updated README

README.md

@@ -1,22 +1,24 @@
-# Advanced Speech Processing with faster-whisper
+Sure, I'd be happy to help you create a README for your project. A README file is essential for explaining your project,
+how to set it up, and how to use it. Here's a basic template for your project:
 
-Welcome to the advanced speech processing utility leveraging the powerful Whisper large-v2 model for the CTranslate2
-framework. This tool is designed for high-performance speech recognition and processing, supporting a wide array of
-languages and the capability to handle video inputs for slide detection and audio transcription.
+---
 
-## Features
+# Faster Whisper Transcription Service
+
+## Overview
 
-- **Language Support**: Extensive language support covering major global languages for speech recognition tasks.
-- **Video Processing**: Download MP4 files from links and extract audio content for transcription.
-- **Slide Detection**: Detect and sort presentation slides from video lectures or meetings.
-- **Audio Transcription**: Leverage the Whisper large-v2 model to transcribe audio content with high accuracy.
+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.
 
-## Getting Started
+## 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.
 
-To begin using this utility, set up the `WhisperModel` from the `faster_whisper` package with the provided language
-configurations. The `EndpointHandler` class is your main interface for processing the data.
 
-### Example Usage
+## Usage
 
 ```python
 import requests
@@ -24,11 +26,9 @@ import os
 
 # Sample data dict with the link to the video file and the desired language for transcription
 DATA = {
-    "inputs": "<base64_encoded_audio_string>",
-    "link": "<your_mp4_video_link>",
-    "language": "en",  # Choose from supported languages
-    "task": "transcribe",
-    "type": "audio"  # Use "link" for video files
+    "inputs": "<base64_encoded_audio>",
+    "language": "en",
+    "task": "transcribe"
 }
 
 HF_ACCESS_TOKEN = os.environ.get("HF_TRANSCRIPTION_ACCESS_TOKEN")
@@ -45,49 +45,12 @@ print(response)
 # The response will contain transcribed audio and detected slides if a video link was provided
 ```
 
-### Processing Video Files
-
-To process video files, the `process_video` function downloads the MP4 file, extracts the audio, and passes it to the
-Whisper model for transcription. It also utilizes the `Detector` and `SlideSorter` classes to identify and sort
-presentation slides within the video.
+## Logging
 
-### Error Handling
-
-Comprehensive logging and error handling are in place to ensure you're informed of each step's success or failure.
-
-## Installation
-
-Ensure that you have the following dependencies installed:
-
-```plaintext
-opencv-python~=4.8.1.78
-numpy~=1.26.1
-Pillow~=10.0.1
-tqdm~=4.66.1
-requests~=2.31.0
-moviepy~=1.0.3
-scipy~=1.11.3
-```
+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.
 
-Install them using pip with the provided `requirements.txt` file:
-
-```bash
-pip install -r requirements.txt
-```
-
-## Languages Supported
-
-This tool supports a plethora of languages, making it highly versatile for global applications. The full list of
-supported languages can be found in the `language` section of the old README.
-
-## License
-
-This project is available under the MIT license.
-
-## More Information
-
-For more information about the original Whisper large-v2 model, please refer to
-its [model card on Hugging Face](https://huggingface.co/openai/whisper-large-v2).
-
----
+## Deployment
 
+This service is intended for deployment on Hugging Face endpoints. Ensure you follow Hugging Face's guidelines for
+deploying model endpoints.