def test_file_hdf5(self, file_name):
"""
Function to extract multi pitch from file. Currently supports only HDF5 files.
"""
mel, stft = self.read_hdf5_file(file_name)
out_mel, out_f0, out_vuv = self.process_file(stft, self.sess)
plot_dict = {"Spec Envelope": {"gt": mel[:,:-6], "op": out_mel[:,:-4]}, "Aperiodic":{"gt": mel[:,-6:-2], "op": out_mel[:,-4:]},\
"F0": {"gt": mel[:,-2], "op": out_f0}, "Vuv": {"gt": mel[:,-1], "op": out_vuv}}
synth = utils.query_yes_no("Synthesize output? ")
if synth:
out_featss = np.concatenate((out_mel[:mel.shape[0]], mel[:out_mel.shape[0],-2:-1], mel[:out_mel.shape[0],-1:]), axis = -1)
audio_out = sig_process.feats_to_audio(out_featss)
sf.write(os.path.join(config.output_dir,'{}_{}_SIN.wav'.format(file_name[:-4], config.singers[speaker_index_2])), audio_out, config.fs)
synth_ori = utils.query_yes_no("Synthesize ground truth with vocoder? ")
if synth_ori:
audio = sig_process.feats_to_audio(mel)
sf.write(os.path.join(config.output_dir,'{}_ori.wav'.format(file_name[:-4])), audio, config.fs)
def test_file_hdf5(self, file_name, speaker_index_2):
"""
Function to extract multi pitch from file. Currently supports only HDF5 files.
"""
mel, notes = self.read_hdf5_file(file_name)
speaker_name = file_name.split('_')[1]
speaker_index = config.singers.index(speaker_name)
speaker_gender = config.genders[speaker_name]
print("Original singer is {}, a human {}".format(
speaker_name, speaker_gender))
speaker_2_gender = config.genders[config.singers[speaker_index_2]]
print("Target singer is {}, a human {}".format(
config.singers[speaker_index_2], speaker_2_gender))
if speaker_gender == "F" and speaker_2_gender == "M":
notes[:, 0] = notes[:, 0] - 12
elif speaker_gender == "M" and speaker_2_gender == "F":
notes[:, 0] = notes[:, 0] + 12
out_mel, out_f0, out_vuv = self.process_file(mel, speaker_index,
speaker_index_2, notes,
self.sess)
plot_dict = {"Spec Envelope": {"gt": mel[:,:-6], "op": out_mel[:,:-4]}, "Aperiodic":{"gt": mel[:,-6:-2], "op": out_mel[:,-4:]},\
"F0": {"gt": mel[:,-2], "op": out_f0, "notes": notes[:,0]}, "Vuv": {"gt": mel[:,-1], "op": out_vuv}}
self.plot_features(plot_dict)
synth = utils.query_yes_no("Synthesize output? ")
if synth:
out_featss = np.concatenate((out_mel, out_f0, out_vuv), axis=-1)
audio_out = sig_process.feats_to_audio(out_featss)
sf.write(
os.path.join(
config.output_dir, './{}_{}_autovc_notes.wav'.format(
file_name[:-5], config.singers[speaker_index_2])),
audio_out, config.fs)
synth_ori = utils.query_yes_no(
"Synthesize ground truth with vocoder? ")
if synth_ori:
audio = sig_process.feats_to_audio(mel)
sf.write(
os.path.join(
config.output_dir,
'./{}_{}_ori.wav'.format(file_name[:-5],
config.singers[speaker_index])),
audio, config.fs)
def test_file_hdf5(self, file_name, speaker_index_2):
"""
Function to extract multi pitch from file. Currently supports only HDF5 files.
