def __getitem__(self, index):
mel_path, wav_path = self.samples_fpaths[index]
# Load the mel spectrogram and adjust its range to [-1, 1]
mel = np.load(mel_path).T.astype(np.float32) / hp.mel_max_abs_value
# Load the wav
wav = np.load(wav_path)
if hp.apply_preemphasis:
wav = audio.pre_emphasis(wav)
wav = np.clip(wav, -1, 1)
# Fix for missing padding # TODO: settle on whether this is any useful
r_pad = (len(wav) // hp.hop_length + 1) * hp.hop_length - len(wav)
wav = np.pad(wav, (0, r_pad), mode='constant')
assert len(wav) >= mel.shape[1] * hp.hop_length
wav = wav[:mel.shape[1] * hp.hop_length]
assert len(wav) % hp.hop_length == 0
# Quantize the wav
if hp.voc_mode == 'RAW':
if hp.mu_law:
quant = audio.encode_mu_law(wav, mu=2**hp.bits)
else:
quant = audio.float_2_label(wav, bits=hp.bits)
elif hp.voc_mode == 'MOL':
quant = audio.float_2_label(wav, bits=16)
return mel.astype(np.float32), quant.astype(np.int64)
def __init__(self, npy_path=None, wav_path=None, speaker_id=0, speaker_nums=2, sample_frames=128, length=-1):
super(SampleDataset, self).__init__()
if npy_path is not None:
self.raw_data = np.load(npy_path)
print('Loading ', npy_path, "\tshape:", self.raw_data.shape)
elif wav_path is not None:
print('Encoding ', wav_path)
wav, sr = librosa.load(wav_path, hparams.sample_rate)
wav = preemphasis(wav, hparams.preemphasis, hparams.preemphasize)
wav = wav / (np.abs(wav).max() * 1.1)
self.wav = audio.encode_mu_law(wav, mu=2 ** hparams.bits)
mel_basis = librosa.filters.mel(hparams.sample_rate, hparams.n_fft, n_mels=hparams.num_mels)
linear_spec = np.abs(
librosa.stft(wav, n_fft=hparams.n_fft, hop_length=hparams.hop_size, win_length=hparams.win_size))
mel_spec = mel_basis.dot(linear_spec)
mel_db = 20 * np.log10(mel_spec)
# print(in_fpath, mel_db.min(), mel_db.max())
self.raw_data = np.clip((mel_db + 120) / 125, 0, 1)
print('Raw_Data Shape:', self.raw_data.shape)
# (num_mels, num_frames)
else:
print("Error! No data input...")
self.speaker = np.zeros(speaker_nums)
self.speaker[speaker_id % speaker_nums] = 1
self.sample_frames = sample_frames
if length > 0:
self.length = length
else:
self.length = max(self.raw_data.shape[1] // sample_frames, 50 * 32)
def __init__(self, video_path, speaker_id=0, speaker_nums=2, sample_frames=128, length=-1, ret_wav=False, use_256=False):
super(SampleVideoDataset, self).__init__()
data_path, video_name = os.path.split(video_path)
folder_path = data_path + '/' + video_name.split('.')[0] + '/'
folder = os.path.exists(folder_path)
if not folder:
print("--- Creating %s... ---" % folder_path)
os.makedirs(folder_path)
reader = imageio.get_reader(video_path, 'ffmpeg', fps=20)
for i, im in enumerate(reader):
imageio.imwrite(folder_path + str(i).zfill(5) + '.jpg', im)
print("--- OK ---")
else:
print("--- %s already exists! ---" % folder_path)
self.list_frame = glob.glob(folder_path + '*.jpg')
self.list_frame.sort()
print("--- Totally %d video frames ---" % len(self.list_frame))
# print(int(hparams.hop_size / hparams.sample_rate * self.sample_frames * 20))
print('Encoding Audio of ', video_path)
wav, sr = librosa.load(video_path, hparams.sample_rate)
if ret_wav:
self.wav = audio.encode_mu_law(wav, mu=2 ** hparams.bits)
mel_basis = librosa.filters.mel(hparams.sample_rate, hparams.n_fft, n_mels=hparams.num_mels)
linear_spec = np.abs(
librosa.stft(wav, n_fft=hparams.n_fft, hop_length=hparams.hop_size, win_length=hparams.win_size))
mel_spec = mel_basis.dot(linear_spec)
mel_db = 20 * np.log10(mel_spec)
# print(in_fpath, mel_db.min(), mel_db.max())
self.raw_data = np.clip((mel_db + 120) / 125, 0, 1)
print('Raw_Data Shape:', self.raw_data.shape)
if np.isnan(self.raw_data).any():
print('!!!There exists np.nan in raw_data!!!')
# (num_mels, num_frames)
self.speaker = np.zeros(speaker_nums)
self.speaker[speaker_id] = 1
self.sample_frames = sample_frames
if length > 0:
self.length = length
else:
self.length = (max(self.raw_data.shape[1] // sample_frames, 50 * 32) // 8) * 8
self.length = self.length // 16 * 16
self.ret_wav = ret_wav
self.transform = transforms.Compose([transforms.Resize(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
if use_256:
self.transform_large = transforms.Compose([transforms.Resize(256),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
else:
self.transform_large = transforms.Compose([transforms.Resize(512),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
self.use_256 = use_256