speech-server

Installation

To set up the environment, follow these steps:

1. Create a virtual environment and install the required packages:

   python3.11 -m venv server
   source server/bin/activate
   pip install -r requirements.txt

   • If you are using the Nemo-based ASR model, use the following requirements file:

   pip install -r requirements_wx_nemo.txt

2. Add the following lines to server/bin/activate to ensure that the necessary libraries are accessible:

   export LD_LIBRARY_PATH=/path/to/environment/server/lib64/python3.11/site-packages/nvidia/cublas/lib:/path/to/environment/server/lib64/python3.11/site-packages/nvidia/cudnn/lib

   If you are using the Nemo version, also add the following:

   export CPATH=$HOME/python-dev/include:$CPATH

3. If Java 11.0 is not installed, set up the Java environment variables. Add the following to server/bin/activate:

   export JAVA_HOME=/path/to/java/installation/jdk-11.0.16.1+1
   export PATH=$JAVA_HOME/bin:$PATH

4. If ffmpeg 7.0.2 is not installed, you may need to download it from here and build it locally. Then, set up the enivronment variables:

   export JAVA_HOME=/path/to/java/installation/jdk-11.0.16.1+1
   export FFMPEG=/path/to/ffmpeg/installation/ffmpeg-7.0.2/build
   export PATH=$JAVA_HOME/bin:$FFMPEG/bin:$PATH

   ffmpeg 7.0.2 is very important to have if we want to make API calls to this server from the Safari browser.

Make sure to replace /path/to/environment/ and /path/to/java/installation/ with the actual paths to your environment and Java installation.

Files Description

• whisperx_model.py: Contains the model definition.
• whisperx_handler.py: Handles data input/output operations.
• archive.sh: Used to create the .mar file for the model in the model_store folder.
• config*.json: Configuration for corresponding models.
• client_webpage.html: An example of a client-side HTML file to send audio this torchserve server and getting the transcription

Browser integration

• The ASR models are being hosted right now on the slatelab server (need to change at some point)
• Port forwarding needed. First run ssh -L 8080:localhost:8080 user@cse-d01187744s.coeit.osu.edu
• Then run client_webpage.html. You will see a page like below.

Model Update Process

To update the model:

1. Always archive the model first by running:

   ./archive.sh

2. After archiving, start the server by running:

   ./start_server.sh

To stop the server, run:

./stop_server.sh

Training and deploying your own models

1. You will need to define my_model.py and my_handler.py (an example is shown for a CTC model I trained)
2. Include all extra files you import under --extra-files as shown in archive.sh
3. Change line 90 in client_webpage.html to your defined endpoint. For example, http://localhost:8080/predictions/my_asr