File size: 1,667 Bytes
b79a71e 8acd0c9 a4bc6fe 8acd0c9 8f3d24a 913390f 8f3d24a abf2043 b79a71e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
---
license: apache-2.0
language:
- en
tags:
- midi
- music
- sentence
- transformers
- embeddings
- midi search
- music search
pretty_name: sourdough
size_categories:
- 1M<n<10M
---
# Sourdough-midi-dataset Sentence Transformers Embeddings
## MIDI files paths/names embeddings for MIDI search
***
### Embeddings are for all files paths/names in [Sourdough-midi-dataset](https://huggingface.co/datasets/BreadAi/Sourdough-midi-dataset) as of 04/07/2025 (Revision cd19431)
***
### Basic use example
#### Install dependencies
```sh
!pip install -U sentence-transformers
!pip install tf-keras
!pip install numpy==1.26.4
!pip install accelerate
!pip install -U tqdm
!pip install -U ipywidgets
```
#### Search for MIDIs
```python
import pickle
import numpy as np
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer("all-mpnet-base-v2")
corpus = pickle.load(open('corpus.pickle', 'rb'))
corpus_embeddings = np.load('corpus-embeddings-all-mpnet-base-v2.npy')
query = ['Hotel California']
query_embedding = model.encode(query, convert_to_tensor=True)
hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=10)
hits = hits[0]
for hit in hits:
print(corpus[hit['corpus_id']], "(Score: {:.4f})".format(hit['score']))
```
####
***
```
@misc {breadai_2025,
author = { {BreadAi} },
title = { Sourdough-midi-dataset (Revision cd19431) },
year = 2025,
url = {\url{https://huggingface.co/datasets/BreadAi/Sourdough-midi-dataset}},
doi = { 10.57967/hf/4743 },
publisher = { Hugging Face }
}
```
***
### Project Los Angeles
### Tegridy Code 2025 |