Comments (2)
Hi! Sorry for the delay in response.
I used a single A100 for all such. And total train time for each component can be parallelized but in general, face model = 1 day, body vq model = 1 day, body diffusion model = 3 days. Of course, everything can be gpu parallelized for faster run time as well.
Inference time depends on the length of sequences. but for instance, to run all sequences in our test set (8 s) for 3 iterations took ~30 minutes - 1 hour.
Inference script can be found here: https://github.com/facebookresearch/audio2photoreal/blob/main/sample/generate.py
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Also how exactly is testing/inference done? On the same four participants ?
Unable to find test script in the repo.
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Related Issues (20)
- Switching from Recording to Uploading Audio in a Demo: Is it Possible? HOT 1
- Why the data is not as in the README ? HOT 2
- Models and pre-requisites models unavailable HOT 3
- Does it support languages other than English? HOT 1
- Models and pre-requisites models unavailable HOT 3
- What model was used to extract the body pose ? HOT 4
- Multiple GPUs DDP error HOT 7
- Data acquisition and processing HOT 3
- The evaluation code for lip reconstructions HOT 1
- Is it possible to run the demo in a laptop without GPU? HOT 3
- How to train a new model from scratch HOT 1
- Visualize 2 avatars in the same scene, just like the introduction page HOT 1
- Replancement of fairseq HOT 1
- Video data HOT 2
- 1
- when i run python -m demo.demo, I test a audio,have a error? HOT 2
- soundfile.LibsndfileError: Error opening '/tmp/audio_tmp_sample0.wav': System error. HOT 1
- About the global rotation and translation of each sequence
- RuntimeError: Not compiled with GPU support ( but i have GPU )
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