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TTSDataset formatting and batch sorting to use pytorch pack for rnns

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This commit is contained in:
Eren Golge
2019-03-06 13:10:05 +01:00
parent 007bef5c35
commit 4326582bb1
3 changed files with 37 additions and 211 deletions
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datasets/TTSDataset.py
+31 -16
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@@ -89,7 +89,8 @@ class MyDataset(Dataset):
def load_phoneme_sequence(self, wav_file, text):
file_name = os.path.basename(wav_file).split('.')[0]
tmp_path = os.path.join(self.phoneme_cache_path, file_name+'_phoneme.npy')
tmp_path = os.path.join(self.phoneme_cache_path,
file_name + '_phoneme.npy')
if os.path.isfile(tmp_path):
try:
text = np.load(tmp_path)
@@ -102,7 +103,9 @@ class MyDataset(Dataset):
np.save(tmp_path, text)
else:
text = np.asarray(
phoneme_to_sequence(text, [self.cleaners], language=self.phoneme_language), dtype=np.int32)
phoneme_to_sequence(
text, [self.cleaners], language=self.phoneme_language),
dtype=np.int32)
np.save(tmp_path, text)
return text
@@ -112,7 +115,7 @@ class MyDataset(Dataset):
mel_name = self.items[idx][2]
linear_name = self.items[idx][3]
text = self.items[idx][0]
if wav_name.split('.')[-1] == 'npy':
wav = self.load_np(wav_name)
else:
@@ -124,13 +127,19 @@ class MyDataset(Dataset):
wav = np.asarray(self.load_wav(wav_file), dtype=np.float32)
mel = None
linear = None
if self.use_phonemes:
text = self.load_phoneme_sequence(wav_file, text)
else:
else:
text = np.asarray(
text_to_sequence(text, [self.cleaners]), dtype=np.int32)
sample = {'text': text, 'wav': wav, 'item_idx': self.items[idx][1], 'mel':mel, 'linear': linear}
sample = {
'text': text,
'wav': wav,
'item_idx': self.items[idx][1],
'mel': mel,
'linear': linear
}
return sample
def sort_items(self):
@@ -151,9 +160,9 @@ class MyDataset(Dataset):
for i in range(len(new_items) // self.batch_group_size):
offset = i * self.batch_group_size
end_offset = offset + self.batch_group_size
temp_items = new_items[offset : end_offset]
temp_items = new_items[offset:end_offset]
random.shuffle(temp_items)
new_items[offset : end_offset] = temp_items
new_items[offset:end_offset] = temp_items
self.items = new_items
if self.verbose:
@@ -181,19 +190,25 @@ class MyDataset(Dataset):
# 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(d["text"]) for d in batch])
text_lenghts, ids_sorted_decreasing = torch.sort(
torch.LongTensor(text_lenghts), dim=0, descending=True)
text_lenghts = np.array([len(x) for x in text])
max_text_len = np.max(text_lenghts)
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]
# if specs are not computed, compute them.
if batch[0]['mel'] is None and batch[0]['linear'] is None:
mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
mel = [
self.ap.melspectrogram(w).astype('float32') for w in wav
]
linear = [
self.ap.spectrogram(w).astype('float32') for w in wav
]
else:
mel = [d['mel'] for d in batch]
linear = [d['linear'] for d in batch]