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| import gradio as gr | |
| from transformers import ( | |
| pipeline, | |
| AutoProcessor, | |
| AutoModelForCausalLM, | |
| AutoTokenizer, | |
| set_seed | |
| ) | |
| from datasets import load_dataset | |
| import torch | |
| import numpy as np | |
| # Set seed | |
| set_seed(42) | |
| # Captioning model | |
| caption_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base") | |
| # GPT-2 model for context generation | |
| gpt2_generator = pipeline("text-generation", model="gpt2") | |
| # SpeechT5 for text-to-speech | |
| synthesiser = pipeline("text-to-speech", model="microsoft/speecht5_tts") | |
| # Load Florence-2-base for OCR | |
| ocr_device = "cuda" if torch.cuda.is_available() else "cpu" | |
| ocr_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 | |
| ocr_model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base", torch_dtype=ocr_dtype, trust_remote_code=True).to(ocr_device) | |
| ocr_processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True) | |
| # Load speaker embedding | |
| embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") | |
| speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) | |
| def process_image(image): | |
| try: | |
| # Generate caption | |
| caption = caption_model(image)[0]['generated_text'] | |
| # Extract OCR text | |
| inputs = ocr_processor(text="<OCR>", images=image, return_tensors="pt").to(ocr_device, ocr_dtype) | |
| generated_ids = ocr_model.generate( | |
| input_ids=inputs["input_ids"], | |
| pixel_values=inputs["pixel_values"], | |
| max_new_tokens=1024, | |
| num_beams=3, | |
| do_sample=False | |
| ) | |
| extracted_text = ocr_processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| # Generate context with GPT-2 | |
| prompt = f"Determine the context of this image based on the caption and extracted text. Caption: {caption}. Extracted text: {extracted_text}. Context:" | |
| context_output = gpt2_generator(prompt, max_length=100, num_return_sequences=1) | |
| context = context_output[0]['generated_text'] | |
| # Text-to-speech | |
| speech = synthesiser(context, forward_params={"speaker_embeddings": speaker_embedding}) | |
| audio = np.array(speech["audio"]) | |
| rate = speech["sampling_rate"] | |
| return (rate, audio), caption, extracted_text, context | |
| except Exception as e: | |
| return None, f"Error: {str(e)}", "", "" | |
| # Gradio UI | |
| iface = gr.Interface( | |
| fn=process_image, | |
| inputs=gr.Image(type='pil', label="Upload an Image"), | |
| outputs=[ | |
| gr.Audio(label="Generated Audio"), | |
| gr.Textbox(label="Generated Caption"), | |
| gr.Textbox(label="Extracted Text (OCR)"), | |
| gr.Textbox(label="Generated Context") | |
| ], | |
| title="SeeSay Contextualizer", | |
| description="Upload an image to generate a caption, extract text, create audio from context, and determine the context using GPT-2 and Florence-2-base." | |
| ) | |
| iface.launch() |