Merge pull request #2 from Hiroshiba/harvest-realtime

Harvest realtime

1 files changed, 157 insertions, 62 deletions

diff --git a/become_yukarin/voice_changer.py b/become_yukarin/voice_changer.py
index 5e0eac0..bed155f 100644
--- a/become_yukarin/voice_changer.py
+++ b/become_yukarin/voice_changer.py
@@ -1,4 +1,5 @@
-from typing import List
+from abc import ABCMeta, abstractproperty, abstractmethod
+from typing import List, Callable, Any
 from typing import NamedTuple
 
 import numpy
@@ -15,7 +16,6 @@ class VoiceChanger(object):
             self,
             acoustic_converter: AcousticConverter,
             super_resolution: SuperResolution,
-            vocoder: Vocoder,
             output_sampling_rate: int = None,
     ) -> None:
         if output_sampling_rate is None:
@@ -23,19 +23,8 @@ class VoiceChanger(object):
 
         self.acoustic_converter = acoustic_converter
         self.super_resolution = super_resolution
-        self.vocoder = vocoder
         self.output_sampling_rate = output_sampling_rate
 
-    def convert_from_wave_path(self, wave_path: str):
-        w_in = self.acoustic_converter._wave_process(wave_path)
-        return self.convert_from_wave(w_in)
-
-    def convert_from_wave(self, wave: Wave):
-        f_in = self.acoustic_converter._feature_process(wave)
-        f_high = self.convert_from_acoustic_feature(f_in)
-        wave = self.vocoder.decode(f_high)
-        return wave
-
     def convert_from_acoustic_feature(self, f_in: AcousticFeature):
         f_low = self.acoustic_converter.convert_to_feature(f_in)
         s_high = self.super_resolution.convert(f_low.spectrogram.astype(numpy.float32))
@@ -43,7 +32,35 @@ class VoiceChanger(object):
         return f_high
 
 
-class Segment(NamedTuple):
+class BaseSegment(ABCMeta):
+    start_time: float
+
+    @property
+    @abstractmethod
+    def time_length(self) -> float:
+        pass
+
+    @property
+    @abstractmethod
+    def end_time(self) -> float:
+        pass
+
+
+class FeatureSegment(NamedTuple, BaseSegment):
+    start_time: float
+    feature: AcousticFeature
+    frame_period: float
+
+    @property
+    def time_length(self):
+        return len(self.feature.f0) * self.frame_period / 1000
+
+    @property
+    def end_time(self):
+        return self.time_length + self.start_time
+
+
+class WaveSegment(NamedTuple, BaseSegment):
     start_time: float
     wave: Wave
 
@@ -59,44 +76,73 @@ class Segment(NamedTuple):
 class VoiceChangerStream(object):
     def __init__(
             self,
-            voice_changer: VoiceChanger,
             sampling_rate: int,
+            frame_period: float,
             in_dtype=numpy.float32,
     ):
-        self.voice_changer = voice_changer
         self.sampling_rate = sampling_rate
+        self.frame_period = frame_period
         self.in_dtype = in_dtype
-        self._data_stream = []  # type: List[Segment]
 
-    @property
-    def vocoder(self):
-        return self.voice_changer.vocoder
+        self.voice_changer: VoiceChanger = None
+        self.vocoder: Vocoder = None
+        self._data_stream = []  # type: List[WaveSegment]
+        self._in_feature_stream = []  # type: List[FeatureSegment]
+        self._out_feature_stream = []  # type: List[FeatureSegment]
 
     def add_wave(self, start_time: float, wave: Wave):
         # validation
         assert wave.sampling_rate == self.sampling_rate
         assert wave.wave.dtype == self.in_dtype
 
-        segment = Segment(start_time=start_time, wave=wave)
+        segment = WaveSegment(start_time=start_time, wave=wave)
         self._data_stream.append(segment)
 
