Video does not have browser-compatible container or codec. Converting to mp4?
HI,When I run gradio and there is an error about converting mp4. But I have the ffmpeg andalso, sudo apt-get install ubuntu-restricted-extras, no help…Is there any suggestion to solve the problem? Thxanaconda3/lib/python3.9/site-packages/gradio/components/video.py:334: UserWarning: Video does not have browser-compatible container or codec. Converting to mp4Full log is ingithub.com/hkchengrex/Tracking-Anything-with-DEVAVideo does not have browser-compatible container or codec. Converting to mp4?opened03:22AM - 26 Sep 23 UTCAK51Hi,

I can run the UI now, thx.
I have the ffmpeg but it said no "browser-com…patible container or codec"?
also, sudo apt-get install ubuntu-restricted-extras, no help...
Is there any suggestion to solve the problem? Thx

```
python demo/demo_gradio.py
/home/ak/anaconda3/lib/python3.9/site-packages/gradio/blocks.py:950: UserWarning: api_name predict already exists, using predict_1
  warnings.warn(
Running on local URL:  http://127.0.0.1:7860

To create a public link, set `share=True` in `launch()`.
Error while flagging: [Errno 2] No such file or directory: 'https://user-images.githubusercontent.com/7107196/265518746-4a00cd0d-f712-447f-82c4-6152addffd6b.mp4'
/home/ak/anaconda3/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343964576/work/aten/src/ATen/native/TensorShape.cpp:3483.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
final text_encoder_type: bert-base-uncased
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Configuration: {'model': './saves/DEVA-propagation.pth', 'output': None, 'save_all': False, 'amp': True, 'key_dim': 64, 'value_dim': 512, 'pix_feat_dim': 512, 'disable_long_term': False, 'max_mid_term_frames': 10, 'min_mid_term_frames': 5, 'max_long_term_elements': 10000, 'num_prototypes': 128, 'top_k': 30, 'mem_every': 5, 'chunk_size': 8, 'size': 480, 'GROUNDING_DINO_CONFIG_PATH': './saves/GroundingDINO_SwinT_OGC.py', 'GROUNDING_DINO_CHECKPOINT_PATH': './saves/groundingdino_swint_ogc.pth', 'DINO_THRESHOLD': 0.35, 'DINO_NMS_THRESHOLD': 0.8, 'SAM_ENCODER_VERSION': 'vit_h', 'SAM_CHECKPOINT_PATH': './saves/sam_vit_h_4b8939.pth', 'MOBILE_SAM_CHECKPOINT_PATH': './saves/mobile_sam.pt', 'SAM_NUM_POINTS_PER_SIDE': 64, 'SAM_NUM_POINTS_PER_BATCH': 64, 'SAM_PRED_IOU_THRESHOLD': 0.88, 'SAM_OVERLAP_THRESHOLD': 0.8, 'img_path': './example/vipseg', 'detection_every': 5, 'num_voting_frames': 3, 'temporal_setting': 'semionline', 'max_missed_detection_count': 10, 'max_num_objects': 200, 'prompt': 'pigs', 'sam_variant': 'original', 'enable_long_term': True, 'enable_long_term_count_usage': True}
  0%|                                                   | 0/789 [00:00<?, ?it/s]/home/ak/anaconda3/lib/python3.9/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
2023-09-26 11:13:16.520850: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-09-26 11:13:16.978377: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-09-26 11:13:17.888671: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
  0%|                                           | 2/789 [00:05<27:41,  2.11s/it]Restricted license - for non-production use only - expires 2024-10-28
100%|█████████████████████████████████████████| 789/789 [06:58<00:00,  1.89it/s]
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/home/ak/anaconda3/lib/python3.9/site-packages/gradio/components/video.py:334: UserWarning: Video does not have browser-compatible container or codec. Converting to mp4
  warnings.warn(
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)


```