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pep8 check

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This commit is contained in:
Eren Golge
2018-04-03 03:24:57 -07:00
parent b4a4377875
commit a9eadd1b8a
23 changed files with 198 additions and 228 deletions
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datasets/LJSpeech.py
+20 -16
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@@ -14,10 +14,10 @@ from TTS.utils.data import (prepare_data, pad_per_step,
class LJSpeechDataset(Dataset):
def __init__(self, csv_file, root_dir, outputs_per_step, sample_rate,
text_cleaner, num_mels, min_level_db, frame_shift_ms,
frame_length_ms, preemphasis, ref_level_db, num_freq, power,
min_seq_len=0):
text_cleaner, num_mels, min_level_db, frame_shift_ms,
frame_length_ms, preemphasis, ref_level_db, num_freq, power,
min_seq_len=0):
with open(csv_file, "r") as f:
self.frames = [line.split('|') for line in f]
self.root_dir = root_dir
@@ -41,11 +41,11 @@ class LJSpeechDataset(Dataset):
def _sort_frames(self):
r"""Sort sequences in ascending order"""
lengths = np.array([len(ins[1]) for ins in self.frames])
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_frames = []
ignored = []
@@ -55,9 +55,10 @@ class LJSpeechDataset(Dataset):
ignored.append(idx)
else:
new_frames.append(self.frames[idx])
print(" | > {} instances are ignored by min_seq_len ({})".format(len(ignored), self.min_seq_len))
print(" | > {} instances are ignored by min_seq_len ({})".format(
len(ignored), self.min_seq_len))
self.frames = new_frames
def __len__(self):
return len(self.frames)
@@ -65,7 +66,8 @@ class LJSpeechDataset(Dataset):
wav_name = os.path.join(self.root_dir,
self.frames[idx][0]) + '.wav'
text = self.frames[idx][1]
text = np.asarray(text_to_sequence(text, [self.cleaners]), dtype=np.int32)
text = np.asarray(text_to_sequence(
text, [self.cleaners]), dtype=np.int32)
wav = np.asarray(self.load_wav(wav_name)[0], dtype=np.float32)
sample = {'text': text, 'wav': wav, 'item_idx': self.frames[idx][0]}
return sample
@@ -96,13 +98,15 @@ class LJSpeechDataset(Dataset):
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
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]
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)
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)
@@ -112,7 +116,7 @@ class LJSpeechDataset(Dataset):
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]
timesteps = mel.shape[2]
# B x T x D
linear = linear.transpose(0, 2, 1)
@@ -125,7 +129,7 @@ class LJSpeechDataset(Dataset):
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;\