def collate_fn(self, batch):
r"""
Perform preprocessing and create a final data batch:
1. PAD sequences with the longest sequence in the batch
2. Convert Audio signal to Spectrograms.
3. PAD sequences that can be divided by r.
4. Convert Numpy to Torch tensors.
"""
# Puts each data field into a tensor with outer dimension batch size
if isinstance(batch[0], collections.Mapping):
keys = list()
wav = [d['wav'] for d in batch]
item_idxs = [d['item_idx'] for d in batch]
text = [d['text'] for d in batch]
text_lenghts = np.array([len(x) for x in text])
max_text_len = np.max(text_lenghts)
linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
mel_lengths = [m.shape[1] + 1 for m in mel] # +1 for zero-frame
# compute 'stop token' targets
stop_targets = [
np.array([0.] * (mel_len - 1)) for mel_len in mel_lengths
]
# PAD stop targets
stop_targets = prepare_stop_target(stop_targets,
self.outputs_per_step)
# PAD sequences with largest length of the batch
text = prepare_data(text).astype(np.int32)
wav = prepare_data(wav)
# PAD features with largest length + a zero frame
linear = prepare_tensor(linear, self.outputs_per_step)
mel = prepare_tensor(mel, self.outputs_per_step)
assert mel.shape[2] == linear.shape[2]
timesteps = mel.shape[2]
# B x T x D
linear = linear.transpose(0, 2, 1)
mel = mel.transpose(0, 2, 1)
# convert things to pytorch
text_lenghts = torch.LongTensor(text_lenghts)
text = torch.LongTensor(text)
linear = torch.FloatTensor(linear)
mel = torch.FloatTensor(mel)
mel_lengths = torch.LongTensor(mel_lengths)
stop_targets = torch.FloatTensor(stop_targets)
return text, text_lenghts, linear, mel, mel_lengths, stop_targets, item_idxs[
0]
raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
found {}".format(type(batch[0]))))
def collate_fn(self, batch):
r"""
Perform preprocessing and create a final data batch:
1. PAD sequences with the longest sequence in the batch
2. Convert Audio signal to Spectrograms.
3. PAD sequences that can be divided by r.
4. Convert Numpy to Torch tensors.
"""
# Puts each data field into a tensor with outer dimension batch size
if isinstance(batch[0], collections.Mapping):
keys = list()
wav = [d['wav'] for d in batch]
item_idxs = [d['item_idx'] for d in batch]
text = [d['text'] for d in batch]
text_lenghts = np.array([len(x) for x in text])
max_text_len = np.max(text_lenghts)
# PAD sequences with largest length of the batch
text = prepare_data(text).astype(np.int32)
wav = prepare_data(wav)
linear = np.array([self.ap.spectrogram(w).astype('float32') for w in wav])
mel = np.array([self.ap.melspectrogram(w).astype('float32') for w in wav])
assert mel.shape[2] == linear.shape[2]
timesteps = mel.shape[2]
# PAD with zeros that can be divided by outputs per step
if (timesteps + 1) % self.outputs_per_step != 0:
pad_len = self.outputs_per_step - \
((timesteps + 1) % self.outputs_per_step)
pad_len += 1
else:
pad_len = 1
linear = pad_per_step(linear, pad_len)
mel = pad_per_step(mel, pad_len)
# reshape jombo
linear = linear.transpose(0, 2, 1)
mel = mel.transpose(0, 2, 1)
# convert things to pytorch
text_lenghts = torch.LongTensor(text_lenghts)
text = torch.LongTensor(text)
linear = torch.FloatTensor(linear)
mel = torch.FloatTensor(mel)
return text, text_lenghts, linear, mel, item_idxs[0]
raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
found {}"
.format(type(batch[0]))))
def collate_fn(self, batch):
r"""
Perform preprocessing and create a final data batch:
1. Sort batch instances by text-length
2. Convert Audio signal to Spectrograms.
3. PAD sequences wrt r.
4. Load to Torch.
"""
# Puts each data field into a tensor with outer dimension batch size
if isinstance(batch[0], collections.Mapping):
text_lenghts = np.array([len(d["text"]) for d in batch])
# sort items with text input length for RNN efficiency
text_lenghts, ids_sorted_decreasing = torch.sort(
torch.LongTensor(text_lenghts), dim=0, descending=True)
wav = [batch[idx]['wav'] for idx in ids_sorted_decreasing]
item_idxs = [
batch[idx]['item_idx'] for idx in ids_sorted_decreasing
]
text = [batch[idx]['text'] for idx in ids_sorted_decreasing]
speaker_name = [
batch[idx]['speaker_name'] for idx in ids_sorted_decreasing
]
# compute features
mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
mel_lengths = [m.shape[1] for m in mel]
# compute 'stop token' targets
stop_targets = [
np.array([0.] * (mel_len - 1) + [1.])
for mel_len in mel_lengths
]
# PAD stop targets
stop_targets = prepare_stop_target(stop_targets,
self.outputs_per_step)
# PAD sequences with longest instance in the batch
text = prepare_data(text).astype(np.int32)
wav = prepare_data(wav)
# PAD features with longest instance
linear = prepare_tensor(linear, self.outputs_per_step)
mel = prepare_tensor(mel, self.outputs_per_step)
assert mel.shape[2] == linear.shape[2]
# B x D x T --> B x T x D
linear = linear.transpose(0, 2, 1)
mel = mel.transpose(0, 2, 1)
# convert things to pytorch
text_lenghts = torch.LongTensor(text_lenghts)
text = torch.LongTensor(text)
linear = torch.FloatTensor(linear).contiguous()
mel = torch.FloatTensor(mel).contiguous()
mel_lengths = torch.LongTensor(mel_lengths)
stop_targets = torch.FloatTensor(stop_targets)
return text, text_lenghts, speaker_name, linear, mel, mel_lengths, \
stop_targets, item_idxs
raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
found {}".format(type(batch[0]))))
