Wordcab Transcribe

Example Project

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Describe the project.

If you want to see...

Key features

• ⚡ Fast: X.
• 🐳 Easy to deploy: X.
• 🔥 Batch requests: X.
• 💸 Cost-effective: X.
• 🫶 Easy-to-use API: X.
• 🤗 MIT License: X.

Requirements

Local development

• Linux (tested on Ubuntu Server 20.04/22.04)
• Python >=3.10, <3.12

Run the API locally 🚀

X

Deployment

• Docker (optional for deployment)
• NVIDIA GPU + NVIDIA Container Toolkit (optional for deployment)

Run the API using Docker

Build the image.

docker build -t my-project:latest .

Run the container.

docker run -d --name wordcab-transcribe \
    --gpus all \
    --shm-size 1g \
    --restart unless-stopped \
    -p 5001:5001 \
    -v ~/.cache:/root/.cache \
    my-project:latest

Run the API behind a reverse proxy

You can run the API behind a reverse proxy like Nginx. We have included a nginx.conf file to help you get started.

# Create a docker network and connect the api container to it
docker network create example-network
docker network connect example-network my-project

# Replace /absolute/path/to/nginx.conf with the absolute path to the nginx.conf
# file on your machine (e.g. /home/user/wordcab-transcribe/nginx.conf).
docker run -d \
    --name nginx \
    --network transcribe \
    -p 80:80 \
    -v /absolute/path/to/nginx.conf:/etc/nginx/nginx.conf:ro \
    nginx

# Check everything is working as expected
docker logs nginx

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⏱️ Profile the API

You can profile the process executions using py-spy as a profiler.

# Launch the container with the cap-add=SYS_PTRACE option
docker run -d --name my-project \
    --gpus all \
    --shm-size 1g \
    --restart unless-stopped \
    --cap-add=SYS_PTRACE \
    -p 5001:5001 \
    -v ~/.cache:/root/.cache \
    my-project:latest

# Enter the container
docker exec -it my-project /bin/bash

# Install py-spy
pip install py-spy

# Find the PID of the process to profile
top  # 28 for example

# Run the profiler
py-spy record --pid 28 --format speedscope -o profile.speedscope.json

# Launch any task on the API to generate some profiling data

# Exit the container and copy the generated file to your local machine
exit
docker cp my-project:/app/profile.speedscope.json profile.speedscope.json

# Go to https://www.speedscope.app/ and upload the file to visualize the profile

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Test the API

X.

🚀 Contributing

Getting started

1. Before you start:

2. Clone the repo

git clone
cd my-project

3. Install dependencies and start coding

pip install -r requirements.txt

4. Run tests

Working workflow

1. Create an issue for the feature or bug you want to work on.
2. Create a branch using the left panel on GitHub.
3. git fetchand git checkout the branch.
4. Make changes and commit.
5. Push the branch to GitHub.
6. Create a pull request and ask for review.
7. Merge the pull request when it's approved and CI passes.
8. Delete the branch.
9. Update your local repo with git fetch and git pull.