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| import os | |
| import argparse | |
| import gradio as gr | |
| from timeit import default_timer as timer | |
| import torch | |
| import numpy as np | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| from model.bart import BartCaptionModel | |
| from utils.audio_utils import load_audio, STR_CH_FIRST | |
| if os.path.isfile("transfer.pth") == False: | |
| torch.hub.download_url_to_file('https://huggingface.co/seungheondoh/lp-music-caps/resolve/main/transfer.pth', 'transfer.pth') | |
| torch.hub.download_url_to_file('https://huggingface.co/seungheondoh/lp-music-caps/resolve/main/folk.wav', 'folk.wav') | |
| torch.hub.download_url_to_file('https://huggingface.co/seungheondoh/lp-music-caps/resolve/main/electronic.mp3', 'electronic.mp3') | |
| torch.hub.download_url_to_file('https://huggingface.co/seungheondoh/lp-music-caps/resolve/main/orchestra.wav', 'orchestra.wav') | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| example_list = ['folk.wav', 'electronic.mp3', 'orchestra.wav'] | |
| model = BartCaptionModel(max_length = 128) | |
| pretrained_object = torch.load('./transfer.pth', map_location='cpu') | |
| state_dict = pretrained_object['state_dict'] | |
| model.load_state_dict(state_dict) | |
| if torch.cuda.is_available(): | |
| torch.cuda.set_device(device) | |
| model = model.cuda(device) | |
| model.eval() | |
| def get_audio(audio_path, duration=10, target_sr=16000): | |
| n_samples = int(duration * target_sr) | |
| audio, sr = load_audio( | |
| path= audio_path, | |
| ch_format= STR_CH_FIRST, | |
| sample_rate= target_sr, | |
| downmix_to_mono= True, | |
| ) | |
| if len(audio.shape) == 2: | |
| audio = audio.mean(0, False) # to mono | |
| input_size = int(n_samples) | |
| if audio.shape[-1] < input_size: # pad sequence | |
| pad = np.zeros(input_size) | |
| pad[: audio.shape[-1]] = audio | |
| audio = pad | |
| ceil = int(audio.shape[-1] // n_samples) | |
| audio = torch.from_numpy(np.stack(np.split(audio[:ceil * n_samples], ceil)).astype('float32')) | |
| return audio | |
| def captioning(audio_path): | |
| audio_tensor = get_audio(audio_path = audio_path) | |
| if torch.cuda.is_available(): | |
| audio_tensor = audio_tensor.to(device) | |
| with torch.no_grad(): | |
| output = model.generate( | |
| samples=audio_tensor, | |
| num_beams=5, | |
| ) | |
| inference = "" | |
| number_of_chunks = range(audio_tensor.shape[0]) | |
| for chunk, text in zip(number_of_chunks, output): | |
| time = f"[{chunk * 10}:00-{(chunk + 1) * 10}:00]" | |
| inference += f"{time}\n{text} \n \n" | |
| return inference | |
| title = "Interactive demo: Music Captioning 🤖🎵" | |
| description = """ | |
| <p style='text-align: center'> LP-MusicCaps: LLM-Based Pseudo Music Captioning</p> | |
| <p style='text-align: center'> SeungHeon Doh, Keunwoo Choi, Jongpil Lee, Juhan Nam, ISMIR 2023</p> | |
| <p style='text-align: center'> <a href='https://arxiv.org/abs/2307.16372' target='_blank'>ArXiv</a> | <a href='https://github.com/seungheondoh/lp-music-caps' target='_blank'>Codes</a> | <a href='https://huggingface.co/datasets/seungheondoh/LP-MusicCaps-MC' target='_blank'>Dataset</a> </p> | |
| <p style='text-align: center'> To use it, simply upload your audio and click 'submit', or click one of the examples to load them. Read more at the links below. </p> | |
| """ | |
| article = "<p style='text-align: center'><a href='https://seungheondoh.github.io/' target='_blank'>Author Info</a> | <a href='https://github.com/seungheondoh' target='_blank'>Github</a></p>" | |
| demo = gr.Interface(fn=captioning, | |
| inputs=gr.Audio(type="filepath"), | |
| outputs=[ | |
| gr.Textbox(label="Caption generated by LP-MusicCaps Transfer Model"), | |
| ], | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article, | |
| cache_examples=False | |
| ) | |
| demo.launch() |