"""
mel, singer_1 = self.read_hdf5_file(file_name)
speaker_name = file_name.split('_')[1]
speaker_index = config.singers.index(speaker_name)
# speaker_file = [x for x in os.listdir(config.emb_dir) if x.endswith('npy') and x.split('_')[1] == speaker_name]
speaker_gender = "M"
print("Original singer is {}, a human {}".format(speaker_name, speaker_gender))
speaker_file_2 = [x for x in os.listdir(config.feats_dir) if x.endswith('hdf5') and x.split('_')[1] == config.singers[speaker_index_2]]
mel_2, singer_2 = self.read_hdf5_file(random.choice(speaker_file_2))
speaker_2_gender = "M"
print("Target singer is {}, a human {}".format(config.singers[speaker_index_2], speaker_2_gender))
out_mel = self.process_file(mel, singer_1, singer_2, self.sess)
plot_dict = {"Spec Envelope": {"gt": mel[:,:-6], "op": out_mel[:,:-4]}, "Aperiodic":{"gt": mel[:,-6:-2], "op": out_mel[:,-4:]}}
self.plot_features(plot_dict)
synth = utils.query_yes_no("Synthesize output? ")
if synth:
if speaker_gender == "F" and speaker_2_gender == "M":
out_featss = np.concatenate((out_mel[:mel.shape[0]], mel[:out_mel.shape[0],-2:-1]-12, mel[:out_mel.shape[0],-1:]), axis = -1)
elif speaker_gender == "M" and speaker_2_gender == "F":
out_featss = np.concatenate((out_mel[:mel.shape[0]], mel[:out_mel.shape[0],-2:-1]+12, mel[:out_mel.shape[0],-1:]), axis = -1)
else:
out_featss = np.concatenate((out_mel[:mel.shape[0]], mel[:out_mel.shape[0],-2:-1], mel[:out_mel.shape[0],-1:]), axis = -1)
audio_out = sig_process.feats_to_audio(out_featss)
audio_out_2 = sig_process.feats_to_audio(mel_2)
sf.write(os.path.join(config.output_dir,'./{}_{}_autovcemb.wav'.format(file_name[:-5], config.singers[speaker_index_2])), audio_out, config.fs)
sf.write(os.path.join(config.output_dir,'./{}_target.wav'.format(file_name[:-5])), audio_out_2, config.fs)
synth_ori = utils.query_yes_no("Synthesize ground truth with vocoder? ")
if synth_ori:
audio = sig_process.feats_to_audio(mel)
sf.write(os.path.join(config.output_dir,'./{}_{}_ori.wav'.format(file_name[:-5], config.singers[speaker_index])), audio, config.fs)
def test_file_wav(self, file_name, speaker_index):
"""
Function to extract multi pitch from file. Currently supports only HDF5 files.
"""
mel, stft = self.read_wav_file(file_name)
out_mel, out_f0, out_vuv = self.process_file(stft, speaker_index,
self.sess)
plot_dict = {"Spec Envelope": {"gt": mel[:,:-6], "op": out_mel[:,:-4]}, "Aperiodic":{"gt": mel[:,-6:-2], "op": out_mel[:,-4:]},\
"F0": {"gt": mel[:,-2], "op": out_f0}, "Vuv": {"gt": mel[:,-1], "op": out_vuv}}
self.plot_features(plot_dict)
synth = utils.query_yes_no("Synthesize output? ")
file_name = file_name.split('/')[-1]
if synth:
gen_change = utils.query_yes_no("Change in gender? ")
if gen_change:
female_male = utils.query_yes_no("Female to male?")
if female_male:
out_featss = np.concatenate(
(out_mel, out_f0 - 12, out_vuv), axis=-1)
else:
out_featss = np.concatenate(
(out_mel, out_f0 + 12, out_vuv), axis=-1)
else:
out_featss = np.concatenate((out_mel, out_f0, out_vuv),
axis=-1)
audio_out = sig_process.feats_to_audio(out_featss)
sf.write(
os.path.join(
config.output_dir,
'{}_{}_SDN.wav'.format(file_name[:-4],
config.singers[speaker_index])),
audio_out, config.fs)
synth_ori = utils.query_yes_no(
"Synthesize ground truth with vocoder? ")
if synth_ori:
audio = sig_process.feats_to_audio(mel)
sf.write(
os.path.join(config.output_dir,
'{}_ori.wav'.format(file_name[:-4])), audio,
config.fs)
def test_file_wav_f0(self, file_name, f0_file, speaker_index):
"""
Function to extract multi pitch from file. Currently supports only HDF5 files.
