
## Introduction

Real-time automated transcription is incredibly useful for anyone who needs to capture spoken content quickly and accurately. Whether you're creating subtitles for videos, transcribing podcast episodes, or documenting meeting notes, having an automated system can save you a lot of time and effort.

In practical terms, automated transcription can be used in various real-world scenarios. For instance, journalists can transcribe interviews on the fly, educators can provide real-time captions for their lectures, and businesses can document conference calls and meetings more efficiently.

[OpenAI Whisper](https://github.com/openai/whisper) is an open-source solution built for this purpose. It uses state-of-the-art machine learning algorithms to transcribe speech with high accuracy, even handling different accents and speaking speeds.

In this tutorial, you will learn how to set up a [Streamlit](https://streamlit.io/) application, integrate OpenAI Whisper for real-time podcast transcription, and deploy the application using Docker and Koyeb, creating a scalable transcription service.

You can consult the [project repository](https://github.com/koyeb/example-whisper-transcription) as work through this guide. You can deploy the podcast transcription application as built in this tutorial using the [Deploy to Koyeb](https://www.koyeb.com/docs/build-and-deploy/deploy-to-koyeb-button) button below:

[![Deploy to Koyeb](https://www.koyeb.com/static/images/deploy/button.svg)](https://app.koyeb.com/deploy?name=whisper-transcription&type=git&repository=koyeb%2Fexample-whisper-transcription&branch=main&builder=dockerfile&instance_type=gpu-nvidia-rtx-4000-sff-ada&env%5B%5D=&ports=8000%3Bhttp%3B%2F)

## Requirements

To successfully follow this tutorial, you will need the following:

- [Git](https://git-scm.com/) installed
- [FFmpeg](https://ffmpeg.org/) installed
- [Python 3.6+](https://www.python.org/downloads/release/python-360/) or later
- A [Koyeb](https://app.koyeb.com/) account

## Demo

Before we jump into the technical details, let me give you a sneak peek of what you will be building in this tutorial:

<iframe
  width="560"
  height="315"
  src="https://www.youtube.com/embed/HlVhwUriUnE"
  title="Using OpenAI Whisper to Transcribe Podcasts"
  frameborder="0"
  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
  referrerpolicy="strict-origin-when-cross-origin"
  allowfullscreen
></iframe>

## Understanding the components

### OpenAI Whisper

OpenAI Whisper is a sophisticated speech-to-text (STT) model designed to transcribe spoken words into written text with high accuracy. Utilizing advanced machine learning algorithms, Whisper is capable of recognizing various accents, dialects, and speaking speeds. It can be integrated into voice assistants, dictation software, and real-time translation services to convert spoken language into text efficiently.

OpenAI Whisper can be used in sectors such as healthcare for medical dictation, in customer service for automated call transcriptions, and in media for generating subtitles for videos and podcasts. Its ability to handle complex speech patterns and languages makes it the go-to service in any application requiring high-quality speech-to-text conversion.

### Streamlit

Streamlit is an open-source Python library designed to create interactive data applications, often referred to as dashboards. It empowers developers to build and share data apps simply and intuitively, eliminating the need for extensive web development expertise.

Streamlit apps are created as Python scripts, which are then executed within the Streamlit environment. The library offers a set of functions that can be used to add interactive elements to the app such as upload file button.

## Steps

To build the transcription service, we'll complete the following steps:

1. [Set up the environment](#set-up-the-environment): Start by setting up your project directory, installing necessary dependencies, and configuring environment variables.
2. [Set up Streamlit](#set-up-streamlit): Next, install Streamlit and create the initial user interface for your application.
3. [Transcribe podcasts with OpenAI Whisper](#transcribe-podcasts-with-open-ai-whisper): Use OpenAI Whisper to transcribe podcasts into text with timestamps.
4. [Dockerize the Streamlit application](#dockerize-the-streamlit-application): Create a Dockerfile to containerize your application for consistent deployment.
5. [Deploy to Koyeb](#deploy-to-koyeb): Finally, deploy your application on the Koyeb platform.

## Set up the environment

Let's start by creating a new Streamlit project. To keep your Python dependencies organized you should create a virtual environment.

