132 lines
4.1 KiB
Python
132 lines
4.1 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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from pathlib import Path
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import numpy as np
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from paddle.io import Dataset, BatchSampler
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from random_cycle import random_cycle
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class MultiSpeakerMelDataset(Dataset):
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"""A 2 layer directory thatn contains mel spectrograms in *.npy format.
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An Example file structure tree is shown below. We prefer to preprocess
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raw datasets and organized them like this.
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dataset_root/
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speaker1/
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utterance1.npy
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utterance2.npy
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utterance3.npy
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speaker2/
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utterance1.npy
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utterance2.npy
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utterance3.npy
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"""
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def __init__(self, dataset_root: Path):
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self.root = Path(dataset_root).expanduser()
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speaker_dirs = [f for f in self.root.glob("*") if f.is_dir()]
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speaker_utterances = {
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speaker_dir: list(speaker_dir.glob("*.npy"))
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for speaker_dir in speaker_dirs
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}
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self.speaker_dirs = speaker_dirs
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self.speaker_to_utterances = speaker_utterances
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# meta data
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self.num_speakers = len(self.speaker_dirs)
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self.num_utterances = np.sum(
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len(utterances)
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for speaker, utterances in self.speaker_to_utterances.items())
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def get_example_by_index(self, speaker_index, utterance_index):
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speaker_dir = self.speaker_dirs[speaker_index]
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fpath = self.speaker_to_utterances[speaker_dir][utterance_index]
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return self[fpath]
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def __getitem__(self, fpath):
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return np.load(fpath)
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def __len__(self):
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return int(self.num_utterances)
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class MultiSpeakerSampler(BatchSampler):
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"""A multi-stratal sampler designed for speaker verification task.
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First, N speakers from all speakers are sampled randomly. Then, for each
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speaker, randomly sample M utterances from their corresponding utterances.
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"""
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def __init__(self,
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dataset: MultiSpeakerMelDataset,
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speakers_per_batch: int,
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utterances_per_speaker: int):
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self._speakers = list(dataset.speaker_dirs)
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self._speaker_to_utterances = dataset.speaker_to_utterances
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self.speakers_per_batch = speakers_per_batch
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self.utterances_per_speaker = utterances_per_speaker
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def __iter__(self):
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# yield list of Paths
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speaker_generator = iter(random_cycle(self._speakers))
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speaker_utterances_generator = {
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s: iter(random_cycle(us))
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for s, us in self._speaker_to_utterances.items()
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}
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while True:
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speakers = []
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for _ in range(self.speakers_per_batch):
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speakers.append(next(speaker_generator))
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utterances = []
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for s in speakers:
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us = speaker_utterances_generator[s]
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for _ in range(self.utterances_per_speaker):
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utterances.append(next(us))
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yield utterances
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class RandomClip(object):
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def __init__(self, frames):
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self.frames = frames
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def __call__(self, spec):
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# spec [T, C]
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T = spec.shape[0]
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start = random.randint(0, T - self.frames)
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return spec[start:start + self.frames, :]
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class Collate(object):
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def __init__(self, num_frames):
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self.random_crop = RandomClip(num_frames)
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def __call__(self, examples):
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frame_clips = [self.random_crop(mel) for mel in examples]
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batced_clips = np.stack(frame_clips)
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return batced_clips
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if __name__ == "__main__":
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mydataset = MultiSpeakerMelDataset(
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Path("/home/chenfeiyu/datasets/SV2TTS/encoder"))
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print(mydataset.get_example_by_index(0, 10))
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