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import os
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import random
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import traceback
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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 module.mel_processing import spectrogram_torch, spec_to_mel_torch
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from text import cleaned_text_to_sequence
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import torch.nn.functional as F
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from tools.my_utils import load_audio
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version = os.environ.get("version", None)
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# ZeroDivisionError fixed by Tybost (https://github.com/RVC-Boss/GPT-SoVITS/issues/79)
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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, hparams, val=False):
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exp_dir = hparams.exp_dir
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self.path2 = "%s/2-name2text.txt" % exp_dir
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self.path4 = "%s/4-cnhubert" % exp_dir
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self.path5 = "%s/5-wav32k" % exp_dir
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assert os.path.exists(self.path2)
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assert os.path.exists(self.path4)
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assert os.path.exists(self.path5)
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names4 = set([name[:-3] for name in list(os.listdir(self.path4))]) # 去除.pt后缀
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names5 = set(os.listdir(self.path5))
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self.phoneme_data = {}
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with open(self.path2, "r", encoding="utf8") as f:
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lines = f.read().strip("\n").split("\n")
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for line in lines:
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tmp = line.split("\t")
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if len(tmp) != 4:
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continue
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self.phoneme_data[tmp[0]] = [tmp[1]]
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self.audiopaths_sid_text = list(set(self.phoneme_data) & names4 & names5)
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tmp = self.audiopaths_sid_text
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leng = len(tmp)
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min_num = 100
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if leng < min_num:
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self.audiopaths_sid_text = []
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for _ in range(max(2, int(min_num / leng))):
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self.audiopaths_sid_text += tmp
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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.val = val
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random.seed(1234)
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random.shuffle(self.audiopaths_sid_text)
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print("phoneme_data_len:", len(self.phoneme_data.keys()))
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print("wav_data_len:", len(self.audiopaths_sid_text))
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audiopaths_sid_text_new = []
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lengths = []
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skipped_phone = 0
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skipped_dur = 0
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for audiopath in tqdm(self.audiopaths_sid_text):
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try:
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phoneme = self.phoneme_data[audiopath][0]
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phoneme = phoneme.split(" ")
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phoneme_ids = cleaned_text_to_sequence(phoneme, version)
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except Exception:
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print(f"{audiopath} not in self.phoneme_data !")
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skipped_phone += 1
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continue
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size = os.path.getsize("%s/%s" % (self.path5, audiopath))
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duration = size / self.sampling_rate / 2
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if duration == 0:
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print(f"Zero duration for {audiopath}, skipping...")
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skipped_dur += 1
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continue
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if 54 > duration > 0.6 or self.val:
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audiopaths_sid_text_new.append([audiopath, phoneme_ids])
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lengths.append(size // (2 * self.hop_length))
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else:
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skipped_dur += 1
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continue
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print("skipped_phone: ", skipped_phone, ", skipped_dur: ", skipped_dur)
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print("total left: ", len(audiopaths_sid_text_new))
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assert len(audiopaths_sid_text_new) > 1 # 至少能凑够batch size，这里todo
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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, phoneme_ids = audiopath_sid_text
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text = torch.FloatTensor(phoneme_ids)
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try:
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spec, wav = self.get_audio("%s/%s" % (self.path5, audiopath))
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with torch.no_grad():
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ssl = torch.load("%s/%s.pt" % (self.path4, audiopath), map_location="cpu")
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if ssl.shape[-1] != spec.shape[-1]:
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typee = ssl.dtype
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ssl = F.pad(ssl.float(), (0, 1), mode="replicate").to(typee)
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ssl.requires_grad = False
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except:
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traceback.print_exc()
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spec = torch.zeros(1025, 100)
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wav = torch.zeros(1, 100 * self.hop_length)
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ssl = torch.zeros(1, 768, 100)
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text = text[-1:]
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print("load audio or ssl error!!!!!!", audiopath)
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return (ssl, spec, wav, text)
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def get_audio(self, filename):
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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
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audio = torch.FloatTensor(audio_array) # /32768
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audio_norm = audio
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audio_norm = audio_norm.unsqueeze(0)
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spec = spectrogram_torch(
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audio_norm, self.filter_length, self.sampling_rate, self.hop_length, self.win_length, center=False
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)
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spec = torch.squeeze(spec, 0)
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return spec, audio_norm
