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import os |
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import random |
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import torch |
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import torch.utils.data |
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from tqdm import tqdm |
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import numpy as np |
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from tools.log import logger |
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import commons |
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from mel_processing import spectrogram_torch, mel_spectrogram_torch |
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from utils import load_wav_to_torch, load_filepaths_and_text |
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from text import cleaned_text_to_sequence |
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from config import config |
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"""Multi speaker version""" |
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class TextAudioSpeakerLoader(torch.utils.data.Dataset): |
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""" |
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1) loads audio, speaker_id, text pairs |
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2) normalizes text and converts them to sequences of integers |
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3) computes spectrograms from audio files. |
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""" |
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def __init__(self, audiopaths_sid_text, hparams): |
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self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text) |
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self.max_wav_value = hparams.max_wav_value |
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self.sampling_rate = hparams.sampling_rate |
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self.filter_length = hparams.filter_length |
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self.hop_length = hparams.hop_length |
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self.win_length = hparams.win_length |
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self.sampling_rate = hparams.sampling_rate |
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self.spk_map = hparams.spk2id |
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self.hparams = hparams |
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self.use_mel_spec_posterior = getattr( |
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hparams, "use_mel_posterior_encoder", False |
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) |
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if self.use_mel_spec_posterior: |
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self.n_mel_channels = getattr(hparams, "n_mel_channels", 80) |
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self.cleaned_text = getattr(hparams, "cleaned_text", False) |
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self.add_blank = hparams.add_blank |
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self.min_text_len = getattr(hparams, "min_text_len", 1) |
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self.max_text_len = getattr(hparams, "max_text_len", 384) |
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random.seed(1234) |
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random.shuffle(self.audiopaths_sid_text) |
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self._filter() |
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def _filter(self): |
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""" |
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Filter text & store spec lengths |
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""" |
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audiopaths_sid_text_new = [] |
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lengths = [] |
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skipped = 0 |
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logger.info("Init dataset...") |
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for _id, spk, language, text, phones, tone, word2ph in tqdm( |
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self.audiopaths_sid_text |
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): |
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audiopath = f"{_id}" |
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if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len: |
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phones = phones.split(" ") |
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tone = [int(i) for i in tone.split(" ")] |
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word2ph = [int(i) for i in word2ph.split(" ")] |
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audiopaths_sid_text_new.append( |
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[audiopath, spk, language, text, phones, tone, word2ph] |
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) |
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lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) |
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else: |
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skipped += 1 |
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logger.info( |
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"skipped: " |
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+ str(skipped) |
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+ ", total: " |
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+ str(len(self.audiopaths_sid_text)) |
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) |
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self.audiopaths_sid_text = audiopaths_sid_text_new |
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self.lengths = lengths |
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def get_audio_text_speaker_pair(self, audiopath_sid_text): |
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audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text |
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bert, ja_bert, en_bert, phones, tone, language = self.get_text( |
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text, word2ph, phones, tone, language, audiopath |
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) |
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spec, wav = self.get_audio(audiopath) |
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sid = torch.LongTensor([int(self.spk_map[sid])]) |
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emo = torch.FloatTensor(np.load(audiopath.replace(".wav", ".emo.npy"))) |
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return (phones, spec, wav, sid, tone, language, bert, ja_bert, en_bert, emo) |
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def get_audio(self, filename): |
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audio, sampling_rate = load_wav_to_torch(filename) |
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if sampling_rate != self.sampling_rate: |
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raise ValueError( |
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"{} {} SR doesn't match target {} SR".format( |
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filename, sampling_rate, self.sampling_rate |
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) |
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) |
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audio_norm = audio / self.max_wav_value |
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audio_norm = audio_norm.unsqueeze(0) |
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spec_filename = filename.replace(".wav", ".spec.pt") |
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if self.use_mel_spec_posterior: |
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spec_filename = spec_filename.replace(".spec.pt", ".mel.pt") |
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try: |
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spec = torch.load(spec_filename) |
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except: |
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if self.use_mel_spec_posterior: |
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spec = mel_spectrogram_torch( |
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audio_norm, |
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self.filter_length, |
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self.n_mel_channels, |
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self.sampling_rate, |
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self.hop_length, |
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self.win_length, |
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self.hparams.mel_fmin, |
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self.hparams.mel_fmax, |
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center=False, |
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) |
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else: |
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spec = spectrogram_torch( |
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audio_norm, |
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self.filter_length, |
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self.sampling_rate, |
