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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import pipeline
from datasets import load_dataset
import soundfile as sf
import torch
# Initialize the chat model
chat_client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
# Initialize the TTS pipeline
tts_synthesizer = pipeline("text-to-speech", model="Futuresony/Output")
# Load the speaker embeddings dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
def chat_with_tts(message, history, system_message, max_tokens, temperature, top_p):
# Step 1: Generate response using the chat model
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for msg in chat_client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = msg.choices[0].delta.content
response += token
# Step 2: Generate speech using TTS
speech = tts_synthesizer(response, forward_params={"speaker_embeddings": speaker_embedding})
output_file = "generated_speech.wav"
sf.write(output_file, speech["audio"], samplerate=speech["sampling_rate"])
# Update the chat history
history.append((message, response))
# Return both text response, audio file, and updated history
return response, output_file, history
# Create the Gradio interface
demo = gr.Interface(
fn=chat_with_tts,
inputs=[
gr.Textbox(label="User Input", placeholder="Type your message..."),
gr.State([]), # Initialize history as an empty list
gr.Textbox(value="You are a friendly chatbot.", label="System Message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
],
outputs=[
gr.Textbox(label="Generated Response"),
gr.Audio(label="Generated Speech"),
gr.State(), # Add State as an output to update the history
],
title="Chat with TTS",
description="Enter text to chat with an AI chatbot. The chatbot will generate a response, which will also be converted to speech using TTS."
)
if __name__ == "__main__":
demo.launch() |