def clotho_collate_fn(batch: MutableSequence[ndarray],
nb_t_steps: Union[AnyStr, Tuple[int, int]],
input_pad_at: str,
output_pad_at: str) \
-> Tuple[Tensor, Tensor]:
"""Pads data.
:param batch: Batch data.
:type batch: list[numpy.ndarray]
:param nb_t_steps: Number of time steps to\
pad/truncate to. Cab use\
'max', 'min', or exact number\
e.g. (1024, 10).
:type nb_t_steps: str|(int, int)
:param input_pad_at: Pad input at the start or\
at the end?
:type input_pad_at: str
:param output_pad_at: Pad output at the start or\
at the end?
:type output_pad_at: str
:return: Padded data.
:rtype: torch.Tensor, torch.Tensor
"""
if type(nb_t_steps) == str:
truncate_fn = max if nb_t_steps.lower() == 'max' else min
in_t_steps = truncate_fn([i[0].shape[0] for i in batch])
out_t_steps = truncate_fn([i[1].shape[0] for i in batch])
else:
in_t_steps, out_t_steps = nb_t_steps
in_dim = batch[0][0].shape[-1]
eos_token = batch[0][1][-1]
PAD = 4367
input_tensor, output_tensor = [], []
for in_b, out_b in batch:
if in_t_steps >= in_b.shape[0]:
padding = pt_zeros(in_t_steps - in_b.shape[0], in_dim).float()
data = [from_numpy(in_b).float()]
if input_pad_at.lower() == 'start':
data.insert(0, padding)
else:
data.append(padding)
tmp_in: Tensor = pt_cat(data)
else:
tmp_in: Tensor = from_numpy(in_b[:in_t_steps, :]).float()
input_tensor.append(tmp_in.unsqueeze_(0))
if out_t_steps >= out_b.shape[0]:
padding = pt_ones(out_t_steps - len(out_b)).mul(PAD).long()
data = [from_numpy(out_b).long()]
if output_pad_at.lower() == 'start':
data.insert(0, padding)
else:
data.append(padding)
tmp_out: Tensor = pt_cat(data)
else:
tmp_out: Tensor = from_numpy(out_b[:out_t_steps]).long()
output_tensor.append(tmp_out.unsqueeze_(0))
input_tensor = pt_cat(input_tensor)
output_tensor = pt_cat(output_tensor)
return input_tensor, output_tensor
def clotho_collate_fn_eval(batch: MutableSequence[ndarray],
nb_t_steps: Union[AnyStr, Tuple[int, int]],
input_pad_at: str,
output_pad_at: str,
split: str,
augment:bool) \
-> Tuple[Tensor, Tensor, Tensor, list]:
"""Pads data.
:param batch: Batch data.
:type batch: list[numpy.ndarray]
:param nb_t_steps: Number of time steps to\
pad/truncate to. Cab use\
'max', 'min', or exact number\
e.g. (1024, 10).
:type nb_t_steps: str|(int, int)
:param input_pad_at: Pad input at the start or\
at the end?
:type input_pad_at: str
:param output_pad_at: Pad output at the start or\
at the end?
:type output_pad_at: str
:return: Padded data.
:rtype: torch.Tensor, torch.Tensor
"""
if type(nb_t_steps) == str:
truncate_fn = max if nb_t_steps.lower() == 'max' else min
in_t_steps = truncate_fn([i[0].shape[0] for i in batch])
out_t_steps = truncate_fn([i[1].shape[0] for i in batch])
else:
in_t_steps, out_t_steps = nb_t_steps
in_dim = batch[0][0].shape[-1]
eos_token = batch[0][1][-1]
batch = sorted(batch, key=lambda x: x[-1], reverse=True)
PAD = 4367
input_tensor, output_tensor = [], []
for in_b, out_b, ref, filename, out_len in batch:
if in_t_steps >= in_b.shape[0]:
padding = pt_zeros(in_t_steps - in_b.shape[0], in_dim).float()
data = [from_numpy(in_b).float()]
if input_pad_at.lower() == 'start':
data.insert(0, padding)
else:
data.append(padding)
tmp_in: Tensor = pt_cat(data)
else:
tmp_in: Tensor = from_numpy(in_b[:in_t_steps, :]).float()
input_tensor.append(tmp_in.unsqueeze_(0))
if out_t_steps >= out_b.shape[0]:
padding = pt_ones(out_t_steps - len(out_b)).mul(PAD).long()
data = [from_numpy(out_b).long()]
if output_pad_at.lower() == 'start':
data.insert(0, padding)
else:
data.append(padding)
tmp_out: Tensor = pt_cat(data)
else:
tmp_out: Tensor = from_numpy(out_b[:out_t_steps]).long()
output_tensor.append(tmp_out.unsqueeze_(0))
input_tensor = pt_cat(input_tensor)
if augment:
input_tensor = spec_augment(input_tensor)
output_tensor = pt_cat(output_tensor)
all_ref = [i[2] for i in batch]
filename = [i[3] for i in batch]
*_, target_len = zip(*batch)
target_len = torch.LongTensor(target_len)
return input_tensor, output_tensor, target_len, all_ref
def clotho_train_collate_fn(batch: MutableSequence[ndarray],
nb_t_steps: Union[AnyStr, Tuple[int, int]],
input_pad_at: str,
output_pad_at: str) \
-> Tuple[Tensor, Tensor, Tensor, Tensor, list]:
"""Pads data.
