リアルタイム機能を切り分け

1 files changed, 0 insertions, 241 deletions

diff --git a/scripts/realtime_voice_changer.py b/scripts/realtime_voice_changer.py
deleted file mode 100644
index bda64dd..0000000
--- a/scripts/realtime_voice_changer.py
+++ /dev/null
@@ -1,241 +0,0 @@
-import librosa
-import world4py
-
-world4py._WORLD_LIBRARY_PATH = 'x64_world.dll'
-
-from pathlib import Path
-from typing import NamedTuple
-from multiprocessing import Queue
-from multiprocessing import Process
-
-import numpy
-import pyaudio
-
-from become_yukarin import AcousticConverter
-from become_yukarin import Vocoder
-from become_yukarin import RealtimeVocoder
-from become_yukarin import SuperResolution
-from become_yukarin import VoiceChanger
-from become_yukarin.config.config import Config
-from become_yukarin.config.config import create_from_json as create_config
-from become_yukarin.config.sr_config import create_from_json as create_sr_config
-from become_yukarin.data_struct import Wave
-from become_yukarin.data_struct import AcousticFeature
-from become_yukarin.voice_changer import VoiceChangerStream
-from become_yukarin.voice_changer import VoiceChangerStreamWrapper
-
-
-class AudioConfig(NamedTuple):
-    rate: int
-    frame_period: float
-    audio_chunk: int
-    convert_chunk: int
-    vocoder_buffer_size: int
-    out_norm: float
-    silent_threshold: float
-
-
-def encode_worker(
-        config: Config,
-        wrapper: VoiceChangerStreamWrapper,
-        audio_config: AudioConfig,
-        queue_input: Queue,
-        queue_output: Queue,
-):
-    wrapper.voice_changer_stream.vocoder = Vocoder(
-        acoustic_feature_param=config.dataset.param.acoustic_feature_param,
-        out_sampling_rate=audio_config.rate,
-    )
-
-    start_time = 0
-    time_length = audio_config.convert_chunk / audio_config.rate
-
-    while True:
-        wave = queue_input.get()
-
-        w = Wave(wave=wave, sampling_rate=audio_config.rate)
-        wrapper.voice_changer_stream.add_wave(start_time=start_time, wave=w)
-        start_time += time_length
-
-        feature = wrapper.pre_convert_next(time_length=time_length)
-        queue_output.put(feature)
-
-
-def convert_worker(
-        config: Config,
-        wrapper: VoiceChangerStreamWrapper,
-        acoustic_converter: AcousticConverter,
-        super_resolution: SuperResolution,
-        audio_config: AudioConfig,
-        queue_input: Queue,
-        queue_output: Queue,
-):
-    wrapper.voice_changer_stream.voice_changer = VoiceChanger(
-        super_resolution=super_resolution,
-        acoustic_converter=acoustic_converter,
-    )
-
-    start_time = 0
-    time_length = audio_config.convert_chunk / audio_config.rate
-    while True:
-        in_feature: AcousticFeature = queue_input.get()
-        wrapper.voice_changer_stream.add_in_feature(
-            start_time=start_time,
-            feature=in_feature,
-            frame_period=audio_config.frame_period,
-        )
-        start_time += time_length
-
-        out_feature = wrapper.convert_next(time_length=time_length)
-        queue_output.put(out_feature)
-
-
-def decode_worker(
-        config: Config,
-        wrapper: VoiceChangerStreamWrapper,
-        audio_config: AudioConfig,
-        queue_input: Queue,
-        queue_output: Queue,
-):
-    wrapper.voice_changer_stream.vocoder = RealtimeVocoder(
-        acoustic_feature_param=config.dataset.param.acoustic_feature_param,
-        out_sampling_rate=audio_config.rate,
-        buffer_size=audio_config.vocoder_buffer_size,
-        number_of_pointers=16,
-    )
-    # vocoder.warm_up(audio_config.vocoder_buffer_size / config.dataset.param.voice_param.sample_rate)
-
-    start_time = 0
-    time_length = audio_config.convert_chunk / audio_config.rate
-    wave_fragment = numpy.empty(0)
-    while True:
-        feature: AcousticFeature = queue_input.get()
-        wrapper.voice_changer_stream.add_out_feature(
-            start_time=start_time,
-            feature=feature,
-            frame_period=audio_config.frame_period,
-        )
-        start_time += time_length
-
-        wave = wrapper.post_convert_next(time_length=time_length).wave
-
-        wave_fragment = numpy.concatenate([wave_fragment, wave])
