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import world4py
world4py._WORLD_LIBRARY_PATH = 'x64_world.dll'
from functools import partial
from pathlib import Path
import signal
import time
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
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:]
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,
)
voice_changer_stream = VoiceChangerStream(
sampling_rate=audio_config.rate,
frame_period=config.dataset.param.acoustic_feature_param.frame_period,
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()
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