Update audio.py

audio.py

@@ -1,51 +1,38 @@
+import streamlit as st
 from io import BytesIO
 from urllib.request import urlopen
 import librosa
-from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor, pipeline
+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")
-
-# Initialize TTS engine
 tts_engine = pyttsx3.init()
 
-# Sample conversation with audio input
-conversation = [
-    {"role": "user", "content": [
-        {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/guess_age_gender.wav"},
-    ]},
-    {"role": "assistant", "content": "Yes, the speaker is female and in her twenties."},
-    {"role": "user", "content": [
-        {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/translate_to_chinese.wav"},
-    ]},
-]
+# Streamlit app UI
+st.title("Text-to-Audio App")
+st.text("This app generates audio from text input using Hugging Face models.")
 
-# Preprocess conversation
-text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
-audios = []
-for message in conversation:
-    if isinstance(message["content"], list):
-        for ele in message["content"]:
-            if ele["type"] == "audio":
-                audios.append(librosa.load(
-                    BytesIO(urlopen(ele['audio_url']).read()),
-                    sr=processor.feature_extractor.sampling_rate)[0]
-                )
+# User input
+text_input = st.text_area("Enter some text for the model:")
+if st.button("Generate Audio"):
+    conversation = [{"role": "user", "content": text_input}]
 
-# Prepare model inputs
-inputs = processor(text=text, audios=audios, return_tensors="pt", padding=True)
-inputs.input_ids = inputs.input_ids.to("cuda")
+    # 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):]
+    # 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]
-print("Model Response:", response)
+    # 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()
+    # Convert response to speech
+    tts_engine.say(response)
+    tts_engine.runAndWait()
+    st.success("Audio generated and played!")