġaumento": 43600, "ìĽĮìĦľ": 43601, "Ġmailbox": 43602, "Ġsteeds": 43603, "ĠPeak": 43604, "å·§": 43605, "Ġwykor": 43606, "Ġprawda": 43607, "иÑĤÑĭ": 43608, "Ġdiscours": 43609, "Ġaccuse": 43610, "cesso": 43611, "uire": 43612, "Ġпопад": 43613, "Ġtha": 43614, "Ġmeasurable": 43615, "beeping": 43616, "ĠInnen": 43617, "ĠпÑıÑĤÑĮ": 43618, "Ġcompeted": 43619, "ĠItalians": 43620, "Ġencontra": 43621, "Ġniew": 43622, "Ġfiltration": 43623, "ĠпÑĢоÑĦеÑģÑģ": 43624, "Ġpajamas": 43625, "Ġcilantro": 43626, "ĠSoc": 43627, "Luc": 43628, "Ġê¹Ģë": 43629, "ĠOdd": 43630, "Ġhydration": 43631, "мов": 43632, "Ġplywood": 43633, "ĠCompetition": 43634, "изнеÑģ": 43635, "flight": 43636, "ĠBeit": 43637, "bourg": 43638, "Ġcoils": 43639, "Ġcâmera": 43640, "Ġamended": 43641, "Äģm": 43642, "Angel": 43643, "ĠStacy": 43644, "flo": 43645, "Ġnormale": 43646, "Ġconsonant": 43647, "Ġaccompanying": 43648, "кÑĸ": 43649, "Ġirritated": 43650, "ĠfÃ¥tt": 43651, "Ġcrocodile": 43652, "IJĺëĬĶ": 43653, "Ġalbeit": 43654, "ĠPhilosophy": 43655, "ç´¯": 43656, "ÅĨ": 43657, "ytic": 43658, "Ġrèg": 43659, "Ġfrança": 43660, "Ġattentive": 43661, "Ham": 43662, "Ġalrededor": 43663, "æĿ¿": 43664, "sei": 43665, "ĠÑģвид": 43666, "Ġgimbal": 43667, "Ġchina": 43668, "ĠðŁİ¶": 43669, "ĠÐĴам": 43670, "Ġstimulating": 43671, "ĠOra": 43672, "ytes": 43673, "Ġheft": 43674, "Ġhaters": 43675, "Ġcomplexes": 43676, "Ġ03": 43677, "ród": 43678, "clear": 43679, "Ġbesteht": 43680, "çķĻè¨Ģ": 43681, "wny": 43682, "moil": 43683, "Ġsloppy": 43684, "Ġinsignificant": 43685, "Ġdubbed": 43686, "Ġëĸł": 43687, "Ġconsigo": 43688, "лÑĥÑĪай": 43689, "Sn": 43690, "Ġ×Ķצ": 43691, "ĠÎĮ": 43692, "Ġnadzie": 43693, "Ġfreshmen": 43694, "taa": 43695, "ĠuwagÄĻ": 43696, "ĠFavorite": 43697, "ĠCriminal": 43698, "Ġeviden": 43699, "Ġsymb": 43700, "Les": 43701, "ĠBeau": 43702, "uned": 43703, "plement": 43704, "Ac": 43705, "Ġdermat": 43706, "ĠNolan": 43707, "Ñĭп": 43708, "Ġsitt": 43709, "Ġeverlasting": 43710, "Ġestavam": 43711, "Ġмик": 43712, "Ġkhác": 43713, "Ġinvit": 43714, "Ġtreble": 43715, "Ġjig": 43716, "mani": 43717, "Ġtuvo": 43718, "ĠRUS": 43719, "ĠErde": 43720, "ĠDziÄĻkujÄĻ": 43721, "Ġblueberries": 43722, "kell": 43723, "acions": 43724, "çĪ·": 43725, "ви": 43726, "LET": 43727, "Ġsprout": 43728, "Ġspor": 43729, "Ġbên": 43730, "ĠMona": 43731, "ĠContain": 43732, "ĠKeys": 43733, "озÑı": 43734, "Ġfunción": 43735, "Ġrappelle": 43736, "Ġevolves": 43737, "Ġscraping": 43738, "Ġcomentários": 43739, "Ġpratique": 43740, "Ġauxiliary": 43741, "ĠSponge": 43742, "Ñģким": 43743, "uvo": 43744, "ĠÑģамо": 43745, "Ġsank": 43746, "Ġhighways": 43747, "Ġinventions": 43748, "Ġиногда": 43749, "Ġcreatively": 43750, "Ġbenchmarks": 43751, "oncé": 43752, "alal": 43753, "Ġsotto": 43754, "Ġcalves": 43755, "ĠMov": 43756, "Ġlavender": 43757, "Ġeyeballs": 43758, "Ġawaiting": 43759, "ĠPaty": 43760, "ÙĦÙĩ": 43761, "Ġembroidery": 43762, "Ġduh": 43763, "Ġcamar": 43764, "ĠBOB": 43765, "Ġspaced": 43766, "ĠgÅĤos": 43767, "аемÑģÑı": 43768, "Ġescapes": 43769, "ĠRogue": 43770, "zcz": 43771, "èŀ": 43772, "¬ë¥¼": 43773, "ĠMoże": 43774, "ĠеÑģÑĤе": 43775, "ĠBurada": 43776, "éĮ²": 43777, "wd": 43778, "uuuu": 43779, "Ġsash": 43780, "ĠLub": 43781, "Ġnotebooks": 43782, "Ġmae": 43783, "Ġconflicting": 43784, "Ġsummertime": 43785, "acas": 43786, "Ġbauen": 43787, "blowing": 43788, "ạo": 43789, "Ġìĸ¸ìłľ": 43790, "ä»ĬæĹ¥ãģ¯": 43791, "ĠSenhor": 43792, "ĠiPhones": 43793, "ĠQuarter": 43794, "ĠìłľëĮĢë¡ľ": 43795, "uÃŁ": 43796, "Ġë§Ī무ë": 43797, "Ġsettlers": 43798, "Ġcrest": 43799, "Ġtransc": 43800, "æĽ¾": 43801, "Ġriots": 43802, "Ġclones": 43803, "ĠOprah": 43804, "ίζ": 43805, "Ġpals": 43806, ".......": 43807, "ãģĶãģĸãģĦãģ¾ãģĻ": 43808, "ĠÑĢоÑģÑģ": 43809, "ĠLaser": 43810, "Ġzaczy": 43811, "Ġsevi": 43812, "Ġregeneration": 43813, "ìĹ¼": 43814, "would": 43815, "Ġüzerine": 43816, "ĠStraÃŁe": 43817, "Ġvengeance": 43818, "Ġrer": 43819, "ĠSafari": 43820, "ĠHEY": 43821, "çķ«": 43822, "Ġsacar": 43823, "Ġimagem": 43824, "ĠBundest": 43825, "mesan": 43826, "ĠPaste": 43827, "Ġsizz": 43828, "ĠпоÑģÑĤÑĥп": 43829, "×Ķ×ķ": 43830, "trad": 43831, "Ġfrançaise": 43832, "ĠBou": 43833, "Ġbarre": 43834, "ĠZhi": 43835, "ĠGeez": 43836, "ihad": 43837, "Ġreconoc": 43838, "Ġpelig": 43839, "Ġindices": 43840, "Ġë°ĶëĢ": 43841, "Ġconduction": 43842, "Ġìķħ": 43843, "Ġzeker": 43844, "Ġfum": 43845, "ĠWür": 43846, "breaker": 43847, "Ġsprite": 43848, "Crowd": 43849, "Ġopener": 43850, "Ġolv": 43851, "Ġbuenas": 43852, "ĠSilk": 43853, "ĠHIM": 43854, "kop": 43855, "compl": 43856, "Ġpossono": 43857, "³Ģ": 43858, "Ġoscillator": 43859, "ĠSith": 43860, "èĥ¡": 43861, "ажи": 43862, "Ġraft": 43863, "hall": 43864, "Ġschneller": 43865, "Ġimporting": 43866, "Ġassembling": 43867, "Ġubiqu": 43868, "Ġactivates": 43869, "acci": 43870, "ĵľë¥¼": 43871, "Ġcomposers": 43872, "ĠACL": 43873, "Conf": 43874, "Ġì½ĺ": 43875, "ĠнекоÑĤоÑĢÑĭе": 43876, "Ġcandies": 43877, "åĬłåħ¥": 43878, "ĠMuss": 43879, "à¹ĥà¸Ĭ": 43880, "Ġduda": 43881, "ником": 43882, "meden": 43883, "Ġìĸ´ëķĮ": 43884, "ĠYeshua": 43885, "zag": 43886, "hodou": 43887, "Ġaloud": 43888, "ĠPalmer": 43889, "imize": 43890, "ãĤ·ãĥ§": 43891, "Ġmaritime": 43892, "Ġcommunal": 43893, "Ġbadges": 43894, "Ġrugby": 43895, "Ġmarshmallow": 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44920, "Ġmacam": 44921, "ĠrzeczywiÅĽcie": 44922, "Ġhovering": 44923, "ège": 44924, "Ġpoorest": 44925, "ĠSched": 44926, "mile": 44927, "issements": 44928, "acÄĥ": 44929, "Ġ립": 44930, "Ġvaccin": 44931, "Ġfuturistic": 44932, "ĠWindow": 44933, "паÑĢ": 44934, "ĠÑĢоÑģ": 44935, "Ġlowers": 44936, "acs": 44937, "ĠÐIJлекÑģанд": 44938, "ĠAlert": 44939, "ieme": 44940, "ĠCaucas": 44941, "Ġjaws": 44942, "Ġhunted": 44943, "ìĹ½": 44944, "ĠبÙĨ": 44945, "Ġ׾׳×ķ": 44946, "Ġturbines": 44947, "Ġlumps": 44948, "ĠAllies": 44949, "ahlt": 44950, "Ġsubscriptions": 44951, "Ġnouveaux": 44952, "uger": 44953, "bones": 44954, "Ġberry": 44955, "ĠìĦłë¬¼": 44956, "ĠManufact": 44957, "ĠLunch": 44958, "ê·¸ëŀĺ": 44959, "Ġhydrated": 44960, "Ġachei": 44961, "ĠYaz": 44962, "ĠTibetan": 44963, "ĠQuantum": 44964, "ĠJerome": 44965, "ĠоÑīÑĥÑī": 44966, "ован": 44967, "motion": 44968, "ĠController": 44969, "energetic": 44970, "ĠÑģкоÑĢо": 44971, "Ġvowels": 44972, "ĠÑĥжаÑģ": 44973, "Ġhoof": 44974, "ĠBullet": 44975, "imagin": 44976, 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45035, "sed": 45036, "ĠíŤ": 45037, "Ġgereki": 45038, "Ġalarms": 45039, "ĠWha": 45040, "ĠMarkus": 45041, "aksi": 45042, "ĠÐIJле": 45043, "klore": 45044, "Ġéner": 45045, "Ġtilde": 45046, "boxing": 45047, "ĠìĦŀ": 45048, "Ġencontramos": 45049, "ĠPhar": 45050, "наком": 45051, "óst": 45052, "Ġİs": 45053, "Ġëĭĺ": 45054, "Ġsquats": 45055, "Ġpretended": 45056, "Ġdez": 45057, "Ġê´ľì°®ìķĦ": 45058, "jach": 45059, "ëĿ¼ê³ł": 45060, "ĠíĻķì§Ħ": 45061, "ĠAnsch": 45062, "imerk": 45063, "Ġconjugate": 45064, "Ġpeninsula": 45065, "Ġgorilla": 45066, "Ġphotographed": 45067, "ĠAunque": 45068, "Ġentren": 45069, "ĠDeutschen": 45070, "ĠAladdin": 45071, "Ġ무ìĦľ": 45072, "ĠStella": 45073, "ĠElection": 45074, "outine": 45075, "Grand": 45076, "ĠWak": 45077, "ĠSergio": 45078, "horse": 45079, "ahon": 45080, "ĠFamilies": 45081, "Ġhating": 45082, "ĠBett": 45083, "à¸Ļะà¸Ħะ": 45084, "Ġcurling": 45085, "ĠIsraelis": 45086, "Ġ׾×IJ×": 45087, "ĠMyers": 45088, "Ġscanned": 45089, "ĠBEC": 45090, "ileri": 45091, "Ġcalle": 45092, "ĠMinh": 45093, "Ġmicron": 45094, "Ġconduc": 45095, "ÃŃv": 45096, "ĠвозÑĮ": 45097, "Ġactionable": 45098, "ĠTrustees": 45099, "Ġtief": 45100, "Ġheaders": 45101, "Ġanimales": 45102, "ìĽĢ": 45103, "лоÑħ": 45104, "unity": 45105, "lya": 45106, "Ġjangan": 45107, "Ġhani": 45108, "Ġcasing": 45109, "Ġjóvenes": 45110, "ĠSplit": 45111, "ĠCarlo": 45112, "ĠBeim": 45113, "å°įä¸įèµ·": 45114, "Ġnuanced": 45115, "Ġteddy": 45116, "ĠClan": 45117, "ächen": 45118, "pier": 45119, "Ġдополн": 45120, "Ġdiaper": 45121, "effective": 45122, "ĠNiagara": 45123, "Ġwart": 45124, "Ġcorro": 45125, "ĠKampf": 45126, "zte": 45127, "Ġdéveloppement": 45128, "Ġattackers": 45129, "ĠSherman": 45130, "Ġ1914": 45131, "Ġmeow": 45132, "ĠPÃ¥": 45133, "ìº": 45134, "cit": 45135, "Ġcoupe": 45136, "Ġê·¸ëĭ¤ìĿĮìĹIJ": 45137, "Ġhumour": 45138, "Ġcole": 45139, "ĠWarning": 45140, "ĠTil": 45141, "calm": 45142, "buat": 45143, "Ġcine": 45144, "kiej": 45145, "Kevin": 45146, "Ġmilligrams": 45147, "×ĵר": 45148, "ariamente": 45149, "Ġoro": 45150, 