def test_preprocessing(feature_type):
# make arguments
args = make_args(feature_type=feature_type)
# prepare dummy wav files
wavdir = "tmp/wav"
if not os.path.exists(wavdir):
os.makedirs(wavdir)
for i in range(5):
make_dummy_wav(wavdir + "/%d.wav" % i, 8000, args.fs)
# feature extract
wav_list = find_files(wavdir, "*.wav")
if not os.path.exists(args.wavdir):
os.makedirs(args.wavdir)
if args.feature_type == "world":
world_feature_extract(wav_list, args)
elif args.feature_type == "melspc":
melspectrogram_extract(wav_list, args)
else:
melcepstrum_extract(wav_list, args)
# calc_stats
file_list = find_files(args.hdf5dir, "*.h5")
calc_stats(file_list, args)
# noise shaping
if feature_type != "melspc":
wav_list = find_files(args.wavdir, "*.wav")
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
if not check_hdf5(args.stats, "/mlsa/coef"):
avg_mcep = read_hdf5(args.stats, args.feature_type + "/mean")
if args.feature_type == "world":
avg_mcep = avg_mcep[args.mcep_dim_start:args.mcep_dim_end]
mlsa_coef = convert_mcep_to_mlsa_coef(avg_mcep, args.mag,
args.mcep_alpha)
write_hdf5(args.stats, "/mlsa/coef", mlsa_coef)
write_hdf5(args.stats, "/mlsa/alpha", args.mcep_alpha)
noise_shaping(wav_list, args)
# remove
shutil.rmtree("tmp")
def main():
"""RUN TRAINING."""
parser = argparse.ArgumentParser()
# path setting
parser.add_argument("--waveforms", required=True,
type=str, help="directory or list of wav files")
parser.add_argument("--feats", required=True,
type=str, help="directory or list of aux feat files")
parser.add_argument("--stats", required=True,
type=str, help="hdf5 file including statistics")
parser.add_argument("--expdir", required=True,
type=str, help="directory to save the model")
parser.add_argument("--feature_type", default="world", choices=["world", "melspc"],
type=str, help="feature type")
# network structure setting
parser.add_argument("--n_quantize", default=256,
type=int, help="number of quantization")
parser.add_argument("--n_aux", default=28,
type=int, help="number of dimension of aux feats")
parser.add_argument("--n_resch", default=512,
type=int, help="number of channels of residual output")
parser.add_argument("--n_skipch", default=256,
type=int, help="number of channels of skip output")
parser.add_argument("--dilation_depth", default=10,
type=int, help="depth of dilation")
parser.add_argument("--dilation_repeat", default=1,
type=int, help="number of repeating of dilation")
parser.add_argument("--kernel_size", default=2,
type=int, help="kernel size of dilated causal convolution")
parser.add_argument("--upsampling_factor", default=80,
type=int, help="upsampling factor of aux features")
parser.add_argument("--use_upsampling_layer", default=True,
type=strtobool, help="flag to use upsampling layer")
parser.add_argument("--use_speaker_code", default=False,
type=strtobool, help="flag to use speaker code")
# network training setting
parser.add_argument("--lr", default=1e-4,
type=float, help="learning rate")
parser.add_argument("--weight_decay", default=0.0,
type=float, help="weight decay coefficient")
parser.add_argument("--batch_length", default=20000,
type=int, help="batch length (if set 0, utterance batch will be used)")
parser.add_argument("--batch_size", default=1,
type=int, help="batch size (if use utterance batch, batch_size will be 1.")
parser.add_argument("--iters", default=200000,
type=int, help="number of iterations")
# other setting
parser.add_argument("--checkpoint_interval", default=10000,
type=int, help="how frequent saving model")
parser.add_argument("--intervals", default=100,
type=int, help="log interval")
parser.add_argument("--seed", default=1,
type=int, help="seed number")
parser.add_argument("--resume", default=None, nargs="?",
type=str, help="model path to restart training")
parser.add_argument("--n_gpus", default=1,
type=int, help="number of gpus")
parser.add_argument("--verbose", default=1,
type=int, help="log level")
args = parser.parse_args()
# set log level
if args.verbose == 1:
logging.basicConfig(level=logging.INFO,
format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
elif args.verbose > 1:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
else:
logging.basicConfig(level=logging.WARNING,
format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
logging.warning("logging is disabled.")
