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#coding: utf-8
import os
import time
import random
import random
import paddle
import paddleaudio
import numpy as np
import soundfile as sf
import paddle.nn.functional as F
from paddle import nn
from paddle.io import DataLoader
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
np.random.seed(1)
random.seed(1)
SPECT_PARAMS = {
"n_fft": 2048,
"win_length": 1200,
"hop_length": 300
}
MEL_PARAMS = {
"n_mels": 80,
"n_fft": 2048,
"win_length": 1200,
"hop_length": 300
}
class MelDataset(paddle.io.Dataset):
def __init__(self,
data_list,
sr=24000,
validation=False,
):
_data_list = [l[:-1].split('|') for l in data_list]
self.data_list = [(path, int(label)) for path, label in _data_list]
self.data_list_per_class = {
target: [(path, label) for path, label in self.data_list if label == target] \
for target in list(set([label for _, label in self.data_list]))}
self.sr = sr
self.to_melspec = paddleaudio.features.MelSpectrogram(**MEL_PARAMS)
self.to_melspec.fbank_matrix[:] = paddle.load(os.path.dirname(__file__) + '/fbank_matrix.pd')['fbank_matrix']
self.mean, self.std = -4, 4
self.validation = validation
self.max_mel_length = 192
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
with paddle.fluid.dygraph.guard(paddle.CPUPlace()):
data = self.data_list[idx]
mel_tensor, label = self._load_data(data)
ref_data = random.choice(self.data_list)
ref_mel_tensor, ref_label = self._load_data(ref_data)
ref2_data = random.choice(self.data_list_per_class[ref_label])
ref2_mel_tensor, _ = self._load_data(ref2_data)
return mel_tensor, label, ref_mel_tensor, ref2_mel_tensor, ref_label
def _load_data(self, path):
wave_tensor, label = self._load_tensor(path)
if not self.validation: # random scale for robustness
random_scale = 0.5 + 0.5 * np.random.random()
wave_tensor = random_scale * wave_tensor
mel_tensor = self.to_melspec(wave_tensor)
mel_tensor = (paddle.log(1e-5 + mel_tensor) - self.mean) / self.std
mel_length = mel_tensor.shape[1]
if mel_length > self.max_mel_length:
random_start = np.random.randint(0, mel_length - self.max_mel_length)
mel_tensor = mel_tensor[:, random_start:random_start + self.max_mel_length]
return mel_tensor, label
def _preprocess(self, wave_tensor, ):
mel_tensor = self.to_melspec(wave_tensor)
mel_tensor = (paddle.log(1e-5 + mel_tensor) - self.mean) / self.std
return mel_tensor
def _load_tensor(self, data):
wave_path, label = data
label = int(label)
wave, sr = sf.read(wave_path)
wave_tensor = paddle.from_numpy(wave).astype(paddle.float32)
return wave_tensor, label
class Collater(object):
"""
Args:
adaptive_batch_size (bool): if true, decrease batch size when long data comes.
"""
def __init__(self, return_wave=False):
self.text_pad_index = 0
self.return_wave = return_wave
self.max_mel_length = 192
self.mel_length_step = 16
self.latent_dim = 16
def __call__(self, batch):
batch_size = len(batch)
nmels = batch[0][0].shape[0]
mels = paddle.zeros((batch_size, nmels, self.max_mel_length)).astype(paddle.float32)
labels = paddle.zeros((batch_size)).astype(paddle.int64)
ref_mels = paddle.zeros((batch_size, nmels, self.max_mel_length)).astype(paddle.float32)
ref2_mels = paddle.zeros((batch_size, nmels, self.max_mel_length)).astype(paddle.float32)
ref_labels = paddle.zeros((batch_size)).astype(paddle.int64)
for bid, (mel, label, ref_mel, ref2_mel, ref_label) in enumerate(batch):
mel_size = mel.shape[1]
mels[bid, :, :mel_size] = mel
ref_mel_size = ref_mel.shape[1]
ref_mels[bid, :, :ref_mel_size] = ref_mel
ref2_mel_size = ref2_mel.shape[1]
ref2_mels[bid, :, :ref2_mel_size] = ref2_mel
labels[bid] = label
ref_labels[bid] = ref_label
z_trg = paddle.randn((batch_size, self.latent_dim))
z_trg2 = paddle.randn((batch_size, self.latent_dim))
mels, ref_mels, ref2_mels = mels.unsqueeze(1), ref_mels.unsqueeze(1), ref2_mels.unsqueeze(1)
return mels, labels, ref_mels, ref2_mels, ref_labels, z_trg, z_trg2
def build_dataloader(path_list,
validation=False,
batch_size=4,
num_workers=1,
collate_config={},
dataset_config={}):
dataset = MelDataset(path_list, validation=validation)
collate_fn = Collater(**collate_config)
data_loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=(not validation),
num_workers=num_workers,
drop_last=(not validation),
collate_fn=collate_fn)
return data_loader
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