Tutorial - Deploy Whisper-large-v3-turbo using Inferless

Whisper-large-v3-turbo is an efficient automatic speech recognition model by OpenAI, featuring 809 million parameters and significantly faster than its predecessor, Whisper large-v3. It excels in diverse applications like transcription and translation, processing audio effectively while handling background noise and various accents.

8.140140460899783 seconds Inference Time: 0.459 seconds for 10 second audio clip

TL;DR:

• Deployment of Whisper-large-v3-turbo model using Transformers.
• You can expect an average latency of 0.459 sec for 10 sec audio clip. This setup has an average cold start time of 8.14 sec.
• Dependencies defined in inferless-runtime-config.yaml.
• GitHub/GitLab template creation with app.py, inferless-runtime-config.yaml and inferless.yaml.
• Model class in app.py with initialize, infer, and finalize functions.
• Custom runtime creation with necessary system and Python packages.
• Model import via GitHub with input_schema.py file.
• Recommended GPU: NVIDIA A100 for optimal performance.
• Custom runtime selection in advanced configuration.
• Final review and deployment on the Inferless platform.

Fork the Repository

Get started by forking the repository. You can do this by clicking on the fork button in the top right corner of the repository page.

This will create a copy of the repository in your own GitHub account, allowing you to make changes and customize it according to your needs.

Create a Custom Runtime in Inferless

To access the custom runtime window in Inferless, simply navigate to the sidebar and click on the Create new Runtime button. A pop-up will appear.

Next, provide a suitable name for your custom runtime and proceed by uploading the inferless-runtime-config.yaml file given above. Finally, ensure you save your changes by clicking on the save button.

Import the Model in Inferless

Log in to your inferless account, select the workspace you want the model to be imported into and click the Add a custom model button.

• Select Github as the method of upload from the Provider list and then select your Github Repository and the branch.
• Choose the type of machine, and specify the minimum and maximum number of replicas for deploying your model.
• Configure Custom Runtime ( If you have pip or apt packages), choose Volume, Secrets and set Environment variables like Inference Timeout / Container Concurrency / Scale Down Timeout
• Once you click “Continue,” click Deploy to start the model import process.

Refer this link for more information on model import.

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Customizing the Code

Open the app.py file. This contains the main code for inference. It has three main functions, initialize, infer and finalize.

Initialize - This function is executed during the cold start and is used to initialize the model. If you have any custom configurations or settings that need to be applied during the initialization, make sure to add them in this function.

Infer - This function is where the inference happens. The argument to this function inputs, is a dictionary containing all the input parameters. The keys are the same as the name given in inputs. Refer to input for more.

def infer(self, inputs):
    audio_url = inputs["audio_url"]

Finalize - This function is used to perform any cleanup activity for example you can unload the model from the gpu by setting self.pipe = None.

For more information refer to the Inferless docs.