mpc001 / visual_speech_recognition_for_multiple_languages Goto Github PK

I tried to reproduce the result of the Mandarin lip reading from the following demo video:

https://youtu.be/FIau-6JA9Po?t=33

I've made a clip "demo_cn.mp4" from this video 0:33-0:41.

My code:

python main.py --config-filename configs/CMLR_V_WER8.0.ini --data-filename inputs/demo_cn.mp4

The output:

load a pre-trained model from: models/CMLR/CMLR_V_WER8.0/model.pth
face tracking speed: 4.90 fps.
hyp: 有一种种的人俗话说的大家人才能真的一年里的一个行

This is different from the one shown in the demo: 中青祝愿大家在新的一年里新春愉快身体健康

I also extracted mouth ROIs from the clip (link).

Would you please let me know if I missed anything?

Do you still have a copy of the CMU-MOSEAS dataset? I've been informed by the authors that they lost all copies of it. If you still have a copy I would immensely appreciate it if you could share it.

Thanks.

Thanks for the releasement. I wonder if the training code will be available in the future? Thanks.

When I install face_detection with code: 'git lfs pull' in ‘tools/readme.md’.
My pycharm told me 'batch response: This repository is over its data quota. Account responsible for LFS bandwidth should purchase more data packs to restore access.'
How can I fix this.

Thanks for releasing the awesome work! I noticed that the Chinese lip reading model is based on the visual modality. I used the visual model but it achieved poor performance on the example video clips like #5. Is there an audio-visual version that hopefully achieves better results?

Thanks.

Hi ， I‘m a beginner in lipreading. I'm curious how low the latency of lip recognition can be? Is there any solution to reduce the delay?
Thank you very much.

Which methods you use to extract landmarks from image?

Hi, thanks for this great work.

I have a question about the section "3.8 Using Additional Training Data" from your paper "Visual Speech Recognition for Multiple Languages in the Wild"

For example, for LRS3 the best WER of 32.1 is achieved by combining the datasets LRW + LRS2 + AVSpeech + LRS3. I was just wondering what way they're combined during training, which of the scenarios would be correct?

Scenario A:

1. Pretrain using LRW + LRS2 + AVSpeech datasets
2. Initialise from 1 above, then train on the LRS3 dataset only

Scenario B:

1. Pretrain using LRW + LRS2 + AVSpeech datasets
2. Initialise from 1 above, then train on LRW + LRS2 + AVSpeech + LRS3 datasets

Would there be a performance difference between these 2 scenarios?

Thanks

Mouth ROIs Cropping should use command "python crop_mouth.py" instead of "python main.py"(this is old one).

and it seems there is No module named 'ibug.face_detection' ?

Thank you for releasing your code to the public, I faced issues when trying to apply crop_mouth.py using the RetinaFace detector while when using MediaPipe it works well!

I have another Q, AVSRDataLoader(modality="video", speed_rate=1, transform=False, detector=cfg.detector, convert_gray=False) I applied it like this when I using MediaPipe detector, which means there is not any transform on frames! because I see there are transforms on videos such as see below

regards

Hello, I'm using your pre-trained model to test the GRID dataset, but for some reason, all the outputs are the same, they are all the same sentence. Can you help me with this?

Do you have any benchmark for inference time ? I have tried with 10-second-length video and it took about 1-2 minutes to print the result.

Hi dear

I would ask how I can deal with data already cropped mouth region with distribution size, I want to apply all pre-processing and data augmentation processes on this project on my own data. But landmarks were not all detected or more of them were none
because face detection was used. so how I can do it to apply this project to my own dataset?

Thank
best regards

Hi, thanks for your excellent work.

I wonder if all released models are language specific? Are there multilingual models?

Thanks.

Hello
So, attempting to use the GPU with a sample video trying to perform lip reading with this simple command:

python main.py --config-filename configs/LRS3_V_WER32.3.ini --data-filename test_video.mp4 --gpu-idx 0

Terminal runs normally, loading the pretrained model etc then I face this error:

load a pre-trained model from: models/LRS3/LRS3_V_WER32.3/model.pth
face tracking speed: 1.51 fps.
Traceback (most recent call last):
  File "main.py", line 210, in <module>
    main()
  File "main.py", line 189, in main
    one_step_inference(
  File "main.py", line 159, in one_step_inference
    output = lipreader(data_filename, landmarks_filename)
  File "Visual_Speech_Recognition_for_Multiple_Languages\lipreading\subroutines.py", line 107, in __call__
    output = self.model.predict(sequence)
  File "Visual_Speech_Recognition_for_Multiple_Languages\lipreading\model.py", line 141, in predict
    nbest_hyps = self.beam_search(
  File "Miniconda3\envs\VSRML\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "Visual_Speech_Recognition_for_Multiple_Languages\espnet\nets\beam_search.py", line 373, in forward
    running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps)
  File "Visual_Speech_Recognition_for_Multiple_Languages\espnet\nets\batch_beam_search.py", line 348, in post_process
    return self._batch_select(running_hyps, remained_ids)
  File "Visual_Speech_Recognition_for_Multiple_Languages\espnet\nets\batch_beam_search.py", line 51, in _batch_select
    score=hyps.score[ids],
RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)

The lip reading task works correctly if I don't specify a GPU parameter and looking around abit, apparently the problem is offloading some variables from CPU to GPU which breaks somewhere during runtime. Any help would be appreciated. Thank you!

hi,

Do you have plan to release code/model of audio-visual speech recognition model proposed in "End-to-end Audio-visual Speech Recognition with Conformers"?

Thanks!

When implementing the CMUMOSEAS_V_ES_WER44.5 model, the execution is broken.

The same does not happen with its other variations (CMUMOSEAS_V_FR_WER58.6 and CMUMOSEAS_V_PT_WER51.4 work fine).

Do you know why? Can anyone help me?

Thanks!

Could you please share the original GRID data set? There are some missing items online.

Am begginer
in python
Select this topic as project in my final year and Using ubuntu version

Wher i want to give path of models in this givencode

As mentioned in S3, the pre-trained models are always trained on the same data as the full model (yet I do not know the pre-training details), and specially the pre-trained VSR model has exactly the same architecture as the full one. So, I wonder why the supervised signals (e.g., intermediate representations) from pre-trained VSR still make sense. Could you give in-depth explanations?

Can you please tell me the life version of pytorch you are using, I have some errors with the 2.0 version. Thank you!