Further improve and add in advanced implementation with RVC load model stuff with conversion tokenizations as well as other shit.

```
import os
import asyncio
import g4f
from g4f.client import Client
from g4f.Provider import OpenaiChat
import sounddevice as sd
import numpy as np
import torch
from scipy.io.wavfile import write

# Configuration
RVC_MODEL_DIR = r"D:\Download\die wit - Weights.gg"  # Directory containing the RVC model files
RVC_MODEL_CHECKPOINT = "model_checkpoint.pth"       # Replace with your model's checkpoint file name
SAMPLE_RATE = 22050                                 # Replace with your model's sample rate

# Function to load your RVC model
def load_rvc_model(model_dir, checkpoint_file):
    print(f"Loading RVC model from directory: {model_dir}")
    checkpoint_path = os.path.join(model_dir, checkpoint_file)
    
    if not os.path.isfile(checkpoint_path):
        raise FileNotFoundError(f"Model checkpoint not found at {checkpoint_path}")

    # Example: Loading a PyTorch model
    # Replace the following lines with your model's specific loading procedures
    try:
        model = torch.load(checkpoint_path, map_location=torch.device('cpu'))
        model.eval()  # Set the model to evaluation mode
        print("RVC model loaded successfully.")
        return model
    except Exception as e:
        raise RuntimeError(f"Failed to load RVC model: {e}")

# Function to preprocess text input (Adjust according to your model's requirements)
def preprocess_text(text):
    # Placeholder: Tokenization and encoding
    # Replace with actual preprocessing steps required by your RVC model
    tokens = text.split()  # Simple whitespace tokenization
    token_ids = [hash(token) % 10000 for token in tokens]  # Example encoding
    input_tensor = torch.tensor(token_ids).unsqueeze(0).float()  # Shape: (1, sequence_length)
    return input_tensor

# Function to synthesize speech using the RVC model
def synthesize_speech(text, model):
    print(f"Synthesizing speech for: {text}")

    # Preprocess the text input
    input_tensor = preprocess_text(text)

    # Generate audio using the model
    with torch.no_grad():
        try:
            # Replace 'generate_audio' with your model's actual method for synthesis
            audio_tensor = model.generate_audio(input_tensor)  # Example method
        except AttributeError:
            # If your model uses a different method, adjust accordingly
            raise NotImplementedError("The model does not have a 'generate_audio' method.")

    # Convert the audio tensor to a NumPy array
    audio_data = audio_tensor.squeeze().cpu().numpy()

    # Normalize the audio to the range [-1, 1]
    max_val = np.max(np.abs(audio_data))
    if max_val > 0:
        audio_data = audio_data / max_val

    return audio_data

# Function to play audio
def play_audio(audio_data, samplerate=SAMPLE_RATE):
    print("Playing audio...")
    sd.play(audio_data, samplerate=samplerate)
    sd.wait()  # Wait until the sound has finished playing
    print("Audio playback finished.")

# Function to handle chatbot interaction
async def chat_with_bot(model):
    client = Client(provider=OpenaiChat)
    print("Chatbot is ready! Type 'exit' to quit.")

    while True:
        user_input = input("You: ")
        if user_input.strip().lower() == 'exit':
            print("Exiting chatbot.")
            break

        try:
            # Get response from the chatbot
            response = await client.chat.completions.create(
                model='gpt-4',  # Ensure this matches the available model
                messages=[{"role": "user", "content": user_input}]
            )

            bot_message = response.choices[0].message.content.strip()
            print(f"Bot: {bot_message}")

            # Synthesize and play the response
            audio_data = synthesize_speech(bot_message, model)
            play_audio(audio_data)

        except Exception as e:
            print(f"An error occurred: {e}")

# Main function
def main():
    # Load the RVC model
    try:
        rvc_model = load_rvc_model(RVC_MODEL_DIR, RVC_MODEL_CHECKPOINT)
    except Exception as e:
        print(f"Error loading model: {e}")
        return

    # Start the chatbot interaction
    try:
        asyncio.run(chat_with_bot(rvc_model))
    except KeyboardInterrupt:
        print("\nChatbot terminated by user.")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")

if __name__ == "__main__":
    main()