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| import gradio as gr | |
| import json | |
| from utils import * | |
| from unidecode import unidecode | |
| from transformers import AutoTokenizer | |
| description = """ | |
| <div> | |
| <a style="display:inline-block" href='https://github.com/microsoft/muzic/tree/main/clamp'><img src='https://img.shields.io/github/stars/microsoft/muzic?style=social' /></a> | |
| <a style='display:inline-block' href='https://ai-muzic.github.io/clamp/'><img src='https://img.shields.io/badge/website-CLaMP-ff69b4.svg' /></a> | |
| <a style="display:inline-block" href="https://huggingface.co/datasets/sander-wood/wikimusictext"><img src="https://img.shields.io/badge/huggingface-dataset-ffcc66.svg"></a> | |
| <a style="display:inline-block" href="https://arxiv.org/pdf/2304.11029.pdf"><img src="https://img.shields.io/badge/arXiv-2304.11029-b31b1b.svg"></a> | |
| <a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/sander-wood/clamp_semantic_music_search?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-md-dark.svg" alt="Duplicate Space"></a> | |
| </div> | |
| ## ℹ️ How to use this demo? | |
| 1. Enter a query in the text box. | |
| 2. Click "Submit" and wait for the result. | |
| 3. It will return the most matching music score from the WikiMusictext dataset (1010 scores in total). | |
| ## ❕Notice | |
| - The text box is case-sensitive. | |
| - You can enter longer text for the text box, but the demo will only use the first 128 tokens. | |
| - The returned results include the title, artist, genre, description, and the score in ABC notation. | |
| - The genre and description may not be accurate, as they are collected from the web. | |
| - The demo is based on CLaMP-S/512, a CLaMP model with 6-layer Transformer text/music encoders and a sequence length of 512. | |
| ## 🔠👉🎵 Semantic Music Search | |
| Semantic search is a technique for retrieving music by open-domain queries, which differs from traditional keyword-based searches that depend on exact matches or meta-information. This involves two steps: 1) extracting music features from all scores in the library, and 2) transforming the query into a text feature. By calculating the similarities between the text feature and the music features, it can efficiently locate the score that best matches the user's query in the library. | |
| """ | |
| examples = [ | |
| "Jazz standard in Minor key with a swing feel.", | |
| "Jazz standard in Major key with a fast tempo.", | |
| "Jazz standard in Blues form with a soulfoul melody.", | |
| "a painting of a starry night with the moon in the sky", | |
| "a green field with a blue sky and clouds", | |
| "a beach with a castle on top of it" | |
| ] | |
| CLAMP_MODEL_NAME = 'sander-wood/clamp-small-512' | |
| QUERY_MODAL = 'text' | |
| KEY_MODAL = 'music' | |
| TOP_N = 1 | |
| TEXT_MODEL_NAME = 'distilroberta-base' | |
| TEXT_LENGTH = 128 | |
| device = torch.device("cpu") | |
| # load CLaMP model | |
| model = CLaMP.from_pretrained(CLAMP_MODEL_NAME) | |
| music_length = model.config.max_length | |
| model = model.to(device) | |
| model.eval() | |
| # initialize patchilizer, tokenizer, and softmax | |
| patchilizer = MusicPatchilizer() | |
| tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_NAME) | |
| softmax = torch.nn.Softmax(dim=1) | |
| def compute_values(Q_e, K_e, t=1): | |
| """ | |
| Compute the values for the attention matrix | |
| Args: | |
| Q_e (torch.Tensor): Query embeddings | |
| K_e (torch.Tensor): Key embeddings | |
| t (float): Temperature for the softmax | |
| Returns: | |
| values (torch.Tensor): Values for the attention matrix | |
| """ | |
| # Normalize the feature representations | |
| Q_e = torch.nn.functional.normalize(Q_e, dim=1) | |
| K_e = torch.nn.functional.normalize(K_e, dim=1) | |
| # Scaled pairwise cosine similarities [1, n] | |
| logits = torch.mm(Q_e, K_e.T) * torch.exp(torch.tensor(t)) | |