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"ĠARM": 45209, "ĠплоÑħо": 45210, "Ġmeillä": 45211, "ĠDion": 45212, "å¼Ģå§ĭ": 45213, "Ġsouha": 45214, "Ġgeschafft": 45215, "Ġconvolution": 45216, "ĠâĢijâĢij": 45217, "Ġ144": 45218, "lingt": 45219, "Ġmännisk": 45220, "Ġgustado": 45221, "Ġcoined": 45222, "ĠLulu": 45223, "å®ĥçļĦ": 45224, "opot": 45225, "ĠPrayer": 45226, "Ġroasting": 45227, "Ġchromosomes": 45228, "飯": 45229, "еле": 45230, "Blue": 45231, "ĠErfolg": 45232, "èĩªçͱ": 45233, "ĠпÑĢидÑĥм": 45234, "Ġrisking": 45235, "ĠGuardians": 45236, "Ġ2024": 45237, "èse": 45238, "ĠбÑĥдÑĤо": 45239, "Ġconserve": 45240, "ĠBringing": 45241, "ĠAstra": 45242, "à¹Ģà¸Ĥ": 45243, "ĠкакÑĥÑİ": 45244, "respace": 45245, "ĠÐŀп": 45246, "ĠвокÑĢÑĥг": 45247, "æħĭ": 45248, "Ġmasked": 45249, "ĠShy": 45250, "ĠNim": 45251, "endas": 45252, "Ġíı¬ìĿ¸": 45253, "Ġ모ìĸij": 45254, "Ġvaleur": 45255, "ĠNegro": 45256, "ĠCDs": 45257, "inkling": 45258, "Ġmontón": 45259, "ĠHond": 45260, "Real": 45261, "Ġfullness": 45262, "ĠWhoops": 45263, "ĠShank": 45264, "ĠBran": 45265, "Ġtransluc": 45266, "Ġerr": 45267, "ĠGardens": 45268, "oyu": 45269, "Ġaffirmative": 45270, "ä¸ĭä¸Ģ": 45271, "Ġpottery": 45272, "live": 45273, "iau": 45274, "mount": 45275, "Ġfluctuations": 45276, "åŁİ": 45277, "ÃŃem": 45278, "Ġpulses": 45279, "Ġcrianças": 45280, "ίαÏĤ": 45281, "Ġbasta": 45282, "ENNIS": 45283, "ĠкоÑĢп": 45284, "ĠFunk": 45285, "ĠéĢĻ": 45286, "Ã¥rt": 45287, "ĠзаÑĤем": 45288, "Ġparasites": 45289, "ãĥĻ": 45290, "Ġairflow": 45291, "ĠXuan": 45292, "Gülme": 45293, "Ġblooming": 45294, "Ġmummy": 45295, "Ġbao": 45296, "ĠClap": 45297, "antics": 45298, "skin": 45299, "centric": 45300, "before": 45301, "ĠRICHARD": 45302, "ĠHahn": 45303, "TAKE": 45304, "ĠÑĤÑĢеÑĤÑĮ": 45305, "Ġpressured": 45306, "ĠKurz": 45307, "isti": 45308, "ĠнаÑĪего": 45309, "Ġsemiconductor": 45310, "ĠClint": 45311, "Ġplup": 45312, "ĠOrigin": 45313, "ĠEvents": 45314, "Ġê±±ìłķ": 45315, "mpfen": 45316, "NEY": 45317, "ĠDW": 45318, "Ġë¶ģíķľ": 45319, "Ġinforms": 45320, "Ġforsk": 45321, "Ġamiga": 45322, "ĠCincinn": 45323, "Str": 45324, "Ġparish": 45325, "Ġ커íĶ": 45326, "Ġsizi": 45327, "Ġplantation": 45328, "Ġbliver": 45329, "ĠполиÑĤ": 45330, "Ġsubdiv": 45331, "Ġrant": 45332, "Ġprincipals": 45333, "åIJ¦": 45334, "Ġkunne": 45335, "ügen": 45336, "arespace": 45337, "Ġvallahi": 45338, "Ġcollapsing": 45339, "اÙĦÙħ": 45340, "Ġlider": 45341, "Ġtama": 45342, "Ġgagner": 45343, "rolle": 45344, "Ġë§IJìĶĢëĵľë": 45345, "Ġcathedral": 45346, "ĠWebs": 45347, "ĠPolitics": 45348, "ãģĹãģ¾": 45349, "ãģ£ãģ¦ãĤĭ": 45350, "ĠDenis": 45351, "Ġtuo": 45352, "Ġrefract": 45353, "Ġdisintegr": 45354, "stes": 45355, "ĠлÑİбов": 45356, "Ġwilt": 45357, "Ġtrusts": 45358, "Ġkomun": 45359, "ĠBasket": 45360, "~!!": 45361, "nae": 45362, "ĠÐļол": 45363, "Ġsyllables": 45364, "ĠHenri": 45365, "ĠNab": 45366, "ÙĪØ¹": 45367, "Ġwn": 45368, "Ġkamp": 45369, "ĠPrague": 45370, "ĠBreakfast": 45371, "Ġê·¸ëŁ´": 45372, "Ġchut": 45373, "Ġ330": 45374, "ĠIndustries": 45375, "ä¸į管": 45376, "ĠiÅŁi": 45377, "ĠGoldman": 45378, "Ġİns": 45379, "ussa": 45380, "ithe": 45381, "ĦIJ": 45382, "ĠSOUND": 45383, "алÑĮнÑĭм": 45384, ".(": 45385, "ĠгоÑĢаз": 45386, "Ġdagegen": 45387, "Ġë®": 45388, "Ġwaiter": 45389, "length": 45390, "ĠÏĥÏĦα": 45391, "Ġchunky": 45392, "Sa": 45393, "Ġrusty": 45394, "ĠJudith": 45395, "750": 45396, "Ġepoxy": 45397, "ì¹ł": 45398, "åı²": 45399, "metro": 45400, "Ġrejecting": 45401, "Ġsquishy": 45402, "Ġplupart": 45403, "Ġméth": 45404, "Ġaspiring": 45405, "ĠDrama": 45406, "Ġuplift": 45407, "§Īëĭ¤": 45408, "................": 45409, "ł¤ìļĶ": 45410, "Ġtécnica": 45411, "Ġpasando": 45412, "Those": 45413, "ĠÑĢаздел": 45414, "Ġmediocre": 45415, "ĠNickel": 45416, "Ġsuperheroes": 45417, "Ġmissionary": 45418, "ĠParece": 45419, "Ġrotational": 45420, "Ġprett": 45421, "ãģĿãģĨãģĿãģĨ": 45422, "Ġlama": 45423, "Ġcanyon": 45424, "Ġbeter": 45425, "ĠProvost": 45426, "Ġhvis": 45427, "Ġdeactiv": 45428, "ĠHels": 45429, "pflicht": 45430, "Something": 45431, "ĠPierce": 45432, "Ġê²Ģì°°": 45433, "lungen": 45434, "Ġsizing": 45435, "Ġlatitude": 45436, "ĠNonetheless": 45437, "omnia": 45438, "ĠSabrina": 45439, "ĠDynamic": 45440, "åĥ¹": 45441, "onta": 45442, "ìĨIJ": 45443, "Ġdirective": 45444, "ĠDepot": 45445, "Ġfueled": 45446, "Ġexpire": 45447, "Ġcomún": 45448, "ĠSexual": 45449, "ĠGore": 45450, "Ġrestless": 45451, "ĠJAKE": 45452, "ÑĤеÑĢеÑģ": 45453, "ĠÑĤÑĢан": 45454, "ĠHolz": 45455, "å°Ĩ": 45456, "ĠActor": 45457, "æĿ¯": 45458, "call": 45459, "Ġemailed": 45460, "ĠPear": 45461, "Ñĥди": 45462, "ÑĢал": 45463, "ĠmÃły": 45464, "ĠCHEERING": 45465, "å®īåħ¨": 45466, "Ġretailer": 45467, "Ġprotr": 45468, "Ġdiscarded": 45469, "ĠHIS": 45470, "Ġevangelical": 45471, "ĠElse": 45472, "Ġexplores": 45473, "Ġcriticizing": 45474, "ifik": 45475, "Ġwhipping": 45476, "Ġopis": 45477, "oused": 45478, "Free": 45479, "ĠíĮ¬": 45480, "Ġmics": 45481, "running": 45482, "Ob": 45483, "itié": 45484, "Ġnecesita": 45485, "ĠDominican": 45486, "ĠBagh": 45487, "Ġtendencies": 45488, "ĠMetropolitan": 45489, "Åijl": 45490, "Ġзнаем": 45491, "ĠZam": 45492, "ĠDeadpool": 45493, "ależ": 45494, "Ġinvestigative": 45495, "ĠPronunciation": 45496, "Ġemulate": 45497, "Ġbanco": 45498, "Ġ-âĻª": 45499, "åĪ»": 45500, "Ġoverarching": 45501, "liches": 45502, "ĠвозвÑĢаÑī": 45503, "ĠScary": 45504, "ĠKia": 45505, "åľŁ": 45506, "ronting": 45507, "inned": 45508, "ĠÛģÙĪ": 45509, "ìĪĺ를": 45510, "ç¾İåij³": 45511, "wel": 45512, "Ġë³Ħë¡ľ": 45513, "Ġunintention": 45514, "aaS": 45515, "Ġnicest": 45516, "ĠTesting": 45517, "ĠISIL": 45518, "ogenous": 45519, "ĠØŁ": 45520, "Ġlieutenant": 45521, "Ġbrauch": 45522, "ĠTir": 45523, "drive": 45524, "Ġtolerant": 45525, "Ġshooters": 45526, "ĠìĺĪë»IJ": 45527, "殺": 45528, "onton": 45529, "Ġteria": 45530, "ietet": 45531, "Ron": 45532, "leigh": 45533, "gae": 45534, "Ġolmak": 45535, "ĠClone": 45536, "sold": 45537, "Ġskeletons": 45538, "Ġincumbent": 45539, "оме": 45540, "CON": 45541, "Ġleven": 45542, "Ġmillennials": 45543, "Ġequator": 45544, "ĠFeder": 45545, "ĠAlexandra": 45546, "Ġvrij": 45547, "ĠHealthcare": 45548, "Ġíķij": 45549, "Ġemphasizing": 45550, "Ġdialogues": 45551, "Ġchilled": 45552, "Ġprow": 45553, "ĠPassion": 45554, "ĠLaden": 45555, "ariest": 45556, "aphrag": 45557, "Ġadditive": 45558, "ĠStaat": 45559, "ĠNept": 45560, "ĠHAM": 45561, "à¹Ģà¸Ń": 45562, "days": 45563, "ĠíĸĪëįĺ": 45564, "Ġvoila": 45565, "ĠÑħл": 45566, "ĠDeutsche": 45567, "quir": 45568, "Open": 45569, "Ġranged": 45570, "Ġlevers": 45571, "ĠMansion": 45572, "pared": 45573, "ĠTitans": 45574, "atoire": 45575, "Ġengages": 45576, "yez": 45577, "naden": 45578, "Ġobstruct": 45579, "ĠEmmy": 45580, "åķĨ": 45581, "°¥": 45582, "Ġtroph": 45583, "Ġtakeaways": 45584, "+.": 45585, "tycznie": 45586, "hésitez": 45587, "ĠpodÃŃa": 45588, "Ġ주ëĬĶ": 45589, "Ġcitation": 45590, "ĠAqua": 45591, "Ġdebugging": 45592, "ван": 45593, "Ġëĭ¹ìĭł": 45594, "ĠاÙĦÙĬ": 45595, "Ġinstantaneous": 45596, "ĠAutumn": 45597, "Ġkepada": 45598, "Ġgetan": 45599, "hini": 45600, "ynthesis": 45601, "ĠпеÑĢи": 45602, "ĠMaced": 45603, "Pac": 45604, "untu": 45605, "Bra": 45606, "ĠгоÑĢаздо": 45607, "Ġ1959": 45608, "ĠÑĤемпеÑĢ": 45609, "Ġsane": 45610, "ĠOUR": 45611, "asu": 45612, "Ġ무ìĹ": 45613, "Ġvalleys": 45614, "Ġlistings": 45615, "Ġprzedstaw": 45616, "Ġgummy": 45617, "Ġcortisol": 45618, "ĠObrig": 45619, "ĠAllied": 45620, "ожÑĥ": 45621, "Ġgénér": 45622, "Ġdocs": 45623, "ĠChili": 45624, "ĠAbdullah": 45625, "Kit": 45626, "Ġcontributors": 45627, "гоÑĢ": 45628, "леÑĢ": 45629, "Ġbinder": 45630, "Ġmodèle": 45631, "íħIJ": 45632, "Ġinteiro": 45633, "mis": 45634, "fera": 45635, "اذ": 45636, "Mania": 45637, "ĠíĻľëıĻ": 45638, "Ġë´IJìļĶ": 45639, "ĠJaz": 45640, "ç»ĵ": 45641, "ÑĸлÑĮки": 45642, "rishna": 45643, "Ġêµ°": 45644, "Ġtamanho": 45645, "Ġappliance": 45646, "ĠResistance": 45647, "ĠLOOK": 45648, "ĠHyp": 45649, "ĠHeil": 45650, "Fire": 45651, "uju": 45652, "Ġheals": 45653, "Ġmalt": 45654, "ĠVERY": 45655, "ĠÑħоÑĩеÑĪÑĮ": 45656, "Ġlinger": 45657, "ĠNarr": 45658, "ĠRegular": 45659, "ĠLoop": 45660, "ĠLeno": 45661, "Ġsortie": 45662, "ĠServe": 45663, "ĠìĿµ": 45664, "ĠLuego": 45665, "ittä": 45666, 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45723, "ĠKarena": 45724, "ĠGuerra": 45725, "è¿ĺæĺ¯": 45726, "Ġsinister": 45727, "Ġpodéis": 45728, "Ġparab": 45729, "Ġoko": 45730, "Ġmatéri": 45731, "Ġcaric": 45732, "sonaro": 45733, "Ġpraticamente": 45734, "ÑĥÑģа": 45735, "Ġcomunque": 