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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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# with torch.no_grad():
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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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def random_slice(self, ssl, wav, mel):
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assert abs(ssl.shape[-1] - wav.shape[-1] // self.hop_length) < 3, ("first", ssl.shape, wav.shape)
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len_mel = mel.shape[1]
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if self.val:
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reference_mel = mel[:, : len_mel // 3]
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return reference_mel, ssl, wav, mel
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dir = random.randint(0, 1)
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sep_point = random.randint(int(len_mel // 3), int(len_mel // 3 * 2))
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if dir == 0:
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reference_mel = mel[:, :sep_point]
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ssl = ssl[:, :, sep_point:]
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wav2 = wav[:, sep_point * self.hop_length :]
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mel = mel[:, sep_point:]
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else:
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reference_mel = mel[:, sep_point:]
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ssl = ssl[:, :, :sep_point]
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wav2 = wav[:, : sep_point * self.hop_length]
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mel = mel[:, :sep_point]
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assert abs(ssl.shape[-1] - wav2.shape[-1] // self.hop_length) < 3, (
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ssl.shape,
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wav.shape,
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wav2.shape,
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mel.shape,
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sep_point,
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self.hop_length,
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sep_point * self.hop_length,
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dir,
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)
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return reference_mel, ssl, wav2, mel
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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(torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True)
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max_ssl_len = max([x[0].size(2) for x in batch])
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max_ssl_len = int(2 * ((max_ssl_len // 2) + 1))
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max_spec_len = max([x[1].size(1) for x in batch])
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max_spec_len = int(2 * ((max_spec_len // 2) + 1))
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max_wav_len = max([x[2].size(1) for x in batch])
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max_text_len = max([x[3].size(0) for x in batch])
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ssl_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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text_lengths = torch.LongTensor(len(batch))
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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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ssl_padded = torch.FloatTensor(len(batch), batch[0][0].size(1), max_ssl_len)
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text_padded = torch.LongTensor(len(batch), max_text_len)
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spec_padded.zero_()
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wav_padded.zero_()
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ssl_padded.zero_()
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text_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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ssl = row[0]
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ssl_padded[i, :, : ssl.size(2)] = ssl[0, :, :]
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ssl_lengths[i] = ssl.size(2)
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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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text = row[3]
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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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return ssl_padded, ssl_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, text_padded, text_lengths
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class TextAudioSpeakerLoaderV3(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, hparams, val=False):
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exp_dir = hparams.exp_dir
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self.path2 = "%s/2-name2text.txt" % exp_dir
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self.path4 = "%s/4-cnhubert" % exp_dir
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self.path5 = "%s/5-wav32k" % exp_dir
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assert os.path.exists(self.path2)
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assert os.path.exists(self.path4)
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assert os.path.exists(self.path5)
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names4 = set([name[:-3] for name in list(os.listdir(self.path4))]) # 去除.pt后缀
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names5 = set(os.listdir(self.path5))
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self.phoneme_data = {}
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with open(self.path2, "r", encoding="utf8") as f:
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lines = f.read().strip("\n").split("\n")
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for line in lines:
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tmp = line.split("\t")
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if len(tmp) != 4:
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continue
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self.phoneme_data[tmp[0]] = [tmp[1]]
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self.audiopaths_sid_text = list(set(self.phoneme_data) & names4 & names5)
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tmp = self.audiopaths_sid_text
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leng = len(tmp)
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min_num = 100
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if leng < min_num:
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self.audiopaths_sid_text = []
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for _ in range(max(2, int(min_num / leng))):
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self.audiopaths_sid_text += tmp
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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.val = val
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random.seed(1234)
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random.shuffle(self.audiopaths_sid_text)
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print("phoneme_data_len:", len(self.phoneme_data.keys()))
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print("wav_data_len:", len(self.audiopaths_sid_text))
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audiopaths_sid_text_new = []
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lengths = []
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skipped_phone = 0
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skipped_dur = 0
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for audiopath in tqdm(self.audiopaths_sid_text):
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try:
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phoneme = self.phoneme_data[audiopath][0]
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phoneme = phoneme.split(" ")
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phoneme_ids = cleaned_text_to_sequence(phoneme, version)
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except Exception:
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print(f"{audiopath} not in self.phoneme_data !")