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self.hop_length, |
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self.win_length, |
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center=False, |
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) |
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spec = torch.squeeze(spec, 0) |
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if config.train_ms_config.spec_cache: |
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torch.save(spec, spec_filename) |
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return spec, audio_norm |
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def get_text(self, text, word2ph, phone, tone, language_str, wav_path): |
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) |
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if self.add_blank: |
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phone = commons.intersperse(phone, 0) |
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tone = commons.intersperse(tone, 0) |
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language = commons.intersperse(language, 0) |
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for i in range(len(word2ph)): |
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word2ph[i] = word2ph[i] * 2 |
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word2ph[0] += 1 |
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bert_path = wav_path.replace(".wav", ".bert.pt") |
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try: |
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bert_ori = torch.load(bert_path) |
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assert bert_ori.shape[-1] == len(phone) |
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except Exception as e: |
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logger.warning("Bert load Failed") |
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logger.warning(e) |
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if language_str == "ZH": |
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bert = bert_ori |
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ja_bert = torch.zeros(1024, len(phone)) |
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en_bert = torch.zeros(1024, len(phone)) |
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elif language_str == "JP": |
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bert = torch.zeros(1024, len(phone)) |
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ja_bert = bert_ori |
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en_bert = torch.zeros(1024, len(phone)) |
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elif language_str == "EN": |
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bert = torch.zeros(1024, len(phone)) |
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ja_bert = torch.zeros(1024, len(phone)) |
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en_bert = bert_ori |
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phone = torch.LongTensor(phone) |
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tone = torch.LongTensor(tone) |
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language = torch.LongTensor(language) |
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return bert, ja_bert, en_bert, phone, tone, language |
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def get_sid(self, sid): |
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sid = torch.LongTensor([int(sid)]) |
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return sid |
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def __getitem__(self, index): |
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return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index]) |
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def __len__(self): |
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return len(self.audiopaths_sid_text) |
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class TextAudioSpeakerCollate: |
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"""Zero-pads model inputs and targets""" |
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def __init__(self, return_ids=False): |
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self.return_ids = return_ids |
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def __call__(self, batch): |
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"""Collate's training batch from normalized text, audio and speaker identities |
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PARAMS |
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------ |
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batch: [text_normalized, spec_normalized, wav_normalized, sid] |
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""" |
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_, ids_sorted_decreasing = torch.sort( |
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torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True |
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) |
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max_text_len = max([len(x[0]) for x in batch]) |
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max_spec_len = max([x[1].size(1) for x in batch]) |
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max_wav_len = max([x[2].size(1) for x in batch]) |
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text_lengths = torch.LongTensor(len(batch)) |
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spec_lengths = torch.LongTensor(len(batch)) |
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wav_lengths = torch.LongTensor(len(batch)) |
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sid = torch.LongTensor(len(batch)) |
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text_padded = torch.LongTensor(len(batch), max_text_len) |
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tone_padded = torch.LongTensor(len(batch), max_text_len) |
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language_padded = torch.LongTensor(len(batch), max_text_len) |
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bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) |
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ja_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) |
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en_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) |
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emo = torch.FloatTensor(len(batch), 1024) |
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spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) |
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wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) |
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text_padded.zero_() |
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tone_padded.zero_() |
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language_padded.zero_() |
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spec_padded.zero_() |
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wav_padded.zero_() |
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bert_padded.zero_() |
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ja_bert_padded.zero_() |
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en_bert_padded.zero_() |
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emo.zero_() |
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for i in range(len(ids_sorted_decreasing)): |
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row = batch[ids_sorted_decreasing[i]] |
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text = row[0] |
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text_padded[i, : text.size(0)] = text |
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text_lengths[i] = text.size(0) |
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spec = row[1] |
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spec_padded[i, :, : spec.size(1)] = spec |
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spec_lengths[i] = spec.size(1) |
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wav = row[2] |
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wav_padded[i, :, : wav.size(1)] = wav |
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wav_lengths[i] = wav.size(1) |
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sid[i] = row[3] |
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tone = row[4] |
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tone_padded[i, : tone.size(0)] = tone |
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language = row[5] |
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language_padded[i, : language.size(0)] = language |
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bert = row[6] |
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bert_padded[i, :, : bert.size(1)] = bert |
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ja_bert = row[7] |
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ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert |
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en_bert = row[8] |
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en_bert_padded[i, :, : en_bert.size(1)] = en_bert |
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emo[i, :] = row[9] |
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return ( |
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text_padded, |
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text_lengths, |