First, create and navigate into a local directory:

```bash copy
# Create and move to the new directory
mkdir example-whisper-koyeb-gpu
cd example-whisper-koyeb-gpu
```

Afterwards, create and activate a new virtual environment:

```bash copy
# Create a virtual environment
python -m venv venv

# Active the virtual environment (Windows)
.\venv\Scripts\activate.bat

# Active the virtual environment (Linux)
source ./venv/bin/activate
```

Now, you can install the required dependencies. Open a `requirements.txt` file in your project directory with the following contents:

```none copy
streamlit
openai-whisper
watchdog
```

Pass the file to `pip` to install the dependencies:

```bash copy
pip install -r requirements.txt
```

For the dependencies, we have included Streamlit for building the web app, OpenAI Whisper for real-time transcription, and watchdog to monitor file system events.

Don't forget to save your dependencies to a `requirements.txt` file:

```bash copy
pip freeze > requirements.txt
```

Now, let's move on to creating a new Streamlit project.

## Set up Streamlit

In this step, you will set up the Streamlit UI that will allow users to upload an audio file, click a button to start the transcribing process, and finally present the segmented transcriptions in an user-friendly manner. All of the logic for the project will reside in this file, so you can start by creating a `app.py` file with the following code:

{/* prettier-ignore-start */}

```python copy
# File: app.py

import streamlit

stream_button_styles = """
<style>
    header { display: none !important; }
</style>
"""

page_styles = """
<style>
    h1 { font-size: 2rem; font-weight: 700; }
    h2 { font-size: 1.7rem; font-weight: 600; }
    .timestamp { color: gray; font-size: 0.9rem; }
</style>
"""

page_title = "Using OpenAI Whisper to Transcribe Podcasts"

page_description = "A demo showcasing the use of OpenAI Whisper to accurately and efficiently convert spoken content from podcasts into written text."

koyeb_box = "To deploy Whisper within minutes, <a href=\"https://koyeb.com/ai\">Koyeb GPUs</a> provide the easiest and most efficient way. Koyeb offers a seamless platform for deploying AI models, leveraging high-performance GPUs to ensure fast and reliable transcriptions."

step_1 = "1. Upload Podcast"

step_2 = "2. Invoke OpenAI Whisper to transcribe podcast 👇🏻"

step_3 = "3. Transcription &nbsp; 🎉"

def unsafe_html(tag, text):
    return streamlit.markdown(f"<{tag}>{text}</{tag}>", unsafe_allow_html=True)

def main():
    # Set title for the page
    streamlit.set_page_config(page_title, layout="centered")
    # Inject hide buttons CSS
    streamlit.markdown(stream_button_styles, unsafe_allow_html=True)
    # Inject page CSS
    streamlit.markdown(page_styles, unsafe_allow_html=True)
    # Create a H1 heading on the page
    streamlit.title(page_title)
    unsafe_html("h2", page_description)
    unsafe_html("p", koyeb_box)
    audio_file = streamlit.file_uploader(step_1, type=["mp3", "mp4", "wav", "m4a"])
    if audio_file:
        # If file is received
        # Write the file on the server
        # Show next step
        unsafe_html("small", step_2)
        if streamlit.button("Transcribe"):
            # Get the transcription
            unsafe_html("small", step_3)
            # Showcase the transcription

if __name__ == "__main__":
    main()
```

{/* prettier-ignore-end */}

The code above does the following:

- Begins by importing the Streamlit module
- Defines CSS for hiding the navigation bar and styling the headings
- Defines text values for the page's title, description, and each step
- Defines the `unsafe_html` function to dynamically create the HTML tags with content
- Accepts an audio file using Streamlit's builtin `file_uploader` function

With this, you have setup a UI that is able to accept podcast audio files from the user. Now, let's move on to transcribing the audio file obtained.

## Transcribe podcasts with OpenAI Whisper

In this step, you will invoke OpenAI Whisper's base model to transcribe an audio file. By default, the model is able to return the timestamps along with the transcription. This enables you to use the generated transcriptions as subtitles as well. Make the following additions in the `app.py` file:

{/* prettier-ignore-start */}

```python copy
# File: app.py

# Existing imports
# . . .
import whisper # [!code ++]

model = whisper.load_model("base") # [!code ++]

# ...

def unsafe_html(tag, text):
    # ...

# Generate transcription of each segment
def timestamp_html(segment): # [!code ++]
    return f'<span class="timestamp">[{segment["start"]:.2f} - {segment["end"]:.2f}]</span> {segment["text"]}' # [!code ++]

# Transcribe an audio file
def transcribe_audio(audio_file): # [!code ++]
    return model.transcribe(audio_file.name) # [!code ++]

# Write the audio file on server
def write_audio(audio_file): # [!code ++]
    with open(audio_file.name, "wb") as f: # [!code ++]
        f.write(audio_file.read()) # [!code ++]

def main():
    # ...
    if audio_file:
        # If file is received
        # Write the file on the server
        write_audio(audio_file) # [!code ++]
        # Show next step
        unsafe_html("small", step_2)
        if streamlit.button("Transcribe"):
            # Get the transcription
            transcript_text = transcribe_audio(audio_file) # [!code ++]
            unsafe_html("small", step_3)
            # Showcase the transcription
            for segment in transcript_text["segments"]: # [!code ++]
                unsafe_html("div", timestamp_html(segment)) # [!code ++]

if __name__ == "__main__":
    main()
```

