unzipped the folder "DeDRM_tools-master.zip".downloaded deDRM from GitHub repository (v 10.0.2).Never had a problem with calibre 4.x and deDRM 6.x but needed to update to deDRM v 10.x. HuBERT Pre-training and Fine-tuning (ASR).Music Source Separation with Hybrid Demucs.Speech Enhancement with MVDR Beamforming.Hello,I can't happily install the latest deDRM plugin. TorchAudio can make use of hardware-based video decoding and encoding supported by underlying FFmpeg libraries that are linked at runtime. Using NVIDIA’s GPU decoder and encoder, it is also possible to pass around CUDA Tensor directly, that is decode video into CUDA tensor or encode video from CUDA tensor, without moving data from/to CPU. This improves the video throughput significantly. However, please note that not all the video formats are supported by hardware acceleration. For the detail on the performance of GPU decoder and encoder please see Hardware-Accelerated Video Decoding and Encoding Overview ¶ This page goes through how to build FFmpeg with hardware acceleration. Using them in TorchAduio requires additional FFmpeg configuration. ![]() In the following, we look into how to enable GPU video decoding with NVIDIA’s Video codec SDK. To use NVENC/NVDEC with TorchAudio, the following items are required. NVIDIA GPU with hardware video decoder/encoder.įFmpeg libraries compiled with NVDEC/NVENC support. ![]() TorchAudio’s official binary distributions are compiled to work with FFmpeg 4 libraries, and they contain the logic required for hardware-based decoding/encoding. In the following, we build FFmpeg 4 libraries with NVDEC/NVENC support. ![]() If you would like to use FFmpeg 5, then you need to build TorchAudio with it. The following procedure was tested on Ubuntu.
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