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skipped_phone += 1
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continue
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size = os.path.getsize("%s/%s" % (self.path5, audiopath))
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duration = size / self.sampling_rate / 2
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if duration == 0:
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print(f"Zero duration for {audiopath}, skipping...")
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skipped_dur += 1
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continue
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if 54 > duration > 0.6 or self.val:
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audiopaths_sid_text_new.append([audiopath, phoneme_ids])
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lengths.append(size // (2 * self.hop_length))
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else:
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skipped_dur += 1
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continue
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print("skipped_phone: ", skipped_phone, ", skipped_dur: ", skipped_dur)
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print("total left: ", len(audiopaths_sid_text_new))
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assert len(audiopaths_sid_text_new) > 1 # 至少能凑够batch size，这里todo
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self.audiopaths_sid_text = audiopaths_sid_text_new
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self.lengths = lengths
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self.spec_min = -12
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self.spec_max = 2
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self.filter_length_mel = self.win_length_mel = 1024
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self.hop_length_mel = 256
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self.n_mel_channels = 100
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self.sampling_rate_mel = 24000
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self.mel_fmin = 0
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self.mel_fmax = None
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def norm_spec(self, x):
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return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
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def get_audio_text_speaker_pair(self, audiopath_sid_text):
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audiopath, phoneme_ids = audiopath_sid_text
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text = torch.FloatTensor(phoneme_ids)
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try:
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spec, mel = self.get_audio("%s/%s" % (self.path5, audiopath))
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with torch.no_grad():
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ssl = torch.load("%s/%s.pt" % (self.path4, audiopath), map_location="cpu")
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if ssl.shape[-1] != spec.shape[-1]:
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typee = ssl.dtype
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ssl = F.pad(ssl.float(), (0, 1), mode="replicate").to(typee)
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ssl.requires_grad = False
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except:
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traceback.print_exc()
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mel = torch.zeros(100, 180)
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# wav = torch.zeros(1, 96 * self.hop_length)
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spec = torch.zeros(1025, 96)
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ssl = torch.zeros(1, 768, 96)