{/* prettier-ignore-end */}

The changes above do the following:

- Import and instantiate the OpenAI Whisper base model
- Define a `timestamp_html` function to display the transcription with **start** and **end** timestamps
- Define a `transcribe_audio` function which invokes the model to generate transcriptions of the audio file
- Define a `write_audio` function to write the audio file on the server
- If an audio file is found, it writes the file on the server
- If the **Transcribe** button is clicked in the UI, `transcribe_audio` and `timestamp_html` functions are invoked to generate and display the transcriptions of the podcast

Now, you can run the Streamlit application with:

```bash copy
streamlit run ./app.py --server.port 8000
```

The application would now be ready on `http://localhost:8000`. Test the application in action by uploading one of your favorite podcasts file and see the transcriptions generated in real-time.

Next, let's dockerize the application to ensure consistency between multiple deployments.

## Dockerize the Streamlit application

Dockerizing deployments creates a consistent and reproducible environment, ensuring that the application runs the same way on any system. It simplifies dependency management and enhances scalability, making deployments more efficient and reliable. To dockerize, create a `Dockerfile` at the root of your project with the following content:

{/* prettier-ignore-start */}

```dockerfile {16,25,27} copy
FROM python:3.9 AS builder

WORKDIR /app

RUN python3 -m venv venv
ENV VIRTUAL_ENV=/app/venv
ENV PATH="$VIRTUAL_ENV/bin:$PATH"

COPY requirements.txt .
RUN pip install -r requirements.txt

FROM python:3.9 AS runner

WORKDIR /app

RUN apt-get update && apt-get install -y ffmpeg && rm -rf /var/lib/apt/lists/*

COPY --from=builder /app/venv venv
COPY app.py app.py

ENV VIRTUAL_ENV=/app/venv
ENV PATH="$VIRTUAL_ENV/bin:$PATH"

EXPOSE 8000

CMD ["streamlit", "run", "./app.py", "--server.port", "8000"]
```

{/* prettier-ignore-end */}

Apart from the usual Dockerfile to deploy Python applications, the following tweaks and additions have been made in this code:

- `RUN apt-get update && apt-get install -y ffmpeg && rm -rf /var/lib/apt/lists/*` is used to install `ffmpeg`, and then clean up package lists to reduce image size
- `EXPOSE 8000` is used to specify the port on which the Streamlit application will run
- `CMD ["streamlit", "run", "./app.py", "--server.port", "8000"]` is used to define the command to start the Streamlit app on port 8000

With everything configured, let's move on to deploy the application to Koyeb.

## Deploy to Koyeb

Now that you have the application running locally you can also deploy it on Koyeb and make it available on the internet.

Create a [new repository on your GitHub account](https://github.com/new) so that you can push your code.

You can download a [standard `.gitignore` file](https://raw.githubusercontent.com/github/gitignore/main/Python.gitignore) for Python from GitHub to exclude certain directories and files from being pushed to the repository:

```bash copy
curl -L https://raw.githubusercontent.com/github/gitignore/main/Python.gitignore -o .gitignore
```

Run the following commands in your terminal to commit and push your code to the repository:

```bash copy
git init
git add app.py Dockerfile requirements.txt .gitignore
git commit -m "first commit"
git branch -M main
git remote add origin [Your GitHub repository URL]
git push -u origin main
```

You should now have all of your local code in your remote repository. Now it is time to deploy the application.

Within the [Koyeb control panel](https://app.koyeb.com/), while on the Overview tab, initiate the app creation and deployment process by clicking **Create Service** and then choosing **Create web service**.

1. Select **GitHub** as the deployment source.
2. Select your repository from the menu. Alternatively, deploy from the [example repository associated with this tutorial](https://github.com/koyeb/example-whisper-transcription) by entering `https://github.com/koyeb/example-whisper-transcription` in the public repository field.
3. In the **Instance** selection, select a GPU Instance.
4. In the **Builder** section, choose **Dockerfile**.
5. Finally, click the **Deploy** button.

