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-### Real Stream Speech-To-Speech based on Hugging Face Open Sources Model GPT-4o 
+# 🎤 Real-Time Speech-to-Speech (S2S) Conversion using Hugging Face Models
+
+**Speech-Ora** is an open-source web app for real-time speech-to-speech conversion, leveraging state-of-the-art models from the Hugging Face ecosystem to create interactions similar to GPT-like conversations.
+
+## 📖 Table of Contents
+* [Overview](#overview)
+  - [System Structure](#system-structure)
+  - [Modularity](#modularity)
+* [Setup Instructions](#setup-instructions)
+* [Usage](#usage)
+  - [Server/Client Approach](#serverclient-approach)
+  - [Local Approach](#local-approach)
+* [Command-Line Usage](#command-line-usage)
+  - [Model Parameters](#model-parameters)
+  - [Generation Parameters](#generation-parameters)
+  - [Key Parameters](#key-parameters)
+* [Docker Setup](#docker-setup)
+
+## Overview
+
+### System Structure
+This repository implements a speech-to-speech Server-Side/Client-side System deign with the following components based Open source model used to built so : 
+
+1. **Voice Activity Detection (VAD)**: Powered by [Silero VAD v5](https://github.com/snakers4/silero-vad).
+2. **Speech to Text (STT)**: Uses Whisper models from the Hugging Face hub.
+3. **Language Model (LM)**: Any Hugging Face instruct model can be used.
+4. **Text to Speech (TTS)**: Uses [Parler-TTS](https://github.com/huggingface/parler-tts) for speech synthesis.
+
+### Modularity
+The pipeline is modular and flexible, allowing customization at each stage:
+- **VAD**: Integrates the [Silero VAD](https://github.com/snakers4/silero-vad).
+- **STT**: Compatible with any Whisper model, including [Distil-Whisper](https://huggingface.co/distil-whisper/distil-large-v3) and multilingual variants.
+- **LM**: Swap language models easily via the Hugging Face model ID.
+- **TTS**: Uses Parler-TTS architecture but supports multiple checkpoints, including multilingual ones.
+
+All components are implemented as independent classes for easy customization.
+
+## Setup Instructions
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/deep-matter/Speech-Ora
+   cd Speech-Ora
+   ```
+
+2. Install the required dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+## Usage
+
+The pipeline supports two primary modes of operation:
+1. **Server/Client Approach**: Models run on a server, and audio input/output are streamed between client and server.
+2. **Local Approach**: Uses a client-server architecture on the same machine using the loopback address.
+
+### Server/Client Approach
+
+1. Start the pipeline on the server:
+   
+
+2. Run the client to send audio input and receive the generated audio:
+  
+
+### Local Approach
+
+Run the pipeline locally by using the loopback address:
+
+
+## Command-Line Usage
+
+The pipeline includes customizable parameters for running the models.
+
+### Model Parameters
+
+- VAD Model: The voice activity detection model.
+- STT Model: Whisper model checkpoint from Hugging Face.
+- LM Model: Any Hugging Face instruct model.
+
+### Generation Parameters
+
+- Sampling Rate: Adjustable sampling rate for audio.
+- Chunk Size: Chunk size for audio processing.
+
+
+
+## Docker Setup
+
+To run the pipeline in a Docker container with GPU access, follow these steps:
+
+ 
+
+### Setup the Environment Set up GPU Access for Docker
+
+here stpes to run docker-Image of full the code run top on Containter . 
+ 
+Before make to have Access into GPU Local host access into your container 
+
+1. Follow the Commands line to setup your own Access Key GPU Locally to used by Docker Image 
+
+```bash
+distribution=$(. /etc/os-release;echo  $ID$VERSION_ID)  
+curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -  
+curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
+```
+2. install Nvidia-Toolkit container
+
+```bash
+sudo apt-get install -y nvidia-container-toolkit
+```
+
+3. Now, configure the Docker daemon to recognize the NVIDIA Container Runtime:
+
+```bash 
+sudo nvidia-ctk runtime configure --runtime=docker
+```
+Now you're ready to run the project in a container with GPU support.
+
+### Build and Run the Docker Container
+
+
+
 
-Speech-Ora open source Web-app for real stream Speech to Seepch using the Open souce to build similar interaction such GPT-4o
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