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| import time | |
| import logging | |
| import os | |
| import random | |
| import traceback | |
| import numpy as np | |
| import torch | |
| import torch.utils.data | |
| from tqdm import tqdm | |
| from module import commons | |
| from module.mel_processing import spectrogram_torch | |
| from text import cleaned_text_to_sequence | |
| from utils import load_wav_to_torch, load_filepaths_and_text | |
| import torch.nn.functional as F | |
| from functools import lru_cache | |
| import requests | |
| from scipy.io import wavfile | |
| from io import BytesIO | |
| from my_utils import load_audio | |
| # ZeroDivisionError fixed by Tybost (https://github.com/RVC-Boss/GPT-SoVITS/issues/79) | |
| class TextAudioSpeakerLoader(torch.utils.data.Dataset): | |
| """ | |
| 1) loads audio, speaker_id, text pairs | |
| 2) normalizes text and converts them to sequences of integers | |
| 3) computes spectrograms from audio files. | |
| """ | |
| def __init__(self, hparams, val=False): | |
| exp_dir = hparams.exp_dir | |
| self.path2 = "%s/2-name2text.txt" % exp_dir | |
| self.path4 = "%s/4-cnhubert" % exp_dir | |
| self.path5 = "%s/5-wav32k" % exp_dir | |
| assert os.path.exists(self.path2) | |
| assert os.path.exists(self.path4) | |
| assert os.path.exists(self.path5) | |
| names4 = set([name[:-3] for name in list(os.listdir(self.path4))]) # 去除.pt后缀 | |
| names5 = set(os.listdir(self.path5)) | |
| self.phoneme_data = {} | |
| with open(self.path2, "r", encoding="utf8") as f: | |
| lines = f.read().strip("\n").split("\n") | |
| for line in lines: | |
| tmp = line.split("\t") | |
| if (len(tmp) != 4): | |
| continue | |
| self.phoneme_data[tmp[0]] = [tmp[1]] | |
| self.audiopaths_sid_text = list(set(self.phoneme_data) & names4 & names5) | |
| tmp = self.audiopaths_sid_text | |
| leng = len(tmp) | |
| min_num = 100 | |
| if (leng < min_num): | |
| self.audiopaths_sid_text = [] | |
| for _ in range(max(2, int(min_num / leng))): | |
| self.audiopaths_sid_text += tmp | |
| self.max_wav_value = hparams.max_wav_value | |
| self.sampling_rate = hparams.sampling_rate | |
| self.filter_length = hparams.filter_length | |
| self.hop_length = hparams.hop_length | |
| self.win_length = hparams.win_length | |
| self.sampling_rate = hparams.sampling_rate | |
| self.val = val | |
| random.seed(1234) | |
| random.shuffle(self.audiopaths_sid_text) | |
| print("phoneme_data_len:", len(self.phoneme_data.keys())) | |
| print("wav_data_len:", len(self.audiopaths_sid_text)) | |
| audiopaths_sid_text_new = [] | |
| lengths = [] | |
| skipped_phone = 0 | |
| skipped_dur = 0 | |
| for audiopath in tqdm(self.audiopaths_sid_text): | |
| try: | |
| phoneme = self.phoneme_data[audiopath][0] | |
| phoneme = phoneme.split(' ') | |
| phoneme_ids = cleaned_text_to_sequence(phoneme) | |
| except Exception: | |
| print(f"{audiopath} not in self.phoneme_data !") | |
| skipped_phone += 1 | |
| continue | |
| size = os.path.getsize("%s/%s" % (self.path5, audiopath)) | |
| duration = size / self.sampling_rate / 2 | |
| if duration == 0: | |
| print(f"Zero duration for {audiopath}, skipping...") | |
| skipped_dur += 1 | |
| continue | |
| if 54 > duration > 0.6 or self.val: | |
| audiopaths_sid_text_new.append([audiopath, phoneme_ids]) | |
| lengths.append(size // (2 * self.hop_length)) | |
| else: | |
| skipped_dur += 1 | |
| continue | |
| print("skipped_phone: ", skipped_phone, ", skipped_dur: ", skipped_dur) | |
| print("total left: ", len(audiopaths_sid_text_new)) | |
| assert len(audiopaths_sid_text_new) > 1 # 至少能凑够batch size，这里todo | |
| self.audiopaths_sid_text = audiopaths_sid_text_new | |
| self.lengths = lengths | |
| def get_audio_text_speaker_pair(self, audiopath_sid_text): | |
| audiopath, phoneme_ids = audiopath_sid_text | |
