リアルタイム機能を切り分け

1 files changed, 0 insertions, 114 deletions

diff --git a/tests/test_voice_changer.py b/tests/test_voice_changer.py
deleted file mode 100644
index 9772bbe..0000000
--- a/tests/test_voice_changer.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import world4py
-world4py._WORLD_LIBRARY_PATH = 'x64_world.dll'
-
-
-from pathlib import Path
-from typing import NamedTuple
-
-import librosa
-import numpy
-
-from become_yukarin import AcousticConverter
-from become_yukarin import RealtimeVocoder
-from become_yukarin import SuperResolution
-from become_yukarin import VoiceChanger
-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.voice_changer import VoiceChangerStream
-from become_yukarin.voice_changer import VoiceChangerStreamWrapper
-
-
-class AudioConfig(NamedTuple):
-    rate: int
-    chunk: int
-    vocoder_buffer_size: int
-    out_norm: float
-
-
-model_base_path = Path('~/Github/become-yukarin/trained/').expanduser()
-test_data_path = Path('tests/test-deep-learning-yuduki-yukari.wav')
-test_output_path = Path('output.wav')
-
-print('model loading...', flush=True)
-
-model_path = model_base_path / Path('pp-weakD-innoise01-tarnoise001/predictor_120000.npz')
-config_path = model_base_path / Path('pp-weakD-innoise01-tarnoise001/config.json')
-config = create_config(config_path)
-acoustic_converter = AcousticConverter(config, model_path)
-print('model 1 loaded!', flush=True)
-
-model_path = model_base_path / Path('sr-noise3/predictor_180000.npz')
-config_path = model_base_path / Path('sr-noise3/config.json')
-sr_config = create_sr_config(config_path)
-super_resolution = SuperResolution(sr_config, model_path)
-print('model 2 loaded!', flush=True)
-
-audio_config = AudioConfig(
-    rate=config.dataset.param.voice_param.sample_rate,
-    chunk=config.dataset.param.voice_param.sample_rate,
-    vocoder_buffer_size=config.dataset.param.voice_param.sample_rate // 16,
-    out_norm=4.5,
-)
-frame_period = config.dataset.param.acoustic_feature_param.frame_period
-
-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,
-)
-
-voice_changer = VoiceChanger(
-    super_resolution=super_resolution,
-    acoustic_converter=acoustic_converter,
-)
-
-voice_changer_stream = VoiceChangerStream(
-    sampling_rate=audio_config.rate,
-    frame_period=acoustic_converter._param.acoustic_feature_param.frame_period,
-    order=acoustic_converter._param.acoustic_feature_param.order,
-    in_dtype=numpy.float32,
-)
-
-voice_changer_stream.voice_changer = voice_changer
-voice_changer_stream.vocoder = vocoder
-
-wrapper = VoiceChangerStreamWrapper(
-    voice_changer_stream=voice_changer_stream,
-    extra_time_pre=1,
-    extra_time=0.2,
-)
-
-raw_wave, _ = librosa.load(str(test_data_path), sr=audio_config.rate)
-wave_out_list = []
-
-start_time = 0
-for i in range(0, len(raw_wave), audio_config.chunk):
-    wave_in = Wave(wave=raw_wave[i:i + audio_config.chunk], sampling_rate=audio_config.rate)
-    wrapper.voice_changer_stream.add_wave(start_time=start_time, wave=wave_in)
-    start_time += len(wave_in.wave) / wave_in.sampling_rate
-
-start_time = 0
-for i in range(len(raw_wave) // audio_config.chunk + 1):
-    feature_in = wrapper.pre_convert_next(time_length=audio_config.chunk / audio_config.rate)
-    wrapper.voice_changer_stream.add_in_feature(start_time=start_time, feature=feature_in, frame_period=frame_period)
-    start_time += audio_config.chunk / audio_config.rate
-    print('pre', i, flush=True)
-
-start_time = 0
-for i in range(len(raw_wave) // audio_config.chunk + 1):
-    feature_out = wrapper.convert_next(time_length=audio_config.chunk / audio_config.rate)
-    wrapper.voice_changer_stream.add_out_feature(start_time=start_time, feature=feature_out, frame_period=frame_period)
-    start_time += audio_config.chunk / audio_config.rate
-    print('cent', i, flush=True)
-
-start_time = 0
-for i in range(len(raw_wave) // audio_config.chunk + 1):
-    wave_out = wrapper.post_convert_next(time_length=audio_config.chunk / audio_config.rate)
-    wave_out_list.append(wave_out)
-    start_time += audio_config.chunk / audio_config.rate
-    print('post', i, flush=True)
-
-out_wave = numpy.concatenate([w.wave for w in wave_out_list]).astype(numpy.float32)
-librosa.output.write_wav(str(test_output_path), out_wave, sr=audio_config.rate)