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Formatting changes and distributed training

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This commit is contained in:
Eren Golge
2019-02-27 09:50:52 +01:00
parent dce1715e0f
commit bf5f18d11e
8 changed files with 277 additions and 356 deletions
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datasets/TTSDataset.py
+21 -17
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@@ -25,7 +25,8 @@ class MyDataset(Dataset):
cached=False,
use_phonemes=True,
phoneme_cache_path=None,
phoneme_language="en-us"):
phoneme_language="en-us",
verbose=False):
"""
Args:
root_path (str): root path for the data folder.
@@ -47,6 +48,7 @@ class MyDataset(Dataset):
phoneme_cache_path (str): path to cache phoneme features.
phoneme_language (str): one the languages from
https://github.com/bootphon/phonemizer#languages
verbose (bool): print diagnostic information.
"""
self.root_path = root_path
self.batch_group_size = batch_group_size
@@ -61,16 +63,17 @@ class MyDataset(Dataset):
self.use_phonemes = use_phonemes
self.phoneme_cache_path = phoneme_cache_path
self.phoneme_language = phoneme_language
self.verbose = verbose
if use_phonemes and not os.path.isdir(phoneme_cache_path):
os.makedirs(phoneme_cache_path)
print(" > DataLoader initialization")
print(" | > Data path: {}".format(root_path))
print(" | > Use phonemes: {}".format(self.use_phonemes))
if use_phonemes:
print(" | > phoneme language: {}".format(phoneme_language))
print(" | > Cached dataset: {}".format(self.cached))
print(" | > Number of instances : {}".format(len(self.items)))
if self.verbose:
print("\n > DataLoader initialization")
print(" | > Data path: {}".format(root_path))
print(" | > Use phonemes: {}".format(self.use_phonemes))
if use_phonemes:
print(" | > phoneme language: {}".format(phoneme_language))
print(" | > Cached dataset: {}".format(self.cached))
print(" | > Number of instances : {}".format(len(self.items)))
self.sort_items()
def load_wav(self, filename):
@@ -125,11 +128,7 @@ class MyDataset(Dataset):
def sort_items(self):
r"""Sort instances based on text length in ascending order"""
lengths = np.array([len(ins[0]) for ins in self.items])
print(" | > Max length sequence: {}".format(np.max(lengths)))
print(" | > Min length sequence: {}".format(np.min(lengths)))
print(" | > Avg length sequence: {}".format(np.mean(lengths)))
idxs = np.argsort(lengths)
new_items = []
ignored = []
@@ -139,11 +138,8 @@ class MyDataset(Dataset):
ignored.append(idx)
else:
new_items.append(self.items[idx])
print(" | > {} instances are ignored ({})".format(
len(ignored), self.min_seq_len))
# shuffle batch groups
if self.batch_group_size > 0:
print(" | > Batch group shuffling is active.")
for i in range(len(new_items) // self.batch_group_size):
offset = i * self.batch_group_size
end_offset = offset + self.batch_group_size
@@ -152,6 +148,14 @@ class MyDataset(Dataset):
new_items[offset : end_offset] = temp_items
self.items = new_items
if self.verbose:
print(" | > Max length sequence: {}".format(np.max(lengths)))
print(" | > Min length sequence: {}".format(np.min(lengths)))
print(" | > Avg length sequence: {}".format(np.mean(lengths)))
print(" | > Num. instances discarded by max-min seq limits: {}".format(
len(ignored), self.min_seq_len))
print(" | > Batch group size: {}.".format(self.batch_group_size))
def __len__(self):
return len(self.items)