mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 19:41:56 +08:00
380 lines
13 KiB
Python
380 lines
13 KiB
Python
import time, logging
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import os
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import random, traceback
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import numpy as np
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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 import commons
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from module.mel_processing import spectrogram_torch
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from text import cleaned_text_to_sequence
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from utils import load_wav_to_torch, load_filepaths_and_text
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import torch.nn.functional as F
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from functools import lru_cache
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import torch
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import requests
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from scipy.io import wavfile
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from io import BytesIO
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# from config import exp_dir
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from my_utils import load_audio
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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)
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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 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(
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"%s/%s.pt" % (self.path4, audiopath), map_location="cpu"
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)
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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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# print(ssl.requires_grad,spec.requires_grad,wav.requires_grad,text.requires_grad)
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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(
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filename, self.sampling_rate
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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
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# print(filename,audio_array.max(),audio_array.min(),audio_array.mean())
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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,
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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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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, (
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"first",
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ssl.shape,
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wav.shape,
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)
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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(
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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_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 (
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ssl_padded,
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ssl_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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text_padded,
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text_lengths,
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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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# print(233333333333333,self.lengths,dir(dataset))
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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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for i in range(len(buckets) - 1, 0, -1):
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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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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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