def main():
"""Run preprocessing process."""
parser = argparse.ArgumentParser(
description=
"Preprocess audio and then extract features (See detail in parallel_wavegan/bin/preprocess.py)."
)
parser.add_argument(
"--wav-scp",
"--scp",
default=None,
type=str,
help=
"kaldi-style wav.scp file. you need to specify either scp or rootdir.")
parser.add_argument(
"--segments",
default=None,
type=str,
help=
"kaldi-style segments file. if use, you must to specify both scp and segments."
)
parser.add_argument(
"--rootdir",
default=None,
type=str,
help=
"directory including wav files. you need to specify either scp or rootdir."
)
parser.add_argument("--dumpdir",
type=str,
required=True,
help="directory to dump feature files.")
parser.add_argument("--config",
type=str,
required=True,
help="yaml format configuration file.")
parser.add_argument(
"--verbose",
type=int,
default=1,
help="logging level. higher is more logging. (default=1)")
parser.add_argument("--num_spk",
type=int,
default=10,
help="number of speakers ")
parser.add_argument("--num_utt",
type=int,
default=20,
help="number of speakers ")
args = parser.parse_args()
# set logger
if args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
elif args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
else:
logging.basicConfig(
level=logging.WARN,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
logging.warning('Skip DEBUG/INFO messages')
# load config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
config.update(vars(args))
# check arguments
if (args.wav_scp is not None and args.rootdir is not None) or \
(args.wav_scp is None and args.rootdir is None):
raise ValueError("Please specify either --rootdir or --wav-scp.")
# get dataset
if args.rootdir is not None:
dataset = AudioDataset(
args.rootdir,
"*.wav",
audio_load_fn=sf.read,
return_utt_id=True,
)
else:
dataset = AudioSCPDataset(
args.wav_scp,
segments=args.segments,
return_utt_id=True,
return_sampling_rate=True,
)
# check directly existence
if not os.path.exists(args.dumpdir):
os.makedirs(args.dumpdir, exist_ok=True)
phase_map = {}
# process each data
for utt_id, (audio, fs) in tqdm(dataset):
# check
assert len(audio.shape) == 1, \
f"{utt_id} seems to be multi-channel signal."
assert np.abs(audio).max() <= 1.0, \
f"{utt_id} seems to be different from 16 bit PCM."
assert fs == config["sampling_rate"], \
f"{utt_id} seems to have a different sampling rate."
spk_id = get_spk_id(utt_id)
if spk_id not in phase_map and len(phase_map.keys()) >= args.num_spk:
break
if spk_id in phase_map and len(phase_map[spk_id]) > args.num_utt:
continue
# trim silence
if config["trim_silence"]:
audio, _ = librosa.effects.trim(
audio,
top_db=config["trim_threshold_in_db"],
frame_length=config["trim_frame_size"],
hop_length=config["trim_hop_size"])
if "sampling_rate_for_feats" not in config:
x = audio
sampling_rate = config["sampling_rate"]
hop_size = config["hop_size"]
else:
print('sampling_rate_for_feats: %d' % config["sampling_rate"])
# NOTE(kan-bayashi): this procedure enables to train the model with different
# sampling rate for feature and audio, e.g., training with mel extracted
# using 16 kHz audio and 24 kHz audio as a target waveform
x = librosa.resample(audio, fs, config["sampling_rate_for_feats"])
sampling_rate = config["sampling_rate_for_feats"]
assert config["hop_size"] * config["sampling_rate_for_feats"] % fs == 0, \
"hop_size must be int value. please check sampling_rate_for_feats is correct."
hop_size = config["hop_size"] * config[
"sampling_rate_for_feats"] // fs
# extract feature
phase = get_angle(x,
sampling_rate=sampling_rate,
hop_size=hop_size,
fft_size=config["fft_size"],
win_length=config["win_length"],
window=config["window"],
num_mels=config["num_mels"],
fmin=config["fmin"],
fmax=config["fmax"])
phase = np.mean(phase, axis=0)
if spk_id not in phase_map:
phase_map[spk_id] = [phase]
else:
phase_map[spk_id].append(phase)
import pdb
pdb.set_trace()
np.save(os.path.join(args.dumpdir, "angles.npy"), phase_map)
def main():
"""Run preprocessing process."""
parser = argparse.ArgumentParser(
description=
"Preprocess audio and then extract features (See detail in parallel_wavegan/bin/preprocess.py)."