# show arguments
for key, value in vars(args).items():
logging.info("%s = %s" % (key, str(value)))
# make experimental directory
if not os.path.exists(args.expdir):
os.makedirs(args.expdir)
# fix seed
os.environ['PYTHONHASHSEED'] = str(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# fix slow computation of dilated conv
# https://github.com/pytorch/pytorch/issues/15054#issuecomment-450191923
torch.backends.cudnn.benchmark = True
# save args as conf
torch.save(args, args.expdir + "/model.conf")
# define network
if args.use_upsampling_layer:
upsampling_factor = args.upsampling_factor
else:
upsampling_factor = 0
model = WaveNet(
n_quantize=args.n_quantize,
n_aux=args.n_aux,
n_resch=args.n_resch,
n_skipch=args.n_skipch,
dilation_depth=args.dilation_depth,
dilation_repeat=args.dilation_repeat,
kernel_size=args.kernel_size,
upsampling_factor=upsampling_factor)
logging.info(model)
model.apply(initialize)
model.train()
if args.n_gpus > 1:
device_ids = range(args.n_gpus)
model = torch.nn.DataParallel(model, device_ids)
model.receptive_field = model.module.receptive_field
if args.n_gpus > args.batch_size:
logging.warning("batch size is less than number of gpus.")
# define optimizer and loss
optimizer = torch.optim.Adam(
model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
criterion = nn.CrossEntropyLoss()
# define transforms
scaler = StandardScaler()
scaler.mean_ = read_hdf5(args.stats, "/" + args.feature_type + "/mean")
scaler.scale_ = read_hdf5(args.stats, "/" + args.feature_type + "/scale")
wav_transform = transforms.Compose([
lambda x: encode_mu_law(x, args.n_quantize)])
feat_transform = transforms.Compose([
lambda x: scaler.transform(x)])
# define generator
if os.path.isdir(args.waveforms):
filenames = sorted(find_files(args.waveforms, "*.wav", use_dir_name=False))
wav_list = [args.waveforms + "/" + filename for filename in filenames]
feat_list = [args.feats + "/" + filename.replace(".wav", ".h5") for filename in filenames]
elif os.path.isfile(args.waveforms):
wav_list = read_txt(args.waveforms)
feat_list = read_txt(args.feats)
else:
logging.error("--waveforms should be directory or list.")
sys.exit(1)
assert len(wav_list) == len(feat_list)
logging.info("number of training data = %d." % len(wav_list))
generator = train_generator(
wav_list, feat_list,
receptive_field=model.receptive_field,
batch_length=args.batch_length,
batch_size=args.batch_size,
feature_type=args.feature_type,
wav_transform=wav_transform,
feat_transform=feat_transform,
shuffle=True,
upsampling_factor=args.upsampling_factor,
use_upsampling_layer=args.use_upsampling_layer,
use_speaker_code=args.use_speaker_code)
# charge minibatch in queue
while not generator.queue.full():
time.sleep(0.1)
# resume model and optimizer
if args.resume is not None and len(args.resume) != 0:
checkpoint = torch.load(args.resume, map_location=lambda storage, loc: storage)
iterations = checkpoint["iterations"]
if args.n_gpus > 1:
model.module.load_state_dict(checkpoint["model"])
else:
model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
logging.info("restored from %d-iter checkpoint." % iterations)
else:
iterations = 0
# check gpu and then send to gpu
if torch.cuda.is_available():
model.cuda()
criterion.cuda()
for state in optimizer.state.values():
for key, value in state.items():
if torch.is_tensor(value):
state[key] = value.cuda()
else:
logging.error("gpu is not available. please check the setting.")
