def prepare_input_sequence(fields,
device,
batch_size=1,
dataset=None,
load_mels=False,
load_pitch=False):
print(fields)
fields['text'] = [
torch.LongTensor(text_to_sequence(t, ['english_cleaners']))
for t in fields['text']
]
#order = np.argsort([-t.size(0) for t in fields['text']])
order = range(len(fields['text']))
fields['text'] = [fields['text'][i] for i in order]
fields['text_lens'] = torch.LongTensor([t.size(0) for t in fields['text']])
if load_mels:
assert 'mel' in fields
fields['mel'] = [
torch.load(Path(dataset, fields['mel'][i])).t() for i in order
]
fields['mel_lens'] = torch.LongTensor(
[t.size(0) for t in fields['mel']])
if load_pitch:
assert 'pitch' in fields
fields['pitch'] = [
torch.load(Path(dataset, fields['pitch'][i])) for i in order
]
fields['pitch_lens'] = torch.LongTensor(
[t.size(0) for t in fields['pitch']])
if 'output' in fields:
fields['output'] = [fields['output'][i] for i in order]
# cut into batches & pad
batches = []
for b in range(0, len(order), batch_size):
batch = {f: values[b:b + batch_size] for f, values in fields.items()}
for f in batch:
if f == 'text':
batch[f] = pad_sequence(batch[f], batch_first=True)
elif f == 'mel' and load_mels:
batch[f] = pad_sequence(batch[f],
batch_first=True).permute(0, 2, 1)
elif f == 'pitch' and load_pitch:
batch[f] = pad_sequence(batch[f], batch_first=True)
if type(batch[f]) is torch.Tensor:
batch[f] = batch[f].to(device)
batches.append(batch)
return batches
def get_text(self, text):
text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners))
return text_norm