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