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---
dataset_info:
features:
- name: audio_tokens
sequence: int64
- name: genre_id
dtype: int64
- name: genre
dtype: string
- name: song_id
dtype: int64
splits:
- name: train
num_bytes: 479627928
num_examples: 19909
- name: test
num_bytes: 122306220
num_examples: 5076
download_size: 123311267
dataset_size: 601934148
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
This dataset contains tokenized audio from [lewtun/music_genres](https://huggingface.co/datasets/lewtun/music_genres) using [SemantiCodec](https://arxiv.org/abs/2405.00233) for performing experiments on AR music generation
The following script is used for tokenization
```python
from datasets import load_dataset, Dataset, DatasetDict
model_id = ""
repo_name = ""
user_name = ""
token = ""
cache_dir = "cache"
vocab_size = 4096
dataset = load_dataset(model_id, cache_dir=cache_dir, trust_remote_code=True)
from semanticodec import SemantiCodec
semanticodec = SemantiCodec(token_rate=100, semantic_vocab_size=vocab_size)
import soundfile as sf
from tqdm import tqdm
import math
dd = {
"train": 0,
"test": 0
}
for split in ["train", "test"]:
tkns = []
for idx in tqdm(range(len(dataset[split]))):
sample = dataset[split][idx]["audio"]
array = sample["array"]
sr = sample["sampling_rate"]
sf.write("output.wav", array, sr)
tokens = semanticodec.encode("output.wav").detach().cpu().numpy().flatten()
tkns.append(tokens)
ds = Dataset.from_dict({
"audio_tokens": tkns,
"genre_id": list(dataset[split]["genre_id"]),
"genre": list(dataset[split]["genre"]),
"song_id": list(dataset[split]["song_id"])
})
dd[split] = ds
dd = DatasetDict(dd)
dd.save_to_disk(repo_name)
dd.push_to_hub(f"{user_name}/{repo_name}", token=token)
``` |