import os
import numpy as np
import collections
import torch
import random
from torch.utils.data import Dataset
from utils.text import text_to_sequence, phoneme_to_sequence, pad_with_eos_bos
from utils.data import prepare_data, prepare_tensor, prepare_stop_target
class MyDataset(Dataset):
def __init__(self,
outputs_per_step,
text_cleaner,
ap,
meta_data,
batch_group_size=0,
min_seq_len=0,
max_seq_len=float("inf"),
use_phonemes=True,
phoneme_cache_path=None,
phoneme_language="en-us",
enable_eos_bos=False,
verbose=False):
"""
Args:
outputs_per_step (int): number of time frames predicted per step.
text_cleaner (str): text cleaner used for the dataset.
ap (TTS.utils.AudioProcessor): audio processor object.
meta_data (list): list of dataset instances.
batch_group_size (int): (0) range of batch randomization after sorting
sequences by length.
min_seq_len (int): (0) minimum sequence length to be processed
by the loader.
max_seq_len (int): (float("inf")) maximum sequence length.
use_phonemes (bool): (true) if true, text converted to phonemes.
phoneme_cache_path (str): path to cache phoneme features.
phoneme_language (str): one the languages from
https://github.com/bootphon/phonemizer#languages
enable_eos_bos (bool): enable end of sentence and beginning of sentences characters.
verbose (bool): print diagnostic information.
"""
self.batch_group_size = batch_group_size
self.items = meta_data
self.outputs_per_step = outputs_per_step
self.sample_rate = ap.sample_rate
self.cleaners = text_cleaner
self.min_seq_len = min_seq_len
self.max_seq_len = max_seq_len
self.ap = ap
self.use_phonemes = use_phonemes
self.phoneme_cache_path = phoneme_cache_path
self.phoneme_language = phoneme_language
self.enable_eos_bos = enable_eos_bos
self.verbose = verbose
if use_phonemes and not os.path.isdir(phoneme_cache_path):
os.makedirs(phoneme_cache_path, exist_ok=True)
if self.verbose:
print("\n > DataLoader initialization")
print(" | > Use phonemes: {}".format(self.use_phonemes))
if use_phonemes:
print(" | > phoneme language: {}".format(phoneme_language))
print(" | > Number of instances : {}".format(len(self.items)))
self.sort_items()
def load_wav(self, filename):
audio = self.ap.load_wav(filename)
return audio
@staticmethod
def load_np(filename):
data = np.load(filename).astype('float32')
return data
def _generate_and_cache_phoneme_sequence(self, text, cache_path):
"""generate a phoneme sequence from text.
since the usage is for subsequent caching, we never add bos and
eos chars here. Instead we add those dynamically later; based on the
config option."""
phonemes = phoneme_to_sequence(text, [self.cleaners],
language=self.phoneme_language,
enable_eos_bos=False)
phonemes = np.asarray(phonemes, dtype=np.int32)
np.save(cache_path, phonemes)
return phonemes
def _load_or_generate_phoneme_sequence(self, wav_file, text):
file_name = os.path.basename(wav_file).split('.')[0]
cache_path = os.path.join(self.phoneme_cache_path,
file_name + '_phoneme.npy')
try:
phonemes = np.load(cache_path)
except FileNotFoundError:
phonemes = self._generate_and_cache_phoneme_sequence(text,
cache_path)
except (ValueError, IOError):
print(" > ERROR: failed loading phonemes for {}. "
"Recomputing.".format(wav_file))
phonemes = self._generate_and_cache_phoneme_sequence(text,
cache_path)
if self.enable_eos_bos:
phonemes = pad_with_eos_bos(phonemes)
return phonemes
def load_data(self, idx):
text, wav_file, speaker_name = self.items[idx]
wav = np.asarray(self.load_wav(wav_file), dtype=np.float32)
if self.use_phonemes:
text = self._load_or_generate_phoneme_sequence(wav_file, text)
else:
text = np.asarray(
text_to_sequence(text, [self.cleaners]), dtype=np.int32)
assert text.size > 0, self.items[idx][1]
assert wav.size > 0, self.items[idx][1]
sample = {
'text': text,
'wav': wav,
'item_idx': self.items[idx][1],
'speaker_name': speaker_name
}
return sample
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])
idxs = np.argsort(lengths)
new_items = []
ignored = []
for i, idx in enumerate(idxs):
length = lengths[idx]
if length < self.min_seq_len or length > self.max_seq_len:
ignored.append(idx)
else:
new_items.append(self.items[idx])
# shuffle batch groups
if self.batch_group_size > 0:
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]
random.shuffle(temp_items)
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 (max={}, min={}) seq limits: {}".format(
self.max_seq_len, self.min_seq_len, len(ignored)))
print(" | > Batch group size: {}.".format(self.batch_group_size))
def __len__(self):
return len(self.items)
def __getitem__(self, idx):
return self.load_data(idx)
def collate_fn(self, batch):
r"""
Perform preprocessing and create a final data batch:
1. PAD sequences with the longest sequence in the batch
2. Convert Audio signal to Spectrograms.
3. PAD sequences that can be divided by r.
4. Convert Numpy to Torch tensors.
"""
# Puts each data field into a tensor with outer dimension batch size
if isinstance(batch[0], collections.Mapping):
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)
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]
speaker_name = [batch[idx]['speaker_name']
for idx in ids_sorted_decreasing]
mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
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
]
# PAD stop targets
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)
wav = prepare_data(wav)
# PAD features with largest length + a zero frame
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]
# B x T x D
linear = linear.transpose(0, 2, 1)
mel = mel.transpose(0, 2, 1)
# convert things to pytorch
text_lenghts = torch.LongTensor(text_lenghts)
text = torch.LongTensor(text)
linear = torch.FloatTensor(linear).contiguous()
mel = torch.FloatTensor(mel).contiguous()
mel_lengths = torch.LongTensor(mel_lengths)
stop_targets = torch.FloatTensor(stop_targets)
return text, text_lenghts, speaker_name, linear, mel, mel_lengths, \
stop_targets, item_idxs
raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
found {}".format(type(batch[0]))))