https://github.com/tddschn/modelz-faster-whisper
https://github.com/tddschn/modelz-faster-whisper
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/tddschn/modelz-faster-whisper
- Owner: tddschn
- Created: 2023-06-15T09:00:34.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-06-15T09:02:01.000Z (almost 3 years ago)
- Last Synced: 2025-06-25T23:46:49.526Z (11 months ago)
- Language: Python
- Size: 19.5 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Modelz faster-whisper
[faster-whisper](https://github.com/guillaumekln/faster-whisper) is a reimplementation of OpenAI's Whisper model using CTranslate2, which is a fast inference engine for Transformer models.
This repository contains the code (Dockerfile, [`build.envd`](https://envd.tensorchord.ai/guide/getting-started.html)) for deploying faster-whisper on [Modelz](https://docs.modelz.ai/).
## Getting Started
This is a template for creating a [Gradio](https://gradio.app/) app on [Modelz](https://modelz.ai/).
Building an Gradio app could be straightforward. You will need to provide three key components:
- A `main.py` file: This file contains the code for making predictions.
- A `requirements.txt` file: This file lists all the dependencies required for the server code to run.
- A `Dockerfile` or a simpler [`build.envd`](https://envd.tensorchord.ai/guide/getting-started.html): This file contains instructions for building a Docker image that encapsulates the server code and its dependencies.
## Build
In the `Dockerfile`, you need to define the instructions for building a Docker image that encapsulates the server code and its dependencies.
In most cases, you could use the template in the repository.
```bash
docker build -t docker.io/USER/IMAGE .
docker push docker.io/USER/IMAGE
```
On the other hand, a [`build.envd`](https://envd.tensorchord.ai/guide/getting-started.html) is a simplified alternative to a Dockerfile. It provides python-based interfaces that contains configuration settings for building a image.
It is easier to use than a Dockerfile as it involves specifying only the dependencies of your machine learning model, not the instructions for CUDA, conda, and other system-level dependencies.
```bash
envd build --output type=image,name=docker.io/USER/IMAGE,push=true
```
## Deploy
Please refer to the [Modelz documentation](https://docs.modelz.ai/gettingstarted/deploy) for more details.