| values = softmax(logits) | |
| return values.squeeze() | |
| def encoding_data(data, modal): | |
| """ | |
| Encode the data into ids | |
| Args: | |
| data (list): List of strings | |
| modal (str): "music" or "text" | |
| Returns: | |
| ids_list (list): List of ids | |
| """ | |
| ids_list = [] | |
| if modal=="music": | |
| for item in data: | |
| patches = patchilizer.encode(item, music_length=music_length, add_eos_patch=True) | |
| ids_list.append(torch.tensor(patches).reshape(-1)) | |
| else: | |
| for item in data: | |
| text_encodings = tokenizer(item, | |
| return_tensors='pt', | |
| truncation=True, | |
| max_length=TEXT_LENGTH) | |
| ids_list.append(text_encodings['input_ids'].squeeze(0)) | |
| return ids_list | |
| def get_features(ids_list, modal): | |
| """ | |
| Get the features from the CLaMP model | |
| Args: | |
| ids_list (list): List of ids | |
| modal (str): "music" or "text" | |
| Returns: | |
| features_list (torch.Tensor): Tensor of features with a shape of (batch_size, hidden_size) | |
| """ | |
| features_list = [] | |
| print("Extracting "+modal+" features...") | |
| with torch.no_grad(): | |
| for ids in tqdm(ids_list): | |
| ids = ids.unsqueeze(0) | |
| if modal=="text": | |
| masks = torch.tensor([1]*len(ids[0])).unsqueeze(0) | |
| features = model.text_enc(ids.to(device), attention_mask=masks.to(device))['last_hidden_state'] | |
| features = model.avg_pooling(features, masks) | |
| features = model.text_proj(features) | |
| else: | |
| masks = torch.tensor([1]*(int(len(ids[0])/PATCH_LENGTH))).unsqueeze(0) | |
| features = model.music_enc(ids, masks)['last_hidden_state'] | |
| features = model.avg_pooling(features, masks) | |
| features = model.music_proj(features) | |
| features_list.append(features[0]) | |
| return torch.stack(features_list).to(device) | |
| def semantic_music_search(query): | |
| """ | |
| Semantic music search | |
| Args: | |
| query (str): Query string | |
| Returns: | |
| output (str): Search result | |
| """ | |
| with open(KEY_MODAL+"_key_cache_"+str(music_length)+".pth", 'rb') as f: | |
| key_cache = torch.load(f) | |
| print("\nQuery: "+query+"\n") | |
| # encode query | |
| query_ids = encoding_data([unidecode(query)], QUERY_MODAL) | |
| query_feature = get_features(query_ids, QUERY_MODAL) | |
| key_filenames = key_cache["filenames"] | |
| key_features = key_cache["features"] | |
| # compute values | |
| values = compute_values(query_feature, key_features) | |
| idx = torch.argsort(values)[-1] | |
| filename = key_filenames[idx].split('/')[-1][:-4] | |
| with open("wikimusictext.json", 'r') as f: | |
| wikimusictext = json.load(f) | |
| for item in wikimusictext: | |
| if item['title']==filename: | |
| # output = "Title:\n" + item['title']+'\n\n' | |
| # output += "Artist:\n" + item['artist']+ '\n\n' | |
| # output += "Genre:\n" + item['genre']+ '\n\n' | |
| # output += "Description:\n" + item['text']+ '\n\n' | |
| # output += "ABC notation:\n" + item['music']+ '\n\n' | |
| print("Title: " + item['title']) | |
| print("Artist: " + item['artist']) | |
| print("Genre: " + item['genre']) | |
| print("Description: " + item['text']) | |
| print("ABC notation:\n" + item['music']) | |
| return item["title"], item["artist"], item["genre"], item["text"], item["music"] | |
| output_title = gr.outputs.Textbox(label="Title") | |
| output_artist = gr.outputs.Textbox(label="Artist") | |
| output_genre = gr.outputs.Textbox(label="Genre") | |
| output_description = gr.outputs.Textbox(label="Description") | |
| output_abc = gr.outputs.Textbox(label="ABC notation") | |
| gr.Interface( | |
| fn=semantic_music_search, | |
| inputs=gr.Textbox(lines=2, placeholder="Describe the music you want to search...", label="Query"), | |
| outputs=[output_title, output_artist, output_genre, output_description, output_abc], | |
| title="🗜️ CLaMP: Semantic Music Search", | |
| description=description, | |
| examples=examples).launch() |