"""
mel, stft = self.read_wav_file(file_name)
f0 = midi_process.open_f0_file(f0_file)
timestamps = np.arange(0, len(mel) * config.hoptime, config.hoptime)
f1 = vamp_notes.note2traj(f0, timestamps)
f1 = sig_process.process_pitch(f1[:, 0])
out_mel, out_f0, out_vuv = self.process_file(stft, speaker_index,
self.sess)
plot_dict = {"Spec Envelope": {"gt": mel[:,:-6], "op": out_mel[:,:-4]}, "Aperiodic":{"gt": mel[:,-6:-2], "op": out_mel[:,-4:]},\
"F0": {"gt": f1[:,0], "op": out_f0}, "Vuv": {"gt": mel[:,-1], "op": out_vuv}}
self.plot_features(plot_dict)
synth = utils.query_yes_no("Synthesize output? ")
file_name = file_name.split('/')[-1]
if synth:
out_featss = np.concatenate((out_mel[:f1.shape[0]], f1), axis=-1)
audio_out = sig_process.feats_to_audio(out_featss)
sf.write(
os.path.join(
config.output_dir,
'{}_{}_SDN_f0_{}.wav'.format(file_name[:-4],
config.singers[speaker_index],
f0_file.split('/')[-1])),
audio_out, config.fs)
synth_ori = utils.query_yes_no(
"Synthesize ground truth with vocoder? ")
if synth_ori:
audio = sig_process.feats_to_audio(mel)
sf.write(
os.path.join(config.output_dir,
'{}_ori.wav'.format(file_name[:-4])), audio,
config.fs)
def test_file_wav(self, file_name, speaker_index_1, speaker_index):
"""
Function to extract multi pitch from file. Currently supports only HDF5 files.
"""
speaker_gender = config.genders[config.singers[speaker_index_1]]
print("Original singer is {}, a human {}".format(
config.singers[speaker_index_1], speaker_gender))
speaker_2_gender = config.genders[config.singers[speaker_index]]
print("Target singer is {}, a human {}".format(
config.singers[speaker_index], speaker_2_gender))
stft, mel = self.read_wav_file(file_name)
out_mel = self.process_file(mel, speaker_index_1, speaker_index,
self.sess)
plot_dict = {
"Spec Envelope": {
"gt": mel[:, :-6],
"op": out_mel[:, :-4]
},
"Aperiodic": {
"gt": mel[:, -6:-2],
"op": out_mel[:, -4:]
}
}
self.plot_features(plot_dict)
synth = utils.query_yes_no("Synthesize output? ")
if synth:
if speaker_gender == "F" and speaker_2_gender == "M":
out_featss = np.concatenate(
(out_mel[:mel.shape[0]], mel[:out_mel.shape[0], -2:-1] -
12, mel[:out_mel.shape[0], -1:]),
axis=-1)
elif speaker_gender == "M" and speaker_2_gender == "F":
out_featss = np.concatenate(
(out_mel[:mel.shape[0]], mel[:out_mel.shape[0], -2:-1] +
12, mel[:out_mel.shape[0], -1:]),
axis=-1)
else:
out_featss = np.concatenate(
(out_mel[:mel.shape[0]], mel[:out_mel.shape[0], -2:-1],
mel[:out_mel.shape[0], -1:]),
axis=-1)
# 0 = mel[:out_mel.shape[0],-2:-1] + np.random.rand(mel[:out_mel.shape[0],-2:-1].shape[0])* 0.5
audio_out = sig_process.feats_to_audio(out_featss)
sf.write(
os.path.join(
config.output_dir, './{}_{}_autovc.wav'.format(
file_name[:-5], config.singers[speaker_index])),
audio_out, config.fs)
synth_ori = utils.query_yes_no(
"Synthesize ground truth with vocoder? ")
if synth_ori:
audio = sig_process.feats_to_audio(mel)
sf.write(
os.path.join(
config.output_dir,
'./{}_{}_ori.wav'.format(file_name[:-5],
config.singers[speaker_index_1])),
audio, config.fs)