)
parser.add_argument(
"--wav-scp",
"--scp",
default=None,
type=str,
help=
"kaldi-style wav.scp file. you need to specify either scp or rootdir.")
parser.add_argument(
"--segments",
default=None,
type=str,
help=
"kaldi-style segments file. if use, you must to specify both scp and segments."
)
parser.add_argument(
"--rootdir",
default=None,
type=str,
help=
"directory including wav files. you need to specify either scp or rootdir."
)
parser.add_argument("--dumpdir",
type=str,
required=True,
help="directory to dump feature files.")
parser.add_argument("--config",
type=str,
required=True,
help="yaml format configuration file.")
parser.add_argument(
"--verbose",
type=int,
default=1,
help="logging level. higher is more logging. (default=1)")
args = parser.parse_args()
# set logger
if args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
elif args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
else:
logging.basicConfig(
level=logging.WARN,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
logging.warning('Skip DEBUG/INFO messages')
# load config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
config.update(vars(args))
# check arguments
if (args.wav_scp is not None and args.rootdir is not None) or \
(args.wav_scp is None and args.rootdir is None):
raise ValueError("Please specify either --rootdir or --wav-scp.")
# get dataset
if args.rootdir is not None:
dataset = AudioDataset(
args.rootdir,
"*.wav",
audio_load_fn=sf.read,
return_utt_id=True,
)
else:
dataset = AudioSCPDataset(
args.wav_scp,
segments=args.segments,
return_utt_id=True,
return_sampling_rate=True,
)
# check directly existence
if not os.path.exists(args.dumpdir):
os.makedirs(args.dumpdir, exist_ok=True)
# process each data
for utt_id, (audio, fs) in tqdm(dataset):
# check
assert len(audio.shape) == 1, \
f"{utt_id} seems to be multi-channel signal."
assert np.abs(audio).max() <= 1.0, \
f"{utt_id} seems to be different from 16 bit PCM."
assert fs == config["sampling_rate"], \
f"{utt_id} seems to have a different sampling rate."
# trim silence
if config["trim_silence"]:
audio, _ = librosa.effects.trim(
audio,
top_db=config["trim_threshold_in_db"],
frame_length=config["trim_frame_size"],
hop_length=config["trim_hop_size"])
if "sampling_rate_for_feats" not in config:
x = audio
sampling_rate = config["sampling_rate"]
hop_size = config["hop_size"]
else:
# NOTE(kan-bayashi): this procedure enables to train the model with different
# sampling rate for feature and audio, e.g., training with mel extracted
# using 16 kHz audio and 24 kHz audio as a target waveform
x = librosa.resample(audio, fs, config["sampling_rate_for_feats"])
sampling_rate = config["sampling_rate_for_feats"]
assert config["hop_size"] * config["sampling_rate_for_feats"] % fs == 0, \
"hop_size must be int value. please check sampling_rate_for_feats is correct."
hop_size = config["hop_size"] * config[
"sampling_rate_for_feats"] // fs
# extract feature
mel = logmelfilterbank(x,
sampling_rate=sampling_rate,
hop_size=hop_size,
fft_size=config["fft_size"],
win_length=config["win_length"],
window=config["window"],
num_mels=config["num_mels"],
fmin=config["fmin"],
fmax=config["fmax"])
# make sure the audio length and feature length are matched
audio = np.pad(audio, (0, config["fft_size"]), mode="reflect")
audio = audio[:len(mel) * config["hop_size"]]
assert len(mel) * config["hop_size"] == len(audio)
# apply global gain
if config["global_gain_scale"] > 0.0:
audio *= config["global_gain_scale"]
if np.abs(audio).max() >= 1.0:
logging.warn(f"{utt_id} causes clipping. "
f"it is better to re-consider global gain scale.")
continue
# save
if config["format"] == "hdf5":
write_hdf5(os.path.join(args.dumpdir, f"{utt_id}.h5"), "wave",
audio.astype(np.float32))
write_hdf5(os.path.join(args.dumpdir, f"{utt_id}.h5"), "feats",
mel.astype(np.float32))
elif config["format"] == "npy":
np.save(os.path.join(args.dumpdir, f"{utt_id}-wave.npy"),
audio.astype(np.float32),
allow_pickle=False)
np.save(os.path.join(args.dumpdir, f"{utt_id}-feats.npy"),
mel.astype(np.float32),
allow_pickle=False)
else:
raise ValueError("support only hdf5 or npy format.")
