https://github.com/brucewind/localwhisperapiservice

https://github.com/brucewind/localwhisperapiservice

openai-whisper transcribe whisper

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/brucewind/localwhisperapiservice
• Owner: BruceWind
• Created: 2024-09-17T09:58:46.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-19T08:21:44.000Z (about 2 months ago)
• Last Synced: 2024-10-12T17:43:02.777Z (about 1 month ago)
• Topics: openai-whisper, transcribe, whisper
• Language: Python
• Homepage:
• Size: 15.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# LocalWhisperAPIService

Whisper Speech Recognition API

This is an API service built on OpenAI's Whisper speech recognition model. It can convert uploaded audio files into text.

## Features

• Supports uploading of various common audio formats, including WAV, MP3, FLAC, OGG, M4A, WebM, AAC, WMA, AIFF, and CAF.

• Based on the Whisper model, supports recognition of multiple languages

• RESTful API interface, convenient for frontend integration

• Health check endpoint for monitoring service status

## Tech Stack

• Python 3.8+

• FastAPI Web framework

• Whisper speech recognition model

• (Other dependencies)

## Installation and Deployment

1. Clone the code repository

2. (Option A) Using virtual environment:

   a. Create a virtual environment:

      python3 -m venv myvenv

   b. Activate the virtual environment:

      - On Windows: `myvenv\Scripts\activate`

      - On macOS and Linux: `source myvenv/bin/activate`

   c. Before installing dependencies, open the `requirements.txt` file and modify the `tokenizers` line:

      - Find the line starting with `https://files.pythonhosted.org/packages/...`

      - Replace it with the appropriate wheel URL for your system. For example:

        - For Python 3.10 on Linux: use the existing URL

        - For other systems: visit https://pypi.org/project/tokenizers/#files, find the appropriate wheel for your system, and use its URL

   d. Install dependencies: `pip install -r requirements.txt`

   e. Start the API service: `uvicorn app.main:app --reload`

3. (Option B) Using Docker:

   a. Build the Docker image:

      `docker build -t localwhisperapi .`

   b. Run the Docker container:

      `docker run -d -p 8000:8000 localwhisperapi`

4. Visit http://localhost:8000/docs to view the API documentation

## Usage

1. Health Check:

   - GET request to the /ping endpoint

   - Returns a status indicating if the service is healthy

2. Transcription:

   - POST audio files to the /transcribe endpoint

   - Supported audio formats: WAV, MP3, FLAC, OGG, M4A, WebM, AAC, WMA, AIFF, CAF

   - The result returned is a JSON object containing the transcribed text content

Example using curl:

Contributing

Issues and Pull Requests are welcome!