45736, "Ġvigilant": 45737, "Ġregimes": 45738, "ĠShooting": 45739, "Ġraids": 45740, "ĠNora": 45741, "ĠWieder": 45742, "mens": 45743, "ĠÑģод": 45744, "Ġê²½ìļ°ìĹIJëĬĶ": 45745, "ĠвÑħод": 45746, "Ġautobi": 45747, "ĠSchn": 45748, "ĠRobbie": 45749, "ĠFitness": 45750, "ĠконÑĦ": 45751, "Ġpenguin": 45752, "моÑĤÑĢÑı": 45753, "Ġминим": 45754, "plays": 45755, "Ġdelegates": 45756, "Mer": 45757, "Ġsistem": 45758, "ĠMichaels": 45759, "male": 45760, "اع": 45761, "Ġcách": 45762, "ĠHä": 45763, "Ġ×Ļ×ķ×ĵ×¢": 45764, "Ġsuperpower": 45765, "Ġstron": 45766, "Ġrover": 45767, "Ġdépend": 45768, "éϳ": 45769, "Ġretiring": 45770, "Ġvampires": 45771, "Ġmerde": 45772, "ĠChanging": 45773, "Ġtame": 45774, "Ġspokesperson": 45775, "Ġcay": 45776, "Ġflirting": 45777, "ĠGrö": 45778, "Ġwär": 45779, "Ġwyb": 45780, "Ġcoeur": 45781, "ạnh": 45782, "ĠìĻĢìĦľ": 45783, "Ġconnais": 45784, "ĠHundreds": 45785, "ĠBea": 45786, "ĠαÏĢ": 45787, "pruch": 45788, "Ġsociedade": 45789, "ĠWhilst": 45790, "ĠKait": 45791, "espace": 45792, "Ġchia": 45793, "ĠErm": 45794, "Ġë°Ķê¿": 45795, "Ġfences": 45796, "ĠMortal": 45797, "ê²ģ": 45798, "ĠгÑĢаÑĦ": 45799, "ĠHomeland": 45800, "ĠJUN": 45801, "isst": 45802, "Ġparlar": 45803, "Ġsporty": 45804, "éo": 45805, "Ġdeepen": 45806, "ĠBehavior": 45807, "éĢı": 45808, "åĵĪåĵĪåĵĪ": 45809, "Ġerrand": 45810, "Ġrotary": 45811, "ĠWellington": 45812, "Wind": 45813, "Ġmesela": 45814, "ảng": 45815, "iende": 45816, "Ġexcell": 45817, "ĠGenius": 45818, "ĠEduardo": 45819, "æľī人": 45820, "ĠÅŁunu": 45821, "Ġİstanbul": 45822, "Ġproduto": 45823, "Ġãħİãħİ": 45824, "OFF": 45825, "Ġwollt": 45826, "çĪĨ": 45827, "Ġëī´ìĬ¤": 45828, "Ġlass": 45829, "Ġhertz": 45830, "Ġaromatic": 45831, "Ġзвон": 45832, "Ġautoc": 45833, "ĠLust": 45834, "Ġ112": 45835, "ĠÎĹ": 45836, "Ġreviewers": 45837, "Ġreceptive": 45838, "å°įäºĨ": 45839, "ând": 45840, "oglo": 45841, "ĠìķĦëĭĻ": 45842, "Ġngo": 45843, "ÑĸÑĤи": 45844, "Ã¥t": 45845, "cono": 45846, "Ġtekrar": 45847, "Ġì£¼ê³ł": 45848, "ĠgelmiÅŁ": 45849, "Ġbedtime": 45850, "ĠArgh": 45851, "ADA": 45852, "ĠгоÑĢода": 45853, "ĠÄĩ": 45854, "Ġalliances": 45855, "giggling": 45856, "Ġyerde": 45857, "Ġspies": 45858, "Ġgutes": 45859, "çi": 45860, "Ġalltid": 45861, "ĠLah": 45862, "ŀIJë": 45863, "ĠdokÅĤad": 45864, "ÙĪÙĬ": 45865, "Ġtoxicity": 45866, "Ġcancellation": 45867, "Ġ1958": 45868, "dro": 45869, "ĠìŀijìĿĢ": 45870, "ĠMotorola": 45871, "Ġmultin": 45872, "Ġenthusiasts": 45873, "ĠMighty": 45874, "ĠCoconut": 45875, ":ãĢĮ": 45876, "ĠPictures": 45877, "Ġsangre": 45878, "Ġblinking": 45879, "olesome": 45880, "ĠìĬ¤íĥĢìĿ¼": 45881, "FP": 45882, "Ġbooming": 45883, "ĠдеÑģÑıÑĤ": 45884, "Ġratchet": 45885, "Ġtimelines": 45886, "leness": 45887, "Ġcages": 45888, "ĠGoodnight": 45889, "ometimes": 45890, "Ġcunning": 45891, "ĠRisk": 45892, "uled": 45893, "dade": 45894, "Ġprata": 45895, "ĠgustarÃŃa": 45896, "amus": 45897, "ĠJinping": 45898, "Ġestrut": 45899, "Ġdescobrir": 45900, "ĠMÄģ": 45901, "ĠAllan": 45902, "ĠåĪĨ": 45903, "Ġ׾ק": 45904, "Ġpreserv": 45905, "ĠStrawberry": 45906, "Äı": 45907, "Lu": 45908, "Ġkro": 45909, "ĠReports": 45910, "ìħĶìķ¼": 45911, "Ġvalt": 45912, "Ġpouvait": 45913, "Ġappar": 45914, "ĠBone": 45915, "Ġpreferably": 45916, "ĠRepública": 45917, "å°±åΰ": 45918, "Ġherzlich": 45919, "Ġchimney": 45920, "Ġçev": 45921, "Ġvisas": 45922, "Ġverr": 45923, "Ġcultivation": 45924, "ĠArmenia": 45925, "ĠвдÑĢÑĥг": 45926, "Ġcockro": 45927, "retched": 45928, "artz": 45929, "ĠлÑİдÑıм": 45930, "ĠpolÃŃticas": 45931, "ĠPanz": 45932, "ĠAKA": 45933, "ĠëĪĮ룬": 45934, "Ġerro": 45935, "Ġcamper": 45936, "Ġ102": 45937, "स": 45938, "done": 45939, "Ġhoard": 45940, "ĠÐŁÐ¾ÑĤом": 45941, "jeong": 45942, "Ġdesta": 45943, "pak": 45944, "Ġinim": 45945, "Ġgrowers": 45946, "ĠMessage": 45947, "Ġelector": 45948, "engage": 45949, "ĠForbes": 45950, "ĠCincinnati": 45951, "Ġdifférence": 45952, "df": 45953, "Ġspar": 45954, "Ġawaits": 45955, "ĠUSSR": 45956, "ĠRising": 45957, "ĠHoÅŁ": 45958, "Ġfooting": 45959, "Ġcondiciones": 45960, "ÑĤоÑĢов": 45961, "Ġclinician": 45962, "ĠDiskuss": 45963, "å£ĵ": 45964, "ר×Ĵ": 45965, "×¥": 45966, "iteit": 45967, "gren": 45968, "Ġcharisma": 45969, "Ġleuke": 45970, "Ġirritating": 45971, "Ġcirca": 45972, "ĠRhodes": 45973, "Ġpior": 45974, "Ġhandicap": 45975, "royable": 45976, "Ġvull": 45977, "OG": 45978, "ĠinÃŃcio": 45979, "ieri": 45980, "Ġsplashing": 45981, "Ġdemise": 45982, "Ġassistir": 45983, "ÑĩÑĤо": 45984, "Ġcovert": 45985, "ĠGud": 45986, "à¸ī": 45987, "klär": 45988, "ĠìŀIJ꾸": 45989, "Ġverändert": 45990, "ĠREM": 45991, "ĠConven": 45992, "atge": 45993, "Ġpierwsze": 45994, "Ġclergy": 45995, "lington": 45996, "liv": 45997, "VPN": 45998, "ĠÑģожал": 45999, "ĠHate": 46000, "ãģ¨ãģĵãĤį": 46001, "ÏĨο": 46002, "ĠRespons": 46003, "озд": 46004, "Ġetmek": 46005, "Ġchemin": 46006, "ÙħØ©": 46007, "Ġê°Ģ족": 46008, "Tre": 46009, "Ġumas": 46010, "ĠBurton": 46011, "Ġpatriarch": 46012, "ĠSmithsonian": 46013, "¥ĺ": 46014, "Moon": 46015, "Air": 46016, "Ġmedios": 46017, "Ġeraser": 46018, "Ġwollten": 46019, "Ġpareil": 46020, "ĠBillie": 46021, "æĬ½": 46022, "еÑĢÑĤв": 46023, "Ġparlament": 46024, "Ġagony": 46025, "ĠQUE": 46026, "sequently": 46027, "Another": 46028, "ĠWhew": 46029, "ĠAnnual": 46030, "Ġseben": 46031, "ìĥģìĿĦ": 46032, "values": 46033, "ŀľë§Į": 46034, "Ġsinon": 46035, "ereal": 46036, "ĠEnlight": 46037, "ĠChemistry": 46038, "ĠCatalunya": 46039, "Ġdoctr": 46040, "anton": 46041, "Ġstuk": 46042, "ĠPlate": 46043, "ĠKardashian": 46044, "Ġfilos": 46045, "ĠWet": 46046, "ĠпопÑĭÑĤ": 46047, "Ġunknowns": 46048, "ĠSchon": 46049, "ĠBaldwin": 46050, "Ġtelescopes": 46051, "ĠGucci": 46052, "oxide": 46053, "ĠConservative": 46054, "ìĦ±ìĿĦ": 46055, "Ġhinaus": 46056, "Power": 46057, "Ġê±´ê°ķ": 46058, "Ġprevail": 46059, "orman": 46060, "machine": 46061, "Ġ1946": 46062, "Ġunbel": 46063, "Ġschaut": 46064, "Ġpiel": 46065, "eenth": 46066, 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48063, "ĠHOR": 48064, "ìĭ¤íŀĪ": 48065, "ilage": 48066, "Ġshaded": 48067, "Ġresumes": 48068, "ĠPeanut": 48069, "ĠMILL": 48070, "apons": 48071, "ĠUFC": 48072, "ĠSole": 48073, "Ġjoystick": 48074, "ĠOlivier": 48075, "warming": 48076, "Ġsyllabus": 48077, "ĠобÑīе": 48078, "Ġhiá»ĩn": 48079, "Ġfesta": 48080, "Ġcradle": 48081, "ĠZac": 48082, "Ġremembrance": 48083, "Ġê°ĻìķĦìĦľ": 48084, "ĠpiÄĻk": 48085, "Ġcoexist": 48086, "ĠVII": 48087, "Ġáreas": 48088, "Ġuważ": 48089, "Ġobservers": 48090, "Ġmänniskor": 48091, "coon": 48092, "ĠDAM": 48093, "Ġnaszym": 48094, "Ġalligator": 48095, "ĠFreeze": 48096, "ĠEstate": 48097, "ĠÑĤÑĢади": 48098, "Ġundercover": 48099, "Ġnies": 48100, "ĠFehler": 48101, "plin": 48102, "ĠKabul": 48103, "ilate": 48104, "Ġê³łìĸij": 48105, "Ġmop": 48106, "ìĦ¼": 48107, "Ġanderer": 48108, "ĠKELL": 48109, "оки": 48110, "ĠжеÑģÑĤ": 48111, "Ġgrazing": 48112, "ĠdaÃŃ": 48113, "Ġcapitalize": 48114, "Ġapex": 48115, "Ġnurturing": 48116, "Ġcortar": 48117, "Ġcontrac": 48118, "ımızı": 48119, "Ġtandem": 48120, "éĥ½æľī": 48121, "gement": 48122, "ĠÑģиÑģÑĤема": 48123, "Ġmanque": 48124, "iajÄħ": 48125, "WOR": 48126, "Ġاب": 48127, "Ġcarts": 48128, "ANO": 48129, "Ġë°Ľê³ł": 48130, "ĠCena": 48131, "ĠBiology": 48132, "idar": 48133, "Ġaż": 48134, "erne": 48135, "anu": 48136, "Ġthanked": 48137, "Ġsubmarines": 48138, "Ġmanic": 48139, "Ġмоз": 48140, "ä¼Ĭ": 48141, "instant": 48142, "essential": 48143, "Ġsamurai": 48144, "Ġpasti": 48145, "Ġalan": 48146, "Ġbroch": 48147, "Ġbaker": 48148, "ĠGuill": 48149, "¨¼": 48150, "Ġwithdrawn": 48151, "ëĭĿ": 48152, "Perfect": 48153, "quency": 48154, "Ġstreamlined": 48155, "Ġ1300": 48156, "´ëıĦ": 48157, "Ġëĸłë": 48158, "Ġãģ¯ãģĦ": 48159, "Ġhvad": 48160, "ä¸Ģå®ļè¦ģ": 48161, "Ġverbally": 48162, "ĠKons": 48163, "Ġì¡°ìĭ¬": 48164, "Ġdiez": 48165, "æİ°æİ°": 48166, "Ġchuckling": 48167, "ĠMih": 48168, "Ġrallies": 48169, "Ġmanter": 48170, "Ġearnest": 48171, "super": 48172, "Ġgece": 48173, "ĠRend": 48174, "ĠGerade": 48175, "jenigen": 48176, "ĠVall": 48177, "ĠìŀĪëĤĺ": 48178, "ĠÑģказала": 48179, "Ġtrabalh": 48180, "ĠнаÑĪем": 48181, "ĠмеÑħ": 48182, "ikit": 48183, "Ġnouns": 48184, "Ġneurological": 48185, "Ġmotivational": 48186, "ĠMcMahon": 48187, "ĠFinished": 48188, "Ġë³´ìĿ´": 48189, "ĠFields": 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, 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48403, "ìĬ¤íĬ¸ë": 48404, "ãĤ¸ãĥ£": 48405, "ĠTurtle": 48406, "ĠRegina": 48407, "ĠLoad": 48408, "does": 48409, "panze": 48410, "¸Ķ": 