def main():
parser = argparse.ArgumentParser(description="Preprocess audio and extract features (see detail in parallel_wavegan/bin/preprocess.py ")
parser.add_argument("--wav-scp","--scp",default=None,type=str,
help="kaldi-styke wav.scp file. you need to specify either scp or rootdir.")
parser.add_argument("--segments",default=None,type=str,
help="kaldi-style segments file. if use you must specify both scp and segments.")
parser.add_argument("--rootdir",default=None,type=str,
help="directory icluding wav files. you need to specify either scp or rootdir.")
parser.add_argument("--dumpdir",type=str,required=True,
help="directory to dump feature files.")
parser.add_argument("--config",type=str,required=True,
help="yaml format configuration file.")
parser.add_argument("--verbose",type=int,default=1,
help="logging level. higher is more logging.")
args = parser.parse_args()
# setting logger
if args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
elif args.verbose > 0:
logging.basicConfig(
level=logging.INFO,format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
else:
logging.basicConfig(
level=logging.WARN,format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
logging.warning('skip DEBUG/INFO messages')
# loading config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.load)
config.update(vars(args))
# checking arguments
if (args.wav_scp is not None and args.rootdir is not None) or \
(args.wav_scp is None and args.rootdir is None):
raise ValueError("Please specify either --wav_scp or --rootdir")
# getting dataset
if args.rootdir is not None:
dataset = AudioDataset(
args.rootdir,"*.wav",
audio_load_fn=sf.read,
return_utt_id=True,
)
else:
dataset = AudioSCPDataset(
args.wav_scp,
segments=args.segments,
return_utt_id=True,
return_sampling_rate=True,
)
# check directory existence
if not os.path.exists(args.dumpdir):
os.makedirs(args.dumpdir, exist_ok=True)
# process each data
for utt_id,(audio,fs) in tqdm(dataset):
# checking
assert len(audio.shape) == 1, f"{utt_id} is multichannel signal."
assert np.abs(audio).max() <= 1.0, f"{utt_id} is different from 16 bit PCM."
assert fs == config['sampling_rate'], f"{utt_id} has different sampling rate."
# trim silence
if config['trim_silence]']:
audio,_ = librosa.effects.trim(audio,
top_db=config['trim_threshold_in_db'],
frame_length=config['trim_frame_size'],
hop_length=config['trim_hop_size'])
if "sampling_rate_for_feats" not in config:
x = audio
sampling_rate = config['sampling_rate']
hop_size = config['hop_size']
else: # here we can train model with different sampling rate for feature and audio
x = librosa.resample(audio, fs, config['sampling_rate_for_feats'])
sampling_rate = config['sampling_rate_for_feats']
assert config['hop_size'] * config['sampling_rate_for_feats'] % fs == 0, \
"hop_size must be int value. please check sampling_rate_for_feats is correct."
hop_size = config['hop_size'] * config['samping_rate_for_feats'] // fs
# extracting feature
mel = logmelfilterbank(x,
sampling_rate = sampling_rate,
hop_size=hop_size,
fft_size=config['fft_size'],
win_length=config['win_length'],
window=config['window'],
num_mels=config['num_mels'],
fmax=config['fmin'],
fmax=config['fmax'])
# making sure the audio length and feature length are matched
audio = np.pad(audio, (0, config['fft_size']), mode="edge")
audio = audio[:len(mel) * config['hop_size']]
assert len(mel) * config['hop_size'] == len(audio)
# apply global gain
if config['global_gain_scale'] > 0.0:
audio *= config['global_gain_scale']
if np.abs(audio).max() >= 1.0:
logging.warn(f"{utt_id} causes clipping. "
f"it is better to reconsider global gain scale.")
continue
if config['format'] == "hdf5":
write_hdf5(os.path.join(args.dumpdir,f"{utt_id}.h5"), "wave", audio.astype(np.float32))
write_hdf5(os.path.join(args.dumpdir, f"{utt_id}.h5"), "feats", mel.astype(np.float32))
elif config['format'] == "npy":
np.save(os.path.join(args.dumpdir, f"{utt_id}-wave.npy"),
audio.astype(np.float32), allow_pickle=False)
np.save(os.path.join(args.dumpdir, f"{utt_id}-feats.npy"),
mel.astype(np.float32), allow_pickle=False)
else:
raise ValueError('support only hdf5 or npy format.')