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text = text[-1:]
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print("load audio or ssl error!!!!!!", audiopath)
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return (ssl, spec, mel, text)
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def get_audio(self, filename):
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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
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audio = torch.FloatTensor(audio_array) # /32768
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audio_norm = audio
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audio_norm = audio_norm.unsqueeze(0)
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audio_array24 = load_audio(
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filename, 24000
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) # load_audio的方法是已经归一化到-1~1之间的，不用再/32768######这里可以用GPU重采样加速
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audio24 = torch.FloatTensor(audio_array24) # /32768
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audio_norm24 = audio24
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audio_norm24 = audio_norm24.unsqueeze(0)
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spec = spectrogram_torch(
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audio_norm, self.filter_length, self.sampling_rate, self.hop_length, self.win_length, center=False
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)
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spec = torch.squeeze(spec, 0)
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spec1 = spectrogram_torch(
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audio_norm24,
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self.filter_length_mel,
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self.sampling_rate_mel,
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self.hop_length_mel,
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self.win_length_mel,
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center=False,
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)
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mel = spec_to_mel_torch(
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spec1, self.filter_length_mel, self.n_mel_channels, self.sampling_rate_mel, self.mel_fmin, self.mel_fmax
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)
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|
mel = torch.squeeze(mel, 0)
|
|
|
mel = self.norm_spec(mel)
|
|
|
# print(1111111,spec.shape,mel.shape)
|
|
|
return spec, mel
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
class TextAudioSpeakerCollateV3:
|
|
|
"""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]
|
|
|
"""
|
|
|
# ssl, spec, wav,mel, text
|
|
|
# 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)
|
|
|
# (ssl, spec,mel, text)
|
|
|
max_ssl_len = max([x[0].size(2) for x in batch])
|
|
|
|
|
|
max_ssl_len1 = int(8 * ((max_ssl_len // 8) + 1))
|
|
|
max_ssl_len = int(2 * ((max_ssl_len // 2) + 1))
|
|
|
|
|
|
# max_ssl_len = int(8 * ((max_ssl_len // 8) + 1))
|
|
|
# max_ssl_len1=max_ssl_len
|
|
|
|
|
|
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])
|
|
|
max_mel_len = int(max_ssl_len1 * 1.25 * 1.5) ###24000/256,32000/640=16000/320
|
|
|
|
|
|
ssl_lengths = torch.LongTensor(len(batch))
|
|
|
spec_lengths = torch.LongTensor(len(batch))
|
|
|
text_lengths = torch.LongTensor(len(batch))
|
|
|
# wav_lengths = torch.LongTensor(len(batch))
|
|
|
mel_lengths = torch.LongTensor(len(batch))
|
|
|
|
|
|
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
|
|
mel_padded = torch.FloatTensor(len(batch), batch[0][2].size(0), max_mel_len)
|
|
|
ssl_padded = torch.FloatTensor(len(batch), batch[0][0].size(1), max_ssl_len)
|
|
|
text_padded = torch.LongTensor(len(batch), max_text_len)
|
|
|
# wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
|
|
|
|
|
spec_padded.zero_()
|
|
|
mel_padded.zero_()
|
|
|
ssl_padded.zero_()
|
|
|
text_padded.zero_()
|
|
|
# wav_padded.zero_()
|
|
|
|
|
|
for i in range(len(ids_sorted_decreasing)):
|
|
|
row = batch[ids_sorted_decreasing[i]]
|
|
|
# ssl, spec, wav,mel, text
|
|
|
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)
|
|
|
|
|
|
mel = row[2]
|
|
|
mel_padded[i, :, : mel.size(1)] = mel
|
|
|
mel_lengths[i] = mel.size(1)
|
|
|
|
|
|
text = row[3]
|
|
|
text_padded[i, : text.size(0)] = text
|
|
|
text_lengths[i] = text.size(0)
|
|
|
|
|
|
# return ssl_padded, spec_padded,mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, wav_padded, wav_lengths,mel_lengths
|
|
|
return ssl_padded, spec_padded, mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, mel_lengths
|
|
|
|
|
|
|
|
|
class TextAudioSpeakerLoaderV4(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, version)
|
|
|
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
|
|
|
self.spec_min = -12
|
|
|
self.spec_max = 2
|
|
|
|
|
|
self.filter_length_mel = self.win_length_mel = 1280
|
|
|
self.hop_length_mel = 320
|
|
|
self.n_mel_channels = 100
|
|
|
self.sampling_rate_mel = 32000
|
|
|
self.mel_fmin = 0
|
|
|
self.mel_fmax = None
|
|
|
|
|
|
def norm_spec(self, x):
|
|
|
return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
|
|
|
|
|
|
def get_audio_text_speaker_pair(self, audiopath_sid_text):
|
|
|
audiopath, phoneme_ids = audiopath_sid_text
|
|
|
text = torch.FloatTensor(phoneme_ids)
|
|
|
try:
|
|
|
spec, mel = 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()
|
|
|
mel = torch.zeros(100, 192)
|
|
|
# wav = torch.zeros(1, 96 * self.hop_length)
|
|
|
spec = torch.zeros(1025, 96)
|
|
|
ssl = torch.zeros(1, 768, 96)
|
|
|
text = text[-1:]
|
|
|
print("load audio or ssl error!!!!!!", audiopath)
|
|
|
return (ssl, spec, mel, 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)
|
|
|
spec1 = spectrogram_torch(audio_norm, 1280, 32000, 320, 1280, center=False)
|
|
|
mel = spec_to_mel_torch(spec1, 1280, 100, 32000, 0, None)
|
|
|
mel = self.norm_spec(torch.squeeze(mel, 0))
|
|
|
return spec, mel
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
class TextAudioSpeakerCollateV4:
|
|
|
"""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]
|
|
|
"""
|
|
|
# ssl, spec, wav,mel, text
|
|
|
# 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)
|
|
|
# (ssl, spec,mel, text)
|
|
|
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))
|
|
|
text_lengths = torch.LongTensor(len(batch))
|
|
|
# wav_lengths = torch.LongTensor(len(batch))
|
|
|
mel_lengths = torch.LongTensor(len(batch))
|
|
|
|
|
|
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
|
|
mel_padded = torch.FloatTensor(len(batch), batch[0][2].size(0), max_spec_len * 2)
|
|
|
ssl_padded = torch.FloatTensor(len(batch), batch[0][0].size(1), max_ssl_len)
|
|
|
text_padded = torch.LongTensor(len(batch), max_text_len)
|
|
|
# wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
|
|
|
|
|
spec_padded.zero_()
|
|
|
mel_padded.zero_()
|
|
|
ssl_padded.zero_()
|
|
|
text_padded.zero_()
|
|
|
# wav_padded.zero_()
|
|
|
|
|
|
for i in range(len(ids_sorted_decreasing)):
|
|
|
row = batch[ids_sorted_decreasing[i]]
|
|
|
# ssl, spec, wav,mel, text
|
|
|
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)
|
|
|
|
|
|
mel = row[2]
|
|
|
mel_padded[i, :, : mel.size(1)] = mel
|
|
|
mel_lengths[i] = mel.size(1)
|
|
|
|
|
|
text = row[3]
|
|
|
text_padded[i, : text.size(0)] = text
|
|
|
text_lengths[i] = text.size(0)
|
|
|
|
|
|
# return ssl_padded, spec_padded,mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, wav_padded, wav_lengths,mel_lengths
|
|
|
return ssl_padded, spec_padded, mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, mel_lengths
|
|
|
|
|
|
|
|
|
class TextAudioSpeakerLoaderV3b(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, version)
|
|
|
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
|
|
|
self.spec_min = -12