48411, "Ġmina": 48412, "ĠLatinos": 48413, "ammers": 48414, "ĠTort": 48415, "ĠBeyonce": 48416, "имоÑģÑĤи": 48417, "ĠвопÑĢоÑģÑĭ": 48418, "Ġbulun": 48419, "èĢĮå·²": 48420, "inek": 48421, "bereich": 48422, "Ġpasture": 48423, "ĠOA": 48424, "ĠMelt": 48425, "ĠEtt": 48426, "ĠDY": 48427, "Ġobwohl": 48428, "Ġleagues": 48429, "ÑĤеÑģÑĮ": 48430, "ĠкÑĥÑģ": 48431, "Ġvors": 48432, "Ġtopp": 48433, "ographical": 48434, "asst": 48435, "Ġlindo": 48436, "Ġë°ĿíĺĶ": 48437, "Ġréfl": 48438, "Ġclimbs": 48439, "Ġvarsa": 48440, "Ġmethyl": 48441, "ĠKarere": 48442, "Æ°á»Ł": 48443, "Rad": 48444, "Ġpreparedness": 48445, "онÑĩ": 48446, "ĠOD": 48447, "ĠCGI": 48448, "Ġम": 48449, "Ġspeechless": 48450, "Ġlasci": 48451, "Ġbolag": 48452, "ĠÑħоÑĩеÑĤÑģÑı": 48453, "Ġgrieving": 48454, "ĠJohannes": 48455, "ĠCarroll": 48456, "adaki": 48457, "άë": 48458, "ĠsÅĤu": 48459, "Ġinnerhalb": 48460, 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48518, "Ġcriticisms": 48519, "ĠRusia": 48520, "म": 48521, "phin": 48522, "ĠCompare": 48523, "ĠBON": 48524, "Ġsneaking": 48525, "ĠRails": 48526, "ĠGeral": 48527, "Ġ1953": 48528, "Hola": 48529, "ĠопÑĭÑĤ": 48530, "Ġrainforest": 48531, "Ġbelum": 48532, "ĠObi": 48533, "ĠISS": 48534, "ãĤĮãģªãģĦ": 48535, "ĠСв": 48536, "Ġblond": 48537, "Ġwzgl": 48538, "ĠpowiedziaÅĤ": 48539, "Ġchoking": 48540, "ĠSongs": 48541, "ĠBiraz": 48542, "Ġyells": 48543, "Ġstylist": 48544, "ÏĮÏĦε": 48545, "Ġschreiben": 48546, "ĠJaw": 48547, "ĠEleven": 48548, "ĠRif": 48549, "/.": 48550, "Ġìĺ¤ëŀľë§Į": 48551, "Ġtreaties": 48552, "uffed": 48553, "ĠâĪĴ": 48554, "Ġroofs": 48555, "à¹Ģส": 48556, "Ġë»": 48557, "Ġsparkle": 48558, "ĠKiev": 48559, "ĠArgu": 48560, "erecht": 48561, "ĠÐĿадо": 48562, "ĠFIL": 48563, "Ġmolta": 48564, "ĠDevi": 48565, "Ġcampe": 48566, "Ġbenevol": 48567, "ĠTough": 48568, "Ġmoim": 48569, "Ġevacuate": 48570, "Ġerrado": 48571, "å©Ĩ": 48572, "ÑĢÑĥго": 48573, "Ġíİĺ": 48574, "ĠÎĵια": 48575, "Ġweaken": 48576, 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48632, "ĠmyÅĽ": 48633, "narrator": 48634, "Ġодном": 48635, "ĠnajwiÄĻ": 48636, "Ġmobilize": 48637, "Ġmillor": 48638, "Ġata": 48639, "æ··": 48640, "ĠpolÃŃtico": 48641, "Ġplead": 48642, "Ġpainters": 48643, "ĠSow": 48644, "оÑĦ": 48645, "ĠìĺĽëĤł": 48646, "ĠÑĩÑĤоб": 48647, "Ġsabor": 48648, "ĠUndert": 48649, "ĠJERRY": 48650, "Å¡ÃŃ": 48651, "Ġë°ĸìĹIJ": 48652, "Ġprécéd": 48653, "Ġannotation": 48654, "ĠInaudible": 48655, "Ġtextured": 48656, "Ġfisherman": 48657, "vordan": 48658, "icherung": 48659, "ĠìłģìĿ´": 48660, "Ġgezeigt": 48661, "Ġmandates": 48662, "Ġbeak": 48663, "ĠTWO": 48664, "ĠAkbar": 48665, "ilian": 48666, "Ġtiếp": 48667, "Ġsuperiority": 48668, "inku": 48669, "Ġlys": 48670, "ĠFCC": 48671, "ĠCPA": 48672, "ustering": 48673, "nicos": 48674, "anja": 48675, "Ġchills": 48676, "ĠCage": 48677, "Ġsealing": 48678, "Ġsaç": 48679, "Ġdedans": 48680, "ĠAlger": 48681, "Ġspezie": 48682, "Ġcoloss": 48683, "ıyı": 48684, "clockwise": 48685, "Ġexactamente": 48686, "Ġiemand": 48687, "amı": 48688, "Ġmandar": 48689, "raj": 48690, "faced": 48691, "agua": 48692, "Ġê¹Ķë": 48693, "Ġinsbesondere": 48694, "Ġdrizzle": 48695, "Ġdiminish": 48696, "ĠYoda": 48697, "AI": 48698, "Ġbilmiyorum": 48699, "ĠMMA": 48700, "ategory": 48701, "ĠпеÑĢеп": 48702, "Ġparticipar": 48703, "Ġnormalized": 48704, "Ġcomplexities": 48705, "æ´²": 48706, "æİ§": 48707, "аÑĢов": 48708, "mist": 48709, "icha": 48710, "Group": 48711, "Ġresiliency": 48712, "Ġnogle": 48713, "ĠCNC": 48714, "prü": 48715, "Ġphysicists": 48716, "нок": 48717, "LI": 48718, "Ġstuffs": 48719, "Ġsistemas": 48720, "Ġinterfering": 48721, "ĠMarvin": 48722, "ército": 48723, "ĠìĹĨê³ł": 48724, "Ġsonic": 48725, "Ġequiv": 48726, "Ġabord": 48727, "ĠRamen": 48728, "Ġ09": 48729, "medim": 48730, "atiques": 48731, "ĠделаÑİÑĤ": 48732, "Ġunanimously": 48733, "Ġskirts": 48734, "ĠíĬ¹ë³Ħ": 48735, "ĠPrix": 48736, "kami": 48737, "Ġfruition": 48738, "Ġbirthdays": 48739, "иком": 48740, "Ġinaugural": 48741, "Ġcorrelate": 48742, "ĠTory": 48743, "ĠëĤĺìģ": 48744, "Ġdew": 48745, "ĠPrecis": 48746, "ihi": 48747, "Ġë¬¸ìłľê°Ģ": 48748, "Ġciting": 48749, "ĠLana": 48750, "ĠKag": 48751, "Ġplaythrough": 48752, "ĠProtocol": 48753, "frist": 48754, "hovah": 48755, "Ġmerciful": 48756, "Ġbilingual": 48757, "ĠGuitar": 48758, "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, ">": 48986, "ĠespÃŃ": 48987, "ĠCOP": 48988, "åŃ©åŃIJ": 48989, "visible": 48990, "ĠпÑĢеÑģÑĤÑĥп": 48991, "Ġstitched": 48992, "à¯Ī.": 48993, "Ġlatent": 48994, "ĠPrab": 48995, "ĠMcN": 48996, "ĠHealing": 48997, "ĠCuriosity": 48998, "cert": 48999, "Ġ민주": 49000, "Ġpatiently": 49001, "ĠYT": 49002, "foreign": 49003, "Ġvẫn": 49004, "Ġindustri": 49005, "Ġcocktails": 49006, "Ġbrighten": 49007, "Ġconsolidated": 49008, "аÑĢд": 49009, "ltry": 49010, "Ġgrille": 49011, "Ġbona": 49012, "Ġdiligently": 49013, "ĠWrestleMania": 49014, "erkt": 49015, "energy": 49016, "999": 49017, "à®ķவ": 49018, "Ġtote": 49019, "iono": 49020, "DIO": 49021, "Ġschizophrenia": 49022, "Ġpostponed": 49023, "ĠQiu": 49024, "ĠÏĥÏħν": 49025, "ĠzdjÄĻ": 49026, "Ġspannend": 49027, "ĠDIS": 49028, "Rel": 49029, "Ġrhin": 49030, "immune": 49031, "Old": 49032, "Ġplötzlich": 49033, "Ġmound": 49034, "Ġastronomical": 49035, "ĠGuid": 49036, "ĠCul": 49037, "HI": 49038, "ĠÅł": 49039, "Ġrepo": 49040, "ĠMaurice": 49041, "ä¸ĢçĤ¹": 49042, "Ġbandits": 49043, "ĠDesktop": 49044, "äss": 49045, "fta": 49046, "Ġlicence": 49047, "Ġimaginar": 49048, "ĠEntreprene": 49049, "xo": 49050, "Ġë§ĽìŀĪëĬĶ": 49051, "Ġ×Ķ×ij": 49052, "Ġpumpkins": 49053, "Ġkanssa": 49054, "ĠjÄĻzy": 49055, "Ġcommunauté": 49056, "bür": 49057, "Ġerhö": 49058, "ĠWolver": 49059, "ĠSharing": 49060, "令": 49061, "Ġpakai": 49062, "Ġinsulted": 49063, "ÐľÑĭ": 49064, "оÑĹ": 49065, "Ġconsiste": 49066, "æĮij": 49067, "Ġyoungsters": 49068, "Ġgleichen": 49069, "weder": 49070, "Ġmote": 49071, "Ġclauses": 49072, "état": 49073, "prus": 49074, "Ġwast": 49075, "ç»ĻæĪij": 49076, "ĠCrisp": 49077, "ĠçĦ¶å¾Į": 49078, "Ġoffenders": 49079, "Ġconvection": 49080, "Ġconfian": 49081, "ollow": 49082, "amet": 49083, "ĠÑĹÑħ": 49084, "第äºĮåĢĭ": 49085, "fficiency": 49086, "Ġunglaub": 49087, "igans": 49088, "Ġmarketed": 49089, "ĠVAN": 49090, "Ġproclaimed": 49091, "Ġcélulas": 49092, "Ġcollide": 49093, "ĠOculus": 49094, "adore": 49095, "Ji": 49096, "Ġsustaining": 49097, "ĠFasc": 49098, "Ġsetzt": 49099, "Ġnosaltres": 49100, "Most": 49101, "ĠвÑĩ": 49102, "Ġnauc": 49103, "ĠBhar": 49104, "çΏçΏ": 49105, "æĪijè·Łä½łè¬Ľ": 49106, "Ġyêu": 49107, "Ġtimest": 49108, "Ġpertama": 49109, "irmi": 49110, "Ġzwr": 49111, "Ġverbess": 49112, "Ġvortex": 49113, "ĠSTACK": 49114, "ثر": 49115, "¹Ħë": 49116, "ĶĶìĺ¤": 49117, "Ġlinkage": 49118, "ĠFraser": 49119, "enario": 49120, "ĠëĿ¼ëĬĶ": 49121, "ĠìĦłë°°": 49122, "hthal": 49123, "Ġê¹Į": 49124, "ĠKhông": 49125, "Ãĥ": 49126, "Ġscrambled": 49127, "ĠEink": 49128, "Ġmicroorgan": 49129, "Ġnarcissist": 49130, "ĠKombat": 49131, "Ġë§¡": 49132, "ĠAGA": 49133, "Ġperfekt": 49134, "ĠSerie": 49135, "determ": 49136, "-'": 49137, "Ġponytail": 49138, "Ġkoska": 49139, "ìĵ": 49140, "Ġobec": 49141, "Ġchests": 49142, "veer": 49143, "Ġuprising": 49144, "Ġstoked": 49145, "associ": 49146, "Ġprodução": 49147, "ĠShape": 49148, "ìłľê°Ģ": 49149, "ĠëͰ": 49150, "Ġjon": 49151, "Ġinadvert": 49152, "antas": 49153, "ĠнаконеÑĨ": 49154, "Ġå°įåķĬ": 49155, "ĠArsenal": 49156, "Ġproteg": 49157, "Ġliberté": 49158, "Ġglare": 49159, "åĪļ": 49160, "å·²ç»ı": 49161, "Ġverein": 49162, "Ġinserts": 49163, "ĠJana": 49164, "Ġwydaje": 49165, "ÅĤum": 49166, "Ġ%.": 49167, "origine": 49168, "Ġsynagogue": 49169, "Ġfallait": 49170, "Ġdisobed": 49171, "Ġantic": 49172, "ĠCycl": 49173, "Ġasynchronous": 49174, "Ġë²Įìį¨": 49175, "Ġgesund": 49176, "Ġgagn": 49177, "Ġpea": 49178, "Ġgrin": 49179, "ést": 49180, "Ġsauc": 49181, "ĠMäd": 49182, "íķ´ëıĦ": 49183, "pps": 49184, "ĠεÏĢι": 49185, "Ġpeuple": 49186, "Ġdeben": 49187, "ĠBree": 49188, "ĠÑĢолÑĮ": 49189, "Ġкаким": 49190, "Ġútil": 49191, "Ġdistributor": 49192, "алÑĭ": 49193, "ĠswojÄħ": 49194, "Ġfolklore": 49195, "Ġreceivers": 49196, "ĠMOO": 49197, "bins": 49198, "astre": 49199, "ìķĪë": 49200, "ĠëĦ£ê³ł": 49201, "Ġmultimedia": 49202, "Ġgebaut": 49203, "овÑĭÑħ": 49204, "ãy": 49205, "Ġdane": 49206, "okol": 49207, "emitism": 49208, "ONEY": 49209, "ĠyaÄŁ": 49210, "Ġchauff": 49211, "容æĺĵ": 49212, "Ġesfuer": 49213, "Äĥn": 49214, "ertas": 49215, "Ġfonctionne": 49216, "omina": 49217, "Ġivory": 