def test_ljspeech_loader(self):
print(" ---- Run data loader for 100 iterations ----")
MAX = 10
RF = 11
C = load_config('test_conf.json')
dataset = LJSpeechDataset(os.path.join(C.data_path, "mels", "meta_fftnet.csv"),
os.path.join(C.data_path, "mels"),
C.sample_rate,
C.num_mels, C.num_freq,
C.min_level_db, C.frame_shift_ms,
C.frame_length_ms, C.preemphasis, C.ref_level_db,
RF, C.min_wav_len, C.max_wav_len)
dataloader = DataLoader(dataset, batch_size=2,
shuffle=False, collate_fn=dataset.collate_fn,
drop_last=True, num_workers=2)
count = 0
last_T = 0
for data in dataloader:
wavs = data[0]
mels = data[1]
print(" > iter: ", count)
assert wavs.shape[1] >= last_T
last_T = wavs.shape[1]
assert wavs.shape[1] == mels.shape[1]
assert wavs.shape[0] == mels.shape[0]
assert wavs.shape[1] > RF
assert wavs.max() > 0 and wavs.mean() > 0
count += 1
if count == MAX:
break
parser = argparse.ArgumentParser()
parser.add_argument(
'--config_path',
type=str,
help='path to config file for training',
)
parser.add_argument('--debug',
type=bool,
default=False,
help='Stop asking for git hash before the run.')
parser.add_argument('--finetune_path', type=str)
args = parser.parse_args()
c = load_config(args.config_path)
# setup output paths and read configs
_ = os.path.dirname(os.path.realpath(__file__))
OUT_PATH = os.path.join(_, c.output_path)
OUT_PATH = create_experiment_folder(OUT_PATH, c.model_name, True)
CHECKPOINT_PATH = os.path.join(OUT_PATH, 'checkpoints')
shutil.copyfile(args.config_path, os.path.join(OUT_PATH, 'config.json'))
# setup TensorBoard
tb = SummaryWriter(OUT_PATH)
# create the FFTNet model
model = FFTNetModel(hid_channels=256,
out_channels=256,
n_layers=c.num_quant,
from multiprocessing import Pool
parser = argparse.ArgumentParser()
parser.add_argument('--data_path', type=str,
help='Folder path to checkpoints.')
parser.add_argument('--out_path', type=str,
help='path to config file for training.')
parser.add_argument('--config', type=str,
help='conf.json file for run settings.')
args = parser.parse_args()
DATA_PATH = args.data_path
OUT_PATH = args.out_path
CONFIG = load_config(args.config)
ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.num_freq, CONFIG.min_level_db,
CONFIG.frame_shift_ms, CONFIG.frame_length_ms, CONFIG.preemphasis,
CONFIG.ref_level_db)
def extract_mel(file_path):
x, fs = sf.read(file_path)
mel = ap.melspectrogram(x.astype('float32'))
file_name = os.path.basename(file_path).replace(".wav","")
mel_file = file_name + ".mel"
np.save(os.path.join(OUT_PATH, mel_file), mel, allow_pickle=False)
mel_len = mel.shape[1]
wav_len = x.shape[0]
return file_path, mel_file, str(wav_len), str(mel_len)
glob_path = os.path.join(DATA_PATH, "**/*.wav")
parser = argparse.ArgumentParser()
parser.add_argument('--restore_path',
type=str,
help='Folder path to checkpoints',
default=0)
parser.add_argument(
'--config_path',
type=str,
help='path to config file for training',
)
parser.add_argument('--debug',
type=bool,
default=False,
help='do not ask for git has before run.')
args = parser.parse_args()
C = load_config(args.config)
def train():
pass
def evaluate():
pass
def main():
if C.max_wav_len < model.receptive_field:
raise RuntimeError(" > Max wav length {} cannot be smaller then\
the model receptive field {}.".format(
c.max_wav_len, model.receptive_field))