| text = torch.FloatTensor(phoneme_ids) | |
| try: | |
| spec, wav = self.get_audio("%s/%s" % (self.path5, audiopath)) | |
| with torch.no_grad(): | |
| ssl = torch.load("%s/%s.pt" % (self.path4, audiopath), map_location="cpu") | |
| if (ssl.shape[-1] != spec.shape[-1]): | |
| typee = ssl.dtype | |
| ssl = F.pad(ssl.float(), (0, 1), mode="replicate").to(typee) | |
| ssl.requires_grad = False | |
| except: | |
| traceback.print_exc() | |
| spec = torch.zeros(1025, 100) | |
| wav = torch.zeros(1, 100 * self.hop_length) | |
| ssl = torch.zeros(1, 768, 100) | |
| text = text[-1:] | |
| print("load audio or ssl error!!!!!!", audiopath) | |
| return (ssl, spec, wav, text) | |
| def get_audio(self, filename): | |
| audio_array = load_audio(filename, self.sampling_rate) # load_audio的方法是已经归一化到-1~1之间的，不用再/32768 | |
| audio = torch.FloatTensor(audio_array) # /32768 | |
| audio_norm = audio | |
| audio_norm = audio_norm.unsqueeze(0) | |
| spec = spectrogram_torch(audio_norm, self.filter_length, self.sampling_rate, self.hop_length, self.win_length, | |
| center=False) | |
| spec = torch.squeeze(spec, 0) | |
| return spec, audio_norm | |
| def get_sid(self, sid): | |
| sid = torch.LongTensor([int(sid)]) | |
| return sid | |
| def __getitem__(self, index): | |
| # with torch.no_grad(): | |
| return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index]) | |
| def __len__(self): | |
| return len(self.audiopaths_sid_text) | |
| def random_slice(self, ssl, wav, mel): | |
| assert abs(ssl.shape[-1] - wav.shape[-1] // self.hop_length) < 3, ( | |
| "first", ssl.shape, wav.shape) | |
| len_mel = mel.shape[1] | |
| if self.val: | |
| reference_mel = mel[:, :len_mel // 3] | |
| return reference_mel, ssl, wav, mel | |
| dir = random.randint(0, 1) | |
| sep_point = random.randint(int(len_mel // 3), int(len_mel // 3 * 2)) | |
| if dir == 0: | |
| reference_mel = mel[:, :sep_point] | |
| ssl = ssl[:, :, sep_point:] | |
| wav2 = wav[:, sep_point * self.hop_length:] | |
| mel = mel[:, sep_point:] | |
| else: | |
| reference_mel = mel[:, sep_point:] | |
| ssl = ssl[:, :, :sep_point] | |
| wav2 = wav[:, :sep_point * self.hop_length] | |
| mel = mel[:, :sep_point] | |
| assert abs(ssl.shape[-1] - wav2.shape[-1] // self.hop_length) < 3, ( | |
| ssl.shape, wav.shape, wav2.shape, mel.shape, sep_point, self.hop_length, sep_point * self.hop_length, dir) | |
| return reference_mel, ssl, wav2, mel | |
| class TextAudioSpeakerCollate(): | |
| """ Zero-pads model inputs and targets | |
| """ | |
| def __init__(self, return_ids=False): | |
| self.return_ids = return_ids | |
| def __call__(self, batch): | |
| """Collate's training batch from normalized text, audio and speaker identities | |
| PARAMS | |
| ------ | |
| batch: [text_normalized, spec_normalized, wav_normalized, sid] | |
| """ | |
| # Right zero-pad all one-hot text sequences to max input length | |
| _, ids_sorted_decreasing = torch.sort( | |
| torch.LongTensor([x[1].size(1) for x in batch]), | |
| dim=0, descending=True) | |
| max_ssl_len = max([x[0].size(2) for x in batch]) | |
| max_ssl_len = int(2 * ((max_ssl_len // 2) + 1)) | |
| max_spec_len = max([x[1].size(1) for x in batch]) | |
| max_spec_len = int(2 * ((max_spec_len // 2) + 1)) | |
| max_wav_len = max([x[2].size(1) for x in batch]) | |
| max_text_len = max([x[3].size(0) for x in batch]) | |
| ssl_lengths = torch.LongTensor(len(batch)) | |
| spec_lengths = torch.LongTensor(len(batch)) | |
| wav_lengths = torch.LongTensor(len(batch)) | |
| text_lengths = torch.LongTensor(len(batch)) | |
| spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) | |
| wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) | |
| ssl_padded = torch.FloatTensor(len(batch), batch[0][0].size(1), max_ssl_len) | |
| text_padded = torch.LongTensor(len(batch), max_text_len) | |
| spec_padded.zero_() | |
