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import streamlit as st
from io import BytesIO
from urllib.request import urlopen
import librosa
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
import pyttsx3 # For text-to-speech
# Load Qwen2Audio model and processor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct")
model = Qwen2AudioForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct", device_map="auto")
tts_engine = pyttsx3.init()
# Streamlit app UI
st.title("Text-to-Audio App")
st.text("This app generates audio from text input using Hugging Face models.")
# User input
text_input = st.text_area("Enter some text for the model:")
if st.button("Generate Audio"):
conversation = [{"role": "user", "content": text_input}]
# Preprocess conversation
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
inputs = processor(text=text, return_tensors="pt", padding=True)
inputs.input_ids = inputs.input_ids.to("cuda")
# Generate response
generate_ids = model.generate(**inputs, max_length=256)
generate_ids = generate_ids[:, inputs.input_ids.size(1):]
# Decode response
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
st.text(f"Model Response: {response}")
# Convert response to speech
tts_engine.say(response)
tts_engine.runAndWait()
st.success("Audio generated and played!")
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