# Video does not have browser-compatible container or codec. Converting to mp4? **URL:** \ **Category:** πŸ”’ Gradio\ **Created:** [September 26, 2023, 7:07am UTC](https://discuss.huggingface.co/t/video-does-not-have-browser-compatible-container-or-codec-converting-to-mp4/56389 "2023-09-26T07:07:23Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![AK51](https://avatars.discourse-cdn.com/v4/letter/a/839c29/32.png) [@AK51](https://discuss.huggingface.co/u/AK51)\ **Post date:** [September 26, 2023, 7:07am UTC](https://discuss.huggingface.co/t/video-does-not-have-browser-compatible-container-or-codec-converting-to-mp4/56389/1 "2023-09-26T07:07:23Z")
HI, When I run gradio and there is an error about converting mp4. But I have the ffmpeg and also, sudo apt-get install ubuntu-restricted-extras, no help… Is there any suggestion to solve the problem? Thx ```auto anaconda3/lib/python3.9/site-packages/gradio/components/video.py:334: UserWarning: Video does not have browser-compatible container or codec. Converting to mp4 ``` Full log is in > > > Hi, > > 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 \[00:00\ warnings.warn("None of the inputs have requires\_grad=True. Gradients will be None") > 2023-09-26 11:13:16.520850: I tensorflow/core/util/port.cc:110\] 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%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 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) > > > \`\`\`