sys.exit(1)
# train
loss = 0
total = 0
for i in six.moves.range(iterations, args.iters):
start = time.time()
(batch_x, batch_h), batch_t = generator.next()
batch_output = model(batch_x, batch_h)
batch_loss = criterion(
batch_output[:, model.receptive_field:].contiguous().view(-1, args.n_quantize),
batch_t[:, model.receptive_field:].contiguous().view(-1))
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
loss += batch_loss.item()
total += time.time() - start
logging.debug("batch loss = %.3f (%.3f sec / batch)" % (
batch_loss.item(), time.time() - start))
# report progress
if (i + 1) % args.intervals == 0:
logging.info("(iter:%d) average loss = %.6f (%.3f sec / batch)" % (
i + 1, loss / args.intervals, total / args.intervals))
logging.info("estimated required time = "
"{0.days:02}:{0.hours:02}:{0.minutes:02}:{0.seconds:02}"
.format(relativedelta(
seconds=int((args.iters - (i + 1)) * (total / args.intervals)))))
loss = 0
total = 0
# save intermidiate model
if (i + 1) % args.checkpoint_interval == 0:
if args.n_gpus > 1:
save_checkpoint(args.expdir, model.module, optimizer, i + 1)
else:
save_checkpoint(args.expdir, model, optimizer, i + 1)
# save final model
if args.n_gpus > 1:
torch.save({"model": model.module.state_dict()}, args.expdir + "/checkpoint-final.pkl")
else:
torch.save({"model": model.state_dict()}, args.expdir + "/checkpoint-final.pkl")
logging.info("final checkpoint created.")
def test_train_generator():
# make dummy wavfiles
wavdir = "data/wav"
if not os.path.exists(wavdir):
os.makedirs(wavdir)
for i in range(5):
make_dummy_wav(wavdir + "/%d.wav" % i)
# make features
feat_args = make_feature_args()
wav_list = find_files(wavdir, "*.wav")
if not os.path.exists(feat_args.wavdir):
os.makedirs(feat_args.wavdir)
feat_args.feature_type = "melspc"
melspectrogram_extract(wav_list, feat_args)
feat_args.feature_type = "world"
world_feature_extract(wav_list, feat_args)
feat_list = find_files(feat_args.hdf5dir, "*.h5")
for ft in ["world", "melspc"]:
# ----------------------------------
# minibatch without upsampling layer
# ----------------------------------
generator_args = make_train_generator_args(wav_list=wav_list,
feat_list=feat_list,
feature_type=ft,
use_upsampling_layer=False,
batch_length=10000,
batch_size=5)
generator = train_generator(**vars(generator_args))
(x, h), t = next(generator)
assert x.size(0) == t.size(0) == h.size(0)
assert x.size(1) == t.size(1) == h.size(2)
# ----------------------------------------
# utterance batch without upsampling layer
# ----------------------------------------
generator_args = make_train_generator_args(wav_list=wav_list,
feat_list=feat_list,
feature_type=ft,
use_upsampling_layer=False,
batch_length=None,
batch_size=5)
generator = train_generator(**vars(generator_args))
(x, h), t = next(generator)
assert x.size(0) == t.size(0) == h.size(0) == 1
assert x.size(1) == t.size(1) == h.size(2)
# -------------------------------
# minibatch with upsampling layer
# -------------------------------
generator_args = make_train_generator_args(wav_list=wav_list,
feat_list=feat_list,
feature_type=ft,
use_upsampling_layer=True,
batch_length=10000,
batch_size=5)
generator = train_generator(**vars(generator_args))
(x, h), t = next(generator)
assert x.size(0) == t.size(0) == h.size(0)
assert x.size(1) == t.size(
1) == h.size(2) * generator_args.upsampling_factor
# -------------------------------------
# utterance batch with upsampling layer
# -------------------------------------
generator_args = make_train_generator_args(wav_list=wav_list,
feat_list=feat_list,
feature_type=ft,
use_upsampling_layer=True,
batch_length=None,
batch_size=5)
generator = train_generator(**vars(generator_args))
(x, h), t = next(generator)
