def melcepstrum_noise_shaping(wav_list, args):
"""APPLY NOISE SHAPING USING STFT-BASED MCEP"""
# define synthesizer
synthesizer = Synthesizer(fs=args.fs, shiftms=args.shiftms, 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.warn("wav file format is not 16 bit PCM.")
x = np.float64(x)
# check sampling frequency
if not fs == args.fs:
logging.error("sampling frequency is not matched.")
sys.exit(1)
# get frame number
num_frames = int(1000 * len(x) / fs / args.shiftms) + 1
# load average mcep
mlsa_coef = read_hdf5(args.stats, "/mcep/mean") * args.mag
mlsa_coef[0] = 0.0
if args.inv:
mlsa_coef[1:] = -1.0 * mlsa_coef[1:]
mlsa_coef = np.float64(np.tile(mlsa_coef, [num_frames, 1]))
# synthesis and write
x_ns = synthesizer.synthesis_diff(x, mlsa_coef, alpha=args.mcep_alpha)
x_ns = low_cut_filter(x_ns, args.fs, cutoff=70)
write_name = args.writedir + "/" + os.path.basename(wav_name)
wavfile.write(write_name, args.fs, np.int16(x_ns))
def noise_shaping(wav_list):
for wav_name in wav_list:
# load wavfile and apply low cut filter
fs, x = wavfile.read(wav_name)
wav_type = x.dtype
x = np.array(x, dtype=np.float64)
# check sampling frequency
if not fs == args.fs:
print("ERROR: sampling frequency is not matched.")
sys.exit(1)
# extract features (only for get the number of frames)
f0, _, _ = feature_extractor.analyze(x)
num_frames = f0.shape[0]
# load average mcep
mlsa_coef = read_hdf5(args.stats, "/mean")
mlsa_coef = mlsa_coef[args.mcep_dim_start:args.
mcep_dim_end] * args.mag
mlsa_coef[0] = 0.0
if args.inv:
mlsa_coef[1:] = -1.0 * mlsa_coef[1:]
mlsa_coef = np.tile(mlsa_coef, [num_frames, 1])
# synthesis and write
x_ns = synthesizer.synthesis_diff(x,
mlsa_coef,
alpha=args.mcep_alpha)
x_ns = low_cut_filter(x_ns, args.fs, cutoff=70)
if wav_type == np.int16:
write_name = args.writedir + "/" + os.path.basename(wav_name)
wavfile.write(write_name, args.fs, np.int16(x_ns))
else:
wavfile.write(write_name, args.fs, x_ns)
def noise_shaping(wav_list, args):
"""APPLY NOISE SHAPING"""
# define feature extractor
feature_extractor = FeatureExtractor(
analyzer="world",
fs=args.fs,
shiftms=args.shiftms,
fftl=args.fftl)
# define synthesizer
synthesizer = Synthesizer(
fs=args.fs,
shiftms=args.shiftms,
fftl=args.fftl)
for i, feat_id in enumerate(wav_list):
logging.info("now processing %s (%d/%d)" % (feat_id, i + 1, len(wav_list)))
# load wavfile and apply low cut filter
wav_filename = args.outdir.replace("feat_id", feat_id)
fs, x = wavfile.read(wav_filename)
wav_type = x.dtype
x = np.array(x, dtype=np.float64)
# check sampling frequency
if not fs == args.fs:
logging.error("sampling frequency is not matched.")
sys.exit(1)
## extract features (only for get the number of frames)
f0, _, _ = feature_extractor.analyze(x)
num_frames = f0.shape[0]
# load average mcep
mlsa_coef = read_hdf5(args.stats, "/%s/mean" % args.feature_type)
mlsa_coef = mlsa_coef[args.mcep_dim_start:args.mcep_dim_end] * args.mag
mlsa_coef[0] = 0.0
if args.inv:
mlsa_coef[1:] = -1.0 * mlsa_coef[1:]
mlsa_coef = np.tile(mlsa_coef, [num_frames, 1])
# synthesis and write
x_ns = synthesizer.synthesis_diff(x, mlsa_coef, alpha=args.mcep_alpha)
x_ns = low_cut_filter(x_ns, args.fs, cutoff=70)
write_name = args.writedir.replace("feat_id", feat_id)
# check directory existence
wav = np.clip(x_ns, -32768, 32767)
if wav_type == np.int16:
wavfile.write(write_name, args.fs, np.int16(wav))
else:
wavfile.write(write_name, args.fs, wav)
def noise_shaping(wav_list):
for wav_name in wav_list:
# load wavfile and apply low cut filter
#fs, x = wavfile.read(wav_name)
#wav_type = x.dtype
#x = np.array(x, dtype=np.float64)
x, fs = sf.read(wav_name)
# check sampling frequency
if not fs == args.fs:
print("ERROR: sampling frequency is not matched.")
sys.exit(1)
# extract features (only for get the number of frames)
#def analyze(wav, fs=22050, minf0=40, maxf0=700, fperiod=5.0, fftl=1024, f0=None, time_axis=None):
#print(fs)
#_, f0, _, _ = analyze(x, fs=fs)
f0, _ = pw.harvest(x, fs, frame_period=5.0)
num_frames = f0.shape[0]
# load average mcep
#mlsa_coef = read_hdf5(args.stats, "/mean_org")
mlsa_coef = read_hdf5(args.stats, "/mean_org_lf0")
#mlsa_coef = read_hdf5(args.stats, "/mean_feat_org_lf0")
#mlsa_coef = mlsa_coef[args.mcep_dim_start:args.mcep_dim_end] * args.mag
mlsa_coef = mlsa_coef[args.mcep_dim_start:] * args.mag
mlsa_coef[0] = 0.0
if args.inv:
mlsa_coef[1:] = -1.0 * mlsa_coef[1:]
mlsa_coef = np.tile(mlsa_coef, [num_frames, 1])
# synthesis and write
x_ns = synthesis_diff(x,
mlsa_coef,
alpha=args.mcep_alpha,
fs=fs,
shiftms=args.shiftms)
x_ns = low_cut_filter(x_ns, args.fs, cutoff=70)
write_name = args.writedir + "/" + os.path.basename(wav_name)
sf.write(write_name, x_ns, args.fs, 'PCM_16')
