Datasets:
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import os
from datasets import load_dataset
from datasets import config
from datasets.utils.py_utils import convert_file_size_to_int
from datasets.table import embed_table_storage
from tqdm import tqdm
def build_parquet(split):
# Source: https://discuss.huggingface.co/t/how-to-save-audio-dataset-with-parquet-format-on-disk/66179
dataset = load_dataset("./src/LADaS.py", split=split, trust_remote_code=True)
max_shard_size = '500MB'
dataset_nbytes = dataset._estimate_nbytes()
max_shard_size = convert_file_size_to_int(max_shard_size or config.MAX_SHARD_SIZE)
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
shards = (dataset.shard(num_shards=num_shards, index=i, contiguous=True) for i in range(num_shards))
def shards_with_embedded_external_files(shards):
for shard in shards:
format = shard.format
shard = shard.with_format("arrow")
shard = shard.map(
embed_table_storage,
batched=True,
batch_size=1000,
keep_in_memory=True,
)
shard = shard.with_format(**format)
yield shard
shards = shards_with_embedded_external_files(shards)
os.makedirs("data", exist_ok=True)
for index, shard in tqdm(
enumerate(shards),
desc="Save the dataset shards",
total=num_shards,
):
shard_path = f"data/{split}-{index:05d}-of-{num_shards:05d}.parquet"
shard.to_parquet(shard_path)
if __name__ == "__main__":
build_parquet("train")
build_parquet("validation")
build_parquet("test")
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