| # **Wav2Lip**: *Accurately Lip-syncing Videos In The Wild* | |
| - slightly modified to run as a Silly Tavern extension by [Mozer](https://github.com/Mozer) | |
| - Now it supports CPU and caching, giving 2x speed-up! | |
| - Full info about this ST extension is here: https://github.com/Mozer/wav2lip_extension | |
| ## original Readme | |
| Are you looking to integrate this into a product? We have a turn-key hosted API with new and improved lip-syncing models here: https://synclabs.so/ | |
| For any other commercial licenscing requests, please contact us at [email protected] or [email protected]. | |
| This code is part of the paper: _A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild_ published at ACM Multimedia 2020. | |
| [](https://paperswithcode.com/sota/lip-sync-on-lrs2?p=a-lip-sync-expert-is-all-you-need-for-speech) | |
| [](https://paperswithcode.com/sota/lip-sync-on-lrs3?p=a-lip-sync-expert-is-all-you-need-for-speech) | |
| [](https://paperswithcode.com/sota/lip-sync-on-lrw?p=a-lip-sync-expert-is-all-you-need-for-speech) | |
| |📑 Original Paper|📰 Project Page|🌀 Demo|⚡ Live Testing|📔 Colab Notebook | |
| |:-:|:-:|:-:|:-:|:-:| | |
| [Paper](http://arxiv.org/abs/2008.10010) | [Project Page](http://cvit.iiit.ac.in/research/projects/cvit-projects/a-lip-sync-expert-is-all-you-need-for-speech-to-lip-generation-in-the-wild/) | [Demo Video](https://youtu.be/0fXaDCZNOJc) | [Interactive Demo](https://bhaasha.iiit.ac.in/lipsync) | [Colab Notebook](https://colab.research.google.com/drive/1tZpDWXz49W6wDcTprANRGLo2D_EbD5J8?usp=sharing) /[Updated Collab Notebook](https://colab.research.google.com/drive/1IjFW1cLevs6Ouyu4Yht4mnR4yeuMqO7Y#scrollTo=MH1m608OymLH) | |
|  | |
| ---------- | |
| **Highlights** | |
| ---------- | |
| - Weights of the visual quality disc has been updated in readme! | |
| - Lip-sync videos to any target speech with high accuracy :100:. Try our [interactive demo](https://bhaasha.iiit.ac.in/lipsync). | |
| - :sparkles: Works for any identity, voice, and language. Also works for CGI faces and synthetic voices. | |
| - Complete training code, inference code, and pretrained models are available :boom: | |
| - Or, quick-start with the Google Colab Notebook: [Link](https://colab.research.google.com/drive/1tZpDWXz49W6wDcTprANRGLo2D_EbD5J8?usp=sharing). Checkpoints and samples are available in a Google Drive [folder](https://drive.google.com/drive/folders/1I-0dNLfFOSFwrfqjNa-SXuwaURHE5K4k?usp=sharing) as well. There is also a [tutorial video](https://www.youtube.com/watch?v=Ic0TBhfuOrA) on this, courtesy of [What Make Art](https://www.youtube.com/channel/UCmGXH-jy0o2CuhqtpxbaQgA). Also, thanks to [Eyal Gruss](https://eyalgruss.com), there is a more accessible [Google Colab notebook](https://j.mp/wav2lip) with more useful features. A tutorial collab notebook is present at this [link](https://colab.research.google.com/drive/1IjFW1cLevs6Ouyu4Yht4mnR4yeuMqO7Y#scrollTo=MH1m608OymLH). | |
| - :fire: :fire: Several new, reliable evaluation benchmarks and metrics [[`evaluation/` folder of this repo]](https://github.com/Rudrabha/Wav2Lip/tree/master/evaluation) released. Instructions to calculate the metrics reported in the paper are also present. | |
| -------- | |
| **Disclaimer** | |
| -------- | |
| All results from this open-source code or our [demo website](https://bhaasha.iiit.ac.in/lipsync) should only be used for research/academic/personal purposes only. As the models are trained on the <a href="http://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrs2.html">LRS2 dataset</a>, any form of commercial use is strictly prohibited. For commercial requests please contact us directly! | |
| Prerequisites | |
| ------------- | |
| - `Python 3.6` | |
| - ffmpeg: `sudo apt-get install ffmpeg` | |
| - Install necessary packages using `pip install -r requirements.txt`. Alternatively, instructions for using a docker image is provided [here](https://gist.github.com/xenogenesi/e62d3d13dadbc164124c830e9c453668). Have a look at [this comment](https://github.com/Rudrabha/Wav2Lip/issues/131#issuecomment-725478562) and comment on [the gist](https://gist.github.com/xenogenesi/e62d3d13dadbc164124c830e9c453668) if you encounter any issues. | |
