Kaldi Release 19.09

The NVIDIA container image for Kaldi, release 19.09, is available on NGC.

Contents of the Kaldi container

This container image contains the complete source of the version of Kaldi in /opt/kaldi. The container also includes the following:

• Ubuntu 18.04 including Python 3.5
• NVIDIA CUDA 10.1.243 including cuBLAS 10.2.1.243
• NVIDIA cuDNN 7.6.3
• NVIDIA NCCL 2.4.8 (optimized for NVLink™ )
• MLNX_OFED
• OpenMPI 3.1.4
• Nsight Compute 2019.4.0
• Nsight Systems 2019.4.2
• TensorRT 6.0.1

Driver Requirements

Release 19.09 is based on NVIDIA CUDA 10.1.243, which requires NVIDIA Driver release 418.xx. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

GPU Requirements

Release 19.09 supports CUDA compute capability 6.0 and higher. This corresponds to GPUs in the Pascal, Volta, and Turing families. Specifically, for a list of GPUs that this compute capability corresponds to, see CUDA GPUs. For additional support details, see Deep Learning Frameworks Support Matrix.

Key Features and Enhancements

This Kaldi release includes the following key features and enhancements.

• Kaldi container image version 19.09 is based on Kaldi 5.5.
• Latest version of NVIDIA cuDNN 7.6.3
• Latest version of TensorRT 6.0.1
• Latest versions of Nsight Compute 2019.4.0 and Nsight Systems 2019.4.2
• Improved benchmark scripts, for example:
  • Unified run_benchmark.sh script so that it can now run single and multi-GPU.
  • Versioned output so that it is not overwritten.
  • Produced human readable transcriptions.
  • Added support for automatic segmentation.
  • Automatically create dataset when wav.scp does not exist.

  See NVREADME.md for more details.

• Ubuntu 18.04 with August 2019 updates

Packaged scripts

The Kaldi container comes with the following scripts:

• /workspace/nvidia-examples/librispeech/prepare.sh which downloads a trained model and data.
• /workspace/nvidia-examples/librispeech/run_benchmark.sh and /workspace/nvidia-examples/librispeech/run_multigpu_benchmark.sh which run inference on the trained model and data.

Known Issues

There are no known issues in this release.