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https://github.com/RVC-Boss/GPT-SoVITS.git
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support sovits v3 lora training, 8G GPU memory is enough
support sovits v3 lora training, 8G GPU memory is enough
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RVC-Boss
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@ -456,6 +456,231 @@ class TextAudioSpeakerCollateV3():
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# return ssl_padded, spec_padded,mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, wav_padded, wav_lengths,mel_lengths
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return ssl_padded, spec_padded,mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths,mel_lengths
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class TextAudioSpeakerLoaderV3b(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,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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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, wav, 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(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
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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(audio_norm, self.filter_length,
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self.sampling_rate, self.hop_length, self.win_length,
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center=False)
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spec = torch.squeeze(spec, 0)
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spec1 = spectrogram_torch(audio_norm24, self.filter_length_mel,self.sampling_rate_mel, self.hop_length_mel, self.win_length_mel,center=False)
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mel = spec_to_mel_torch(spec1, self.filter_length_mel, self.n_mel_channels, self.sampling_rate_mel, self.mel_fmin, self.mel_fmax)
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mel = torch.squeeze(mel, 0)
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mel=self.norm_spec(mel)
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# print(1111111,spec.shape,mel.shape)
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return spec, mel,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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class TextAudioSpeakerCollateV3b():
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""" Zero-pads model inputs and targets
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"""
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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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#ssl, spec, wav,mel, text
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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]),
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dim=0, descending=True)
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#(ssl, spec,mel, text)
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max_ssl_len = max([x[0].size(2) for x in batch])
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max_ssl_len1 = int(8 * ((max_ssl_len // 8) + 1))
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max_ssl_len = int(2 * ((max_ssl_len // 2) + 1))
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# max_ssl_len = int(8 * ((max_ssl_len // 8) + 1))
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# max_ssl_len1=max_ssl_len
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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[4].size(0) for x in batch])
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max_mel_len=int(max_ssl_len1*1.25*1.5)###24000/256,32000/640=16000/320
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ssl_lengths = torch.LongTensor(len(batch))
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spec_lengths = torch.LongTensor(len(batch))
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text_lengths = torch.LongTensor(len(batch))
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wav_lengths = torch.LongTensor(len(batch))
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mel_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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mel_padded = torch.FloatTensor(len(batch), batch[0][3].size(0), max_mel_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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wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
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spec_padded.zero_()
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mel_padded.zero_()
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ssl_padded.zero_()
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text_padded.zero_()
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wav_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, spec, wav,mel, text
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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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mel = row[3]
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mel_padded[i, :, :mel.size(1)] = mel
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mel_lengths[i] = mel.size(1)
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text = row[4]
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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, spec_padded,mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, wav_padded, wav_lengths,mel_lengths
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# return ssl_padded, spec_padded,mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths,mel_lengths
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class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
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"""
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@ -145,7 +145,7 @@ def mel_spectrogram_torch(
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return_complex=False,
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)
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spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
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spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-9)
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spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
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spec = spectral_normalize_torch(spec)
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@ -1099,17 +1099,15 @@ class CFM(torch.nn.Module):
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return x
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def forward(self, x1, x_lens, prompt_lens, mu, use_grad_ckpt):
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b, _, t = x1.shape
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# random timestep
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t = torch.rand([b], device=mu.device, dtype=x1.dtype)
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x0 = torch.randn_like(x1,device=mu.device)
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vt = x1 - x0
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xt = x0 + t[:, None, None] * vt
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dt = torch.zeros_like(t,device=mu.device)
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prompt = torch.zeros_like(x1)
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for bib in range(b):
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prompt[bib, :, :prompt_lens[bib]] = x1[bib, :, :prompt_lens[bib]]
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xt[bib, :, :prompt_lens[bib]] = 0
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for i in range(b):
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prompt[i, :, :prompt_lens[i]] = x1[i, :, :prompt_lens[i]]
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xt[i, :, :prompt_lens[i]] = 0
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gailv=0.3# if ttime()>1736250488 else 0.1
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if random.random() < gailv:
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base = torch.randint(2, 8, (t.shape[0],), device=mu.device)
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@ -1128,14 +1126,15 @@ class CFM(torch.nn.Module):
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vt_pred = self.estimator(xt, prompt, x_lens, t,dt, mu, use_grad_ckpt).transpose(2,1)
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loss = 0
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# print(45555555,estimator_out.shape,u.shape,x_lens,prompt_lens)#45555555 torch.Size([7, 465, 100]) torch.Size([7, 100, 465]) tensor([461, 461, 451, 451, 442, 442, 442], device='cuda:0') tensor([ 96, 93, 185, 59, 244, 262, 294], device='cuda:0')
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for bib in range(b):
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loss += self.criterion(vt_pred[bib, :, prompt_lens[bib]:x_lens[bib]], vt[bib, :, prompt_lens[bib]:x_lens[bib]])
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for i in range(b):
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loss += self.criterion(vt_pred[i, :, prompt_lens[i]:x_lens[i]], vt[i, :, prompt_lens[i]:x_lens[i]])