| - Face detection [pre-trained model](https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth) should be downloaded to `face_detection/detection/sfd/s3fd.pth`. Alternative [link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/prajwal_k_research_iiit_ac_in/EZsy6qWuivtDnANIG73iHjIBjMSoojcIV0NULXV-yiuiIg?e=qTasa8) if the above does not work. | |
| Getting the weights | |
| ---------- | |
| | Model | Description | Link to the model | | |
| | :-------------: | :---------------: | :---------------: | | |
| | Wav2Lip | Highly accurate lip-sync | [Link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/Eb3LEzbfuKlJiR600lQWRxgBIY27JZg80f7V9jtMfbNDaQ?e=TBFBVW) | | |
| | Wav2Lip + GAN | Slightly inferior lip-sync, but better visual quality | [Link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/EdjI7bZlgApMqsVoEUUXpLsBxqXbn5z8VTmoxp55YNDcIA?e=n9ljGW) | | |
| | Expert Discriminator | Weights of the expert discriminator | [Link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/EQRvmiZg-HRAjvI6zqN9eTEBP74KefynCwPWVmF57l-AYA?e=ZRPHKP) | | |
| | Visual Quality Discriminator | Weights of the visual disc trained in a GAN setup | [Link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/EQVqH88dTm1HjlK11eNba5gBbn15WMS0B0EZbDBttqrqkg?e=ic0ljo) | | |
| Lip-syncing videos using the pre-trained models (Inference) | |
| ------- | |
| You can lip-sync any video to any audio: | |
| ```bash | |
| python inference.py --checkpoint_path <ckpt> --face <video.mp4> --audio <an-audio-source> | |
| ``` | |
| The result is saved (by default) in `results/result_voice.mp4`. You can specify it as an argument, similar to several other available options. The audio source can be any file supported by `FFMPEG` containing audio data: `*.wav`, `*.mp3` or even a video file, from which the code will automatically extract the audio. | |
| ##### Tips for better results: | |
| - Experiment with the `--pads` argument to adjust the detected face bounding box. Often leads to improved results. You might need to increase the bottom padding to include the chin region. E.g. `--pads 0 20 0 0`. | |
| - If you see the mouth position dislocated or some weird artifacts such as two mouths, then it can be because of over-smoothing the face detections. Use the `--nosmooth` argument and give it another try. | |
| - Experiment with the `--resize_factor` argument, to get a lower-resolution video. Why? The models are trained on faces that were at a lower resolution. You might get better, visually pleasing results for 720p videos than for 1080p videos (in many cases, the latter works well too). | |
| - The Wav2Lip model without GAN usually needs more experimenting with the above two to get the most ideal results, and sometimes, can give you a better result as well. | |
| Preparing LRS2 for training | |
| ---------- | |
| Our models are trained on LRS2. See [here](#training-on-datasets-other-than-lrs2) for a few suggestions regarding training on other datasets. | |
| ##### LRS2 dataset folder structure | |
| ``` | |
| data_root (mvlrs_v1) | |
| ├── main, pretrain (we use only main folder in this work) | |
| | ├── list of folders | |
| | │ ├── five-digit numbered video IDs ending with (.mp4) | |
| ``` | |
| Place the LRS2 filelists (train, val, test) `.txt` files in the `filelists/` folder. | |
| ##### Preprocess the dataset for fast training | |
| ```bash | |
| python preprocess.py --data_root data_root/main --preprocessed_root lrs2_preprocessed/ | |
| ``` | |
| Additional options like `batch_size` and the number of GPUs to use in parallel to use can also be set. | |
| ##### Preprocessed LRS2 folder structure | |
| ``` | |
| preprocessed_root (lrs2_preprocessed) | |
| ├── list of folders | |
| | ├── Folders with five-digit numbered video IDs | |
| | │ ├── *.jpg | |
| | │ ├── audio.wav | |
| ``` | |
| Train! | |
| ---------- | |
