mirror of https://github.com/coqui-ai/TTS.git
TTSDataset formatting and batch sorting to use pytorch pack for rnns
This commit is contained in:
Eren Golge
parent
007bef5c35
commit
4326582bb1
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@ -89,7 +89,8 @@ class MyDataset(Dataset):
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def load_phoneme_sequence(self, wav_file, text):
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file_name = os.path.basename(wav_file).split('.')[0]
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tmp_path = os.path.join(self.phoneme_cache_path, file_name+'_phoneme.npy')
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tmp_path = os.path.join(self.phoneme_cache_path,
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file_name + '_phoneme.npy')
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if os.path.isfile(tmp_path):
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try:
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text = np.load(tmp_path)
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@ -102,7 +103,9 @@ class MyDataset(Dataset):
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np.save(tmp_path, text)
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else:
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text = np.asarray(
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phoneme_to_sequence(text, [self.cleaners], language=self.phoneme_language), dtype=np.int32)
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phoneme_to_sequence(
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text, [self.cleaners], language=self.phoneme_language),
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dtype=np.int32)
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np.save(tmp_path, text)
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return text
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@ -112,7 +115,7 @@ class MyDataset(Dataset):
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mel_name = self.items[idx][2]
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linear_name = self.items[idx][3]
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text = self.items[idx][0]
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if wav_name.split('.')[-1] == 'npy':
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wav = self.load_np(wav_name)
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else:
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@ -124,13 +127,19 @@ class MyDataset(Dataset):
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wav = np.asarray(self.load_wav(wav_file), dtype=np.float32)
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mel = None
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linear = None
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if self.use_phonemes:
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text = self.load_phoneme_sequence(wav_file, text)
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else:
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else:
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text = np.asarray(
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text_to_sequence(text, [self.cleaners]), dtype=np.int32)
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sample = {'text': text, 'wav': wav, 'item_idx': self.items[idx][1], 'mel':mel, 'linear': linear}
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sample = {
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'text': text,
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'wav': wav,
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'item_idx': self.items[idx][1],
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'mel': mel,
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'linear': linear
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}
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return sample
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def sort_items(self):
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@ -151,9 +160,9 @@ class MyDataset(Dataset):
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for i in range(len(new_items) // self.batch_group_size):
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offset = i * self.batch_group_size
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end_offset = offset + self.batch_group_size
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temp_items = new_items[offset : end_offset]
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temp_items = new_items[offset:end_offset]
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random.shuffle(temp_items)
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new_items[offset : end_offset] = temp_items
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new_items[offset:end_offset] = temp_items
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self.items = new_items
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if self.verbose:
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@ -181,19 +190,25 @@ class MyDataset(Dataset):
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# Puts each data field into a tensor with outer dimension batch size
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if isinstance(batch[0], collections.Mapping):
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keys = list()
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wav = [d['wav'] for d in batch]
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item_idxs = [d['item_idx'] for d in batch]
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text = [d['text'] for d in batch]
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text_lenghts = np.array([len(d["text"]) for d in batch])
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text_lenghts, ids_sorted_decreasing = torch.sort(
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torch.LongTensor(text_lenghts), dim=0, descending=True)
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text_lenghts = np.array([len(x) for x in text])
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max_text_len = np.max(text_lenghts)
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wav = [batch[idx]['wav'] for idx in ids_sorted_decreasing]
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item_idxs = [
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batch[idx]['item_idx'] for idx in ids_sorted_decreasing
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]
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text = [batch[idx]['text'] for idx in ids_sorted_decreasing]
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# if specs are not computed, compute them.
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if batch[0]['mel'] is None and batch[0]['linear'] is None:
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mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
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linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
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mel = [
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self.ap.melspectrogram(w).astype('float32') for w in wav
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]
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linear = [
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self.ap.spectrogram(w).astype('float32') for w in wav
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]
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else:
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mel = [d['mel'] for d in batch]
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linear = [d['linear'] for d in batch]
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@ -1,179 +0,0 @@
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import os
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import random
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import numpy as np
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import collections
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import librosa
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import torch
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from tqdm import tqdm
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from torch.utils.data import Dataset
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from utils.text import text_to_sequence
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from datasets.preprocess import tts_cache
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from utils.data import (prepare_data, pad_per_step, prepare_tensor,
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prepare_stop_target)
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class MyDataset(Dataset):
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# TODO: Merge to TTSDataset.py, but it is not fast as it is supposed to be
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def __init__(self,
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root_path,
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meta_file,
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outputs_per_step,
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text_cleaner,
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ap,
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batch_group_size=0,
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min_seq_len=0,
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**kwargs
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):
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self.root_path = root_path
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self.batch_group_size = batch_group_size
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self.feat_dir = os.path.join(root_path, 'loader_data')
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self.items = tts_cache(root_path, meta_file)
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self.outputs_per_step = outputs_per_step
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self.sample_rate = ap.sample_rate
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self.cleaners = text_cleaner
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self.min_seq_len = min_seq_len
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self.wavs = None
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self.mels = None
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self.linears = None
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print(" > Reading LJSpeech from - {}".format(root_path))
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print(" | > Number of instances : {}".format(len(self.items)))
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self.sort_items()
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self.fill_data()
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def fill_data(self):
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if self.wavs is None and self.mels is None:
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self.wavs = []
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self.mels = []
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self.linears = []
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self.texts = []
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for item in tqdm(self.items):
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wav_file = item[0]
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mel_file = item[1]
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linear_file = item[2]
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text = item[-1]
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wav = self.load_np(wav_file)
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mel = self.load_np(mel_file)
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linear = self.load_np(linear_file)
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self.wavs.append(wav)
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self.mels.append(mel)
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self.linears.append(linear)
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self.texts.append(np.asarray(
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text_to_sequence(text, [self.cleaners]), dtype=np.int32))
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print(" > Data loaded to memory")
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def load_wav(self, filename):
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try:
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audio = librosa.core.load(filename, sr=self.sample_rate)
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return audio
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except RuntimeError as e:
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print(" !! Cannot read file : {}".format(filename))
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def load_np(self, filename):
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data = np.load(filename).astype('float32')
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return data
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def sort_items(self):
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r"""Sort text sequences in ascending order"""
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lengths = np.array([len(ins[-1]) for ins in self.items])
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print(" | > Max length sequence {}".format(np.max(lengths)))
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print(" | > Min length sequence {}".format(np.min(lengths)))
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print(" | > Avg length sequence {}".format(np.mean(lengths)))
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idxs = np.argsort(lengths)
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new_frames = []
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ignored = []
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for i, idx in enumerate(idxs):
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length = lengths[idx]
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if length < self.min_seq_len:
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ignored.append(idx)
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else:
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new_frames.append(self.items[idx])
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print(" | > {} instances are ignored by min_seq_len ({})".format(
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len(ignored), self.min_seq_len))
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# shuffle batch groups
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if self.batch_group_size > 0:
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print(" | > Batch group shuffling is active.")
