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| import gradio as gr | |
| import requests | |
| from PIL import Image | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| import torch | |
| import soundfile as sf | |
| from diffusers import StableAudioPipeline | |
| import os | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '' | |
| from huggingface_hub import login | |
| from torch.nn.utils.parametrizations import weight_norm | |
| login(token=os.environ["HF_TOKEN"]) | |
| device = torch.device("cpu") | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cpu") | |
| pipe = StableAudioPipeline.from_pretrained("stabilityai/stable-audio-open-1.0") | |
| pipe = pipe.to("cpu") | |
| #img_url = 'https://www.caracteristicass.de/wp-content/uploads/2023/02/imagenes-artisticas.jpg' | |
| class Aspecto(): | |
| pass | |
| screen = Aspecto() | |
| with gr.Blocks(theme=gr.themes.Ocean(primary_hue="pink", neutral_hue="indigo", font=[gr.themes.GoogleFont("Montserrat"), "Playwrite England SemiJoine", "Quicksand"])) as demo: | |
| textbox = gr.Textbox(label="Url") | |
| with gr.Row(): | |
| button = gr.Button("Describir", variant="primary") | |
| clear = gr.Button("Borrar") | |
| output = gr.Textbox(label="Resumen") | |
| output2 = gr.Audio(label="Audio") | |
| def describir(url): | |
| raw_image = Image.open(requests.get(url, stream=True).raw).convert('RGB') | |
| inputs = processor(raw_image, return_tensors="pt").to("cpu") | |
| out = model.generate(**inputs) | |
| leer(processor.decode(out[0], skip_special_tokens=True)) | |
| return processor.decode(out[0], skip_special_tokens=True) | |
| def leer(texto): | |
| prompt = texto | |
| negative_prompt = "Low quality." | |
| # set the seed for generator | |
| generator = torch.Generator("cpu").manual_seed(0) | |
| # run the generation | |
| audio = pipe( | |
| prompt, | |
| negative_prompt=negative_prompt, | |
| num_inference_steps=200, | |
| audio_end_in_s=10.0, | |
| num_waveforms_per_prompt=3, | |
| generator=generator, | |
| ).audios | |
| salida = audio[0].T.float().cpu().numpy() | |
| sf.write("demo.wav", salida, pipe.vae.sampling_rate) | |
| return sf.read("demo.wav") | |
| button.click(describir, [textbox], output, leer, [output], output2) | |
| demo.launch(debug=True) |