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from functools import partial
from pathlib import Path
from typing import Optional
import chainer
import numpy
import pysptk
import pyworld
from become_yukarin.config import Config
from become_yukarin.data_struct import AcousticFeature
from become_yukarin.data_struct import Wave
from become_yukarin.dataset.dataset import AcousticFeatureDenormalizeProcess
from become_yukarin.dataset.dataset import AcousticFeatureLoadProcess
from become_yukarin.dataset.dataset import AcousticFeatureNormalizeProcess
from become_yukarin.dataset.dataset import AcousticFeatureProcess
from become_yukarin.dataset.dataset import DecodeFeatureProcess
from become_yukarin.dataset.dataset import EncodeFeatureProcess
from become_yukarin.dataset.dataset import WaveFileLoadProcess
from become_yukarin.model import create_predictor
class VoiceChanger(object):
def __init__(self, config: Config, model_path: Path):
self.config = config
self.model_path = model_path
self.model = model = create_predictor(config.model)
chainer.serializers.load_npz(str(model_path), model)
self._param = param = config.dataset.param
self._wave_process = WaveFileLoadProcess(
sample_rate=param.voice_param.sample_rate,
top_db=param.voice_param.top_db,
)
self._feature_process = AcousticFeatureProcess(
frame_period=param.acoustic_feature_param.frame_period,
order=param.acoustic_feature_param.order,
alpha=param.acoustic_feature_param.alpha,
)
_acoustic_feature_load_process = AcousticFeatureLoadProcess()
input_mean = _acoustic_feature_load_process(config.dataset.input_mean_path, test=True)
input_var = _acoustic_feature_load_process(config.dataset.input_var_path, test=True)
target_mean = _acoustic_feature_load_process(config.dataset.target_mean_path, test=True)
target_var = _acoustic_feature_load_process(config.dataset.target_var_path, test=True)
self._feature_normalize = AcousticFeatureNormalizeProcess(
mean=input_mean,
var=input_var,
)
self._feature_denormalize = AcousticFeatureDenormalizeProcess(
mean=target_mean,
var=target_var,
)
feature_sizes = AcousticFeature.get_sizes(
sampling_rate=param.voice_param.sample_rate,
order=param.acoustic_feature_param.order,
)
self._encode_feature = EncodeFeatureProcess(config.dataset.features)
self._decode_feature = DecodeFeatureProcess(config.dataset.features, feature_sizes)
def __call__(self, voice_path: Path, out_sampling_rate: Optional[int] = None):
input = input_wave = self._wave_process(str(voice_path), test=True)
if out_sampling_rate is None:
out_sampling_rate = input_wave.sampling_rate
input = input_feature = self._feature_process(input, test=True)
input = self._feature_normalize(input, test=True)
input = self._encode_feature(input, test=True)
converter = partial(chainer.dataset.convert.concat_examples, padding=0)
inputs = converter([input])
with chainer.using_config('train', False):
out = self.model(inputs).data[0]
out = self._decode_feature(out, test=True)
out = AcousticFeature(
f0=out.f0,
spectrogram=out.spectrogram,
aperiodicity=out.aperiodicity,
mfcc=out.mfcc,
voiced=input_feature.voiced,
)
out = self._feature_denormalize(out, test=True)
out = AcousticFeature(
f0=out.f0,
spectrogram=out.spectrogram,
aperiodicity=input_feature.aperiodicity,
mfcc=out.mfcc,
voiced=out.voiced,
)
fftlen = pyworld.get_cheaptrick_fft_size(input_wave.sampling_rate)
spectrogram = pysptk.mc2sp(
out.mfcc,
alpha=self._param.acoustic_feature_param.alpha,
fftlen=fftlen,
)
out = AcousticFeature(
f0=out.f0,
spectrogram=spectrogram,
aperiodicity=out.aperiodicity,
mfcc=out.mfcc,
voiced=out.voiced,
).astype(numpy.float64)
out = pyworld.synthesize(
f0=out.f0.ravel(),
spectrogram=out.spectrogram,
aperiodicity=out.aperiodicity,
fs=out_sampling_rate,
frame_period=self._param.acoustic_feature_param.frame_period,
)
return Wave(out, sampling_rate=out_sampling_rate)
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