Mingu Kim
89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
import gradio as gr
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from transformers import pipeline
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import edge_tts
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import asyncio
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asr = pipeline("automatic-speech-recognition", model="openai/whisper-base")
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generator = pipeline("text-generation", model="Qwen/Qwen2.5-0.5B-Instruct")
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voice_txt, current_answer = "", ""
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async def text_to_voice(text):
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voice = "ko-KR-InJoonNeural"
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communicate = edge_tts.Communicate(text, voice)
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await communicate.save("answer.mp3")
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def make_voice():
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global current_answer
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if not current_answer:
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return None
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asyncio.run(text_to_voice(current_answer))
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return "answer.mp3"
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def change_txt(file):
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global voice_txt
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result = asr(file, return_timestamps=True)
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voice_txt = result['text']
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return voice_txt
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def question_answer(question):
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"""_summary_
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question 답변 생성 후 리턴
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음성내용 -> 텍스트 변환한거 보내주고, 질문도 보내주고
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Args:
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question (_type_): _description_
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"""
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global voice_txt, current_answer
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if not voice_txt:
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return "음성을 텍스트로 변환한 후 질문하세요."
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prompt = f"""
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다음 음성 내용을 참고해서 질문에 답변하세요
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음성내용 :
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{voice_txt}
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질문 :
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{question}
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답변:
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"""
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result = generator(prompt, max_new_tokens=50, return_full_text = False, do_sample= False, pad_token_id=generator.tokenizer.eos_token_id)
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current_answer = result[0]['generated_text'].strip()
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return current_answer
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with gr.Blocks(title="AI 음성 챗봇") as demo:
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gr.Markdown("## AI 음성 비서")
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with gr.Row():
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with gr.Column(scale=1):
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file = gr.Audio(type="filepath")
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txt_btn = gr.Button("텍스트 변환")
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with gr.Column(scale=1):
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out = gr.Textbox(label = "텍스트 변환", lines=3)
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with gr.Row():
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with gr.Column(scale=1):
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question = gr.Textbox(label="질문하기")
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question_btn = gr.Button("질문하기")
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with gr.Column(scale=1):
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answer = gr.Textbox(label = "answer", placeholder="답변")
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voice_btn = gr.Button("답변 음성 전환")
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with gr.Row():
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audio_output = gr.Audio(label="AI 음성 답변", autoplay=True)
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txt_btn.click(fn=change_txt, inputs=file, outputs=out)
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question_btn.click(fn=question_answer, inputs = question, outputs = answer)
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voice_btn.click(fn=make_voice, outputs= audio_output)
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demo.launch() |