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import typing
from abc import ABCMeta, abstractmethod
from pathlib import Path
from typing import Callable
from typing import Dict
from typing import List
import chainer
import librosa
import numpy
import pysptk
import pyworld
from ..config import DatasetConfig
from ..data_struct import AcousticFeature
from ..data_struct import Wave
class BaseDataProcess(metaclass=ABCMeta):
@abstractmethod
def __call__(self, data, test):
pass
class LambdaProcess(BaseDataProcess):
def __init__(self, process: Callable[[any, bool], any]):
self._process = process
def __call__(self, data, test):
return self._process(data, test)
class DictKeyReplaceProcess(BaseDataProcess):
def __init__(self, key_map: Dict[str, str]):
self._key_map = key_map
def __call__(self, data: Dict[str, any], test):
return {key_after: data[key_before] for key_after, key_before in self._key_map}
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 WaveFileLoadProcess(BaseDataProcess):
def __init__(self, sample_rate: int, top_db: float, dtype=numpy.float32):
self._sample_rate = sample_rate
self._top_db = top_db
self._dtype = dtype
def __call__(self, data: str, test):
wave = librosa.core.load(data, sr=self._sample_rate, dtype=self._dtype)[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, dtype=numpy.float32):
self._frame_period = frame_period
self._order = order
self._alpha = alpha
self._dtype = dtype
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.astype(self._dtype),
spectrogram=spectrogram.astype(self._dtype),
aperiodicity=aperiodicity.astype(self._dtype),
mfcc=mfcc.astype(self._dtype),
)
class AcousticFeatureLoadProcess(BaseDataProcess):
def __init__(self):
pass
def __call__(self, path: Path, test):
d = numpy.load(path).item() # type: dict
return AcousticFeature(
f0=d['f0'],
spectrogram=d['spectrogram'],
aperiodicity=d['aperiodicity'],
mfcc=d['mfcc'],
)
class AcousticFeatureNormalizeProcess(BaseDataProcess):
def __init__(self, mean: AcousticFeature, var: AcousticFeature):
self._mean = mean
self._var = var
def __call__(self, data: AcousticFeature, test):
return AcousticFeature(
f0=(data.f0 - self._mean.f0) / numpy.sqrt(self._var.f0),
spectrogram=(data.spectrogram - self._mean.spectrogram) / numpy.sqrt(self._var.spectrogram),
aperiodicity=(data.aperiodicity - self._mean.aperiodicity) / numpy.sqrt(self._var.aperiodicity),
mfcc=(data.mfcc - self._mean.mfcc) / numpy.sqrt(self._var.mfcc),
)
class AcousticFeatureDenormalizeProcess(BaseDataProcess):
def __init__(self, mean: AcousticFeature, var: AcousticFeature):
self._mean = mean
self._var = var
def __call__(self, data: AcousticFeature, test):
return AcousticFeature(
f0=data.f0 * numpy.sqrt(self._var.f0) + self._mean.f0,
spectrogram=data.spectrogram * numpy.sqrt(self._var.spectrogram) + self._mean.spectrogram,
aperiodicity=data.aperiodicity * numpy.sqrt(self._var.aperiodicity) + self._mean.aperiodicity,
mfcc=data.mfcc * numpy.sqrt(self._var.mfcc) + self._mean.mfcc,
)
class EncodeFeatureProcess(BaseDataProcess):
def __init__(self, targets: List[str]):
self._targets = targets
def __call__(self, data: AcousticFeature, test):
feature = numpy.concatenate([getattr(data, t) for t in self._targets])
feature = feature.T
return feature
class DecodeFeatureProcess(BaseDataProcess):
def __init__(self, targets: List[str]):
self._targets = targets
def __call__(self, data: numpy.ndarray, test):
# TODO: implement for other features
data = data.T
return AcousticFeature(
f0=numpy.nan,
spectrogram=numpy.nan,
aperiodicity=numpy.nan,
mfcc=data,
)
class ShapeAlignProcess(BaseDataProcess):
def __call__(self, data, test):
data1, data2 = data['input'], data['target']
m = max(data1.shape[1], data2.shape[1])
data1 = numpy.pad(data1, ((0, 0), (0, m - data1.shape[1])), mode='constant')
data2 = numpy.pad(data2, ((0, 0), (0, m - data2.shape[1])), mode='constant')
data['input'], data['target'] = data1, data2
return data
class DataProcessDataset(chainer.dataset.DatasetMixin):
def __init__(self, data: typing.List, data_process: BaseDataProcess):
self._data = data
self._data_process = data_process
def __len__(self):
return len(self._data)
def get_example(self, i):
return self._data_process(data=self._data[i], test=not chainer.config.train)
def create(config: DatasetConfig):
import glob
input_paths = list(sorted([Path(p) for p in glob.glob(config.input_glob)]))
target_paths = list(sorted([Path(p) for p in glob.glob(config.target_glob)]))
assert len(input_paths) == len(target_paths)
acoustic_feature_load_process = AcousticFeatureLoadProcess()
input_mean = acoustic_feature_load_process(config.input_mean_path, test=True)
input_var = acoustic_feature_load_process(config.input_var_path, test=True)
target_mean = acoustic_feature_load_process(config.target_mean_path, test=True)
target_var = acoustic_feature_load_process(config.target_var_path, test=True)
# {input_path, target_path}
data_process = ChainProcess([
SplitProcess(dict(
input=ChainProcess([
LambdaProcess(lambda d, test: d['input_path']),
acoustic_feature_load_process,
AcousticFeatureNormalizeProcess(mean=input_mean, var=input_var),
EncodeFeatureProcess(['mfcc']),
]),
target=ChainProcess([
LambdaProcess(lambda d, test: d['target_path']),
acoustic_feature_load_process,
AcousticFeatureNormalizeProcess(mean=target_mean, var=target_var),
EncodeFeatureProcess(['mfcc']),
]),
)),
ShapeAlignProcess(),
])
num_test = config.num_test
pairs = [
dict(input_path=input_path, target_path=target_path)
for input_path, target_path in zip(input_paths, target_paths)
]
numpy.random.RandomState(config.seed).shuffle(pairs)
train_paths = pairs[num_test:]
test_paths = pairs[:num_test]
train_for_evaluate_paths = train_paths[:num_test]
return {
'train': DataProcessDataset(train_paths, data_process),
'test': DataProcessDataset(test_paths, data_process),
'train_eval': DataProcessDataset(train_for_evaluate_paths, data_process),
}
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