def melcepstrum_extract(wav_list, args):
"""EXTRACT MEL CEPSTRUM."""
# define feature extractor
for i, wav_name in enumerate(wav_list):
logging.info("now processing %s (%d/%d)" %
(wav_name, i + 1, len(wav_list)))
# load wavfile and apply low cut filter
fs, x = wavfile.read(wav_name)
if x.dtype != np.int16:
logging.warning("wav file format is not 16 bit PCM.")
x = np.array(x, dtype=np.float64)
if args.highpass_cutoff != 0:
x = low_cut_filter(x, fs, cutoff=args.highpass_cutoff)
# check sampling frequency
if not fs == args.fs:
logging.error("sampling frequency is not matched.")
sys.exit(1)
# extract features
shiftl = int(args.shiftms * fs * 0.001)
mcep = stft_mcep(x, args.fftl, shiftl, args.mcep_dim, args.mcep_alpha)
# save to hdf5
hdf5name = args.hdf5dir + "/" + os.path.basename(wav_name).replace(
".wav", ".h5")
write_hdf5(hdf5name, "/mcep", np.float32(mcep))
# overwrite wav file
if args.highpass_cutoff != 0 and args.save_wav:
wavfile.write(args.wavdir + "/" + os.path.basename(wav_name), fs,
np.int16(x))
def world_feature_extract(wav_list, args):
"""EXTRACT WORLD FEATURE VECTOR."""
# define feature extractor
feature_extractor = FeatureExtractor(analyzer="world",
fs=args.fs,
shiftms=args.shiftms,
minf0=args.minf0,
maxf0=args.maxf0,
fftl=args.fftl)
for i, wav_name in enumerate(wav_list):
logging.info("now processing %s (%d/%d)" %
(wav_name, i + 1, len(wav_list)))
# load wavfile and apply low cut filter
fs, x = wavfile.read(wav_name)
if x.dtype != np.int16:
logging.warning("wav file format is not 16 bit PCM.")
x = np.array(x, dtype=np.float64)
if args.highpass_cutoff != 0:
x = low_cut_filter(x, fs, cutoff=args.highpass_cutoff)
# check sampling frequency
if not fs == args.fs:
logging.error("sampling frequency is not matched.")
sys.exit(1)
# extract features
f0, _, _ = feature_extractor.analyze(x)
uv, cont_f0 = convert_continuos_f0(f0)
cont_f0_lpf = low_pass_filter(cont_f0,
int(1.0 / (args.shiftms * 0.001)),
cutoff=20)
codeap = feature_extractor.codeap()
mcep = feature_extractor.mcep(dim=args.mcep_dim, alpha=args.mcep_alpha)
# concatenate
cont_f0_lpf = np.expand_dims(cont_f0_lpf, axis=-1)
uv = np.expand_dims(uv, axis=-1)
feats = np.concatenate([uv, cont_f0_lpf, mcep, codeap], axis=1)
# save to hdf5
hdf5name = args.hdf5dir + "/" + os.path.basename(wav_name).replace(
".wav", ".h5")
write_hdf5(hdf5name, "/world", feats)
# overwrite wav file
if args.highpass_cutoff != 0 and args.save_wav:
wavfile.write(args.wavdir + "/" + os.path.basename(wav_name), fs,
np.int16(x))
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 melspectrogram_extract(wav_list, args):
"""EXTRACT MEL SPECTROGRAM."""
# define feature extractor
for i, wav_name in enumerate(wav_list):
logging.info("now processing %s (%d/%d)" %
(wav_name, i + 1, len(wav_list)))
# load wavfile and apply low cut filter
fs, x = wavfile.read(wav_name)
if x.dtype != np.int16:
logging.warning("wav file format is not 16 bit PCM.")
x = np.array(x, dtype=np.float64)
if args.highpass_cutoff != 0:
x = low_cut_filter(x, fs, cutoff=args.highpass_cutoff)
# check sampling frequency
if not fs == args.fs:
logging.error("sampling frequency is not matched.")
sys.exit(1)
# extract features
x_norm = x / (np.iinfo(np.int16).max + 1)
shiftl = int(args.shiftms * fs * 0.001)
mspc = librosa.feature.melspectrogram(
x_norm,
fs,
n_fft=args.fftl,
hop_length=shiftl,
n_mels=args.mspc_dim,
fmin=args.fmin if args.fmin is not None else 0,
fmax=args.fmax if args.fmax is not None else fs // 2,
power=1.0)
mspc = np.log10(np.maximum(EPS, mspc.T))
# save to hdf5
hdf5name = args.hdf5dir + "/" + os.path.basename(wav_name).replace(
".wav", ".h5")
write_hdf5(hdf5name, "/melspc", np.float32(mspc))
# overwrite wav file
if args.highpass_cutoff != 0 and args.save_wav:
wavfile.write(args.wavdir + "/" + os.path.basename(wav_name), fs,
np.int16(x))
def calc_stats(file_list, args):
"""CALCULATE STATISTICS."""
scaler = StandardScaler()
# process over all of data
for i, filename in enumerate(file_list):
logging.info("now processing %s (%d/%d)" %
(filename, i + 1, len(file_list)))
feat = read_hdf5(filename, "/" + args.feature_type)
scaler.partial_fit(feat)
# add uv term
mean = scaler.mean_
scale = scaler.scale_
if args.feature_type == "world":
mean[0] = 0.0
scale[0] = 1.0
# write to hdf5
write_hdf5(args.stats, "/" + args.feature_type + "/mean", np.float32(mean))
write_hdf5(args.stats, "/" + args.feature_type + "/scale",
np.float32(scale))
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()