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from transformers import AutoProcessor, BlipForConditionalGeneration, AutoTokenizer | |
import librosa | |
import numpy as np | |
import torch | |
import open_clip | |
# Carga el modelo de clasificación de imagen a texto | |
blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
blip_model_large.to(device) | |
##### IMAGE MODEL TO TEXT, MODEL 1 | |
def generate_caption(processor, model, image, tokenizer=None, use_float_16=False): | |
inputs = processor(images=image, return_tensors="pt").to(device) | |
if use_float_16: | |
inputs = inputs.to(torch.float16) | |
generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50) | |
if tokenizer is not None: | |
generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
else: | |
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
return generated_caption | |
def generate_caption_coca(model, transform, image): | |
im = transform(image).unsqueeze(0).to(device) | |
with torch.no_grad(), torch.cuda.amp.autocast(): | |
generated = model.generate(im, seq_len=20) | |
return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "") | |
#####END IMAGE MODEL TO TEXT |