assert x.size(0) == t.size(0) == h.size(0) == 1
assert x.size(1) == t.size(
1) == h.size(2) * generator_args.upsampling_factor
def test_decode_generator():
# make dummy wavfiles
wavdir = "data/wav"
if not os.path.exists(wavdir):
os.makedirs(wavdir)
for i in range(5):
make_dummy_wav(wavdir + "/%d.wav" % i)
# make features
feat_args = make_feature_args()
wav_list = find_files(wavdir, "*.wav")
if not os.path.exists(feat_args.wavdir):
os.makedirs(feat_args.wavdir)
feat_args.feature_type = "melspc"
melspectrogram_extract(wav_list, feat_args)
feat_args.feature_type = "world"
world_feature_extract(wav_list, feat_args)
feat_list = find_files(feat_args.hdf5dir, "*.h5")
for ft in ["world", "melspc"]:
# ----------------------------------
# non-batch without upsampling layer
# ----------------------------------
generator_args = make_decode_generator_args(feat_list=feat_list,
feature_type=ft,
use_upsampling_layer=False,
batch_size=1)
generator = decode_generator(**vars(generator_args))
_, (x, h, n_samples) = next(generator)
assert x.size(0) == h.size(0) == 1
assert h.size(2) == n_samples + 1
# -------------------------------
# non-batch with upsampling layer
# -------------------------------
generator_args = make_decode_generator_args(feat_list=feat_list,
feature_type=ft,
use_upsampling_layer=True,
batch_size=1)
generator = decode_generator(**vars(generator_args))
_, (x, h, n_samples) = next(generator)
assert x.size(0) == h.size(0) == 1
assert h.size(2) * generator_args.upsampling_factor == n_samples + 1
# ----------------------------------
# minibatch without upsampling layer
# ----------------------------------
generator_args = make_decode_generator_args(feat_list=feat_list,
feature_type=ft,
use_upsampling_layer=False,
batch_size=5)
generator = decode_generator(**vars(generator_args))
_, (batch_x, batch_h, n_samples_list) = next(generator)
assert batch_x.size(0) == batch_h.size(0) == len(n_samples_list)
assert batch_h.size(2) == max(n_samples_list) + 1
# -------------------------------
# minibatch with upsampling layer
# -------------------------------
generator_args = make_decode_generator_args(feat_list=feat_list,
feature_type=ft,
use_upsampling_layer=True,
batch_size=5)
generator = decode_generator(**vars(generator_args))
_, (batch_x, batch_h, n_samples_list) = next(generator)
assert batch_x.size(0) == batch_h.size(0) == len(n_samples_list)
assert batch_h.size(2) * generator_args.upsampling_factor == max(
n_samples_list) + 1
def main():
"""RUN DECODING."""
parser = argparse.ArgumentParser()
# decode setting
parser.add_argument("--seed_waveforms",
required=True,
type=str,
help="directory or list of wav files")
parser.add_argument("--checkpoint",
required=True,
type=str,
help="model file")
parser.add_argument("--outdir",
required=True,
type=str,
help="directory to save generated samples")
parser.add_argument("--config",
default=None,
type=str,
help="configure file")
parser.add_argument("--fs", default=16000, type=int, help="sampling rate")
parser.add_argument("--batch_size",
default=1,
type=int,
help="number of batches to decode of batch_length")
parser.add_argument("--speaker_code", type=int, help="speaker code")
# other setting
parser.add_argument("--intervals",
default=1000,
type=int,
help="log interval")
parser.add_argument("--seed", default=1, type=int, help="seed number")
parser.add_argument("--verbose", default=1, type=int, help="log level")
args = parser.parse_args()
# set log level
if args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
elif args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
else:
logging.basicConfig(
level=logging.WARNING,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
logging.warning("logging is disabled.")
