What Is Kaldi?
The Kaldi Speech Recognition Toolkit project began in 2009 at Johns Hopkins University with the intent of developing techniques to reduce both the cost and time required to build speech recognition systems. While originally focused on ASR support for new languages and domains, the Kaldi project has steadily grown in size and capabilities, enabling hundreds of researchers to participate in advancing the field. Now the de-facto speech recognition toolkit in the community, Kaldi helps to enable speech services used by millions of people every day.
This container has Kaldi pre-built and ready to use in
/opt/kaldi, however, if you want to rebuild, run:
> make -j -C /opt/kaldi/src/
In addition, the source can be found in
Before you can run an NGC deep learning framework container, your Docker environment must support NVIDIA GPUs. To run a container, issue the appropriate command as explained in the Running A Container chapter in the NVIDIA Containers And Frameworks User Guide and specify the registry, repository, and tags. For more information about using NGC, refer to the NGC Container User Guide.
The method implemented in your system depends on the DGX OS version installed (for DGX systems), the specific NGC Cloud Image provided by a Cloud Service Provider, or the software that you have installed in preparation for running NGC containers on TITAN PCs, Quadro PCs, or vGPUs.
- Select the Tags tab and locate the container image release that you want to run.
- In the Pull Tag column, click the icon to copy the
- Open a command prompt and paste the pull command. The pulling of the container image begins. Ensure the pull completes successfully before proceeding to the next step.
- Run the container image.
If you have Docker 19.03 or later, a typical command to launch the container is:
docker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/nvidia/kaldi:xx.xx-py3
If you have Docker 19.02 or earlier, a typical command to launch the container is:
nvidia-docker run -it --rm -v local_dir:container_dir nvcr.io/nvidia/kaldi:xx.xx-py3
-itmeans run in interactive mode
--rmwill delete the container when finished
-vis the mounting directory
local_diris the directory or file from your host system (absolute path) that you want to access from inside your container. For example, the
local_dirin the following path is
If you are inside the container, for example,
ls /data/mnist, you will see the same files as if you issued the
ls /home/jsmith/data/mnist command from outside the container.
container_diris the target directory when you are inside your container. For example,
/data/mnistis the target directory in the example:
xx.xxis the container version. For example,
/workspace/README.md inside the container for information on customizing your Kaldi image.
An example has been provided and can be found here: nvidia-examples/librispeech/
To run the example you will first have to prepare the model:
Once the model is prepared you can run a speech to text benchmark as follows:
For the latest Release Notes, see the Kaldi Release Notes Documentation website.
For a full list of the supported software and specific versions that come packaged with this framework based on the container image, see the Frameworks Support Matrix.
To review known CVEs on the 21.07 image, please refer to the Known Issues section of the Product Release Notes.
By pulling and using the container, you accept the terms and conditions of this End User License Agreement.