| # Faster Whisper Transcription Service | |
| ## Overview | |
| This project uses the `faster_whisper` Python package to provide an API endpoint for audio transcription. It utilizes | |
| OpenAI's Whisper model (large-v3) for accurate and efficient speech-to-text conversion. The service is designed to be | |
| deployed on Hugging Face endpoints. | |
| ## Features | |
| - **Efficient Transcription**: Utilizes the large-v3 Whisper model for high-quality transcription. | |
| - **Multilingual Support**: Supports transcription in various languages, with default language set to German (de). | |
| - **Segmented Output**: Returns transcribed text with segment IDs and timestamps for each transcribed segment. | |
| ## Usage | |
| ```python | |
| import requests | |
| import os | |
| # Sample data dict with the link to the video file and the desired language for transcription | |
| DATA = { | |
| "inputs": "<base64_encoded_audio>", | |
| "language": "de", | |
| "task": "transcribe" | |
| } | |
| HF_ACCESS_TOKEN = os.environ.get("HF_TRANSCRIPTION_ACCESS_TOKEN") | |
| API_URL = os.environ.get("HF_TRANSCRIPTION_ENDPOINT") | |
| HEADERS = { | |
| "Authorization": HF_ACCESS_TOKEN, | |
| "Content-Type": "application/json" | |
| } | |
| response = requests.post(API_URL, headers=HEADERS, json=DATA) | |
| print(response) | |
| ``` | |
| ## Logging | |
| Logging is set up to debug level, providing detailed information during the transcription process, including the length | |
| of decoded bytes, the progress of segments being transcribed, and a confirmation once the inference is completed. | |
| ## Deployment | |
| This service is intended for deployment on Hugging Face endpoints. Ensure you follow Hugging Face's guidelines for | |
| deploying model endpoints. | |