☆☆☆☆☆ (0) Rate this solution
This document has instructions to run a WaveNet FP32 inference using Intel® Optimizations for TensorFlow*.
Quick Start Scripts
||Runs inference with a pretrained model|
To run on bare metal, the following prerequisites must be installed in your environment:
- Python* 3
- Clone the tensorflow-wavenet repo and get pull request #352 for the CPU optimizations. The path to the cloned repo will be passed as the model source directory when running the launch script.
git clone https://github.com/ibab/tensorflow-wavenet.git cd tensorflow-wavenet/ git fetch origin pull/352/head:cpu_optimized git checkout cpu_optimized
After installing the prerequisites, download and untar the model package. Set environment variables for the path to your
TF_WAVENET_DIR and an
OUTPUT_DIR where log files will be written, then run a quickstart script.
TF_WAVENET_DIR=<tensorflow-wavenet directory> OUTPUT_DIR=<directory where log files will be written> wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/wavenet-fp32-inference.tar.gz tar -xzf wavenet-fp32-inference.tar.gz cd wavenet-fp32-inference quickstart/fp32_inference.sh
Documentation and Sources
LEGAL NOTICE: By accessing, downloading or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software included with the Software Package. Please refer to the license file for additional details.
Related Containers and Solutions
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.