# show arguments
for key, value in vars(args).items():
logging.info("%s = %s" % (key, str(value)))
# check arguments
if args.config is None:
args.config = os.path.dirname(args.checkpoint) + "/model.conf"
if not os.path.exists(args.config):
raise FileNotFoundError("config file is missing (%s)." % (args.config))
# check directory existence
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
if os.path.isdir(args.seed_waveforms):
filenames = sorted(
find_files(args.seed_waveforms, "*.wav", use_dir_name=False))
wav_list = [
args.seed_waveforms + "/" + filename for filename in filenames
]
elif os.path.isfile(args.seed_waveforms):
wav_list = read_txt(args.seed_waveforms)
else:
logging.error("--seed_waveforms should be directory or list.")
sys.exit(1)
# if speaker code is specified then filter out all other speakers if files conform to wave set pattern
if not args.speaker_code is None:
try:
wav_list = [
file for file in wav_list
if parse_wave_file_name(file)[1] == args.speaker_code
]
except ValueError:
# files not in wave set format, then original list will be kept
pass
# fix seed
os.environ['PYTHONHASHSEED'] = str(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# fix slow computation of dilated conv
# https://github.com/pytorch/pytorch/issues/15054#issuecomment-450191923
torch.backends.cudnn.benchmark = True
# load config
config = torch.load(args.config)
wav_transform = transforms.Compose(
[lambda x: encode_mu_law(x, config.n_quantize)])
# set default gpu and do not track gradient
torch.set_grad_enabled(False)
# define model and load parameters
model = WaveNet(n_quantize=config.n_quantize,
n_aux=1,
n_resch=config.n_resch,
n_skipch=config.n_skipch,
dilation_depth=config.dilation_depth,
dilation_repeat=config.dilation_repeat,
kernel_size=config.kernel_size)
checkpoint = torch.load(args.checkpoint,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint["model"])
model.eval()
if torch.cuda.is_available():
model.cuda()
# extract train iterations
train_iterations = checkpoint["iterations"]
# choose seed sample
wav_id = np.random.randint(0, high=len(wav_list))
wav_file = wav_list[wav_id]
seed_x, _rate = sf.read(wav_file, dtype=np.float32)
assert args.fs == _rate, f"expected sample rate is {args.fs}, {wav_file} is {_rate}"
tot_sample_length = config.batch_length + model.receptive_field
max_sample_id = seed_x.shape[0] - tot_sample_length
assert max_sample_id >= 0
sample_id = np.random.randint(0, max_sample_id)
seed_sample = seed_x[sample_id:sample_id + tot_sample_length]
# take code from filename if none
speaker_code = parse_wave_file_name(
wav_file)[1] if args.speaker_code is None else args.speaker_code
# log decoding info
output_fn_info = f"I{train_iterations}-R{args.seed}-S{speaker_code}-W{Path(wav_file).stem}"
length_sec = args.batch_size * config.batch_length / args.fs
logging.info(
f"generating {args.batch_size} batches of {config.batch_length} samples = {length_sec} seconds"
f" into {output_fn_info} set")
# write seed sample
sf.write(args.outdir + f"/seed_sample-{output_fn_info}.wav", seed_sample,
args.fs, "PCM_16")
logging.info("wrote seed_sample.wav in %s." % args.outdir)
# decode
new_samples = args.batch_size * config.batch_length
x, h = decode_from_wav(seed_sample,
new_samples,
wav_transform=wav_transform,
speaker_code=speaker_code)
def save_samples(samples, file_name):
wav_data = decode_mu_law(samples, config.n_quantize)
sf.write(file_name, wav_data, args.fs, "PCM_16")
def progress_callback(samples, no_samples, elapsed):
save_samples(samples,
args.outdir + "/" + f"decoded.t.all-{output_fn_info}.wav")
save_samples(samples[-no_samples:],
args.outdir + "/" + f"decoded.t.new-{output_fn_info}.wav")
logging.info("decoding (length = %d)" % h.shape[2])
samples = model.fast_generate(x,
h,
new_samples,
args.intervals,
callback=progress_callback)
# samples = model.generate(x, h, new_samples, args.intervals)
logging.info(f"decoded {len(seed_sample)}")
save_samples(samples, args.outdir + "/" + f"decoded-{output_fn_info}.wav")
logging.info("wrote decoded.wav in %s." % args.outdir)
def main():
"""RUN FEATURE EXTRACTION IN PARALLEL."""
parser = argparse.ArgumentParser(
description="making feature file argsurations.")