49218, "ĠYoutuber": 49219, "ĠSkywalker": 49220, "иÑĩеÑģкаÑı": 49221, "toi": 49222, "Ġveya": 49223, "Ġgelernt": 49224, "Ġchancellor": 49225, "ĠStatistics": 49226, "Ġwelded": 49227, "Ġondan": 49228, "ĠSei": 49229, "Ġmedically": 49230, "Ġenergized": 49231, "ĠVia": 49232, "Ġвик": 49233, "Ġuninter": 49234, "Ġhighness": 49235, "ĠíĮĶë": 49236, "Ġamplified": 49237, "ĠSergey": 49238, "ĠMins": 49239, "warm": 49240, "pell": 49241, "ophile": 49242, "Ġhè": 49243, "ĠBelo": 49244, "ĠSketch": 49245, "Ġcharacterization": 49246, "ansen": 49247, "ĠÑĤÑĥÑĢ": 49248, "Ġãħĭãħĭãħĭ": 49249, "Note": 49250, "ĠkoÅŁ": 49251, "Ġciert": 49252, "flu": 49253, "Ġbaht": 49254, "ĠDowntown": 49255, "ĠCRIS": 49256, "odie": 49257, "140": 49258, "Ġlitres": 49259, "Ġgriev": 49260, "æ§ĺ": 49261, "Ġì͍ê°Ģ": 49262, "Ġsucceeds": 49263, "Ġ__": 49264, "enting": 49265, "Ġvimos": 49266, "Ġsì": 49267, "defense": 49268, "ĠMcD": 49269, "ĠMarion": 49270, "ĠDont": 49271, "ĠDDR": 49272, "ĠLazar": 49273, "ĠDAR": 49274, "Ġkuv": 49275, "Kn": 49276, "Ġsembla": 49277, "Ġairborne": 49278, "ĠViolence": 49279, "ëIJIJ": 49280, "Ġrestraint": 49281, "Ġwhistles": 49282, "Ġscolded": 49283, "Ġacceso": 49284, "Ġabsolutamente": 49285, "ĠTyl": 49286, "ĠSap": 49287, "¶Ģë¶Ħ": 49288, "itäten": 49289, "adem": 49290, "Ġý": 49291, "Ġprescribe": 49292, "ĠMage": 49293, "ĠHelena": 49294, "å¾Īæľī": 49295, "亲": 49296, "vt": 49297, "Ġvienen": 49298, "Ġsneez": 49299, "Ġmolé": 49300, "Æ°á»Łng": 49301, "Ġtransporting": 49302, "ĠLean": 49303, "Ġkung": 49304, "ÑĥÑĢа": 49305, "ÏĦÎŃ": 49306, "utches": 49307, "onders": 49308, "liyor": 49309, "Nat": 49310, "Ġzij": 49311, "Ġmammal": 49312, "Ġkäyt": 49313, "ĠJoanna": 49314, "sent": 49315, "Ġस": 49316, "Ġvested": 49317, "ĠErfahrung": 49318, "okee": 49319, "Ġclipping": 49320, "ĠListening": 49321, "Ġ(#": 49322, "fö": 49323, "Ġvidare": 49324, "Ġbrittle": 49325, "ĠSTART": 49326, "ĠDamas": 49327, "ĠYog": 49328, "ãĤĵãģ¨": 49329, "gart": 49330, "Ġverlier": 49331, "Ġheartfelt": 49332, "ĠdoÅĽÄĩ": 49333, "ì¹ĺê°Ģ": 49334, ".»": 49335, "Ġmaximal": 49336, "Ġdistintos": 49337, "ĠìĻľëĥIJíķĺë©´": 49338, "Ġsailed": 49339, "Ġconveyed": 49340, "ĠTinder": 49341, "ĠSUPER": 49342, "ниÑĨÑĥ": 49343, "controlled": 49344, "Ġfunz": 49345, "Ġbastards": 49346, "ĠGinsburg": 49347, "Ġnuovo": 49348, "ĠPere": 49349, "ĠJES": 49350, "ĠDingen": 49351, "ĠBets": 49352, "umba": 49353, "acción": 49354, "ĠìŀĪì§Ģë§Į": 49355, "Ġretra": 49356, "ĠLaurent": 49357, "Ġpozy": 49358, "Ġgrooves": 49359, "Ġmáquina": 49360, "Ġminion": 49361, "Ġdeinen": 49362, "ĠShaun": 49363, "×Ļ×Ļ": 49364, "Ġhonorary": 49365, "osaurus": 49366, "Ġzeit": 49367, "Ġespecie": 49368, "ĠBCE": 49369, "аÑĤе": 49370, "Justin": 49371, "ĠWheels": 49372, "ĠìĿ´íķ´": 49373, "ĠبÙĬÙĨ": 49374, "Ġpropulsion": 49375, "Ġperceber": 49376, "ĠNewman": 49377, "å´": 49378, "culosis": 49379, "Mi": 49380, "ĠаккÑĥ": 49381, "Ġmastering": 49382, "Ġläh": 49383, "Ġfists": 49384, "ä»Ķ": 49385, "Ġmarinade": 49386, "Lilly": 49387, "Ġëħ¸ëł¥": 49388, "ĠYH": 49389, "Ġurgently": 49390, "Ġinformational": 49391, "Ġacordo": 49392, "izzy": 49393, "ãģĦãģı": 49394, "ìĿ´ìĸ´": 49395, "imar": 49396, "ĠëĤĺìĺ¤ë": 49397, "Ġtwenties": 49398, "Ġrasp": 49399, "Ġbumpy": 49400, "بة": 49401, "worker": 49402, "Ġquickest": 49403, "Ġattaches": 49404, "виг": 49405, "ĠëĤĺíĥĢë": 49406, "Ġpuree": 49407, "Ġoversized": 49408, "Ġstirred": 49409, "Ġjakim": 49410, "Ġhomicide": 49411, "ãĤĤãģĹ": 49412, "iscilla": 49413, "Ġì±Ļ": 49414, "Ġspeculative": 49415, "Ġassists": 49416, "main": 49417, "jähr": 49418, "indet": 49419, "ĠÅŁur": 49420, "Ġforecasts": 49421, "Ġdiversion": 49422, "Ġtare": 49423, "Ġogl": 49424, "ĠOrganisation": 49425, "ĠChevy": 49426, "Ġbaja": 49427, "andır": 49428, "ĠÙĪÙĦا": 49429, "Ġradiant": 49430, "Ġliaison": 49431, "Ġdemokrat": 49432, "ĠMARC": 49433, "ÏĢοÏħ": 49434, "Ġrunt": 49435, "Ġprécis": 49436, "Ġgeven": 49437, "Ġvéhic": 49438, "ĠJESS": 49439, "STR": 49440, "Ġìĸĺë": 49441, "Ġvisionary": 49442, "Ġburadan": 49443, "ĠãģĤãĤĬ": 49444, "Ġrebirth": 49445, "Ġexhibited": 49446, "ĠMetall": 49447, "olie": 49448, "elyn": 49449, "Ġflavours": 49450, "Ġescrito": 49451, "ĠDelete": 49452, "ĠìķĮìķĺìĸ´": 49453, "ĠÑĥкÑĢаÑĹн": 49454, "Ġinterrupting": 49455, "Ġidentific": 49456, "ĠSuzuki": 49457, "ĠLanding": 49458, "ä»¶äºĭæĥħ": 49459, "andi": 49460, "Ġestran": 49461, "Ġcouleur": 49462, "Ġagrad": 49463, "ĠSny": 49464, "Ġà®ĩல": 49465, "Ġander": 49466, "Ġrua": 49467, "Ġprise": 49468, "Ġlaure": 49469, "ĠíĬĢ": 49470, "Ġmoderation": 49471, "Ġerfahren": 49472, "Ġdeconst": 49473, "ĠReese": 49474, "ĠPK": 49475, "etos": 49476, "ãģĵãĤĮãģ§": 49477, "ĠGravity": 49478, "ĠEren": 49479, "Ġoverboard": 49480, "Ġmüsst": 49481, "ĠEmail": 49482, "еÑĢм": 49483, "ydi": 49484, "iÄĻdzy": 49485, "ĠLOU": 49486, "ĠFuÃŁball": 49487, "ĠRD": 49488, "alts": 49489, "ĠìĬ¤íĬ¸ë": 49490, "ĠÐļÑĢаÑģ": 49491, "Ġtelev": 49492, "ĠÑĢо": 49493, "Ġresignation": 49494, "Ġjingle": 49495, "ĠStudien": 49496, "ĠIX": 49497, "ĠSentinel": 49498, "ĠPang": 49499, "éĦ": 49500, "Jake": 49501, "Ġpersonagem": 49502, "Ġmédia": 49503, "ĠChern": 49504, "antically": 49505, "Ġthá»Ŀi": 49506, "Ġparalysis": 49507, "Ġjapanese": 49508, "Ġconex": 49509, "Ġefic": 49510, "Ġunderside": 49511, "Ġneol": 49512, "Ġfian": 49513, "имоÑģÑĤÑĮ": 49514, "Ġquirky": 49515, "Ġpista": 49516, "ĠClement": 49517, "nothing": 49518, "ĠпоеÑħ": 49519, "Ġhorrend": 49520, "Ġconsolidate": 49521, "ploys": 49522, "emaker": 49523, "Jennifer": 49524, "Ġnuméro": 49525, "Ġfamoso": 49526, "ĠNeptune": 49527, "ĠíĸĪìĸ´": 49528, "ĠпÑĢезид": 49529, "Ġsitcom": 49530, "Ġserio": 49531, "Ġmue": 49532, "Ġglands": 49533, "Ġbörjar": 49534, "ĠYJ": 49535, "ĠRiot": 49536, "paragus": 49537, "Ġsegurança": 49538, "Ġimmature": 49539, "ĠMadonna": 49540, "à¸į": 49541, "Ġlingering": 49542, "Ġacesso": 49543, "ĠOrient": 49544, "ĠRecomm": 49545, "Ġcomplac": 49546, "founded": 49547, "attend": 49548, "Ġcielo": 49549, "ĠZhan": 49550, "naires": 49551, "cco": 49552, "Ġ×IJ׳": 49553, "Ġstata": 49554, "Ġcontradictory": 49555, "ĠSé": 49556, "ĠSAN": 49557, "ĠConnie": 49558, "Ġëĭ¹ìĭľ": 49559, "ĠÑģамой": 49560, "Ġmajestic": 49561, "ĠPenguin": 49562, "ĠCOME": 49563, "ÃŃcios": 49564, "pero": 49565, "Ġmg": 49566, "Ġfauc": 49567, "Ġcorrer": 49568, "ĠGottes": 49569, "ĠAnglo": 49570, "Har": 49571, "á»Ĺi": 49572, "Ġvitesse": 49573, "Ġannouncer": 49574, "ĠOmaha": 49575, "kum": 49576, "Ġspared": 49577, "ĠÑĢаза": 49578, "ĠполÑĥÑĩиÑĤÑģÑı": 49579, "Ġtähän": 49580, "Ġпонад": 49581, "Ġpertaining": 49582, "ĠRate": 49583, "iern": 49584, "Gold": 49585, "Ġteste": 49586, "ĠdeÄŁild": 49587, "Ġdamping": 49588, "ĠPartnership": 49589, "zysta": 49590, "geld": 49591, "Ġsmokes": 49592, "ĠMarriage": 49593, "쪽ìĹIJ": 49594, "èħ³": 49595, "isce": 49596, "Ġtryna": 49597, "ĠDirectory": 49598, "ĠëĤĺìĺ¬": 49599, "Ġshameful": 49600, "Ġmentre": 49601, "Ġassigning": 49602, "æĺ¯éĢĻæ¨£": 49603, "Ġrepertoire": 49604, "Ġobjetos": 49605, "稱": 49606, "Ġunderworld": 49607, "Ġendeavors": 49608, "Ġignite": 49609, "ĠÙĪØ¬": 49610, "Ġexperient": 49611, "ĠÐĹап": 49612, "ĠзаклÑİÑĩ": 49613, "Ġvoltages": 49614, "Ġniego": 49615, "Ġdeficits": 49616, "Ġbuenos": 49617, "ĠSleeping": 49618, "ĠSalem": 49619, "Ġunlocking": 49620, "Ġinteracted": 49621, "Ġentendeu": 49622, "ĠSuperintendent": 49623, "Ġszczegól": 49624, "Ġquas": 49625, "Ġpaling": 49626, "Ġkho": 49627, "بØŃ": 49628, "Ġcolabor": 49629, "ĠпÑĢигоÑĤов": 49630, "Ġmauv": 49631, "ĠJudas": 49632, "ĠAssist": 49633, "ĠÑĤеÑĢÑĢи": 49634, "ĠнаÑģколÑĮко": 49635, "Ġsubsidy": 49636, "ĠEmbassy": 49637, "Ġdagen": 49638, "ĠSanto": 49639, "èά": 49640, "ש×ķ×ij": 49641, "Ġabruptly": 49642, "ĠAdapt": 49643, "Ġvaak": 49644, "Ġpostal": 49645, "Ġinvestir": 49646, "Ġfiquei": 49647, "Ġdowntime": 49648, "ĠWebb": 49649, "ĠNCAA": 49650, "ĠEstoy": 49651, "олоÑĤ": 49652, "ĠìĤ¬ê±´": 49653, "Ġnationalist": 49654, "ĠKathryn": 49655, "ĠKop": 49656, "éª": 49657, "Sean": 49658, "ONA": 49659, "ĠBj": 49660, "×¢×Ŀ": 49661, "ÃŃb": 49662, "idamente": 49663, "Ġглаза": 49664, "Ġunnie": 49665, "Ġgemaakt": 49666, "ĠINTERVIEWER": 49667, "ĠHaut": 49668, "ίο": 49669, "geois": 49670, "wydd": 49671, "Ġколи": 49672, "Ġtightened": 49673, "Ġplanners": 49674, "Ġherum": 49675, "Ġgörün": 49676, "Ġelectronically": 49677, "Ġceram": 49678, "Ġëĭ¤ìĸijíķľ": 49679, "Ġepilepsy": 49680, "ĠeÄŁ": 49681, "lins": 49682, "ĠShiny": 49683, "æł¡": 49684, "ĠÑģолн": 49685, "Ġmacaron": 49686, "Ġimpacto": 