| import gradio as gr | |
| from transformers import AutoProcessor, MusicgenForConditionalGeneration | |
| import numpy as np | |
| title = "Musicalization System of Painting Demo" | |
| description = "Pui Ching Middle School: Musicalization System of Painting Demo" | |
| def generate_music(text): | |
| tag2text_checkpoint = "./tag2text_swin_14m.pth" | |
| tag2text_model = tag2text(pretrained=tag2text_checkpoint, image_size=image_size, vit='swin_b').eval().to(device) | |
| res = inference_tag2text(image, tagging_model, specified_tags) | |
| processor = AutoProcessor.from_pretrained("facebook/musicgen-small") | |
| model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small") | |
| inputs = processor( | |
| text=[text], | |
| padding=True, | |
| return_tensors="pt", | |
| ) | |
| audio_values = model.generate(**inputs, max_new_tokens=256) | |
| sampling_rate = model.audio_encoder.config.sampling_rate | |
| target_dtype = np.int16 | |
| max_range = np.iinfo(target_dtype).max | |
| audio_values = audio_values[0, 0].numpy() | |
| return sampling_rate, (audio_values * max_range).astype(np.int16) | |
| iface = gr.Interface( | |
| fn=generate_music, | |
| title=title, | |
| description=description, | |
| inputs=gr.Text(label="Content"), | |
| outputs=gr.Audio(label='Generated Music')) | |
| iface.launch() |