144 lines
10 KiB
Markdown
144 lines
10 KiB
Markdown
# **Wav2Lip**: *Accurately Lip-syncing Videos In The Wild*
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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.
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[](https://paperswithcode.com/sota/lip-sync-on-lrs2?p=a-lip-sync-expert-is-all-you-need-for-speech)
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[](https://paperswithcode.com/sota/lip-sync-on-lrs3?p=a-lip-sync-expert-is-all-you-need-for-speech)
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[](https://paperswithcode.com/sota/lip-sync-on-lrw?p=a-lip-sync-expert-is-all-you-need-for-speech)
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[[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) | [[ReSyncED] (coming soon)](#)
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<img src="https://drive.google.com/uc?export=view&id=1Wn0hPmpo4GRbCIJR8Tf20Akzdi1qjjG9"/>
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----------
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**Highlights**
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----------
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- Lip-sync videos to any target speech with high accuracy :100:. Try our [interactive demo](https://bhaasha.iiit.ac.in/lipsync).
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- :sparkles: Works for any identity, voice, and language. Also works for CGI faces and synthetic voices.
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- Complete training code, inference code, and pretrained models are available :boom:
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- 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).
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- :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.
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--------
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**Disclaimer**
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--------
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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 prohibhited. Please contact us for all further queries.
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Prerequisites
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-------------
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- `Python 3.6`
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- ffmpeg: `sudo apt-get install ffmpeg`
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- 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.
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- 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.
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Getting the weights
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----------
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| Model | Description | Link to the model |
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| :-------------: | :---------------: | :---------------: |
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| Wav2Lip | Highly accurate lip-sync | [Link](https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/Eb3LEzbfuKlJiR600lQWRxgBIY27JZg80f7V9jtMfbNDaQ?e=TBFBVW) |
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| 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) |
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| 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) |
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Lip-syncing videos using the pre-trained models (Inference)
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-------
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You can lip-sync any video to any audio:
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```bash
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python inference.py --checkpoint_path <ckpt> --face <video.mp4> --audio <an-audio-source>
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```
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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.
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##### Tips for better results:
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- 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`.
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- 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 another try.
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- Experiment with the `--resize_factor` argument, to get a lower resolution video. Why? The models are trained on faces which 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).
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- 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.
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Preparing LRS2 for training
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----------
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Our models are trained on LRS2. See [here](#training-on-datasets-other-than-lrs2) for a few suggestions regarding training on other datasets.
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##### LRS2 dataset folder structure
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```
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data_root (mvlrs_v1)
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├── main, pretrain (we use only main folder in this work)
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| ├── list of folders
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| │ ├── five-digit numbered video IDs ending with (.mp4)
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```
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Place the LRS2 filelists (train, val, test) `.txt` files in the `filelists/` folder.
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##### Preprocess the dataset for fast training
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```bash
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python preprocess.py --data_root data_root/main --preprocessed_root lrs2_preprocessed/
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```
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Additional options like `batch_size` and number of GPUs to use in parallel to use can also be set.
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##### Preprocessed LRS2 folder structure
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```
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preprocessed_root (lrs2_preprocessed)
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├── list of folders
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| ├── Folders with five-digit numbered video IDs
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| │ ├── *.jpg
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| │ ├── audio.wav
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```
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Train!
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----------
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There are two major steps: (i) Train the expert lip-sync discriminator, (ii) Train the Wav2Lip model(s).
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##### Training the expert discriminator
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You can download [the pre-trained weights](#getting-the-weights) if you want to skip this step. To train it:
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```bash
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python color_syncnet_train.py --data_root lrs2_preprocessed/ --checkpoint_dir <folder_to_save_checkpoints>
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```
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##### Training the Wav2Lip models
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You can either train the model without the additional visual quality disriminator (< 1 day of training) or use the discriminator (~2 days). For the former, run:
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```bash
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python wav2lip_train.py --data_root lrs2_preprocessed/ --checkpoint_dir <folder_to_save_checkpoints> --syncnet_checkpoint_path <path_to_expert_disc_checkpoint>
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```
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To train with the visual quality discriminator, you should run `hq_wav2lip_train.py` instead. The arguments for both the files are similar. In both the 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.
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Training on datasets other than LRS2
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------------------------------------
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Training on other datasets might require modifications to the code. Please read the following before you raise an issue:
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- 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.
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- You must train the expert discriminator for your own dataset before training Wav2Lip.
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- If it is your own dataset downloaded from the web, in most cases, needs to be sync-corrected.
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- Be mindful of the FPS of the videos of your dataset. Changes to FPS would need significant code changes.
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- 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.
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When raising an issue on this topic, please let us know that you are aware of all these points.
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Evaluation
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Will be updated.
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License and Citation
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The software can only be used for personal/research/non-commercial purposes. Please cite the following paper if you use this code:
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```
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@inproceedings{10.1145/3394171.3413532,
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author = {Prajwal, K R and Mukhopadhyay, Rudrabha and Namboodiri, Vinay P. and Jawahar, C.V.},
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title = {A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild},
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year = {2020},
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isbn = {9781450379885},
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publisher = {Association for Computing Machinery},
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address = {New York, NY, USA},
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url = {https://doi.org/10.1145/3394171.3413532},
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doi = {10.1145/3394171.3413532},
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booktitle = {Proceedings of the 28th ACM International Conference on Multimedia},
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pages = {484–492},
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numpages = {9},
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keywords = {lip sync, talking face generation, video generation},
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location = {Seattle, WA, USA},
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series = {MM '20}
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}
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```
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Acknowledgements
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Parts of the code structure is 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.
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