49687, "ĠVegan": 49688, "zeÅĦ": 49689, "ĠRapha": 49690, "ĠPars": 49691, "ĠLEO": 49692, "ãģĬãģ£": 49693, "cü": 49694, "Ġ׾×Ķ×Ļ×ķת": 49695, "Ġähnlich": 49696, "Ġfloss": 49697, "ĠAZ": 49698, "Ġmöchten": 49699, "Ġgrooming": 49700, "Ġgrasses": 49701, "ranch": 49702, "Ġrecibir": 49703, "Ġbouncy": 49704, "ĠHobby": 49705, "Ġviktig": 49706, "Ġbegitu": 49707, "ĠPicasso": 49708, "ĠKush": 49709, "모": 49710, "Ġobstruction": 49711, "Ġë¶ĦìľĦ": 49712, "Ġmicrob": 49713, "ĠWestminster": 49714, "rops": 49715, "dul": 49716, "Ġdevo": 49717, "ĠLehrer": 49718, "ĠAdvisor": 49719, "ucken": 49720, "ĠбÑĥм": 49721, "Ġflattering": 49722, "ĠTruman": 49723, "ĠSempre": 49724, "ĠMcCain": 49725, "ĠHindus": 49726, "Julia": 49727, "Ġwatershed": 49728, "Ġlush": 49729, "ìłĦë": 49730, "Before": 49731, "ĠÐĴÑĤоÑĢ": 49732, "ĠSaaS": 49733, "Ġsitzt": 49734, "Ġbeetle": 49735, "ĠEssential": 49736, "enko": 49737, "ĠëķĮëıĦ": 49738, "Ġrevving": 49739, "Ġpoorer": 49740, "Ġcoerc": 49741, "Ġidee": 49742, "Ġcoû": 49743, "alet": 49744, "Ġzdrow": 49745, "Ġfender": 49746, "growth": 49747, "DING": 49748, "Ġzde": 49749, "ä¸ĬéĿ¢": 49750, "ENTS": 49751, "Ġfacets": 49752, "éļª": 49753, "ushima": 49754, "ĠÅŁeh": 49755, "Ġparasite": 49756, "Ġlapse": 49757, "ĠMeer": 49758, "ĠKund": 49759, "Ġslog": 49760, "Ġbrunch": 49761, "ĠChart": 49762, "arz": 49763, "ĠMUS": 49764, "Ġoffenses": 49765, "Ġinglés": 49766, "Ġfoliage": 49767, "oplan": 49768, "Aut": 49769, "ĠJacqu": 49770, "tak": 49771, "iembre": 49772, "Ġxen": 49773, "Ġnominees": 49774, "Ġbiomedical": 49775, "ésus": 49776, "Ġestuv": 49777, "ÏĦÏĮ": 49778, "ATHAN": 49779, "Ġíķľëį°": 49780, "Ġheed": 49781, "crosstalk": 49782, "Bill": 49783, "Ġspouses": 49784, "ĠÑģÑİж": 49785, "Ġverso": 49786, "ĠSven": 49787, "ĠCau": 49788, "cuz": 49789, "Ġë³´ìĦ¸ìļĶ": 49790, "ĠÑħозÑı": 49791, "Ġmocking": 49792, "ĠOna": 49793, "ĠDá": 49794, "Ġfruitful": 49795, "Ġbanquet": 49796, "udding": 49797, "inctions": 49798, "dert": 49799, "sud": 49800, "Ġdescon": 49801, "ĠJC": 49802, "Ġ§": 49803, "Ġpubli": 49804, "ëĪĪ": 49805, "éģķãģĨ": 49806, "Ġentschieden": 49807, "ĠROI": 49808, "ãģįãģŁ": 49809, "ĠìĥĿê²¼": 49810, "Ġkäytt": 49811, "yani": 49812, "shaw": 49813, "Ġunleash": 49814, "Ġmanne": 49815, "Ġhistogram": 49816, "æĬ¥": 49817, "à¸Ńะà¹Ħร": 49818, "Ġgn": 49819, "Ġfella": 49820, "Ġeinges": 49821, "ĠBuilt": 49822, "Ġrepresenta": 49823, "Ġpunishing": 49824, "Ġoutsiders": 49825, "нÑĥÑĤÑĮÑģÑı": 49826, "current": 49827, "Ġfamiliarity": 49828, "Ġдив": 49829, "Ġprojets": 49830, "Ġaqueles": 49831, "ĠGlue": 49832, "those": 49833, "Ġinception": 49834, "Ġaquellos": 49835, "Ġillusions": 49836, "Ġattends": 49837, "rese": 49838, "Ġswarm": 49839, "Ġswab": 49840, "Ġregardez": 49841, "Ġposição": 49842, "Ġakhir": 49843, "Ġextracting": 49844, "Ġanecdote": 49845, "ĠTale": 49846, "Ġвин": 49847, "Ġabges": 49848, "ĠoluÅŁ": 49849, "Ġcomplicado": 49850, "Ġcovari": 49851, "ÑĸÑĤÑĮ": 49852, "Der": 49853, "Ġ×Ļ×Ķ": 49854, "Form": 49855, "Ġìĸ´ì¨Įëĵł": 49856, "Ġreadable": 49857, "Ġinhibit": 49858, "Ġdecipher": 49859, "ĠAngry": 49860, "pg": 49861, "வத": 49862, "ĠÑģобÑģÑĤвенно": 49863, "Ġsamh": 49864, "Ġescr": 49865, "Ġencompasses": 49866, "Ġauster": 49867, "Ġconfisc": 49868, "ĠMandal": 49869, "Ġ}": 49870, "atcher": 49871, "=#": 49872, "çļĦæĹ¶åĢĻ": 49873, "Ġкино": 49874, "Ġstal": 49875, "lungs": 49876, "Ġvole": 49877, "Ġrequis": 49878, "ĠãĤĪ": 49879, "Ġpén": 49880, "Ġlecturer": 49881, "Ġinscription": 49882, "Ġcervical": 49883, "ĠTreasure": 49884, "ĠJW": 49885, 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"ĠÑģпоÑĢÑĤ": 49941, "Ġбег": 49942, "Ġnectar": 49943, "ultura": 49944, "imsical": 49945, "ĮĢ를": 49946, "å¹´åīį": 49947, "ãģĹãĤĥ": 49948, "Ġvisão": 49949, "éģİä¾Ĩ": 49950, "ÿÿÿÿÿÿÿÿ": 49951, "attform": 49952, "Ġë§ŀëĬĶ": 49953, "Ġpilgrimage": 49954, "Ġmating": 49955, "ĠReaper": 49956, "ĠBref": 49957, "çĶŁæ´»": 49958, "Ġ×ij×ĵ": 49959, "Ġnovamente": 49960, "Ġgrilling": 49961, "ĠWireless": 49962, "ĠRomanian": 49963, "ÒĽ": 49964, "ìľłë": 49965, "hait": 49966, "ĠBora": 49967, "ARRY": 49968, "Ġhypotheses": 49969, "马": 49970, "ikut": 49971, "ĠìķĦë²Ħ": 49972, "ĠÑĸз": 49973, "Ġnationale": 49974, "تÙī": 49975, "üllt": 49976, "Ġéléments": 49977, "ĠWare": 49978, "Ġ(-": 49979, "алÑĮном": 49980, "Ġindict": 49981, "ĠStones": 49982, "ãģŁãĤģ": 49983, "explosion": 49984, "ĠëĥĦìĥĪ": 49985, "Ġfelic": 49986, "Ġjudiciary": 49987, "Ġincarnation": 49988, "Ġinning": 49989, "Ġformul": 49990, "Ġshipment": 49991, "Ġreindeer": 49992, "æĴŃ": 49993, "ĠознаÑĩ": 49994, "Ġenvol": 49995, "undy": 49996, "ĠзнаÑĤÑĮ": 49997, 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================================================
FILE: musetalk/whisper/whisper/audio.py
================================================
import os
from functools import lru_cache
from typing import Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
def load_audio(file: str, sr: int = SAMPLE_RATE):
"""
Open an audio file and read as mono waveform, resampling as necessary
Parameters
----------
file: str
The audio file to open
sr: int
The sample rate to resample the audio if necessary
Returns
-------
A NumPy array containing the audio waveform, in float32 dtype.
"""
try:
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
out, _ = (
ffmpeg.input(file, threads=0)
.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
)
except ffmpeg.Error as e:
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
"""
Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
"""
if torch.is_tensor(array):
if array.shape[axis] > 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": "humored",
"humouring": "humoring",
"humourless": "humorless",
"humours": "humors",
"hybridise": "hybridize",
"hybridised": "hybridized",
"hybridises": "hybridizes",
"hybridising": "hybridizing",
"hypnotise": "hypnotize",
"hypnotised": "hypnotized",
"hypnotises": "hypnotizes",
"hypnotising": "hypnotizing",
"hypothesise": "hypothesize",
"hypothesised": "hypothesized",
"hypothesises": "hypothesizes",
"hypothesising": "hypothesizing",
"idealisation": "idealization",
"idealise": "idealize",
"idealised": "idealized",
"idealises": "idealizes",
"idealising": "idealizing",
"idolise": "idolize",
"idolised": "idolized",
"idolises": "idolizes",
"idolising": "idolizing",
"immobilisation": "immobilization",
"immobilise": "immobilize",
"immobilised": "immobilized",
"immobiliser": "immobilizer",
"immobilisers": "immobilizers",
"immobilises": "immobilizes",
"immobilising": "immobilizing",
"immortalise": "immortalize",
"immortalised": "immortalized",
"immortalises": "immortalizes",
"immortalising": "immortalizing",
"immunisation": "immunization",
"immunise": "immunize",
"immunised": "immunized",
"immunises": "immunizes",
"immunising": "immunizing",
"impanelled": "impaneled",
"impanelling": "impaneling",
"imperilled": "imperiled",
"imperilling": "imperiling",
"individualise": "individualize",
"individualised": "individualized",
"individualises": "individualizes",
"individualising": "individualizing",
"industrialise": "industrialize",
"industrialised": "industrialized",
"industrialises": "industrializes",
"industrialising": "industrializing",
"inflexion": "inflection",
"inflexions": "inflections",
"initialise": "initialize",
"initialised": "initialized",
"initialises": "initializes",
"initialising": "initializing",
"initialled": "initialed",
"initialling": "initialing",
"instal": "install",
"instalment": "installment",
"instalments": "installments",
"instals": "installs",
"instil": "instill",
"instils": "instills",
"institutionalisation": "institutionalization",
"institutionalise": "institutionalize",
"institutionalised": "institutionalized",
"institutionalises": "institutionalizes",
"institutionalising": "institutionalizing",
"intellectualise": "intellectualize",
"intellectualised": "intellectualized",
"intellectualises": "intellectualizes",
"intellectualising": "intellectualizing",
"internalisation": "internalization",
"internalise": "internalize",
"internalised": "internalized",
"internalises": "internalizes",
"internalising": "internalizing",
"internationalisation": "internationalization",
"internationalise": "internationalize",
"internationalised": "internationalized",
"internationalises": "internationalizes",
"internationalising": "internationalizing",
"ionisation": "ionization",
"ionise": "ionize",