| wav_padded.zero_() | |
| ssl_padded.zero_() | |
| text_padded.zero_() | |
| for i in range(len(ids_sorted_decreasing)): | |
| row = batch[ids_sorted_decreasing[i]] | |
| ssl = row[0] | |
| ssl_padded[i, :, :ssl.size(2)] = ssl[0, :, :] | |
| ssl_lengths[i] = ssl.size(2) | |
| spec = row[1] | |
| spec_padded[i, :, :spec.size(1)] = spec | |
| spec_lengths[i] = spec.size(1) | |
| wav = row[2] | |
| wav_padded[i, :, :wav.size(1)] = wav | |
| wav_lengths[i] = wav.size(1) | |
| text = row[3] | |
| text_padded[i, :text.size(0)] = text | |
| text_lengths[i] = text.size(0) | |
| return ssl_padded, ssl_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, text_padded, text_lengths | |
| class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler): | |
| """ | |
| Maintain similar input lengths in a batch. | |
| Length groups are specified by boundaries. | |
| Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}. | |
| It removes samples which are not included in the boundaries. | |
| Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded. | |
| """ | |
| def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True): | |
| super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) | |
| self.lengths = dataset.lengths | |
| self.batch_size = batch_size | |
| self.boundaries = boundaries | |
| self.buckets, self.num_samples_per_bucket = self._create_buckets() | |
| self.total_size = sum(self.num_samples_per_bucket) | |
| self.num_samples = self.total_size // self.num_replicas | |
| def _create_buckets(self): | |
| buckets = [[] for _ in range(len(self.boundaries) - 1)] | |
| for i in range(len(self.lengths)): | |
| length = self.lengths[i] | |
| idx_bucket = self._bisect(length) | |
| if idx_bucket != -1: | |
| buckets[idx_bucket].append(i) | |
| i = len(buckets) - 1 | |
| while i >= 0: | |
| if len(buckets[i]) == 0: | |
| buckets.pop(i) | |
| self.boundaries.pop(i + 1) | |
| i -= 1 | |
| num_samples_per_bucket = [] | |
| for i in range(len(buckets)): | |
| len_bucket = len(buckets[i]) | |
| total_batch_size = self.num_replicas * self.batch_size | |
| rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size | |
| num_samples_per_bucket.append(len_bucket + rem) | |
| return buckets, num_samples_per_bucket | |
| def __iter__(self): | |
| g = torch.Generator() | |
| g.manual_seed(self.epoch) | |
| indices = [] | |
| if self.shuffle: | |
| for bucket in self.buckets: | |
| indices.append(torch.randperm(len(bucket), generator=g).tolist()) | |
| else: | |
| for bucket in self.buckets: | |
| indices.append(list(range(len(bucket)))) | |
| batches = [] | |
| for i in range(len(self.buckets)): | |
| bucket = self.buckets[i] | |
| len_bucket = len(bucket) | |
| ids_bucket = indices[i] | |
| num_samples_bucket = self.num_samples_per_bucket[i] | |
| rem = num_samples_bucket - len_bucket | |
| ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)] | |
| ids_bucket = ids_bucket[self.rank::self.num_replicas] | |
| for j in range(len(ids_bucket) // self.batch_size): | |
| batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]] | |
| batches.append(batch) | |
| if self.shuffle: | |
| batch_ids = torch.randperm(len(batches), generator=g).tolist() | |
| batches = [batches[i] for i in batch_ids] | |
| self.batches = batches | |
| assert len(self.batches) * self.batch_size == self.num_samples | |
| return iter(self.batches) | |
| def _bisect(self, x, lo=0, hi=None): | |
| if hi is None: | |
| hi = len(self.boundaries) - 1 | |
| if hi > lo: | |
| mid = (hi + lo) // 2 | |
| if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]: | |
| return mid | |
| elif x <= self.boundaries[mid]: | |
| return self._bisect(x, lo, mid) | |
| else: | |
| return self._bisect(x, mid + 1, hi) | |
| else: | |
| return -1 | |
| def __len__(self): | |
| return self.num_samples // self.batch_size |