Comments (3)
In previous script, nnabla and openvino were not separated.
So we prepared script that can be run in openvino only environment.
#30
In my environment, original d3net-mss takes 554m49sec by cpu, but openvino (4cpus) takes 8m53sec.
from ai-research-code.
Thank you for checking!
Yes, it takes a bit long time. We would like to share how to reduce inference time.
Please give us some time to check and summarize.
from ai-research-code.
We've added an inference script which runs the model on OpenVINO runtime by Intel which should be faster.
See https://github.com/sony/ai-research-code/blob/master/d3net/music-source-separation/README.md#inference-with-openvino or our colab demo to run the OpenVINO model in Python.
from ai-research-code.
Related Issues (20)
- Chinese supported? HOT 2
- Adding additional speakers - transfer learning
- NVC-Net Training HOT 3
- [Mixed Precision DNNs]: ImageNet codebase? HOT 1
- 【NVC-Net】RuntimeError: target_specific error in backward_impl. Failed `status == CUDNN_STATUS_SUCCESS`: UNKNOWN HOT 1
- 【NVC-Net】ImportError: libcudart.so.10.2: cannot open shared object file: No such file or directory HOT 6
- 【NVC-Net】Mutli-GPU training multiple models? HOT 2
- No pretrained NVC model HOT 5
- Memory allocation failed HOT 16
- MobileNet implementation for Mixed Precision DNNs
- Segmentation fault and RuntimeError: value error in setup_impl HOT 3
- resuming training from checkpoint HOT 2
- Question about Mixed Precision DNNs HOT 5
- [Quantized Depth Completion] Questions about implementation details HOT 5
- pretrained NVC model HOT 1
- NVCnet g_loss_con=0.0000 while training HOT 2
- [NVC-Net] About 16 kHz training and model convergence HOT 2
- [NVC-NET]Inference in CPU environment HOT 2
- [X-UMX] Bad performance when using --targets HOT 1
- hi,where is tvc-gmm code? HOT 2
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