-    def remove_wave(self, end_time: float):
+    def add_in_feature(self, start_time: float, feature: AcousticFeature, frame_period: float):
+        # validation
+        assert frame_period == self.frame_period
+        assert feature.f0.dtype == self.in_dtype
+
+        segment = FeatureSegment(start_time=start_time, feature=feature, frame_period=self.frame_period)
+        self._in_feature_stream.append(segment)
+
+    def add_out_feature(self, start_time: float, feature: AcousticFeature, frame_period: float):
+        # validation
+        assert frame_period == self.frame_period
+
+        segment = FeatureSegment(start_time=start_time, feature=feature, frame_period=self.frame_period)
+        self._out_feature_stream.append(segment)
+
+    def remove(self, end_time: float):
         self._data_stream = list(filter(lambda s: s.end_time > end_time, self._data_stream))
+        self._in_feature_stream = list(filter(lambda s: s.end_time > end_time, self._in_feature_stream))
+        self._out_feature_stream = list(filter(lambda s: s.end_time > end_time, self._out_feature_stream))
+
+    @staticmethod
+    def fetch(
+            start_time: float,
+            time_length: float,
+            data_stream: List[BaseSegment],
+            rate: float,
+            pad_function: Callable[[int], Any],
+            pick_function: Callable[[Any, int, int], Any],
+            concat_function: Callable[[List], Any],
+            extra_time: float = 0,
+    ):
+        start_time -= extra_time
+        time_length += extra_time * 2
 
-    def convert_to_feature(self, start_time: float, time_length: float):
         end_time = start_time + time_length
         buffer_list = []
-        stream = filter(lambda s: not (end_time < s.start_time or s.end_time < start_time), self._data_stream)
+        stream = filter(lambda s: not (end_time < s.start_time or s.end_time < start_time), data_stream)
 
         start_time_buffer = start_time
         remaining_time = time_length
         for segment in stream:
             # padding
             if segment.start_time > start_time_buffer:
-                pad = numpy.zeros(
-                    shape=int((segment.start_time - start_time_buffer) * self.sampling_rate),
-                    dtype=self.in_dtype,
-                )
+                length = int((segment.start_time - start_time_buffer) * rate)
+                pad = pad_function(length)
                 buffer_list.append(pad)
                 start_time_buffer = segment.start_time
 
@@ -105,9 +151,9 @@ class VoiceChangerStream(object):
             else:
                 one_time_length = remaining_time
 
-            first_index = int((start_time_buffer - segment.start_time) * self.sampling_rate)
-            last_index = int(first_index + one_time_length * self.sampling_rate)
-            one_buffer = segment.wave.wave[first_index:last_index]
+            first_index = int((start_time_buffer - segment.start_time) * rate)
+            last_index = int(first_index + one_time_length * rate)
+            one_buffer = pick_function(segment, first_index, last_index)
             buffer_list.append(one_buffer)
 
             start_time_buffer += one_time_length
@@ -117,44 +163,67 @@ class VoiceChangerStream(object):
                 break
         else:
             # last padding
-            pad = numpy.zeros(shape=int((end_time - start_time_buffer) * self.sampling_rate), dtype=self.in_dtype)
+            length = int((end_time - start_time_buffer) * rate)
+            pad = pad_function(length)
             buffer_list.append(pad)
 
-        buffer = numpy.concatenate(buffer_list)
-        in_wave = Wave(wave=buffer, sampling_rate=self.sampling_rate)
-        in_feature = self.vocoder.encode(in_wave)
-        out_feature = self.voice_changer.convert_from_acoustic_feature(in_feature)
-        return out_feature
+        buffer = concat_function(buffer_list)
+        return buffer
 
-    def convert(self, start_time: float, time_length: float):
-        feature = self.convert_to_feature(start_time=start_time, time_length=time_length)
-        out_wave = self.vocoder.decode(
-            acoustic_feature=feature,
+    def pre_convert(self, start_time: float, time_length: float, extra_time: float):
+        wave = self.fetch(
+            start_time=start_time,
+            time_length=time_length,
+            extra_time=extra_time,
+            data_stream=self._data_stream,
+            rate=self.sampling_rate,
+            pad_function=lambda length: numpy.zeros(shape=length, dtype=self.in_dtype),
+            pick_function=lambda segment, first, last: segment.wave.wave[first:last],
+            concat_function=numpy.concatenate,
         )
-        return out_wave
+        in_wave = Wave(wave=wave, sampling_rate=self.sampling_rate)
+        in_feature = self.vocoder.encode(in_wave)
 
-    def convert_with_extra_time(self, start_time: float, time_length: float, extra_time: float):
-        """
-        :param extra_time: 音声変換時に余分に使うデータの時間長。ゼロパディングを防ぐ。
-        """
-        frame_period = self.vocoder.acoustic_feature_param.frame_period
+        pad = int(extra_time / (self.vocoder.acoustic_feature_param.frame_period / 1000))
+        in_feature = in_feature.pick(pad, -pad)
+        return in_feature
 
