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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
from asr import transcribe_auto # ASR function
# Initialize Chat Model
chat_client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
# Initialize Facebook TTS Model
tts_synthesizer = pipeline("text-to-speech", model="Futuresony/Output")
# Load Speaker Embeddings for TTS
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
def speech_to_chat(audio, history, system_message, max_tokens, temperature, top_p):
# Step 1: Transcribe Speech to Text
transcribed_text = transcribe_auto(audio)
# Step 2: Generate Chat Response
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": transcribed_text})
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 3: Convert Chat Response to Speech
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 Chat History
history.append((transcribed_text, response))
# Return transcribed text, chatbot response, generated speech, and updated history
return transcribed_text, response, output_file, history
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("<h2 style='text-align: center;'>Real-time ASR → Chat → TTS</h2>")
with gr.Row():
audio_input = gr.Audio(source="microphone", type="filepath", label="🎤 Speak Here")
transcribed_text_output = gr.Textbox(label="📝 Transcribed Text", interactive=False)
chat_response_output = gr.Textbox(label="🤖 AI Response", interactive=False)
audio_output = gr.Audio(label="🔊 AI Speech Output")
submit_button = gr.Button("🎙️ Speak & Generate Response")
system_msg = gr.Textbox(value="You are a friendly chatbot.", label="System Message")
max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens")
temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
chat_history = gr.State([]) # Store conversation history
submit_button.click(
fn=speech_to_chat,
inputs=[audio_input, chat_history, system_msg, max_tokens, temperature, top_p],
outputs=[transcribed_text_output, chat_response_output, audio_output, chat_history],
)
# Run the App
if __name__ == "__main__":
demo.launch()
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