Once the application is deployed, you can visit the Koyeb service URL (ending in `.koyeb.app`) to access the Streamlit application.

## Conclusion

In this tutorial, you built a podcast transcription application with the Streamlit framework and OpenAI Whisper. During the process, you learned how to invoke the OpenAI Whisper model in Python to generate transcription with timestamps, and how to use the Streamlit framework to quickly prototype a user interface with a functioning upload button in a few lines of code.

Given that the application was deployed using the Git deployment option, subsequent code push to the deployed branch will automatically initiate a new build for your application. Changes to your application will become live once the deployment is successful. In the event of a failed deployment, Koyeb retains the most recent operational production deployment, ensuring the uninterrupted operation of your application.


Learn how to use OpenAI Whisper to build an app to generate transcription of podcast audio files in real-time.

Using OpenAI Whisper to Transcribe Podcasts on Koyeb


## Introduction

[Continue](https://continue.dev) is an open-source AI code assistant that connects any models and context to build custom autocomplete prompts and chat experiences inside the IDE, like VS Code and JetBrains.

[Ollama](https://ollama.com/) is a self-hosted AI solution to run open-source large language models on your own infrastructure, and [Codestral](https://ollama.com/library/codestral) is [MistralAI's](https://mistral.ai/) first-ever code model designed for code generation tasks.

In this guide, we will demonstrate how to use Continue with [Ollama](/deploy/ollama), the [Mistral Codestral](https://mistral.ai/news/codestral/) model, and [Koyeb GPUs](/blog/gpus-public-preview-run-ai-workloads-on-h100-a100-l40s-and-more) to build a custom, self-hosted AI code assistant.

When complete, you will have a private AI code assistant for autocomplete prompts and chat available within VS Code and JetBrains.

## Requirements

To successfully follow and complete this guide, you need:

- A [Koyeb account](https://app.koyeb.com) to deploy and run Ollama
- [VS Code](https://code.visualstudio.com/) or [JetBrains](https://www.jetbrains.com/) installed on your machine

## Steps

To complete this guide and build a custom AI code assistant using Continue, Ollama, Codestral, and Koyeb GPUs, you need to follow these steps:

1. [**Deploy Ollama on Koyeb's GPUs**](#deploy-ollama-on-koyebs-gpus)
2. [**Install and configure the Continue package in VS Code**](#install-and-configure-the-continue-package-in-vs-code)
3. [**Get started with your custom AI code assistant**](#get-started-with-your-custom-ai-code-assistant)

## Deploy Ollama on Koyeb's GPUs

To get started, we will deploy Ollama on Koyeb's GPUs. Ollama will be used to run the Mistral Codestral model on a Koyeb RTX 4000 SFF ADA which is ideal for cost-effective AI inference and running open-source large language models.

To create and deploy Ollama on Koyeb, we will use the [Deploy to Koyeb](https://www.koyeb.com/docs/build-and-deploy/deploy-to-koyeb-button) button below:

[![Deploy to Koyeb](https://www.koyeb.com/static/images/deploy/button.svg)](https://app.koyeb.com/deploy?name=ollama&type=docker&image=ollama%2Follama&command=serve&instance_type=gpu-nvidia-rtx-4000-sff-ada&env%5B%5D=&ports=11434%3Bhttp%3B%2F)

On the service configuration page, you can customize the [Service](/docs/reference/services) name, [Instance](/docs/reference/instances) type, and other settings to match your requirements.

When you are ready, click the **Deploy** button to create the service and start the deployment process.

After a few seconds, your Ollama service will be deployed and running on Koyeb.

The next step is to pull the Mistral Codestral model to use it with Ollama. To do so, retrieve the Service URL from the Koyeb dashboard and run the following command in your terminal:

```bash
curl https://<YOUR_SUBDOMAIN>.koyeb.app/api/pull -d '{
  "name": "codestral"
}'
```

<Admonition>Take care to replace the base URL ending in `koyeb.app` with your actual service URL.</Admonition>

Ollama will pull the Mistral Codestral model and prepare it for use. This might take a few moments. Once it's done, we can move to the next step and configure Continue to use the `ollama` provider.

## Install and configure the Continue package in VS Code

With Ollama deployed, we will show how to configure Continue for VS Code to use `ollama` as a provider. For JetBrains, please refer to the [Continue documentation](https://docs.continue.dev/quickstart#jetbrains).