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for i in range(len(new_frames) // self.batch_group_size):
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offset = i * self.batch_group_size
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end_offset = offset + self.batch_group_size
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temp_frames = new_frames[offset : end_offset]
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random.shuffle(temp_frames)
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new_frames[offset : end_offset] = temp_frames
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self.items = new_frames
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def __len__(self):
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return len(self.items)
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def __getitem__(self, idx):
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text = self.texts[idx]
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wav = self.wavs[idx]
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mel = self.mels[idx]
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linear = self.linears[idx]
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sample = {
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'text': text,
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'wav': wav,
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'item_idx': self.items[idx][0],
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'mel': mel,
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'linear': linear
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}
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return sample
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def collate_fn(self, batch):
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r"""
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Perform preprocessing and create a final data batch:
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1. PAD sequences with the longest sequence in the batch
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2. Convert Audio signal to Spectrograms.
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3. PAD sequences that can be divided by r.
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4. Convert Numpy to Torch tensors.
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"""
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# Puts each data field into a tensor with outer dimension batch size
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if isinstance(batch[0], collections.Mapping):
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keys = list()
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wav = [d['wav'] for d in batch]
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item_idxs = [d['item_idx'] for d in batch]
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text = [d['text'] for d in batch]
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mel = [d['mel'] for d in batch]
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linear = [d['linear'] for d in batch]
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text_lenghts = np.array([len(x) for x in text])
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max_text_len = np.max(text_lenghts)
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mel_lengths = [m.shape[1] + 1 for m in mel] # +1 for zero-frame
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# compute 'stop token' targets
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stop_targets = [
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np.array([0.] * (mel_len - 1)) for mel_len in mel_lengths
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]
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# PAD stop targets
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stop_targets = prepare_stop_target(stop_targets,
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self.outputs_per_step)
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# PAD sequences with largest length of the batch
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text = prepare_data(text).astype(np.int32)
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wav = prepare_data(wav)
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# PAD features with largest length + a zero frame
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linear = prepare_tensor(linear, self.outputs_per_step)
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mel = prepare_tensor(mel, self.outputs_per_step)
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timesteps = mel.shape[2]
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# B x T x D
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linear = linear.transpose(0, 2, 1)
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mel = mel.transpose(0, 2, 1)
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# convert things to pytorch
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text_lenghts = torch.LongTensor(text_lenghts)
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text = torch.LongTensor(text)
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linear = torch.FloatTensor(linear)
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mel = torch.FloatTensor(mel)
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mel_lengths = torch.LongTensor(mel_lengths)
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stop_targets = torch.FloatTensor(stop_targets)
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return text, text_lenghts, linear, mel, mel_lengths, stop_targets, item_idxs
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raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
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found {}".format(type(batch[0]))))
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@ -50,22 +50,12 @@ class MSELossMasked(nn.Module):
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Returns:
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loss: An average loss value masked by the length.
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"""
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input = input.contiguous()
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target = target.contiguous()
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# logits_flat: (batch * max_len, dim)
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input = input.view(-1, input.shape[-1])
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# target_flat: (batch * max_len, dim)
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target_flat = target.view(-1, target.shape[-1])
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# losses_flat: (batch * max_len, dim)
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losses_flat = functional.mse_loss(
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input, target_flat, size_average=False, reduce=False)
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# losses: (batch, max_len, dim)
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losses = losses_flat.view(*target.size())
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# mask: (batch, max_len, 1)
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mask = sequence_mask(
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sequence_length=length, max_len=target.size(1)).unsqueeze(2)
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losses = losses * mask.float()
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loss = losses.sum() / (length.float().sum() * float(target.shape[2]))
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sequence_length=length, max_len=target.size(1)).unsqueeze(2).float()
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mask = mask.expand_as(input)
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loss = functional.mse_loss(
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input * mask, target * mask, reduction="sum")
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loss = loss / mask.sum()
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return loss
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