| .. | ||
| Colab_Jasper_TRT_inference_demo.ipynb | ||
| example1.wav | ||
| example2.wav | ||
| example3.wav | ||
| example4.wav | ||
| JasperTRT.ipynb | ||
| JasperTRTIS.ipynb | ||
| keynote.wav | ||
| README.md | ||
Jasper notebooks
This folder provides different notebooks to run Jasper inference step by step.
Table Of Contents
- Jasper Jupyter Notebook for TensorRT
- Jasper Colab Notebook for TensorRT
- Jasper Jupyter Notebook for TensorRT Inference Server
Jasper Jupyter Notebook for TensorRT
Requirements
./trt/ contains a Dockerfile which extends the PyTorch 19.09-py3 NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:
- NVIDIA Turing or Volta based GPU
- NVIDIA Docker
- PyTorch 19.09-py3 NGC container
- NVIDIA machine learning repository and NVIDIA cuda repository for NVIDIA TensorRT 6
- NVIDIA Volta or Turing based GPU
- Pretrained Jasper Model Checkpoint
Quick Start Guide
Running the following scripts will build and launch the container containing all required dependencies for both TensorRT as well as native PyTorch. This is necessary for using inference with TensorRT and can also be used for data download, processing and training of the model.
1. Clone the repository.
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/PyTorch/SpeechRecognition/Jasper
2. Build the Jasper PyTorch with TRT 6 container:
bash trt/scripts/docker/build.sh
3. Create directories
Prepare to start a detached session in the NGC container. Create three directories on your local machine for dataset, checkpoint, and result, respectively, naming "data" "checkpoint" "result":
mkdir data checkpoint result
4. Download the checkpoint
Download the checkpoint file jasperpyt_fp16 from NGC Model Repository:
to the directory: checkpoint
The Jasper PyTorch container will be launched in the Jupyter notebook. Within the container, the contents of the root repository will be copied to the /workspace/jasper directory.
The /datasets, /checkpoints, /results directories are mounted as volumes and mapped to the corresponding directories "data" "checkpoint" "result" on the host.
5. Run the notebook
For running the notebook on your local machine, run:
jupyter notebook -- notebooks/JasperTRT.ipynb
For running the notebook on another machine remotely, run:
jupyter notebook --ip=0.0.0.0 --allow-root
And navigate a web browser to the IP address or hostname of the host machine at port 8888: http://[host machine]:8888
Use the token listed in the output from running the jupyter command to log in, for example: http://[host machine]:8888/?token=aae96ae9387cd28151868fee318c3b3581a2d794f3b25c6b
Jasper Colab Notebook for TensorRT
Requirements
./trt/ contains a Dockerfile which extends the PyTorch 19.09-py3 NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:
- NVIDIA Turing or Volta based GPU
- NVIDIA Docker
- PyTorch 19.09-py3 NGC container
- NVIDIA machine learning repository and NVIDIA cuda repository for NVIDIA TensorRT 6
- NVIDIA Volta or Turing based GPU
- Pretrained Jasper Model Checkpoint
Quick Start Guide
Running the following scripts will build and launch the container containing all required dependencies for both TensorRT as well as native PyTorch. This is necessary for using inference with TensorRT and can also be used for data download, processing and training of the model.
1. Clone the repository.
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/PyTorch/SpeechRecognition/Jasper
2. Build the Jasper PyTorch with TRT 6 container:
bash trt/scripts/docker/build.sh
3. Create directories
Prepare to start a detached session in the NGC container. Create three directories on your local machine for dataset, checkpoint, and result, respectively, naming "data" "checkpoint" "result":
mkdir data checkpoint result
4. Download the checkpoint
Download the checkpoint file jasperpyt_fp16 from NGC Model Repository:
to the directory: checkpoint
The Jasper PyTorch container will be launched in the Jupyter notebook. Within the container, the contents of the root repository will be copied to the /workspace/jasper directory.
The /datasets, /checkpoints, /results directories are mounted as volumes and mapped to the corresponding directories "data" "checkpoint" "result" on the host.
5. Run the notebook
2deaddbc2ea58d5318b06203ae30ace2dd576ecb For running the notebook on your local machine, run:
jupyter notebook -- notebooks/Colab_Jasper_TRT_inference_demo.ipynb
For running the notebook on another machine remotely, run:
jupyter notebook --ip=0.0.0.0 --allow-root
And navigate a web browser to the IP address or hostname of the host machine at port 8888: http://[host machine]:8888
Use the token listed in the output from running the jupyter command to log in, for example: http://[host machine]:8888/?token=aae96ae9387cd28151868fee318c3b3581a2d794f3b25c6b
Jasper Jupyter Notebook for TensorRT Inference Server
This notebook can be executed from Google Colab by supplying the notebook Github URL or by open this link directly.