-        if len(wave_fragment) >= audio_config.audio_chunk:
-            wave, wave_fragment = wave_fragment[:audio_config.audio_chunk], wave_fragment[audio_config.audio_chunk:]
-
-            power = librosa.core.power_to_db(numpy.abs(librosa.stft(wave)) ** 2).mean()
-            if power >= audio_config.silent_threshold:
-                queue_output.put(wave)
-
-
-def main():
-    print('model loading...', flush=True)
-
-    queue_input_wave = Queue()
-    queue_input_feature = Queue()
-    queue_output_feature = Queue()
-    queue_output_wave = Queue()
-
-    model_path = Path('./trained/pp-weakD-innoise01-tarnoise001/predictor_120000.npz')
-    config_path = Path('./trained/pp-weakD-innoise01-tarnoise001/config.json')
-    config = create_config(config_path)
-    acoustic_converter = AcousticConverter(config, model_path, gpu=0)
-    print('model 1 loaded!', flush=True)
-
-    model_path = Path('./trained/sr-noise3/predictor_180000.npz')
-    config_path = Path('./trained/sr-noise3/config.json')
-    sr_config = create_sr_config(config_path)
-    super_resolution = SuperResolution(sr_config, model_path, gpu=0)
-    print('model 2 loaded!', flush=True)
-
-    audio_instance = pyaudio.PyAudio()
-    audio_config = AudioConfig(
-        rate=config.dataset.param.voice_param.sample_rate,
-        frame_period=config.dataset.param.acoustic_feature_param.frame_period,
-        audio_chunk=config.dataset.param.voice_param.sample_rate,
-        convert_chunk=config.dataset.param.voice_param.sample_rate,
-        vocoder_buffer_size=config.dataset.param.voice_param.sample_rate // 16,
-        out_norm=2.5,
-        silent_threshold=-99.0,
-    )
-
-    voice_changer_stream = VoiceChangerStream(
-        sampling_rate=audio_config.rate,
-        frame_period=config.dataset.param.acoustic_feature_param.frame_period,
-        order=config.dataset.param.acoustic_feature_param.order,
-        in_dtype=numpy.float32,
-    )
-
-    wrapper = VoiceChangerStreamWrapper(
-        voice_changer_stream=voice_changer_stream,
-        extra_time_pre=0.2,
-        extra_time=0.1,
-    )
-
-    process_encoder = Process(target=encode_worker, kwargs=dict(
-        config=config,
-        wrapper=wrapper,
-        audio_config=audio_config,
-        queue_input=queue_input_wave,
-        queue_output=queue_input_feature,
-    ))
-    process_encoder.start()
-
-    process_converter = Process(target=convert_worker, kwargs=dict(
-        config=config,
-        wrapper=wrapper,
-        acoustic_converter=acoustic_converter,
-        super_resolution=super_resolution,
-        audio_config=audio_config,
-        queue_input=queue_input_feature,
-        queue_output=queue_output_feature,
-    ))
-    process_converter.start()
-
-    process_decoder = Process(target=decode_worker, kwargs=dict(
-        config=config,
-        wrapper=wrapper,
-        audio_config=audio_config,
-        queue_input=queue_output_feature,
-        queue_output=queue_output_wave,
-    ))
-    process_decoder.start()
-
-    audio_stream = audio_instance.open(
-        format=pyaudio.paFloat32,
-        channels=1,
-        rate=audio_config.rate,
-        frames_per_buffer=audio_config.audio_chunk,
-        input=True,
-        output=True,
-    )
-
-    # process_converter.join()
-
-    while True:
-        # input audio
-        in_data = audio_stream.read(audio_config.audio_chunk)
-        wave = numpy.fromstring(in_data, dtype=numpy.float32)
-        print('input', len(wave), flush=True)
-        queue_input_wave.put(wave)
-
-        print('queue_input_wave', queue_input_wave.qsize(), flush=True)
-        print('queue_input_feature', queue_input_feature.qsize(), flush=True)
-        print('queue_output_feature', queue_output_feature.qsize(), flush=True)
-        print('queue_output_wave', queue_output_wave.qsize(), flush=True)
-
-        # output
-        try:
-            wave = queue_output_wave.get_nowait()
-        except:
-            wave = None
-
-        if wave is not None:
-            print('output', len(wave), flush=True)
-            wave *= audio_config.out_norm
-            b = wave.astype(numpy.float32).tobytes()
-            audio_stream.write(b)
-
-
-if __name__ == '__main__':
-    main()