def inference(model_itr, bar):
bar.next()
model_path = Path('05output/predictor_' + str(model_itr) + '.npz')
config_path = Path('recipe/config.json')
config = create_config(config_path)
acoustic_converter = AcousticConverter(config, model_path, gpu=0)
wave = acoustic_converter(voice_path="01input02/music0001_80.wav")
librosa.output.write_wav('inference_output_' + str(model_itr) + '.wav',
wave.wave,
wave.sampling_rate,
norm=True)
def process(p: Path, acoustic_converter: AcousticConverter):
try:
if p.suffix in ['.npy', '.npz']:
fn = glob.glob(str(input_wave_directory / p.stem) + '.*')[0]
p = Path(fn)
wave = acoustic_converter(p)
librosa.output.write_wav(str(output / p.stem) + '.wav', wave.wave, wave.sampling_rate, norm=True)
except:
import traceback
print('error!', str(p))
print(traceback.format_exc())
for model_name in args.model_names:
base_model = model_directory / model_name
config = create_config(base_model / 'config.json')
input_paths = list(sorted([Path(p) for p in glob.glob(str(config.dataset.input_glob))]))
numpy.random.RandomState(config.dataset.seed).shuffle(input_paths)
path_train = input_paths[0]
path_test = input_paths[-1]
if it is not None:
model_path = base_model / 'predictor_{}.npz'.format(it)
else:
model_paths = base_model.glob('predictor_*.npz')
model_path = list(sorted(model_paths, key=extract_number))[-1]
print(model_path)
acoustic_converter = AcousticConverter(config, model_path, gpu=gpu)
output = Path('./output').absolute() / base_model.name
fn = glob.glob(str(input_wave_directory / p.stem) + '.*')[0]
p = Path(fn)
wave = acoustic_converter(p)
librosa.output.write_wav(str(output / p.stem) + '.wav',
wave.wave,
wave.sampling_rate,
norm=True)
except:
import traceback
print('error!', str(p))
print(traceback.format_exc())
for model_name in args.model_names:
base_model = model_directory / model_name
config = create_config(base_model / 'config.json')
input_paths = list(
sorted([Path(p) for p in glob.glob(str(config.dataset.input_glob))]))
numpy.random.RandomState(config.dataset.seed).shuffle(input_paths)
path_train = input_paths[0]
path_test = input_paths[-1]
if it is not None:
model_path = base_model / 'predictor_{}.npz'.format(it)
else:
model_paths = base_model.glob('predictor_*.npz')
model_path = list(sorted(model_paths, key=extract_number))[-1]
print(model_path)
acoustic_converter = AcousticConverter(config, model_path, gpu=gpu)
parser.add_argument('--f0_floor1', type=float, default=71)
parser.add_argument('--f0_ceil1', type=float, default=800)
parser.add_argument('--f0_floor2', type=float, default=71)
parser.add_argument('--f0_ceil2', type=float, default=800)
parser.add_argument('--ignore_feature',
nargs='+',
default=['spectrogram', 'aperiodicity'])
parser.add_argument('--disable_alignment', action='store_true')
parser.add_argument('--enable_overwrite', action='store_true')
arguments = parser.parse_args()
pprint(dir(arguments))
pre_convert = arguments.pre_converter1_config is not None
if pre_convert:
config = create_config(arguments.pre_converter1_config)
pre_converter1 = AcousticConverter(config, arguments.pre_converter1_model)
else:
pre_converter1 = None
def generate_feature(path1, path2):
out1 = Path(arguments.output1_directory, path1.stem + '.npy')
out2 = Path(arguments.output2_directory, path2.stem + '.npy')
if out1.exists() and out2.exists() and not arguments.enable_overwrite:
return
# load wave and padding
wave_file_load_process = WaveFileLoadProcess(
sample_rate=arguments.sample_rate,
top_db=arguments.top_db,
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('harvest-innoise03/predictor_1390000.npz')
config_path = model_base_path / Path('harvest-innoise03/config.json')
config = create_config(config_path)
acoustic_converter = AcousticConverter(config, model_path, gpu=0)
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, gpu=0)
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,
)
def main():
print('model loading...', flush=True)
queue_input_wave = Queue()
queue_output_wave = Queue()
model_path = Path('./trained/harvest-innoise03/predictor_1390000.npz')
config_path = Path('./trained/harvest-innoise03/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,
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,
)
process_converter = Process(target=convert_worker,
kwargs=dict(
config=config,
audio_config=audio_config,
acoustic_converter=acoustic_converter,
super_resolution=super_resolution,
queue_input_wave=queue_input_wave,
queue_output_wave=queue_output_wave,
))
process_converter.start()
signal.signal(signal.SIGINT,
lambda signum, frame: process_converter.terminate())
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)
# 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)
parser.add_argument('--alpha', type=float, default=base_acoustic_feature_param.alpha)
parser.add_argument('--f0_estimating_method', type=str, default=base_acoustic_feature_param.f0_estimating_method)
parser.add_argument('--f0_floor1', type=float, default=71)
parser.add_argument('--f0_ceil1', type=float, default=800)
parser.add_argument('--f0_floor2', type=float, default=71)
parser.add_argument('--f0_ceil2', type=float, default=800)
parser.add_argument('--ignore_feature', nargs='+', default=['spectrogram', 'aperiodicity'])
parser.add_argument('--disable_alignment', action='store_true')
parser.add_argument('--enable_overwrite', action='store_true')
arguments = parser.parse_args()
pprint(dir(arguments))
pre_convert = arguments.pre_converter1_config is not None
if pre_convert:
config = create_config(arguments.pre_converter1_config)
pre_converter1 = AcousticConverter(config, arguments.pre_converter1_model)
else:
pre_converter1 = None
def generate_feature(path1, path2):
out1 = Path(arguments.output1_directory, path1.stem + '.npy')
out2 = Path(arguments.output2_directory, path2.stem + '.npy')
if out1.exists() and out2.exists() and not arguments.enable_overwrite:
return
# load wave and padding
wave_file_load_process = WaveFileLoadProcess(
sample_rate=arguments.sample_rate,
top_db=arguments.top_db,
