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import json
import os
import typing
from abc import ABCMeta, abstractmethod
from typing import NamedTuple
import nnmnkwii.preprocessing
import chainer
import librosa
import numpy
import pysptk
import pyworld
class Wave(NamedTuple):
wave: numpy.ndarray
sampling_rate: int
class AcousticFeature(NamedTuple):
f0: numpy.ndarray
spectrogram: numpy.ndarray
aperiodicity: numpy.ndarray
mfcc: numpy.ndarray
class BaseDataProcess(metaclass=ABCMeta):
@abstractmethod
def __call__(self, data, test):
pass
class ChainProcess(BaseDataProcess):
def __init__(self, process: typing.Iterable[BaseDataProcess]):
self._process = process
def __call__(self, data, test):
for p in self._process:
data = p(data, test)
return data
class SplitProcess(BaseDataProcess):
def __init__(self, process: typing.Dict[str, typing.Optional[BaseDataProcess]]):
self._process = process
def __call__(self, data, test):
data = {
k: p(data, test) if p is not None else data
for k, p in self._process.items()
}
return data
class DataProcessDataset(chainer.dataset.DatasetMixin):
def __init__(self, data: typing.List, data_process: BaseDataProcess, test):
self._data = data
self._data_process = data_process
self._test = test
def __len__(self):
return len(self._data)
def get_example(self, i):
return self._data_process(data=self._data[i], test=self._test)
class WaveFileLoadProcess(BaseDataProcess):
def __init__(self, sample_rate: int, top_db: float):
self._sample_rate = sample_rate
self._top_db = top_db
def __call__(self, data: str, test):
wave = librosa.core.load(data, sr=self._sample_rate)[0]
wave = librosa.effects.remix(wave, intervals=librosa.effects.split(wave, top_db=self._top_db))
return Wave(wave, self._sample_rate)
class AcousticFeatureProcess(BaseDataProcess):
def __init__(self, frame_period, order, alpha):
self._frame_period = frame_period
self._order = order
self._alpha = alpha
def __call__(self, data: Wave, test):
x = data.wave.astype(numpy.float64)
fs = data.sampling_rate
_f0, t = pyworld.dio(x, fs, frame_period=self._frame_period)
f0 = pyworld.stonemask(x, _f0, t, fs)
spectrogram = pyworld.cheaptrick(x, f0, t, fs)
aperiodicity = pyworld.d4c(x, f0, t, fs)
mfcc = pysptk.sp2mc(spectrogram, order=self._order, alpha=self._alpha)
return AcousticFeature(
f0=f0,
spectrogram=spectrogram,
aperiodicity=aperiodicity,
mfcc=mfcc,
)
# data_process = ChainProcess([
# SplitProcess(dict(
# input=ChainProcess([
# WaveFileLoadProcess(),
# AcousticFeatureProcess(),
# ]),
# tareget=ChainProcess([
# WaveFileLoadProcess(),
# AcousticFeatureProcess(),
# ]),
# )),
#
# PILImageProcess(mode='RGB'),
# RandomFlipImageProcess(p_flip_horizontal=0.5, p_flip_vertical=0),
# RandomResizeImageProcess(min_short=128, max_short=160),
# RandomCropImageProcess(crop_width=128, crop_height=128),
# RgbImageArrayProcess(),
# SplitProcess({
# 'target': None,
# 'raw_line': RawLineImageArrayProcess(),
# })
# ])
#
#
# def choose(config: DatasetConfig):
# if config.images_glob is not None:
# import glob
# paths = glob.glob(config.images_glob)
# paths = data_filter(
# datas=paths,
# keys=list(map(lambda p: os.path.basename(p), paths)),
# filter_func=filter_image,
# num_process=None,
# cache_path=config.cache_path,
# )
# paths = list(paths)
# else:
# paths = json.load(open(config.images_list))
#
# num_test = config.num_test
# train_paths = paths[num_test:]
# test_paths = paths[:num_test]
# train_for_evaluate_paths = train_paths[:num_test]
#
# return {
# 'train': DataProcessDataset(train_paths, data_process, test=False),
# 'test': DataProcessDataset(test_paths, data_process, test=True),
# 'train_eval': DataProcessDataset(train_for_evaluate_paths, data_process, test=True),
# }
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