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PaddleSpeech/paddlespeech/vector/exps/ge2e/speaker_verification_datase...

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
from pathlib import Path
import numpy as np
from paddle.io import BatchSampler
from paddle.io import Dataset
from paddlespeech.vector.exps.ge2e.random_cycle import random_cycle
class MultiSpeakerMelDataset(Dataset):
"""A 2 layer directory thatn contains mel spectrograms in *.npy format.
An Example file structure tree is shown below. We prefer to preprocess
raw datasets and organized them like this.
dataset_root/
speaker1/
utterance1.npy
utterance2.npy
utterance3.npy
speaker2/
utterance1.npy
utterance2.npy
utterance3.npy
"""
def __init__(self, dataset_root: Path):
self.root = Path(dataset_root).expanduser()
speaker_dirs = [f for f in self.root.glob("*") if f.is_dir()]
speaker_utterances = {
speaker_dir: list(speaker_dir.glob("*.npy"))
for speaker_dir in speaker_dirs
}
self.speaker_dirs = speaker_dirs
self.speaker_to_utterances = speaker_utterances
# meta data
self.num_speakers = len(self.speaker_dirs)
self.num_utterances = np.sum(
len(utterances)
for speaker, utterances in self.speaker_to_utterances.items())
def get_example_by_index(self, speaker_index, utterance_index):
speaker_dir = self.speaker_dirs[speaker_index]
fpath = self.speaker_to_utterances[speaker_dir][utterance_index]
return self[fpath]
def __getitem__(self, fpath):
return np.load(fpath)
def __len__(self):
return int(self.num_utterances)
class MultiSpeakerSampler(BatchSampler):
"""A multi-stratal sampler designed for speaker verification task.
First, N speakers from all speakers are sampled randomly. Then, for each
speaker, randomly sample M utterances from their corresponding utterances.
"""
def __init__(self,
dataset: MultiSpeakerMelDataset,
speakers_per_batch: int,
utterances_per_speaker: int):
self._speakers = list(dataset.speaker_dirs)
self._speaker_to_utterances = dataset.speaker_to_utterances
self.speakers_per_batch = speakers_per_batch
self.utterances_per_speaker = utterances_per_speaker
def __iter__(self):
# yield list of Paths
speaker_generator = iter(random_cycle(self._speakers))
speaker_utterances_generator = {
s: iter(random_cycle(us))
for s, us in self._speaker_to_utterances.items()
}
while True:
speakers = []
for _ in range(self.speakers_per_batch):
speakers.append(next(speaker_generator))
utterances = []
for s in speakers:
us = speaker_utterances_generator[s]
for _ in range(self.utterances_per_speaker):
utterances.append(next(us))
yield utterances
class RandomClip(object):
def __init__(self, frames):
self.frames = frames
def __call__(self, spec):
# spec [T, C]
T = spec.shape[0]
start = random.randint(0, T - self.frames)
return spec[start:start + self.frames, :]
class Collate(object):
def __init__(self, num_frames):
self.random_crop = RandomClip(num_frames)
def __call__(self, examples):
frame_clips = [self.random_crop(mel) for mel in examples]
batced_clips = np.stack(frame_clips)
return batced_clips
if __name__ == "__main__":
mydataset = MultiSpeakerMelDataset(
Path("/home/chenfeiyu/datasets/SV2TTS/encoder"))
print(mydataset.get_example_by_index(0, 10))