parser.add_argument("--waveforms",
default=None,
help="directory or list of filename of input wavfile")
parser.add_argument("--hdf5dir",
default=None,
help="directory to save hdf5")
parser.add_argument("--wavdir",
default=None,
help="directory to save of preprocessed wav file")
parser.add_argument("--fs",
default=16000,
type=int,
help="Sampling frequency")
parser.add_argument("--shiftms",
default=5,
type=float,
help="Frame shift in msec")
parser.add_argument("--feature_type",
default="world",
choices=["world", "melspc", "mcep"],
type=str,
help="feature type")
parser.add_argument("--mspc_dim",
default=80,
type=int,
help="Dimension of mel spectrogram")
parser.add_argument("--minf0", default=40, type=int, help="minimum f0")
parser.add_argument("--maxf0", default=400, type=int, help="maximum f0")
parser.add_argument("--mcep_dim",
default=24,
type=int,
help="Dimension of mel cepstrum")
parser.add_argument("--mcep_alpha",
default=0.41,
type=float,
help="Alpha of mel cepstrum")
parser.add_argument("--fftl", default=1024, type=int, help="FFT length")
parser.add_argument("--highpass_cutoff",
default=70,
type=int,
help="Cut off frequency in lowpass filter")
parser.add_argument("--save_wav",
default=True,
type=strtobool,
help="Whether to save filtered wav file")
parser.add_argument("--n_jobs",
default=10,
type=int,
help="number of parallel jobs")
parser.add_argument("--verbose",
default=1,
type=int,
help="log message level")
args = parser.parse_args()
# set log level
if args.verbose == 1:
logging.basicConfig(
level=logging.INFO,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
elif args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
else:
logging.basicConfig(
level=logging.WARNING,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
logging.warning("logging is disabled.")
# show arguments
for key, value in vars(args).items():
logging.info("%s = %s" % (key, str(value)))
# read list
if os.path.isdir(args.waveforms):
file_list = sorted(find_files(args.waveforms, "*.wav"))
else:
file_list = read_txt(args.waveforms)
logging.info("number of utterances = %d" % len(file_list))
# check directory existence
if not os.path.exists(args.wavdir) and args.highpass_cutoff != 0:
os.makedirs(args.wavdir)
if not os.path.exists(args.hdf5dir):
os.makedirs(args.hdf5dir)
# divide list
file_lists = np.array_split(file_list, args.n_jobs)
file_lists = [f_list.tolist() for f_list in file_lists]
# multi processing
processes = []
if args.feature_type == "world":
target_fn = world_feature_extract
elif args.feature_type == "melspc":
target_fn = melspectrogram_extract
else:
target_fn = melcepstrum_extract
for f in file_lists:
p = mp.Process(target=target_fn, args=(
f,
args,
))
p.start()
processes.append(p)
# wait for all process
for p in processes:
p.join()
def main(args):
"""RUN DECODING."""
# set log level
if args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
elif args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
else:
logging.basicConfig(
level=logging.WARNING,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
logging.warning("logging is disabled.")
# show arguments
for key, value in vars(args).items():
logging.info("%s = %s" % (key, str(value)))
# check arguments
if args.stats is None:
args.stats = os.path.dirname(args.checkpoint) + "/stats.h5"
if args.config is None:
args.config = os.path.dirname(args.checkpoint) + "/model.conf"
if not os.path.exists(args.stats):
raise FileNotFoundError("statistics file is missing (%s)." %
(args.stats))
if not os.path.exists(args.config):
raise FileNotFoundError("config file is missing (%s)." % (args.config))
# check directory existence
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# fix seed
os.environ['PYTHONHASHSEED'] = str(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# fix slow computation of dilated convargs.feats
# https://github.com/pytorch/pytorch/issues/15054#issuecomment-450191923
torch.backends.cudnn.benchmark = True
# load config
config = torch.load(args.config)
# get file list
if os.path.isdir(args.feats):
feat_list = sorted(find_files(args.feats, "*.h5"))
elif os.path.isfile(args.feats):
feat_list = read_txt(args.feats)
else:
logging.error("--feats should be directory or list.")
