app.py

import gradio as gr 
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

def alexa(audio):
    converted_text = speech_to_text(audio)
    generated_text = text_generation(converted_text)
    speech = text_to_speech(generated_text)
    return speech

def speech_to_text(audio):
    audio_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")
    if audio == None:
        raise gr.Error("Please, submit an audio file")
    else:  
        text = audio_to_text(audio)["text"]
    return text

def text_generation(text):
    torch.random.manual_seed(0)

    model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-128k-instruct", 
    torch_dtype="auto", 
    trust_remote_code=True, )
    tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")

    messages = [
        {"role": "user", "content": text}
    ]
    
    generation_args = {
        "max_new_tokens": 500,
        "return_full_text": False,
        "temperature": 0.0,
        "do_sample": False,
    }
    
    text_gen= pipeline("text-generation", model="microsoft/Phi-3-mini-128k-instruct", trust_remote_code = True)
    response = text_gen(messages, **generation_args)
    return response[0]["generated_text"]

def text_to_speech(text):
    text_to_audio = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
    narrated_text = text_to_audio(text)
    return (narrated_text["sampling_rate"], narrated_text["audio"][0] )


gr.Interface(
  fn=alexa,
  inputs=gr.Audio(type="filepath"),
  outputs=[gr.Audio(label="Narration", type="numpy",  autoplay=True)], live=True).launch().share=True