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BUG fixes and more visualization changes

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This commit is contained in:
Eren Golge
2018-02-08 05:57:43 -08:00
parent 3cafc6568c
commit 584c8fbf5e
4 changed files with 36 additions and 23 deletions
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datasets/LJSpeech.py
+14 -11
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@@ -29,7 +29,7 @@ class LJSpeechDataset(Dataset):
def load_wav(self, filename):
try:
audio = librosa.load(filename, sr=self.sample_rate)
audio = librosa.core.load(filename, sr=self.sample_rate)
return audio
except RuntimeError as e:
print(" !! Cannot read file : {}".format(filename))
@@ -43,7 +43,7 @@ class LJSpeechDataset(Dataset):
text = self.frames.ix[idx, 1]
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}
sample = {'text': text, 'wav': wav, 'item_idx': self.frames.ix[idx, 0]}
return sample
def get_dummy_data(self):
@@ -55,33 +55,36 @@ class LJSpeechDataset(Dataset):
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(x) for x in text])
max_text_len = np.max(text_lenghts)
wav = [d['wav'] for d in batch]
# PAD sequences with largest length of the batch
text = prepare_data(text).astype(np.int32)
wav = prepare_data(wav)
magnitude = np.array([self.ap.spectrogram(w) for w in wav])
mel = np.array([self.ap.melspectrogram(w) for w in wav])
linear = np.array([self.ap.spectrogram(w).astype('float32') for w in wav])
mel = np.array([self.ap.melspectrogram(w).astype('float32') for w in wav])
assert mel.shape[2] == linear.shape[2]
timesteps = mel.shape[2]
# PAD with zeros that can be divided by outputs per step
if timesteps % self.outputs_per_step != 0:
magnitude = pad_per_step(magnitude, self.outputs_per_step)
mel = pad_per_step(mel, self.outputs_per_step)
# if timesteps % self.outputs_per_step != 0:
linear = pad_per_step(linear, self.outputs_per_step)
mel = pad_per_step(mel, self.outputs_per_step)
# reshape jombo
magnitude = magnitude.transpose(0, 2, 1)
linear = linear.transpose(0, 2, 1)
mel = mel.transpose(0, 2, 1)
text_lenghts = torch.LongTensor(text_lenghts)
text = torch.LongTensor(text)
magnitude = torch.FloatTensor(magnitude)
linear = torch.FloatTensor(linear)
mel = torch.FloatTensor(mel)
return text, text_lenghts, magnitude, mel
return text, text_lenghts, linear, mel, item_idxs[0]
raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
found {}"