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https://github.com/askrella/speech-rest-api
Transcription and TTS Rest API (OpenAI Whisper, Speechbrain)
https://github.com/askrella/speech-rest-api
artificial-intelligence openai python3 speech-recognition speech-to-text text-to-speech whisper whisper-ai
Last synced: 22 days ago
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Transcription and TTS Rest API (OpenAI Whisper, Speechbrain)
- Host: GitHub
- URL: https://github.com/askrella/speech-rest-api
- Owner: askrella
- Created: 2023-02-20T18:42:34.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-02-01T20:27:36.000Z (12 months ago)
- Last Synced: 2024-02-08T02:59:53.491Z (12 months ago)
- Topics: artificial-intelligence, openai, python3, speech-recognition, speech-to-text, text-to-speech, whisper, whisper-ai
- Language: Python
- Homepage:
- Size: 31.3 KB
- Stars: 76
- Watchers: 7
- Forks: 22
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
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README
# Speech REST API
This project provides a simple Flask API to transcribe speech from an audio file using the Whisper speech recognition library.
The API loads a pre-trained deep learning model to detect the spoken language and transcribe the speech to text.
It also provides an endpoint to generate speech from text using the Tacotron2 and HiFiGAN models.
## Requirements
- Python 3.9 or later
## Installation
```bash
# Clone the repository
git clone https://github.com/askrella/speech-rest-api.git
# Navigate to the project directory
cd speech-rest-api
# Install ffmpeg (Ubuntu & Debian)
sudo apt update && sudo apt install ffmpeg -y
# Install the dependencies
pip install -r requirements.txt
# (Optional) Set PORT environment variable
export PORT=3000
# Run the REST API
python app.py
```
## Documentation
This endpoint generates speech from a text using the Tacotron2 and HiFiGAN models.
```bash
POST http://localhost:80/tts
```
The request body must be a JSON object with the following field:
- text (required): The text you want to hear spoken in the audio file.
Example:
```json
{
"text": "Hello, I can do some text to speech. Thats awesome!"
}
```
The response is an audio file in WAV format containing the generated speech.
Here's an example curl command that generates speech from a list of input sentences:
```sh
curl -X POST \
-H "Content-Type: application/json" \
--data '{"text": "Hello, how are you? My name is Shubh. It is a pleasure to meet you."}' \
http://localhost:80/tts \
--output output.wav
```
This curl command sends a JSON payload with a list of three input sentences to the /tts endpoint, and saves the resulting audio file to output.wav.
This endpoint transcribes audio files using the Whisper model.
```bash
POST http://localhost:80/transcribe
```
The request body must be a form data object containing an audio file.
Example:
```form
audio=@path/to/your/audio/file.wav
```
The response is a JSON object containing the detected language and the transcribed text.
Example:
```json
{
"language": "en-US",
"text": "Hello, how are you?"
}
```
Here's an example curl command that transcribes an audio file:
```css
curl -X POST \
-F "audio=@path/to/your/audio/file.wav" \
http://localhost:80/transcribe
```
This curl command sends an HTTP POST request to the /transcribe endpoint with a form data containing the audio file. The detected language and transcribed text are returned as a JSON object.