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