flow.txt

Create a gradio which acts a basic voice assient capable of executing task 
make the applicatio in python and deplyoble on hugging face spaces
use the transformers api from hugging face where every possible 

Start by using fasterwhisper to convert speach into text; do this in chunks
detect if the user stops talking and use senteinet anaylsis to figure if the user stopped to think or asked a question 
if the user aksed a question feed it to chat gpt-oss-120b 
give chat gpt acees to a bunch of get info; get booking dates; create; booking; take order 
get a resoponce from chat gpt and play and conver it audio using Higgs Audio V2 [https://huggingface.co/bosonai/higgs-audio-v2-generation-3B-base]
feed this audio responce back to gradio app; make sure all the models are avible and ready at the same time for real time conversation


Creating a Voice Assistant with Gradio for Hugging Face Spaces

This explanation details how to build a sophisticated voice assistant using Gradio that can be deployed on Hugging Face Spaces. The assistant will provide real-time interaction capabilities with speech-to-text conversion, intelligent conversation analysis, and natural-sounding responses.

## System Architecture

The voice assistant consists of several key components working together:

1. **Speech-to-Text (STT)** - Using Faster Whisper for efficient and accurate transcription
2. **Conversation Analysis** - Sentiment analysis to detect user intent and conversation flow
3. **Natural Language Processing** - GPT model integration for intelligent responses
4. **Text-to-Speech (TTS)** - Higgs Audio V2 for high-quality voice synthesis
5. **User Interface** - Gradio for a clean, accessible web interface

## Detailed Implementation Steps

### 1. Speech-to-Text Processing

The system will use Faster Whisper, an optimized version of OpenAI's Whisper model, to convert user speech to text:

- Implement chunk-based audio processing to handle continuous speech
- Configure the model to process audio in near real-time (16kHz sampling rate)
- Optimize for latency by using a smaller model variant for initial deployment
- Implement adaptive silence detection to identify when a user has finished speaking
- Batch process audio frames to balance accuracy and responsiveness


### 2. Conversation Flow Analysis

The system will use sentiment analysis and conversational cues to determine user intent:

- Implement a pause detection algorithm that analyzes audio for natural breaks
- Use transformer-based sentiment analysis to distinguish between:
  - Questions requiring information
  - Thinking pauses (user contemplating)
  - Statements or commands
- Track conversation context to improve understanding of follow-up queries
- Calculate confidence scores for detected intents to handle ambiguous cases

```

### 3. GPT Model Integration

The system will use a powerful language model to generate contextually relevant responses:

- Integrate with Hugging Face's `gpt-oss-120b` or equivalent open-source large language model
- Provide the model with specialized system prompts for different tasks:
  - Information retrieval (get_info)
  - Booking management (get_booking_dates, create_booking)
  - Order processing (take_order)
- Implement context management to maintain conversation history
- Use function calling capabilities to execute specific tasks based on user requests
- Apply rate limiting and token optimization for efficient resource usage


### 4. Text-to-Speech Synthesis

The system will convert text responses to natural-sounding speech:

- Integrate Higgs Audio V2 model from Hugging Face for high-quality voice synthesis
- Configure voice parameters (pitch, speed, style) for natural conversation
- Implement streaming audio playback to minimize perceived latency
- Cache common responses to improve performance
- Add prosody and emphasis based on sentiment and content type



### 5. Gradio Interface Implementation

The system will use Gradio to create an intuitive, accessible user interface:

- Design a clean interface with audio input and output components
- Implement WebRTC for low-latency audio streaming
- Add visual feedback indicators for system status (listening, processing, speaking)
- Include text display of transcriptions and responses for accessibility
- Provide controls for adjusting voice parameters and conversation settings
- Ensure responsive design for both desktop and mobile use

## Hugging Face Spaces Deployment

To deploy the application on Hugging Face Spaces:

1. Create a `requirements.txt` file with all necessary dependencies:
   - gradio
   - transformers
   - torch
   - faster-whisper
   - numpy
   - scipy
   - ffmpeg-python

3. Implement model caching:
   - Use Hugging Face's model caching mechanisms to improve loading times
   - Implement progressive loading to make the interface available quickly

4. Create a `README.md` with clear usage instructions and capabilities

5. Add a Spaces SDK configuration file to specify resource requirements