| |
| import numpy as np |
| import gradio as gr |
| from transformers import pipeline |
|
|
| |
| tts = pipeline("text-to-speech", model="facebook/mms-tts-eng") |
|
|
| |
| chat = pipeline( |
| "text-generation", |
| model="Qwen/Qwen2.5-0.5B-Instruct", |
| device_map="auto", |
| ) |
|
|
|
|
| def speak(text): |
| """Plain text -> speech.""" |
| out = tts(text) |
| audio = np.squeeze(out["audio"]) |
| return out["sampling_rate"], audio |
|
|
|
|
| def converse(user_message, history): |
| """User text -> LLM reply -> speech of the reply.""" |
| history = history or [] |
| messages = [{"role": "system", "content": "You are a friendly assistant. Keep replies short."}] |
| for u, a in history: |
| messages += [{"role": "user", "content": u}, {"role": "assistant", "content": a}] |
| messages.append({"role": "user", "content": user_message}) |
|
|
| reply = chat(messages, max_new_tokens=150, do_sample=True, temperature=0.7)[0]["generated_text"][-1]["content"] |
| history.append((user_message, reply)) |
| return history, speak(reply), "" |
|
|
|
|
| with gr.Blocks(title="Text to Audio") as demo: |
| gr.Markdown("# Text → Audio") |
|
|
| with gr.Tab("Text to Speech"): |
| txt = gr.Textbox(label="Enter text", lines=3) |
| btn = gr.Button("Generate audio") |
| out_audio = gr.Audio(label="Output") |
| btn.click(speak, txt, out_audio) |
|
|
| with gr.Tab("Voice Conversation"): |
| chatbot = gr.Chatbot(label="Conversation") |
| msg = gr.Textbox(label="Your message") |
| reply_audio = gr.Audio(label="Spoken reply", autoplay=True) |
| msg.submit(converse, [msg, chatbot], [chatbot, reply_audio, msg]) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