Get started by installing the [Continue VS Code extension](https://marketplace.visualstudio.com/items?itemName=Continue.continue).
This will open the Continue extension page for VS Code. Click the **Install** button to install the extension.

Once the install has completed, open the `~/.continue/config.json` file on your machine and edit it to match the format below:

```json
{
  "models": [
    {
      "title": "Codestral on Koyeb",
      "apiBase": "https://<YOUR_SUBDOMAIN>.koyeb.app/",
      "provider": "ollama",
      "model": "llama3:8b"
    }
  ]
}
```

The above configuration tells Continue to:

1. use the `ollama` provider
2. use the Mistral Codestral model
3. use the Ollama Instance located at the Koyeb Service URL

<Admonition>Take care to replace the `apiBase` value with your Ollama Service URL.</Admonition>

Restart VS Code to apply the changes and get started using the AI code assistant.

## Get started with your Custom AI code assistant

Use the following shortcuts to access Continue and interact with the AI code assistant:

- cmd+L (MacOS)
- ctrl-L (Windows / Linux)

You can now start asking questions about your codebase, get autocomplete suggestions, and more.

<Banner
  title="Blazing-Fast AI Deployments"
  description="Enjoy automatic continuous deployment, global load balancing, real-time metrics and monitoring, autoscaling, and more."
  type="claim-free"
  buttonText="Deploy Now"
  buttonLink="https://app.koyeb.com/"
/>

## Conclusion

In this guide, we demonstrated how to use Continue, Ollama, MistralAI's Codestral, and Koyeb GPUs to build a custom autocomplete and chat experience inside of VS Code.
This tutorial covers the basics of how to get started using Continue. To go further, be sure to check out the [Continue documentation](https://docs.continue.dev/intro)
to learn more about how to use Continue.


Run your AI workloads worldwide on high-performance infrastructure.

Global deployments of LLMs and AI apps

This guide shows how to use Continue with Ollama, a self-hosted AI solution to run the Mistral Codestral model on Koyeb GPUs

Use Continue, Ollama, Codestral, and Koyeb GPUs to Build a Custom AI Code Assistant

Deploy your AI applications across continents and run on an edge network of 250 locations.

Low latency AI workloads

Learn how to set up a vLLM Instance to run inference workloads and host your own OpenAI-compatible API on Koyeb.

Deploy the vLLM Inference Engine to Run Large Language Models (LLM) on Koyeb

Learn how to use Groq, speech-to-text (STT), and text-to-speech (TTS) to build an app to automatically translate between languages in real-time.

Using Groq to Build a Real-Time Language Translation App

Deploy LLaVA and your favorite cutting-edge AI tech across continents. No credit card required.

This tutorial walks through how to build a multimodal vision chat app powered by LLaVa, Chainlit, and Replicate.

Build a Multimodal Chat App using LLava, Chainlit, and Replicate

Deploy your favorite cutting-edge AI tech across continents. No credit card required.

This tutorial walks through how to build a chatbot powered by MistralAI, with FastAPI as the backend and FastUI as the front end.

Use MistralAI, FastAPI, and FastUI to Build a Conversational AI Chatbot

Leverage Koyeb's high-end bare metal machines for your AI workloads. No credit card required.

Run high-performance AI workloads

Learn step-by-step how to set up and utilize AutoGen within Chainlit. You'll discover how to create and interact with AI personas that are tailored to your specific needs, be it scriptwriting for YouTube content or ideating SaaS products.

Use AutoGen, Chainlit, and OpenAI to Generate Dynamic AI Personas

Deploy your fulll stack apps next to fully-managed serverless PostgreSQL databases on high-end infrastructure. No credit card required.

Serverless PostgreSQL with built-in pgvector

In this tutorial, we showcase how to deploy a FAQ search service built with Hugging Face's Inference API, pgvector, Koyeb's Managed Postgres. The optimized FAQ Search leverages sentence similarity searching to provide the most relevant results to a user's search terms.

Use pgvector and Hugging Face to Build an Optimized FAQ Search with Sentence Similarity

Deploy LangChain, Mistral 7B, and your favorite cutting-edge AI tech across continents. No credit card required.

Run global AI workloads

This guide explains how to build a YouTube video summarization using Langchain, Deepgram, and Mistral 7B. Deploy your AI workload on Koyeb to enjoy high-performance microVMs, seamless scaling, and fast global deployments.