407 lines
14 KiB
Python
407 lines
14 KiB
Python
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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from loguru 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，get_bert
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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", 300)
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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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# Store spectrogram lengths for Bucketing
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# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
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# spec_length = wav_length // hop_length
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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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# separate filename, speaker_id and text
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audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
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bert, ja_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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return (phones, spec, wav, sid, tone, language, bert, ja_bert)
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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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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 = torch.load(bert_path)
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assert bert.shape[-1] == len(phone)
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except:
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bert = get_bert(text, word2ph, language_str)
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torch.save(bert, bert_path)
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assert bert.shape[-1] == len(phone), phone
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if language_str == "ZH":
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bert = bert
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ja_bert = torch.zeros(768, len(phone))
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elif language_str == "JA":
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ja_bert = bert
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bert = torch.zeros(1024, len(phone))
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else:
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bert = torch.zeros(1024, len(phone))
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ja_bert = torch.zeros(768, len(phone))
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assert bert.shape[-1] == len(phone), (
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bert.shape,
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len(phone),
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sum(word2ph),
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p1,
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p2,
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t1,
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t2,
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pold,
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pold2,
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word2ph,
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text,
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w2pho,
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)
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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, 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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# Right zero-pad all one-hot text sequences to max input length
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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), 768, max_text_len)
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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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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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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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)
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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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# When one bucket is not traversed
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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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# deterministically shuffle based on epoch
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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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# add extra samples to make it evenly divisible
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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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# subsample
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ids_bucket = ids_bucket[self.rank :: self.num_replicas]
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# batching
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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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