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spec_padded, |
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spec_lengths, |
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wav_padded, |
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wav_lengths, |
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sid, |
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tone_padded, |
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language_padded, |
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bert_padded, |
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ja_bert_padded, |
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en_bert_padded, |
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emo, |
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) |
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class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler): |
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""" |
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Maintain similar input lengths in a batch. |
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Length groups are specified by boundaries. |
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Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}. |
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It removes samples which are not included in the boundaries. |
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Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded. |
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""" |
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def __init__( |
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self, |
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dataset, |
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batch_size, |
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boundaries, |
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num_replicas=None, |
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rank=None, |
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shuffle=True, |
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): |
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super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) |
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self.lengths = dataset.lengths |
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self.batch_size = batch_size |
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self.boundaries = boundaries |
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self.buckets, self.num_samples_per_bucket = self._create_buckets() |
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self.total_size = sum(self.num_samples_per_bucket) |
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self.num_samples = self.total_size // self.num_replicas |
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def _create_buckets(self): |
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buckets = [[] for _ in range(len(self.boundaries) - 1)] |
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for i in range(len(self.lengths)): |
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length = self.lengths[i] |
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idx_bucket = self._bisect(length) |
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if idx_bucket != -1: |
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buckets[idx_bucket].append(i) |
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try: |
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for i in range(len(buckets) - 1, 0, -1): |
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if len(buckets[i]) == 0: |
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buckets.pop(i) |
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self.boundaries.pop(i + 1) |
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assert all(len(bucket) > 0 for bucket in buckets) |
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except Exception as e: |
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print("Bucket warning ", e) |
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for i in range(len(buckets) - 1, -1, -1): |
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if len(buckets[i]) == 0: |
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buckets.pop(i) |
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self.boundaries.pop(i + 1) |
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num_samples_per_bucket = [] |
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for i in range(len(buckets)): |
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len_bucket = len(buckets[i]) |
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total_batch_size = self.num_replicas * self.batch_size |
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rem = ( |
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total_batch_size - (len_bucket % total_batch_size) |
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) % total_batch_size |
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num_samples_per_bucket.append(len_bucket + rem) |
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return buckets, num_samples_per_bucket |
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def __iter__(self): |
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g = torch.Generator() |
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g.manual_seed(self.epoch) |
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indices = [] |
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if self.shuffle: |
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for bucket in self.buckets: |
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indices.append(torch.randperm(len(bucket), generator=g).tolist()) |
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else: |
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for bucket in self.buckets: |
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indices.append(list(range(len(bucket)))) |
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batches = [] |
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for i in range(len(self.buckets)): |
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bucket = self.buckets[i] |
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len_bucket = len(bucket) |
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if len_bucket == 0: |
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continue |
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ids_bucket = indices[i] |
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num_samples_bucket = self.num_samples_per_bucket[i] |
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rem = num_samples_bucket - len_bucket |
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ids_bucket = ( |
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ids_bucket |
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+ ids_bucket * (rem // len_bucket) |
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+ ids_bucket[: (rem % len_bucket)] |
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) |
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ids_bucket = ids_bucket[self.rank :: self.num_replicas] |
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for j in range(len(ids_bucket) // self.batch_size): |
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batch = [ |
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bucket[idx] |
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for idx in ids_bucket[ |
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j * self.batch_size : (j + 1) * self.batch_size |
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] |
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] |
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batches.append(batch) |
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if self.shuffle: |
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batch_ids = torch.randperm(len(batches), generator=g).tolist() |
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batches = [batches[i] for i in batch_ids] |
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self.batches = batches |
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assert len(self.batches) * self.batch_size == self.num_samples |
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return iter(self.batches) |
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def _bisect(self, x, lo=0, hi=None): |
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if hi is None: |
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hi = len(self.boundaries) - 1 |
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if hi > lo: |
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mid = (hi + lo) // 2 |
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if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]: |
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return mid |
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elif x <= self.boundaries[mid]: |
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return self._bisect(x, lo, mid) |
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else: |
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return self._bisect(x, mid + 1, hi) |
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else: |
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return -1 |
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def __len__(self): |
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return self.num_samples // self.batch_size |
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