:param batch: Batch data.
:type batch: list[numpy.ndarray]
:param nb_t_steps: Number of time steps to\
pad/truncate to. Cab use\
'max', 'min', or exact number\
e.g. (1024, 10).
:type nb_t_steps: str|(int, int)
:param input_pad_at: Pad input at the start or\
at the end?
:type input_pad_at: str
:param output_pad_at: Pad output at the start or\
at the end?
:type output_pad_at: str
:return: Padded data.
:rtype: torch.Tensor, torch.Tensor
"""
def make_seq_even(sequences, audio_lengths):
even_seqs = []
even_len = []
for i, s in enumerate(sequences):
if len(s) % 2 != 0:
even_seqs.append(s[:-1])
even_len.append(audio_lengths[i] - 1)
else:
even_seqs.append(s)
even_len.append(audio_lengths[i])
return even_seqs, even_len
if type(nb_t_steps) == str:
truncate_fn = max if nb_t_steps.lower() == 'max' else min
in_t_steps = truncate_fn([i[0].shape[0] for i in batch])
out_t_steps = truncate_fn([i[1].shape[0] for i in batch])
else:
in_t_steps, out_t_steps = nb_t_steps
in_dim = batch[0][0].shape[-1]
eos_token = batch[0][1][-1]
input_tensor, output_tensor = [], []
audio_lengths, text_lengths = [], []
file_ids_list = []
for in_b, out_b, fileid_b in batch:
audio_lengths.append(in_b.shape[0])
# print("toto", out_b.shape)
text_lengths.append(out_b.shape[0])
file_ids_list.extend(fileid_b)
if in_t_steps >= in_b.shape[0]:
padding = pt_zeros(in_t_steps - in_b.shape[0], in_dim).float()
data = [from_numpy(in_b).float()]
if input_pad_at.lower() == 'start':
data.insert(0, padding)
else:
data.append(padding)
tmp_in: Tensor = pt_cat(data)
else:
tmp_in: Tensor = from_numpy(in_b[:in_t_steps, :]).float()
# input_tensor.append(tmp_in.unsqueeze_(0))
input_tensor.append(tmp_in)
if out_t_steps >= out_b.shape[0]:
padding = pt_ones(out_t_steps - len(out_b)).mul(eos_token).long()
data = [from_numpy(out_b).long()]
if output_pad_at.lower() == 'start':
data.insert(0, padding)
else:
data.append(padding)
tmp_out: Tensor = pt_cat(data)
else:
tmp_out: Tensor = from_numpy(out_b[:out_t_steps]).long()
# output_tensor.append(tmp_out.unsqueeze_(0))
output_tensor.append(tmp_out)
# we sort by increasing lengths
# print("audio_lengths", audio_lengths)
audio_sorted_indices = sorted(range(len(audio_lengths)),
key=lambda k: audio_lengths[k])
audio_batch_sorted = [input_tensor[i] for i in audio_sorted_indices]
audio_lengths_sorted = [audio_lengths[i] for i in audio_sorted_indices]
# print("before, audio_sorted_indices", audio_sorted_indices)
# print("audio_lengths_sorted", audio_lengths_sorted)
# get text with the audio_sorted_indices indices
text_batch_sorted = [
output_tensor[i].unsqueeze_(0) for i in audio_sorted_indices
]
text_lengths = [text_lengths[i] for i in audio_sorted_indices]
# print("text_lengths", text_lengths)
# print("before, text_lengths", text_lengths)
# make all audio tensors to even length
even_audio_batch_sorted, even_audio_lengths_sorted = make_seq_even(
audio_batch_sorted, audio_lengths_sorted)
# reverse lists: largest sequence first (needed for packed sequences)
# audio_sorted_indices = audio_sorted_indices[::-1]
even_audio_lengths_sorted = even_audio_lengths_sorted[::-1]
even_audio_batch_sorted = even_audio_batch_sorted[::-1]
text_batch_sorted = text_batch_sorted[::-1]
text_lengths = text_lengths[::-1]
text_lengths = LongTensor(text_lengths)
# print("text_lengths tensor", text_lengths)
text_batch_sorted = pt_cat(text_batch_sorted)
even_audio_lengths_sorted = LongTensor(even_audio_lengths_sorted)
# we pad the sequences and get a tensor
input_tensor = rnn_utils.pad_sequence(
even_audio_batch_sorted) # size: T, B, F=40
# let's sort the file ids list with the sorted indices:
# print("????", len(audio_sorted_indices))
# print("????", len(file_ids_list), file_ids_list)
file_ids_list_sorted = [file_ids_list[ind] for ind in audio_sorted_indices]
file_ids_list_sorted = file_ids_list_sorted[::-1]
# print("????", len(file_ids_list_sorted), file_ids_list_sorted)
# print('input_tensor', input_tensor.size())
# print("text_batch_sorted", text_batch_sorted)
# print("even_audio_lengths_sorted tensor", even_audio_lengths_sorted)
# print("text_lengths", text_lengths)
# print("x_pad", x_pad.size())
# for i in range(len(audio_batch)):
# print(i, audio_lengths_sorted[i], audio_batch_sorted[i].size(), x_pad[:,i,:].size(), text_lengths[i], padded_text[i].size())
return input_tensor, text_batch_sorted, even_audio_lengths_sorted, text_lengths, file_ids_list_sorted