sys.exit(1)
# prepare the file list for parallel decoding
feat_lists = np.array_split(feat_list, args.n_gpus)
feat_lists = [f_list.tolist() for f_list in feat_lists]
# define transform
scaler = StandardScaler()
scaler.mean_ = read_hdf5(args.stats, "/" + config.feature_type + "/mean")
scaler.scale_ = read_hdf5(args.stats, "/" + config.feature_type + "/scale")
wav_transform = transforms.Compose(
[lambda x: encode_mu_law(x, config.n_quantize)])
feat_transform = transforms.Compose([lambda x: scaler.transform(x)])
f0_transform = transforms.Compose(
[partial(shift_semi_tone_f0_pulse, shift=args.f0_shift)])
# define gpu decode function
def gpu_decode(feat_list, gpu):
# set default gpu and do not track gradient
torch.cuda.set_device(gpu)
torch.set_grad_enabled(False)
# define model and load parameters
if config.use_upsampling_layer:
upsampling_factor = config.upsampling_factor
else:
upsampling_factor = 0
if args.use_pulse:
_WaveNet = WaveNetPulse
else:
_WaveNet = WaveNet
config.n_aux = 28
model = _WaveNet(n_quantize=config.n_quantize,
n_aux=config.n_aux,
n_resch=config.n_resch,
n_skipch=config.n_skipch,
dilation_depth=config.dilation_depth,
dilation_repeat=config.dilation_repeat,
kernel_size=config.kernel_size,
upsampling_factor=upsampling_factor)
model.load_state_dict(
torch.load(args.checkpoint,
map_location=lambda storage, loc: storage)["model"])
model.eval()
model.cuda()
print(args.use_pulse)
# define generator
generator = decode_generator(
feat_list,
batch_size=args.batch_size,
feature_type=config.feature_type,
wav_transform=wav_transform,
feat_transform=feat_transform,
f0_transform=f0_transform,
upsampling_factor=config.upsampling_factor,
use_upsampling_layer=config.use_upsampling_layer,
use_speaker_code=config.use_speaker_code,
use_pulse=args.use_pulse)
# decode
if args.batch_size > 1:
for feat_ids, (batch_x, batch_h, batch_p,
n_samples_list) in generator:
logging.info("decoding start")
samples_list = model.batch_fast_generate(
batch_x,
batch_h,
n_samples_list,
batch_p,
intervals=args.intervals)
for feat_id, samples in zip(feat_ids, samples_list):
wav = decode_mu_law(samples, config.n_quantize)
sf.write(args.outdir + "/" + feat_id + ".wav", wav,
args.fs, "PCM_16")
logging.info("wrote %s.wav in %s." %
(feat_id, args.outdir))
else:
raise NotImplementedError
# parallel decode
processes = []
for gpu, feat_list in enumerate(feat_lists):
p = mp.Process(target=gpu_decode, args=(
feat_list,
gpu,
))
p.start()
processes.append(p)
# wait for all process
for p in processes:
p.join()
def main():
"""RUN NOISE SHAPING IN PARALLEL."""
parser = argparse.ArgumentParser(
description="making feature file argsurations.")
parser.add_argument(
"--waveforms", default=None,
help="directory or list of filename of input wavfile")
parser.add_argument(
"--stats", default=None,
help="filename of hdf5 format")
parser.add_argument(
"--outdir", default=None,
help="directory to save preprocessed wav file")
parser.add_argument(
"--fs", default=16000,
type=int, help="Sampling frequency")
parser.add_argument(
"--shiftms", default=5,
type=float, help="Frame shift in msec")
parser.add_argument(
"--feature_type", default="world", choices=["world", "mcep", "melspc"],
type=str, help="feature type")
parser.add_argument(
"--mcep_dim_start", default=2,
type=int, help="Start index of mel cepstrum")
parser.add_argument(
"--mcep_dim_end", default=27,
type=int, help="End index of mel cepstrum")
parser.add_argument(
"--mcep_alpha", default=0.41,
type=float, help="Alpha of mel cepstrum")
parser.add_argument(
"--mag", default=0.5,
type=float, help="magnification of noise shaping")
parser.add_argument(
"--verbose", default=1,
type=int, help="log message level")
parser.add_argument(
'--n_jobs', default=10,
type=int, help="number of parallel jobs")
parser.add_argument(
'--inv', default=False, type=strtobool,
help="if True, inverse filtering will be performed")
args = parser.parse_args()
# set log level
if args.verbose == 1:
logging.basicConfig(level=logging.INFO,
format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
elif args.verbose > 1:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
else:
logging.basicConfig(level=logging.WARNING,
format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
logging.warning("logging is disabled.")
