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gpt_sovits_v3

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gpt_sovits_v3
This commit is contained in:
RVC-Boss
2025-02-11 21:07:26 +08:00
committed by GitHub
parent fa42d26d0e
commit 25cb1bf400
2 changed files with 474 additions and 5 deletions
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229
GPT_SoVITS/module/data_utils.py
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@@ -9,7 +9,7 @@ import torch.utils.data
from tqdm import tqdm
from module import commons
from module.mel_processing import spectrogram_torch
from module.mel_processing import spectrogram_torch,spec_to_mel_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
@@ -170,8 +170,6 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
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
"""
@@ -232,7 +230,232 @@ class TextAudioSpeakerCollate():
text_lengths[i] = text.size(0)
return ssl_padded, ssl_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, text_padded, text_lengths
class TextAudioSpeakerLoaderV3(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 = 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, 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
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 DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
"""