Local Whisper Docker API

Run OpenAI's Whisper speech-to-text model locally in a Docker container, with a simple Python API for file uploads and transcription.

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Features

• No API keys required – runs entirely on your machine.
• Transcribe large files – not limited by OpenAI's 25MB API restriction.
• Simple HTTP API – upload audio/video files and get JSON transcriptions.
• No extra Python dependencies – uses only Python standard library and Whisper CLI.

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Quick Start

1. Clone the repository

git clone https://github.com/jaypetersdotdev/local-whisper.git
cd local-whisper

2. Build the Docker image

docker build -t whisper-api .

3. Run the Docker container

docker run -p 5001:5001 whisper-api

• The API will be available at http://localhost:5001/transcribe

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Usage

Transcribe an audio or video file

Send a POST request with your file as form-data (field name: file):

Using curl:

curl -F "file=@your-audio-file.mp3" http://localhost:5001/transcribe

Using n8n

• Use the "HTTP Request" node
• Method: POST
• URL: http://localhost:5001/transcribe
• Request Format: Form-Data
• Field Name: file
• Type: File
• Value: (reference to your binary file, e.g. {{$binary.data}})

Response

You will receive a JSON object with the transcription and segment timings.

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Requirements

• Docker
• (No OpenAI API key required)
• Sufficient CPU and RAM for Whisper (large files/models require more resources)

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Notes

• This API is for local or trusted use only. The multipart parser is minimal and not hardened for public internet exposure.
• The default model is small. You can change this in api_server.py if you want to use a different Whisper model.

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Community & Support

• YouTube channel: Jay Peters - @jaypetersdotdev
• FREE workflows and resources: Learn Automation & AI

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License

MIT (or your preferred license)

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Contributing

Pull requests and issues welcome!