| There are two major steps: (i) Train the expert lip-sync discriminator, (ii) Train the Wav2Lip model(s). | |
| ##### Training the expert discriminator | |
| You can download [the pre-trained weights](#getting-the-weights) if you want to skip this step. To train it: | |
| ```bash | |
| python color_syncnet_train.py --data_root lrs2_preprocessed/ --checkpoint_dir <folder_to_save_checkpoints> | |
| ``` | |
| ##### Training the Wav2Lip models | |
| You can either train the model without the additional visual quality discriminator (< 1 day of training) or use the discriminator (~2 days). For the former, run: | |
| ```bash | |
| python wav2lip_train.py --data_root lrs2_preprocessed/ --checkpoint_dir <folder_to_save_checkpoints> --syncnet_checkpoint_path <path_to_expert_disc_checkpoint> | |
| ``` | |
| To train with the visual quality discriminator, you should run `hq_wav2lip_train.py` instead. The arguments for both files are similar. In both cases, you can resume training as well. Look at `python wav2lip_train.py --help` for more details. You can also set additional less commonly-used hyper-parameters at the bottom of the `hparams.py` file. | |
| Training on datasets other than LRS2 | |
| ------------------------------------ | |
| Training on other datasets might require modifications to the code. Please read the following before you raise an issue: | |
| - You might not get good results by training/fine-tuning on a few minutes of a single speaker. This is a separate research problem, to which we do not have a solution yet. Thus, we would most likely not be able to resolve your issue. | |
| - You must train the expert discriminator for your own dataset before training Wav2Lip. | |
| - If it is your own dataset downloaded from the web, in most cases, needs to be sync-corrected. | |
| - Be mindful of the FPS of the videos of your dataset. Changes to FPS would need significant code changes. | |
| - The expert discriminator's eval loss should go down to ~0.25 and the Wav2Lip eval sync loss should go down to ~0.2 to get good results. | |
| When raising an issue on this topic, please let us know that you are aware of all these points. | |
| We have an HD model trained on a dataset allowing commercial usage. The size of the generated face will be 192 x 288 in our new model. | |
| Evaluation | |
| ---------- | |
| Please check the `evaluation/` folder for the instructions. | |
| License and Citation | |
| ---------- | |
| This repository can only be used for personal/research/non-commercial purposes. However, for commercial requests, please contact us directly at [email protected] or [email protected]. We have an HD model trained on a dataset allowing commercial usage. The size of the generated face will be 192 x 288 in our new model. Please cite the following paper if you use this repository: | |
| ``` | |
| @inproceedings{10.1145/3394171.3413532, | |
| author = {Prajwal, K R and Mukhopadhyay, Rudrabha and Namboodiri, Vinay P. and Jawahar, C.V.}, | |
| title = {A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild}, | |
| year = {2020}, | |
| isbn = {9781450379885}, | |
| publisher = {Association for Computing Machinery}, | |
| address = {New York, NY, USA}, | |
| url = {https://doi.org/10.1145/3394171.3413532}, | |
| doi = {10.1145/3394171.3413532}, | |
| booktitle = {Proceedings of the 28th ACM International Conference on Multimedia}, | |
| pages = {484–492}, | |
| numpages = {9}, | |
| keywords = {lip sync, talking face generation, video generation}, | |
| location = {Seattle, WA, USA}, | |
| series = {MM '20} | |
| } | |
| ``` | |
| Acknowledgments | |
| ---------- | |
| Parts of the code structure are inspired by this [TTS repository](https://github.com/r9y9/deepvoice3_pytorch). We thank the author for this wonderful code. The code for Face Detection has been taken from the [face_alignment](https://github.com/1adrianb/face-alignment) repository. We thank the authors for releasing their code and models. We thank [zabique](https://github.com/zabique) for the tutorial collab notebook. | |