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://sync.so/ For any other commercial / enterprise requests, please contact us at pavan@synclabs.so and prady@sync.so To reach out to the authors directly you can reach us at prajwal@synclabs.so, rudrabha@sync.so. 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.

Highlights

• Weights of the visual quality disc has been updated in readme!
• Lip-sync videos to any target speech with high accuracy 💯. Try our interactive demo.
• ✨ 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 💥
• Or, quick-start with the Google Colab Notebook: Link. Checkpoints and samples are available in a Google Drive folder as well. There is also a tutorial video on this, courtesy of What Make Art. Also, thanks to Eyal Gruss, there is a more accessible Google Colab notebook with more useful features. A tutorial collab notebook is present at this link.
• 🔥 🔥 Several new, reliable evaluation benchmarks and metrics [evaluation/ folder of this repo] released. Instructions to calculate the metrics reported in the paper are also present.

For any other commercial / enterprise requests, please contact us at

• 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. Have a look at this comment and comment on the gist if you encounter any issues.
• Face detection pre-trained model should be downloaded to face_detection/detection/sfd/s3fd.pth. Alternative link if the above does not work. Getting the weights

You can lip-sync any video to any audio:

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.

• 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 for a few suggestions regarding training on other datasets.

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.

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_root (lrs2_preprocessed)
├── list of folders
|	├── Folders with five-digit numbered video IDs
|	│   ├── *.jpg
|	│   ├── audio.wav

There are two major steps: (i) Train the expert lip-sync discriminator, (ii) Train the Wav2Lip model(s).

You can download the pre-trained weights if you want to skip this step. To train it:

python color_syncnet_train.py --data_root lrs2_preprocessed/ --checkpoint_dir <folder_to_save_checkpoints>

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:

python wav2lip_train.py --data_root lrs2_preprocessed/ --checkpoint_dir <folder_to_save_checkpoints> --syncnet_checkpoint_path <path_to_expert_disc_checkpoint>

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

This repository can only be used for personal/research/non-commercial purposes. However, for commercial requests, please contact us directly at rudrabha@synclabs.so or prajwal@synclabs.so. We have a turn-key hosted API with new and improved lip-syncing models here: https://synclabs.so/ The size of the generated face will be 192 x 288 in our new models. 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}
}

Parts of the code structure are inspired by this TTS repository. We thank the author for this wonderful code. The code for Face Detection has been taken from the face_alignment repository. We thank the authors for releasing their code and models. We thank zabique for the tutorial collab notebook.

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