"ionised": "ionized",
"ioniser": "ionizer",
"ionisers": "ionizers",
"ionises": "ionizes",
"ionising": "ionizing",
"italicise": "italicize",
"italicised": "italicized",
"italicises": "italicizes",
"italicising": "italicizing",
"itemise": "itemize",
"itemised": "itemized",
"itemises": "itemizes",
"itemising": "itemizing",
"jeopardise": "jeopardize",
"jeopardised": "jeopardized",
"jeopardises": "jeopardizes",
"jeopardising": "jeopardizing",
"jewelled": "jeweled",
"jeweller": "jeweler",
"jewellers": "jewelers",
"jewellery": "jewelry",
"judgement": "judgment",
"kilogramme": "kilogram",
"kilogrammes": "kilograms",
"kilometre": "kilometer",
"kilometres": "kilometers",
"labelled": "labeled",
"labelling": "labeling",
"labour": "labor",
"laboured": "labored",
"labourer": "laborer",
"labourers": "laborers",
"labouring": "laboring",
"labours": "labors",
"lacklustre": "lackluster",
"legalisation": "legalization",
"legalise": "legalize",
"legalised": "legalized",
"legalises": "legalizes",
"legalising": "legalizing",
"legitimise": "legitimize",
"legitimised": "legitimized",
"legitimises": "legitimizes",
"legitimising": "legitimizing",
"leukaemia": "leukemia",
"levelled": "leveled",
"leveller": "leveler",
"levellers": "levelers",
"levelling": "leveling",
"libelled": "libeled",
"libelling": "libeling",
"libellous": "libelous",
"liberalisation": "liberalization",
"liberalise": "liberalize",
"liberalised": "liberalized",
"liberalises": "liberalizes",
"liberalising": "liberalizing",
"licence": "license",
"licenced": "licensed",
"licences": "licenses",
"licencing": "licensing",
"likeable": "likable",
"lionisation": "lionization",
"lionise": "lionize",
"lionised": "lionized",
"lionises": "lionizes",
"lionising": "lionizing",
"liquidise": "liquidize",
"liquidised": "liquidized",
"liquidiser": "liquidizer",
"liquidisers": "liquidizers",
"liquidises": "liquidizes",
"liquidising": "liquidizing",
"litre": "liter",
"litres": "liters",
"localise": "localize",
"localised": "localized",
"localises": "localizes",
"localising": "localizing",
"louvre": "louver",
"louvred": "louvered",
"louvres": "louvers",
"lustre": "luster",
"magnetise": "magnetize",
"magnetised": "magnetized",
"magnetises": "magnetizes",
"magnetising": "magnetizing",
"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": "mechanizing",
"mediaeval": "medieval",
"memorialise": "memorialize",
"memorialised": "memorialized",
"memorialises": "memorializes",
"memorialising": "memorializing",
"memorise": "memorize",
"memorised": "memorized",
"memorises": "memorizes",
"memorising": "memorizing",
"mesmerise": "mesmerize",
"mesmerised": "mesmerized",
"mesmerises": "mesmerizes",
"mesmerising": "mesmerizing",
"metabolise": "metabolize",
"metabolised": "metabolized",
"metabolises": "metabolizes",
"metabolising": "metabolizing",
"metre": "meter",
"metres": "meters",
"micrometre": "micrometer",
"micrometres": "micrometers",
"militarise": "militarize",
"militarised": "militarized",
"militarises": "militarizes",
"militarising": "militarizing",
"milligramme": "milligram",
"milligrammes": "milligrams",
"millilitre": "milliliter",
"millilitres": "milliliters",
"millimetre": "millimeter",
"millimetres": "millimeters",
"miniaturisation": "miniaturization",
"miniaturise": "miniaturize",
"miniaturised": "miniaturized",
"miniaturises": "miniaturizes",
"miniaturising": "miniaturizing",
"minibusses": "minibuses",
"minimise": "minimize",
"minimised": "minimized",
"minimises": "minimizes",
"minimising": "minimizing",
"misbehaviour": "misbehavior",
"misdemeanour": "misdemeanor",
"misdemeanours": "misdemeanors",
"misspelt": "misspelled",
"mitre": "miter",
"mitres": "miters",
"mobilisation": "mobilization",
"mobilise": "mobilize",
"mobilised": "mobilized",
"mobilises": "mobilizes",
"mobilising": "mobilizing",
"modelled": "modeled",
"modeller": "modeler",
"modellers": "modelers",
"modelling": "modeling",
"modernise": "modernize",
"modernised": "modernized",
"modernises": "modernizes",
"modernising": "modernizing",
"moisturise": "moisturize",
"moisturised": "moisturized",
"moisturiser": "moisturizer",
"moisturisers": "moisturizers",
"moisturises": "moisturizes",
"moisturising": "moisturizing",
"monologue": "monolog",
"monologues": "monologs",
"monopolisation": "monopolization",
"monopolise": "monopolize",
"monopolised": "monopolized",
"monopolises": "monopolizes",
"monopolising": "monopolizing",
"moralise": "moralize",
"moralised": "moralized",
"moralises": "moralizes",
"moralising": "moralizing",
"motorised": "motorized",
"mould": "mold",
"moulded": "molded",
"moulder": "molder",
"mouldered": "moldered",
"mouldering": "moldering",
"moulders": "molders",
"mouldier": "moldier",
"mouldiest": "moldiest",
"moulding": "molding",
"mouldings": "moldings",
"moulds": "molds",
"mouldy": "moldy",
"moult": "molt",
"moulted": "molted",
"moulting": "molting",
"moults": "molts",
"moustache": "mustache",
"moustached": "mustached",
"moustaches": "mustaches",
"moustachioed": "mustachioed",
"multicoloured": "multicolored",
"nationalisation": "nationalization",
"nationalisations": "nationalizations",
"nationalise": "nationalize",
"nationalised": "nationalized",
"nationalises": "nationalizes",
"nationalising": "nationalizing",
"naturalisation": "naturalization",
"naturalise": "naturalize",
"naturalised": "naturalized",
"naturalises": "naturalizes",
"naturalising": "naturalizing",
"neighbour": "neighbor",
"neighbourhood": "neighborhood",
"neighbourhoods": "neighborhoods",
"neighbouring": "neighboring",
"neighbourliness": "neighborliness",
"neighbourly": "neighborly",
"neighbours": "neighbors",
"neutralisation": "neutralization",
"neutralise": "neutralize",
"neutralised": "neutralized",
"neutralises": "neutralizes",
"neutralising": "neutralizing",
"normalisation": "normalization",
"normalise": "normalize",
"normalised": "normalized",
"normalises": "normalizes",
"normalising": "normalizing",
"odour": "odor",
"odourless": "odorless",
"odours": "odors",
"oesophagus": "esophagus",
"oesophaguses": "esophaguses",
"oestrogen": "estrogen",
"offence": "offense",
"offences": "offenses",
"omelette": "omelet",
"omelettes": "omelets",
"optimise": "optimize",
"optimised": "optimized",
"optimises": "optimizes",
"optimising": "optimizing",
"organisation": "organization",
"organisational": "organizational",
"organisations": "organizations",
"organise": "organize",
"organised": "organized",
"organiser": "organizer",
"organisers": "organizers",
"organises": "organizes",
"organising": "organizing",
"orthopaedic": "orthopedic",
"orthopaedics": "orthopedics",
"ostracise": "ostracize",
"ostracised": "ostracized",
"ostracises": "ostracizes",
"ostracising": "ostracizing",
"outmanoeuvre": "outmaneuver",
"outmanoeuvred": "outmaneuvered",
"outmanoeuvres": "outmaneuvers",
"outmanoeuvring": "outmaneuvering",
"overemphasise": "overemphasize",
"overemphasised": "overemphasized",
"overemphasises": "overemphasizes",
"overemphasising": "overemphasizing",
"oxidisation": "oxidization",
"oxidise": "oxidize",
"oxidised": "oxidized",
"oxidises": "oxidizes",
"oxidising": "oxidizing",
"paederast": "pederast",
"paederasts": "pederasts",
"paediatric": "pediatric",
"paediatrician": "pediatrician",
"paediatricians": "pediatricians",
"paediatrics": "pediatrics",
"paedophile": "pedophile",
"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",
"patronises": "patronizes",
"patronising": "patronizing",
"patronisingly": "patronizingly",
"pedalled": "pedaled",
"pedalling": "pedaling",
"pedestrianisation": "pedestrianization",
"pedestrianise": "pedestrianize",
"pedestrianised": "pedestrianized",
"pedestrianises": "pedestrianizes",
"pedestrianising": "pedestrianizing",
"penalise": "penalize",
"penalised": "penalized",
"penalises": "penalizes",
"penalising": "penalizing",
"pencilled": "penciled",
"pencilling": "penciling",
"personalise": "personalize",
"personalised": "personalized",
"personalises": "personalizes",
"personalising": "personalizing",
"pharmacopoeia": "pharmacopeia",
"pharmacopoeias": "pharmacopeias",
"philosophise": "philosophize",
"philosophised": "philosophized",
"philosophises": "philosophizes",
"philosophising": "philosophizing",
"philtre": "filter",
"philtres": "filters",
"phoney": "phony",
"plagiarise": "plagiarize",
"plagiarised": "plagiarized",
"plagiarises": "plagiarizes",
