File size: 3,357 Bytes
d1d89ce 39d1328 43282ee 39d1328 f754b4d 39d1328 04a31f5 39d1328 d1d89ce 39d1328 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
import spaces
import os
import re
import gradio as gr
import torch
from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration
# Model path and configuration
model_path = "./model"
base_model_id = "Qwen/Qwen2-Audio-7B-Instruct"
# Load the model and processor
def load_model():
# Load the processor from the base model
processor = AutoProcessor.from_pretrained(
base_model_id,
trust_remote_code=True,
)
# Load the base model
model = Qwen2AudioForConditionalGeneration.from_pretrained(
base_model_id,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
model.eval()
return model, processor
# Initialize model and processor
model, processor = load_model()
# Function to extract components from model output
def extract_components(text):
thinking = ""
semantic = ""
answer = ""
# Extract thinking
think_match = re.search(r"<think>(.*?)</think>", text, re.DOTALL)
if think_match:
thinking = think_match.group(1).strip()
# Extract semantic elements
semantic_match = re.search(r"<semantic_elements>(.*?)</semantic_elements>", text, re.DOTALL)
if semantic_match:
semantic = semantic_match.group(1).strip()
# Extract answer
answer_match = re.search(r"<answer>(.*?)</answer>", text, re.DOTALL)
if answer_match:
answer = answer_match.group(1).strip()
return thinking, semantic, answer
@spaces.GPU
def process_audio(audio_file):
# Load and process the audio
sampling_rate = processor.feature_extractor.sampling_rate
# Create conversation format
conversation = [
{"role": "user", "content": [
{"type": "audio", "audio": audio_file},
{"type": "text", "text": "Describe the audio in detail."}
]}
]
# Format the chat
chat_text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
# Process the inputs
inputs = processor(
text=chat_text,
audios=[audio_file],
return_tensors="pt",
sampling_rate=sampling_rate,
).to(model.device)
# Generate the output
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=768,
do_sample=False,
)
# Decode the output
generated_text = processor.tokenizer.decode(outputs[0], skip_special_tokens=False)
assistant_text = generated_text.split("<|im_start|>assistant\n")[-1].split("<|im_end|>")[0].strip()
# Extract components
thinking, semantic, answer = extract_components(assistant_text)
return thinking, semantic, answer
# Create Gradio interface
demo = gr.Interface(
fn=process_audio,
inputs=gr.Audio(type="filepath", label="Upload Audio"),
outputs=[
gr.Textbox(label="Thinking Process", lines=10),
gr.Textbox(label="Semantic Elements", lines=5),
gr.Textbox(label="Answer", lines=5)
],
title="Qwen2Audio Audio Description Demo",
description="Upload an audio file and the model will provide detailed analysis and description.",
examples=[], # Add example files here if available
cache_examples=False,
)
# Launch the app
if __name__ == "__main__":
demo.launch()
|