# show arguments
for key, value in vars(args).items():
logging.info("%s = %s" % (key, str(value)))
# read list
if os.path.isdir(args.waveforms):
file_list = sorted(find_files(args.waveforms, "*.wav"))
else:
file_list = read_txt(args.waveforms)
logging.info("number of utterances = %d" % len(file_list))
# check directory existence
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# divide list
file_lists = np.array_split(file_list, args.n_jobs)
file_lists = [f_list.tolist() for f_list in file_lists]
# calculate MLSA coef ans save it
if not check_hdf5(args.stats, "/mlsa/coef"):
avg_mcep = read_hdf5(args.stats, args.feature_type + "/mean")
if args.feature_type == "world":
avg_mcep = avg_mcep[args.mcep_dim_start:args.mcep_dim_end]
mlsa_coef = convert_mcep_to_mlsa_coef(avg_mcep, args.mag, args.mcep_alpha)
write_hdf5(args.stats, "/mlsa/coef", mlsa_coef)
write_hdf5(args.stats, "/mlsa/alpha", args.mcep_alpha)
# multi processing
processes = []
if args.feature_type == "melspc":
# TODO(kan-bayashi): implement noise shaping using melspectrogram
raise NotImplementedError("currently, support only world and mcep.")
for f in file_lists:
p = mp.Process(target=noise_shaping, args=(f, args,))
p.start()
processes.append(p)
# wait for all process
for p in processes:
p.join()
def get_dataloader(self):
args = self.args
# define transforms
scaler = StandardScaler()
scaler.mean_ = read_hdf5(args.stats, "/" + args.feature_type + "/mean")
scaler.scale_ = read_hdf5(args.stats,
"/" + args.feature_type + "/scale")
wav_transform = transforms.Compose(
[lambda x: encode_mu_law(x, args.n_quantize)])
feat_transform = transforms.Compose([lambda x: scaler.transform(x)])
# define generator
if os.path.isdir(args.waveforms):
filenames = sorted(
find_files(args.waveforms, "*.wav", use_dir_name=False))
wav_list_train = [
args.waveforms + "/" + filename for filename in filenames
]
feat_list_train = [
args.feats + "/" + filename.replace(".wav", ".h5")
for filename in filenames
]
elif os.path.isfile(args.waveforms):
wav_list_train = read_txt(args.waveforms)
feat_list_train = read_txt(args.feats)
else:
logging.error("--waveforms should be directory or list.")
sys.exit(1)
assert len(wav_list_train) == len(feat_list_train)
logging.info("number of training data = %d." % len(wav_list_train))
generator = data_generator(
wav_list_train,
feat_list_train,
receptive_field=self.model.receptive_field,
batch_length=args.batch_length,
batch_size=args.batch_size,
feature_type=args.feature_type,
wav_transform=wav_transform,
feat_transform=feat_transform,
shuffle=True,
upsampling_factor=args.upsampling_factor,
use_upsampling_layer=args.use_upsampling_layer,
use_speaker_code=args.use_speaker_code,
use_pulse=args.use_pulse)
test_generator = data_generator(
wav_list_test[:args.batch_size],
feat_list_test[:args.batch_size],
receptive_field=self.model.receptive_field,
batch_length=args.batch_length,
batch_size=args.batch_size,
feature_type=args.feature_type,
wav_transform=wav_transform,
feat_transform=feat_transform,
shuffle=False,
upsampling_factor=args.upsampling_factor,
use_upsampling_layer=args.use_upsampling_layer,
use_speaker_code=args.use_speaker_code,
use_pulse=args.use_pulse)
# charge minibatch in queue
while not generator.queue.full():
time.sleep(0.1)
return generator, test_generator