"plagiarising": "plagiarizing",
"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",
"pretence": "pretense",
"pretences": "pretenses",
"primaeval": "primeval",
"prioritisation": "prioritization",
"prioritise": "prioritize",
"prioritised": "prioritized",
"prioritises": "prioritizes",
"prioritising": "prioritizing",
"privatisation": "privatization",
"privatisations": "privatizations",
"privatise": "privatize",
"privatised": "privatized",
"privatises": "privatizes",
"privatising": "privatizing",
"professionalisation": "professionalization",
"professionalise": "professionalize",
"professionalised": "professionalized",
"professionalises": "professionalizes",
"professionalising": "professionalizing",
"programme": "program",
"programmes": "programs",
"prologue": "prolog",
"prologues": "prologs",
"propagandise": "propagandize",
"propagandised": "propagandized",
"propagandises": "propagandizes",
"propagandising": "propagandizing",
"proselytise": "proselytize",
"proselytised": "proselytized",
"proselytiser": "proselytizer",
"proselytisers": "proselytizers",
"proselytises": "proselytizes",
"proselytising": "proselytizing",
"psychoanalyse": "psychoanalyze",
"psychoanalysed": "psychoanalyzed",
"psychoanalyses": "psychoanalyzes",
"psychoanalysing": "psychoanalyzing",
"publicise": "publicize",
"publicised": "publicized",
"publicises": "publicizes",
"publicising": "publicizing",
"pulverisation": "pulverization",
"pulverise": "pulverize",
"pulverised": "pulverized",
"pulverises": "pulverizes",
"pulverising": "pulverizing",
"pummelled": "pummel",
"pummelling": "pummeled",
"pyjama": "pajama",
"pyjamas": "pajamas",
"pzazz": "pizzazz",
"quarrelled": "quarreled",
"quarrelling": "quarreling",
"radicalise": "radicalize",
"radicalised": "radicalized",
"radicalises": "radicalizes",
"radicalising": "radicalizing",
"rancour": "rancor",
"randomise": "randomize",
"randomised": "randomized",
"randomises": "randomizes",
"randomising": "randomizing",
"rationalisation": "rationalization",
"rationalisations": "rationalizations",
"rationalise": "rationalize",
"rationalised": "rationalized",
"rationalises": "rationalizes",
"rationalising": "rationalizing",
"ravelled": "raveled",
"ravelling": "raveling",
"realisable": "realizable",
"realisation": "realization",
"realisations": "realizations",
"realise": "realize",
"realised": "realized",
"realises": "realizes",
"realising": "realizing",
"recognisable": "recognizable",
"recognisably": "recognizably",
"recognisance": "recognizance",
"recognise": "recognize",
"recognised": "recognized",
"recognises": "recognizes",
"recognising": "recognizing",
"reconnoitre": "reconnoiter",
"reconnoitred": "reconnoitered",
"reconnoitres": "reconnoiters",
"reconnoitring": "reconnoitering",
"refuelled": "refueled",
"refuelling": "refueling",
"regularisation": "regularization",
"regularise": "regularize",
"regularised": "regularized",
"regularises": "regularizes",
"regularising": "regularizing",
"remodelled": "remodeled",
"remodelling": "remodeling",
"remould": "remold",
"remoulded": "remolded",
"remoulding": "remolding",
"remoulds": "remolds",
"reorganisation": "reorganization",
"reorganisations": "reorganizations",
"reorganise": "reorganize",
"reorganised": "reorganized",
"reorganises": "reorganizes",
"reorganising": "reorganizing",
"revelled": "reveled",
"reveller": "reveler",
"revellers": "revelers",
"revelling": "reveling",
"revitalise": "revitalize",
"revitalised": "revitalized",
"revitalises": "revitalizes",
"revitalising": "revitalizing",
"revolutionise": "revolutionize",
"revolutionised": "revolutionized",
"revolutionises": "revolutionizes",
"revolutionising": "revolutionizing",
"rhapsodise": "rhapsodize",
"rhapsodised": "rhapsodized",
"rhapsodises": "rhapsodizes",
"rhapsodising": "rhapsodizing",
"rigour": "rigor",
"rigours": "rigors",
"ritualised": "ritualized",
"rivalled": "rivaled",
"rivalling": "rivaling",
"romanticise": "romanticize",
"romanticised": "romanticized",
"romanticises": "romanticizes",
"romanticising": "romanticizing",
"rumour": "rumor",
"rumoured": "rumored",
"rumours": "rumors",
"sabre": "saber",
"sabres": "sabers",
"saltpetre": "saltpeter",
"sanitise": "sanitize",
"sanitised": "sanitized",
"sanitises": "sanitizes",
"sanitising": "sanitizing",
"satirise": "satirize",
"satirised": "satirized",
"satirises": "satirizes",
"satirising": "satirizing",
"saviour": "savior",
"saviours": "saviors",
"savour": "savor",
"savoured": "savored",
"savouries": "savories",
"savouring": "savoring",
"savours": "savors",
"savoury": "savory",
"scandalise": "scandalize",
"scandalised": "scandalized",
"scandalises": "scandalizes",
"scandalising": "scandalizing",
"sceptic": "skeptic",
"sceptical": "skeptical",
"sceptically": "skeptically",
"scepticism": "skepticism",
"sceptics": "skeptics",
"sceptre": "scepter",
"sceptres": "scepters",
"scrutinise": "scrutinize",
"scrutinised": "scrutinized",
"scrutinises": "scrutinizes",
"scrutinising": "scrutinizing",
"secularisation": "secularization",
"secularise": "secularize",
"secularised": "secularized",
"secularises": "secularizes",
"secularising": "secularizing",
"sensationalise": "sensationalize",
"sensationalised": "sensationalized",
"sensationalises": "sensationalizes",
"sensationalising": "sensationalizing",
"sensitise": "sensitize",
"sensitised": "sensitized",
"sensitises": "sensitizes",
"sensitising": "sensitizing",
"sentimentalise": "sentimentalize",
"sentimentalised": "sentimentalized",
"sentimentalises": "sentimentalizes",
"sentimentalising": "sentimentalizing",
"sepulchre": "sepulcher",
"sepulchres": "sepulchers",
"serialisation": "serialization",
"serialisations": "serializations",
"serialise": "serialize",
"serialised": "serialized",
"serialises": "serializes",
"serialising": "serializing",
"sermonise": "sermonize",
"sermonised": "sermonized",
"sermonises": "sermonizes",
"sermonising": "sermonizing",
"sheikh": "sheik",
"shovelled": "shoveled",
"shovelling": "shoveling",
"shrivelled": "shriveled",
"shrivelling": "shriveling",
"signalise": "signalize",
"signalised": "signalized",
"signalises": "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
================================================
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
================================================
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
系统: 欢迎使用livetalking,请点击"开始连接"按钮开始对话。
发送
按住说话,松开发送
================================================
FILE: web/echo.html
================================================
================================================
FILE: web/echoapi.html
================================================
================================================
FILE: web/rtcpush.html
================================================
WebRTC webcam
Use STUN server
Start
Send
Media
================================================
FILE: web/rtcpushapi-asr.html
================================================
WebRTC webcam
Use STUN server
Start
Send
Media
================================================
FILE: web/rtcpushapi.html
================================================
WebRTC webcam
Use STUN server
Start
Send
Media
================================================
FILE: web/rtcpushchat.html
================================================
WebRTC webcam
Use STUN server
Start
Send
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
Use STUN server
Start
Stop
Send
================================================
FILE: web/webrtcapi-asr.html
================================================
WebRTC webcam
Use STUN server
Start
Stop
Start Recording
Stop Recording
Send
================================================
FILE: web/webrtcapi-custom.html
================================================
WebRTC webcam
Use STUN server
Start
Stop
Send
切换视频
================================================
FILE: web/webrtcapi.html
================================================
WebRTC webcam
Use STUN server
Start
Stop
Start Recording
Stop Recording
Send
================================================
FILE: web/webrtcchat.html
================================================
WebRTC webcam
Use STUN server
Start
Stop
Start Recording
Stop Recording
Send
================================================
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)