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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,
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)
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