リアルタイム機能を切り分け

1 files changed, 0 insertions, 252 deletions

diff --git a/become_yukarin/voice_changer.py b/become_yukarin/voice_changer.py
index 7f7bbe4..698244c 100644
--- a/become_yukarin/voice_changer.py
+++ b/become_yukarin/voice_changer.py
@@ -1,15 +1,8 @@
-from abc import ABCMeta, abstractproperty, abstractmethod
-from typing import List, Callable, Any
-from typing import NamedTuple
-
 import numpy
 
-from become_yukarin.param import Param
 from .acoustic_converter import AcousticConverter
 from .data_struct import AcousticFeature
-from .data_struct import Wave
 from .super_resolution import SuperResolution
-from .vocoder import Vocoder
 
 
 class VoiceChanger(object):
@@ -31,248 +24,3 @@ class VoiceChanger(object):
         s_high = self.super_resolution.convert(f_low.spectrogram.astype(numpy.float32))
         f_high = self.super_resolution.convert_to_feature(s_high, f_low)
         return f_high
-
-
-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
-
-    @property
-    def time_length(self):
-        return len(self.wave.wave) / self.wave.sampling_rate
-
-    @property
-    def end_time(self):
-        return self.time_length + self.start_time
-
-
-class VoiceChangerStream(object):
-    def __init__(
-            self,
-            sampling_rate: int,
-            frame_period: float,
-            order: int,
-            in_dtype=numpy.float32,
-    ):
-        self.sampling_rate = sampling_rate
-        self.frame_period = frame_period
-        self.order = order
-        self.in_dtype = in_dtype
-
-        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 = WaveSegment(start_time=start_time, wave=wave)
-        self._data_stream.append(segment)
-
-    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
-
-        end_time = start_time + time_length
-        buffer_list = []
-        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:
-                length = int((segment.start_time - start_time_buffer) * rate)
-                pad = pad_function(length)
-                buffer_list.append(pad)
-                start_time_buffer = segment.start_time
-
-            if remaining_time > segment.end_time - start_time_buffer:
-                one_time_length = segment.end_time - start_time_buffer
-            else:
-                one_time_length = remaining_time
-
-            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
-            remaining_time -= one_time_length
-
-            if start_time_buffer >= end_time:
-                break
-        else:
-            # last padding
-            length = int((end_time - start_time_buffer) * rate)
-            pad = pad_function(length)
-            buffer_list.append(pad)
-
-        buffer = concat_function(buffer_list)
-        return buffer
-
-    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,
-        )
-        in_wave = Wave(wave=wave, sampling_rate=self.sampling_rate)
-        in_feature = self.vocoder.encode(in_wave)
-
-        pad = int(extra_time / (self.vocoder.acoustic_feature_param.frame_period / 1000))
-        in_feature = in_feature.pick(pad, -pad)
-        return in_feature
-
-    def convert(self, start_time: float, time_length: float, extra_time: float):
-        sizes = AcousticFeature.get_sizes(sampling_rate=self.sampling_rate, order=self.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)
-
-        pad = int(extra_time * 1000 / self.frame_period)
-        out_feature = out_feature.pick(pad, -pad)
-        return out_feature
-
-    def post_convert(self, start_time: float, time_length: float):
-        sizes = AcousticFeature.get_sizes(sampling_rate=self.sampling_rate, order=self.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=out_feature,
-        )
-        return out_wave
-
-
-class VoiceChangerStreamWrapper(object):
-    def __init__(
-            self,
-            voice_changer_stream: VoiceChangerStream,
-            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_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(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)