-        start_time -= extra_time
-        time_length += extra_time * 2
+    def convert(self, start_time: float, time_length: float, extra_time: float):
+        order = self.voice_changer.acoustic_converter.config.dataset.param.acoustic_feature_param.order
+        sizes = AcousticFeature.get_sizes(sampling_rate=self.sampling_rate, order=order)
+        keys = ['f0', 'aperiodicity', 'mfcc', 'voiced']
+        in_feature = self.fetch(
+            start_time=start_time,
+            time_length=time_length,
+            extra_time=extra_time,
+            data_stream=self._in_feature_stream,
+            rate=1000 / self.frame_period,
+            pad_function=lambda length: AcousticFeature.silent(length, sizes=sizes, keys=keys),
+            pick_function=lambda segment, first, last: segment.feature.pick(first, last),
+            concat_function=lambda buffers: AcousticFeature.concatenate(buffers, keys=keys),
+        )
+        out_feature = self.voice_changer.convert_from_acoustic_feature(in_feature)
 
-        extra_feature = self.convert_to_feature(start_time=start_time, time_length=time_length)
+        pad = int(extra_time * 1000 / self.frame_period)
+        out_feature = out_feature.pick(pad, -pad)
+        return out_feature
 
-        pad = int(extra_time / (frame_period / 1000))
-        feature = AcousticFeature(
-            f0=extra_feature.f0[pad:-pad],
-            spectrogram=extra_feature.spectrogram[pad:-pad],
-            aperiodicity=extra_feature.aperiodicity[pad:-pad],
-            mfcc=extra_feature.mfcc[pad:-pad],
-            voiced=extra_feature.voiced[pad:-pad],
+    def post_convert(self, start_time: float, time_length: float):
+        order = self.voice_changer.acoustic_converter.config.dataset.param.acoustic_feature_param.order
+        sizes = AcousticFeature.get_sizes(sampling_rate=self.sampling_rate, order=order)
+        keys = ['f0', 'aperiodicity', 'spectrogram', 'voiced']
+        out_feature = self.fetch(
+            start_time=start_time,
+            time_length=time_length,
+            data_stream=self._out_feature_stream,
+            rate=1000 / self.frame_period,
+            pad_function=lambda length: AcousticFeature.silent(length, sizes=sizes, keys=keys),
+            pick_function=lambda segment, first, last: segment.feature.pick(first, last),
+            concat_function=lambda buffers: AcousticFeature.concatenate(buffers, keys=keys),
         )
 
         out_wave = self.vocoder.decode(
-            acoustic_feature=feature,
+            acoustic_feature=out_feature,
         )
         return out_wave
 
@@ -163,20 +232,46 @@ class VoiceChangerStreamWrapper(object):
     def __init__(
             self,
             voice_changer_stream: VoiceChangerStream,
-            extra_time: float = 0.0
+            extra_time_pre: float = 0.0,
+            extra_time: float = 0.0,
     ):
         self.voice_changer_stream = voice_changer_stream
+        self.extra_time_pre = extra_time_pre
         self.extra_time = extra_time
+        self._current_time_pre = 0
         self._current_time = 0
+        self._current_time_post = 0
+
+    def pre_convert_next(self, time_length: float):
+        in_feature = self.voice_changer_stream.pre_convert(
+            start_time=self._current_time_pre,
+            time_length=time_length,
+            extra_time=self.extra_time_pre,
+        )
+        self._current_time_pre += time_length
+        return in_feature
 
     def convert_next(self, time_length: float):
-        out_wave = self.voice_changer_stream.convert_with_extra_time(
+        out_feature = self.voice_changer_stream.convert(
             start_time=self._current_time,
             time_length=time_length,
             extra_time=self.extra_time,
         )
         self._current_time += time_length
+        return out_feature
+
+    def post_convert_next(self, time_length: float):
+        out_wave = self.voice_changer_stream.post_convert(
+            start_time=self._current_time_post,
+            time_length=time_length,
+        )
+        self._current_time_post += time_length
         return out_wave
 
-    def remove_previous_wave(self):
-        self.voice_changer_stream.remove_wave(end_time=self._current_time - self.extra_time)
+    def remove_previous(self):
+        end_time = min(
+            self._current_time_pre - self.extra_time_pre,
+            self._current_time - self.extra_time,
+            self._current_time_post,
+        )
+        self.voice_changer_stream.remove(end_time=end_time)