41 lines
1.8 KiB
Python
41 lines
1.8 KiB
Python
from models.encoder.data_objects.random_cycler import RandomCycler
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from models.encoder.data_objects.utterance import Utterance
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from pathlib import Path
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# Contains the set of utterances of a single speaker
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class Speaker:
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def __init__(self, root: Path):
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self.root = root
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self.name = root.name
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self.utterances = None
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self.utterance_cycler = None
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def _load_utterances(self):
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with self.root.joinpath("_sources.txt").open("r") as sources_file:
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sources = [l.split(",") for l in sources_file]
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sources = {frames_fname: wave_fpath for frames_fname, wave_fpath in sources}
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self.utterances = [Utterance(self.root.joinpath(f), w) for f, w in sources.items()]
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self.utterance_cycler = RandomCycler(self.utterances)
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def random_partial(self, count, n_frames):
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"""
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Samples a batch of <count> unique partial utterances from the disk in a way that all
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utterances come up at least once every two cycles and in a random order every time.
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:param count: The number of partial utterances to sample from the set of utterances from
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that speaker. Utterances are guaranteed not to be repeated if <count> is not larger than
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the number of utterances available.
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:param n_frames: The number of frames in the partial utterance.
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:return: A list of tuples (utterance, frames, range) where utterance is an Utterance,
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frames are the frames of the partial utterances and range is the range of the partial
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utterance with regard to the complete utterance.
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"""
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if self.utterances is None:
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self._load_utterances()
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utterances = self.utterance_cycler.sample(count)
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a = [(u,) + u.random_partial(n_frames) for u in utterances]
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return a
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