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https://github.com/innovatorved/whisper.api
This project provides an API with user level access support to transcribe speech to text using a finetuned and processed Whisper ASR model.
https://github.com/innovatorved/whisper.api
asr hacktoberfest innovatorved transcribe whisper
Last synced: about 1 month ago
JSON representation
This project provides an API with user level access support to transcribe speech to text using a finetuned and processed Whisper ASR model.
- Host: GitHub
- URL: https://github.com/innovatorved/whisper.api
- Owner: innovatorved
- License: mit
- Created: 2023-08-12T19:34:34.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-25T17:51:10.000Z (12 months ago)
- Last Synced: 2024-09-27T06:22:30.194Z (about 1 month ago)
- Topics: asr, hacktoberfest, innovatorved, transcribe, whisper
- Language: Python
- Homepage: https://innovatorved-whisper-api.hf.space/
- Size: 521 KB
- Stars: 863
- Watchers: 14
- Forks: 34
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
---
title: whisper.api
emoji: 😶🌫️
colorFrom: purple
colorTo: gray
sdk: docker
app_file: Dockerfile
app_port: 7860
---
## Whisper API - Speech to Text Transcription
This open source project provides a self-hostable API for speech to text transcription using a finetuned Whisper ASR model. The API allows you to easily convert audio files to text through HTTP requests. Ideal for adding speech recognition capabilities to your applications.
Key features:
- Uses a finetuned Whisper model for accurate speech recognition
- Simple HTTP API for audio file transcription
- User level access with API keys for managing usage
- Self-hostable code for your own speech transcription service
- Quantized model optimization for fast and efficient inference
- Open source implementation for customization and transparency
This repository contains code to deploy the API server along with finetuning and quantizing models. Check out the documentation for getting started!
## Installation
To install the necessary dependencies, run the following command:
```bash
# Install ffmpeg for Audio Processing
sudo apt install ffmpeg
# Install Python Package
pip install -r requirements.txt
```
## Running the Project
To run the project, use the following command:
```bash
uvicorn app.main:app --reload
```
## Get Your token
To get your token, use the following command:
```bash
curl -X 'POST' \
'https://innovatorved-whisper-api.hf.space/api/v1/users/get_token' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"email": "[email protected]",
"password": "password"
}'
```
## Example to Transcribe a File
To upload a file and transcribe it, use the following command:
Note: The token is a dummy token and will not work. Please use the token provided by the admin.
Here are the available models:
- tiny.en
- tiny.en.q5
- base.en.q5
```bash
# Modify the token and audioFilePath
curl -X 'POST' \
'http://localhost:8000/api/v1/transcribe/?model=tiny.en.q5' \
-H 'accept: application/json' \
-H 'Authentication: e9b7658aa93342c492fa64153849c68b8md9uBmaqCwKq4VcgkuBD0G54FmsE8JT' \
-H 'Content-Type: multipart/form-data' \
-F '[email protected];type=audio/wav'
```
## License
[MIT](https://choosealicense.com/licenses/mit/)
## Reference & Credits
- [https://github.com/openai/whisper](https://github.com/openai/whisper)
- [https://openai.com/blog/whisper/](https://openai.com/blog/whisper/)
- [https://github.com/ggerganov/whisper.cpp](https://github.com/ggerganov/whisper.cpp)
## Authors
- [Ved Gupta](https://www.github.com/innovatorved)
## 🚀 About Me
Just try to be a developer!
## Support
For support, email [email protected]