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README.md

@@ -27,7 +27,7 @@ This code is part of the paper: _A Lip Sync Expert Is All You Need for Speech to
 --------
 **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!
+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. For commercial requests please contact us directly!
 
 Prerequisites
 -------------
@@ -55,8 +55,8 @@ The result is saved (by default) in `results/result_voice.mp4`. You can specify
 
 ##### 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). 
+- 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. 
+- 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). 
 - 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
@@ -78,7 +78,7 @@ Place the LRS2 filelists (train, val, test) `.txt` files in the `filelists/` fol
 ```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.
+Additional options like `batch_size` and number of GPUs to use in parallel to use can also be set.
 
 ##### Preprocessed LRS2 folder structure
 ```
@@ -99,12 +99,12 @@ You can download [the pre-trained weights](#getting-the-weights) if you want to
 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: 
+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: 
 ```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.
+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.
 
 Training on datasets other than LRS2
 ------------------------------------
@@ -126,7 +126,7 @@ 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:
+Theis 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.},
@@ -147,6 +147,6 @@ series = {MM '20}
 ```
 
 
-Acknowledgments
+Acknowledgements
 ----------
-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.
+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. We thank [zabique](https://github.com/zabique) for the tutorial collab notebook.

face_detection/detection/sfd/sfd_detector.py

@@ -14,19 +14,20 @@
 
 
 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)
+    @classmethod
+    def load_model(cls, device):
+        path_to_detector = os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth')
 
         # 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()
+        cls.face_detector = s3fd()
+        cls.face_detector.load_state_dict(model_weights)
+        cls.face_detector.to(device)
+        cls.face_detector.eval()
 
     def detect_from_image(self, tensor_or_path):
         image = self.tensor_or_path_to_ndarray(tensor_or_path)