56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
from models.encoder.data_objects.random_cycler import RandomCycler
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from models.encoder.data_objects.speaker_batch import SpeakerBatch
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from models.encoder.data_objects.speaker import Speaker
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from models.encoder.params_data import partials_n_frames
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from torch.utils.data import Dataset, DataLoader
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from pathlib import Path
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# TODO: improve with a pool of speakers for data efficiency
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class SpeakerVerificationDataset(Dataset):
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def __init__(self, datasets_root: Path):
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self.root = datasets_root
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speaker_dirs = [f for f in self.root.glob("*") if f.is_dir()]
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if len(speaker_dirs) == 0:
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raise Exception("No speakers found. Make sure you are pointing to the directory "
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"containing all preprocessed speaker directories.")
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self.speakers = [Speaker(speaker_dir) for speaker_dir in speaker_dirs]
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self.speaker_cycler = RandomCycler(self.speakers)
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def __len__(self):
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return int(1e10)
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def __getitem__(self, index):
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return next(self.speaker_cycler)
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def get_logs(self):
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log_string = ""
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for log_fpath in self.root.glob("*.txt"):
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with log_fpath.open("r") as log_file:
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log_string += "".join(log_file.readlines())
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return log_string
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class SpeakerVerificationDataLoader(DataLoader):
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def __init__(self, dataset, speakers_per_batch, utterances_per_speaker, sampler=None,
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batch_sampler=None, num_workers=0, pin_memory=False, timeout=0,
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worker_init_fn=None):
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self.utterances_per_speaker = utterances_per_speaker
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super().__init__(
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dataset=dataset,
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batch_size=speakers_per_batch,
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shuffle=False,
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sampler=sampler,
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batch_sampler=batch_sampler,
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num_workers=num_workers,
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collate_fn=self.collate,
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pin_memory=pin_memory,
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drop_last=False,
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timeout=timeout,
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worker_init_fn=worker_init_fn
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)
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def collate(self, speakers):
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return SpeakerBatch(speakers, self.utterances_per_speaker, partials_n_frames)
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