|
|
|
self.spec_max = 2
|
|
|
|
|
|
self.filter_length_mel = self.win_length_mel = 1024
|
|
|
self.hop_length_mel = 256
|
|
|
self.n_mel_channels = 100
|
|
|
self.sampling_rate_mel = 24000
|
|
|
self.mel_fmin = 0
|
|
|
self.mel_fmax = None
|
|
|
|
|
|
def norm_spec(self, x):
|
|
|
return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
|
|
|
|
|
|
def get_audio_text_speaker_pair(self, audiopath_sid_text):
|
|
|
audiopath, phoneme_ids = audiopath_sid_text
|
|
|
text = torch.FloatTensor(phoneme_ids)
|
|
|
try:
|
|
|
spec, mel, 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()
|
|
|
mel = torch.zeros(100, 180)
|
|
|
wav = torch.zeros(1, 96 * self.hop_length)
|
|
|
spec = torch.zeros(1025, 96)
|
|
|
ssl = torch.zeros(1, 768, 96)
|
|
|
text = text[-1:]
|
|
|
print("load audio or ssl error!!!!!!", audiopath)
|
|
|
return (ssl, spec, wav, mel, 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)
|
|
|
audio_array24 = load_audio(
|
|
|
filename, 24000
|
|
|
) # load_audio的方法是已经归一化到-1~1之间的，不用再/32768######这里可以用GPU重采样加速
|
|
|
audio24 = torch.FloatTensor(audio_array24) # /32768
|
|
|
audio_norm24 = audio24
|
|
|
audio_norm24 = audio_norm24.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)
|
|
|
|
|
|
spec1 = spectrogram_torch(
|
|
|
audio_norm24,
|
|
|
self.filter_length_mel,
|
|
|
self.sampling_rate_mel,
|
|
|
self.hop_length_mel,
|
|
|
self.win_length_mel,
|
|
|
center=False,
|
|
|
)
|
|
|
mel = spec_to_mel_torch(
|
|
|
spec1, self.filter_length_mel, self.n_mel_channels, self.sampling_rate_mel, self.mel_fmin, self.mel_fmax
|
|
|
)
|
|
|
mel = torch.squeeze(mel, 0)
|
|
|
mel = self.norm_spec(mel)
|
|
|
# print(1111111,spec.shape,mel.shape)
|
|
|
return spec, mel, 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)
|
|
|
|
|
|
|
|
|
class TextAudioSpeakerCollateV3b:
|
|
|
"""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]
|
|
|
"""
|
|
|
# ssl, spec, wav,mel, text
|
|
|
# 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)
|
|
|
# (ssl, spec,mel, text)
|
|
|
max_ssl_len = max([x[0].size(2) for x in batch])
|
|
|
|
|
|
max_ssl_len1 = int(8 * ((max_ssl_len // 8) + 1))
|
|
|
max_ssl_len = int(2 * ((max_ssl_len // 2) + 1))
|
|
|
|
|
|
# max_ssl_len = int(8 * ((max_ssl_len // 8) + 1))
|
|
|
# max_ssl_len1=max_ssl_len
|
|
|
|
|
|
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[4].size(0) for x in batch])
|
|
|
max_mel_len = int(max_ssl_len1 * 1.25 * 1.5) ###24000/256,32000/640=16000/320
|
|
|
|
|
|
ssl_lengths = torch.LongTensor(len(batch))
|
|
|
spec_lengths = torch.LongTensor(len(batch))
|
|
|
text_lengths = torch.LongTensor(len(batch))
|
|
|
wav_lengths = torch.LongTensor(len(batch))
|
|
|
mel_lengths = torch.LongTensor(len(batch))
|
|
|
|
|
|
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
|
|
mel_padded = torch.FloatTensor(len(batch), batch[0][3].size(0), max_mel_len)
|
|
|
ssl_padded = torch.FloatTensor(len(batch), batch[0][0].size(1), max_ssl_len)
|
|
|
text_padded = torch.LongTensor(len(batch), max_text_len)
|
|
|
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
|
|
|
|
|
spec_padded.zero_()
|
|
|
mel_padded.zero_()
|
|
|
ssl_padded.zero_()
|
|
|
text_padded.zero_()
|
|
|
wav_padded.zero_()
|
|
|
|
|
|
for i in range(len(ids_sorted_decreasing)):
|
|
|
row = batch[ids_sorted_decreasing[i]]
|
|
|
# ssl, spec, wav,mel, text
|
|
|
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)
|
|
|
|
|
|
mel = row[3]
|
|
|
mel_padded[i, :, : mel.size(1)] = mel
|
|
|
mel_lengths[i] = mel.size(1)
|
|
|
|
|
|
text = row[4]
|
|
|
text_padded[i, : text.size(0)] = text
|
|
|
text_lengths[i] = text.size(0)
|
|
|
|
|
|
return (
|
|
|
ssl_padded,
|
|
|
spec_padded,
|
|
|
mel_padded,
|
|
|
ssl_lengths,
|
|
|
spec_lengths,
|
|
|
text_padded,
|
|
|
text_lengths,
|
|
|
wav_padded,
|
|
|
wav_lengths,
|
|
|
mel_lengths,
|
|
|
)
|
|
|
# return ssl_padded, spec_padded,mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths,mel_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
|