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import numpy as np
import pandas as pd
from pathlib import Path
from src.datasets.base_dataset import SimpleAudioFakeDataset
class InTheWildDataset(SimpleAudioFakeDataset):
def __init__(
self,
path,
subset="train",
transform=None,
seed=None,
partition_ratio=(0.7, 0.15),
split_strategy="random"
):
super().__init__(subset=subset, transform=transform)
self.path = path
self.read_samples()
self.partition_ratio = partition_ratio
self.seed = seed
def read_samples(self):
path = Path(self.path)
meta_path = path / "meta.csv"
self.samples = pd.read_csv(meta_path)
self.samples["path"] = self.samples["file"].apply(lambda n: str(path / n))
self.samples["file"] = self.samples["file"].apply(lambda n: Path(n).stem)
self.samples["label"] = self.samples["label"].map({"bona-fide": "bonafide", "spoof": "spoof"})
self.samples["attack_type"] = self.samples["label"].map({"bonafide": "-", "spoof": "X"})
self.samples.rename(columns={'file': 'sample_name', 'speaker': 'user_id'}, inplace=True)
def split_samples_per_speaker(self, samples):
speaker_list = pd.Series(samples["user_id"].unique())
speaker_list = speaker_list.sort_values()
speaker_list = speaker_list.sample(frac=1, random_state=self.seed)
speaker_list = list(speaker_list)
p, s = self.partition_ratio
subsets = np.split(speaker_list, [int(p * len(speaker_list)), int((p + s) * len(speaker_list))])
speaker_subset = dict(zip(['train', 'test', 'val'], subsets))[self.subset]
return self.samples[self.samples["user_id"].isin(speaker_subset)]
if __name__ == "__main__":
dataset = InTheWildDataset(
path="../datasets/release_in_the_wild",
subset="val",
seed=242,
split_strategy="per_speaker"
)
print(len(dataset))
print(len(dataset.samples["user_id"].unique()))
print(dataset.samples["user_id"].unique())
print(dataset[0])
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