Instructions to use Pierfrancesco/adt-str with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pierfrancesco/adt-str with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Pierfrancesco/adt-str", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,236 Bytes
162fa19 | 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 | from pathlib import Path
def midi_explorer(dataset, path): # path(str)->paths(list)
if dataset == "lakh":
return list(Path(path).glob("*.mid"))
if dataset == "lakh_matched":
return list(Path(path).rglob("*.mid"))
if dataset == "guitarset":
return list(Path(path).glob("*.jams"))
if dataset == "maestro":
files = []
with open(path.split(",")[0] + "/maestro-v3.0.0.csv", "r", encoding="utf-8") as file:
reader = csv.reader(file)
next(reader)
for row in reader:
if row[2] == path.split(",")[1]:
files.append(path.split(",")[0] + "/" + row[4])
return files
if dataset == "slakh":
return list(Path(path).glob("**/MIDI/*.mid"))
if dataset == "molina":
return list(Path(path).glob("*.GroundTruth.txt"))
if dataset == "phenicx":
return list(Path(path).glob("*.mid"))
if dataset == "urmp":
return list(Path(path).rglob("Notes*.txt"))
if dataset == "gaps":
return list(Path(path).glob("*.mid"))
if dataset == "other":
return list(Path(path).glob("*.mid"))
if dataset == "maps":
return list(Path(path).glob("*.txt"))
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