2 files changed, 87 insertions, 1 deletions
diff --git a/become_yukarin/model/sr_model.py b/become_yukarin/model/sr_model.py
index 64158ca..8f9a34b 100644
--- a/become_yukarin/model/sr_model.py
+++ b/become_yukarin/model/sr_model.py
@@ -111,7 +111,11 @@ class SRDiscriminator(chainer.Chain):
         return h
 
 
+def create_predictor_sr(config: SRModelConfig):
+    return SRPredictor(in_ch=1, out_ch=1)
+
+
 def create_sr(config: SRModelConfig):
-    predictor = SRPredictor(in_ch=1, out_ch=1)
+    predictor = create_predictor_sr()
     discriminator = SRDiscriminator(in_ch=1, out_ch=1)
     return predictor, discriminator
diff --git a/become_yukarin/super_resolution.py b/become_yukarin/super_resolution.py
new file mode 100644
index 0000000..535af57
--- /dev/null
+++ b/become_yukarin/super_resolution.py
@@ -0,0 +1,82 @@
+from functools import partial
+from pathlib import Path
+from typing import Optional
+
+import chainer
+import numpy
+import pyworld
+
+from become_yukarin.config.sr_config import SRConfig
+from become_yukarin.data_struct import AcousticFeature
+from become_yukarin.data_struct import Wave
+from become_yukarin.dataset.dataset import LowHighSpectrogramFeatureLoadProcess
+from become_yukarin.dataset.dataset import LowHighSpectrogramFeatureProcess
+from become_yukarin.dataset.dataset import WaveFileLoadProcess
+from become_yukarin.model.sr_model import create_predictor_sr
+
+
+class SuperResolution(object):
+    def __init__(self, config: SRConfig, model_path: Path):
+        self.config = config
+        self.model_path = model_path
+
+        self.model = model = create_predictor_sr(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=None,
+        )
+        self._low_high_spectrogram_process = LowHighSpectrogramFeatureProcess(
+            frame_period=param.acoustic_feature_param.frame_period,
+            order=param.acoustic_feature_param.order,
+            alpha=param.acoustic_feature_param.alpha,
+        )
+        self._low_high_spectrogram_load_process = LowHighSpectrogramFeatureLoadProcess(
+            validate=True,
+        )
+
+    def convert(self, input: numpy.ndarray) -> numpy.ndarray:
+        converter = partial(chainer.dataset.convert.concat_examples, padding=0)
+        inputs = converter([numpy.log(input)[:, :-1]])
+
+        with chainer.using_config('train', False):
+            out = self.model(inputs).data[0]
+
+        out = out[0]
+        out[:, out.shape[1]] = out[:, -1]
+        return out
+
+    def convert_to_audio(
+            self,
+            input: numpy.ndarray,
+            acoustic_feature: AcousticFeature,
+            sampling_rate: Optional[int] = None,
+    ):
+        out = pyworld.synthesize(
+            f0=acoustic_feature.f0.ravel(),
+            spectrogram=input.astype(numpy.float64),
+            aperiodicity=acoustic_feature.aperiodicity,
+            fs=sampling_rate,
+            frame_period=self._param.acoustic_feature_param.frame_period,
+        )
+        return Wave(out, sampling_rate=sampling_rate)
+
+    def convert_from_audio_path(self, input: Path):
+        input = self._wave_process(str(input), test=True)
+        input = self._low_high_spectrogram_process(input, test=True)
+        return self.convert(input.low)
+
+    def convert_from_feature_path(self, input: Path):
+        input = self._low_high_spectrogram_load_process(input, test=True)
+        return self.convert(input.low)
+
+    def __call__(
+            self,
+            input: numpy.ndarray,
+            acoustic_feature: AcousticFeature,
+            sampling_rate: Optional[int] = None,
+    ):
+        high = self.convert(input)
+        return self.convert_to_audio(high, acoustic_feature=acoustic_feature, sampling_rate=sampling_rate)