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loss /= b
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return loss#, estimator_out + (1 - self.sigma_min) * z
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return loss
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def set_no_grad(net_g):
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for name, param in net_g.named_parameters():
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param.requires_grad=False
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class SynthesizerTrnV3(nn.Module):
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"""
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@ -1210,7 +1209,6 @@ class SynthesizerTrnV3(nn.Module):
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bins=1024
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)
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self.freeze_quantizer=freeze_quantizer
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inter_channels2=512
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self.bridge=nn.Sequential(
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nn.Conv1d(inter_channels, inter_channels2, 1, stride=1),
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@ -1219,6 +1217,10 @@ class SynthesizerTrnV3(nn.Module):
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self.wns1=Encoder(inter_channels2, inter_channels2, inter_channels2, 5, 1, 8,gin_channels=gin_channels)
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self.linear_mel=nn.Conv1d(inter_channels2,100,1,stride=1)
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self.cfm = CFM(100,DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),)#text_dim is condition feature dim
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if self.freeze_quantizer==True:
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set_no_grad(self.ssl_proj)
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set_no_grad(self.quantizer)
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set_no_grad(self.enc_p)
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def forward(self, ssl, y, mel,ssl_lengths,y_lengths, text, text_lengths,mel_lengths, use_grad_ckpt):#ssl_lengths no need now
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with autocast(enabled=False):
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@ -1229,13 +1231,13 @@ class SynthesizerTrnV3(nn.Module):
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if self.freeze_quantizer:
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self.ssl_proj.eval()#
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self.quantizer.eval()
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self.enc_p.eval()
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ssl = self.ssl_proj(ssl)
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quantized, codes, commit_loss, quantized_list = self.quantizer(
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ssl, layers=[0]
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)
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with maybe_no_grad:
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quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
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x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
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quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
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x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
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fea=self.bridge(x)
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fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
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fea, y_mask_ = self.wns1(fea, mel_lengths, ge)##If the 1-minute fine-tuning works fine, no need to manually adjust the learning rate.
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@ -1274,3 +1276,148 @@ class SynthesizerTrnV3(nn.Module):
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ssl = self.ssl_proj(x)
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quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
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return codes.transpose(0,1)
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class SynthesizerTrnV3b(nn.Module):
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"""
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Synthesizer for Training
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"""
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def __init__(self,
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spec_channels,
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segment_size,
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inter_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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resblock,
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resblock_kernel_sizes,
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resblock_dilation_sizes,
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upsample_rates,
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upsample_initial_channel,
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upsample_kernel_sizes,
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n_speakers=0,
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gin_channels=0,
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use_sdp=True,
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semantic_frame_rate=None,
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freeze_quantizer=None,
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**kwargs):
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super().__init__()
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self.spec_channels = spec_channels
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self.inter_channels = inter_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.resblock = resblock
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self.resblock_kernel_sizes = resblock_kernel_sizes
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self.resblock_dilation_sizes = resblock_dilation_sizes
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self.upsample_rates = upsample_rates
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self.upsample_initial_channel = upsample_initial_channel
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self.upsample_kernel_sizes = upsample_kernel_sizes
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self.segment_size = segment_size
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self.n_speakers = n_speakers
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self.gin_channels = gin_channels
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self.model_dim=512
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self.use_sdp = use_sdp
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self.enc_p = TextEncoder(inter_channels,hidden_channels,filter_channels,n_heads,n_layers,kernel_size,p_dropout)
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# self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)###Rollback
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self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)###Rollback
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self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
|
||||
upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
||||
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
|
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gin_channels=gin_channels)
|
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self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
||||
|
||||
|
||||
ssl_dim = 768
|
||||
assert semantic_frame_rate in ['25hz', "50hz"]
|
||||
self.semantic_frame_rate = semantic_frame_rate
|
||||
if semantic_frame_rate == '25hz':
|
||||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2)
|
||||
else:
|
||||
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
|
||||
|
||||
self.quantizer = ResidualVectorQuantizer(
|
||||
dimension=ssl_dim,
|
||||
n_q=1,
|
||||
bins=1024
|
||||
)
|
||||
self.freeze_quantizer=freeze_quantizer
|
||||
|
||||
inter_channels2=512
|
||||
self.bridge=nn.Sequential(
|
||||
nn.Conv1d(inter_channels, inter_channels2, 1, stride=1),
|
||||
nn.LeakyReLU()
|
||||
)
|
||||
self.wns1=Encoder(inter_channels2, inter_channels2, inter_channels2, 5, 1, 8,gin_channels=gin_channels)
|
||||
self.linear_mel=nn.Conv1d(inter_channels2,100,1,stride=1)
|
||||
self.cfm = CFM(100,DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),)#text_dim is condition feature dim
|
||||
|
||||
|
||||
def forward(self, ssl, y, mel,ssl_lengths,y_lengths, text, text_lengths,mel_lengths):#ssl_lengths no need now
|
||||
with autocast(enabled=False):
|
||||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
|
||||
ge = self.ref_enc(y[:,:704] * y_mask, y_mask)
|
||||
# ge = self.ref_enc(y * y_mask, y_mask)#change back, new spec setting is whole 24k
|
||||
# ge=None
|
||||
maybe_no_grad = torch.no_grad() if self.freeze_quantizer else contextlib.nullcontext()
|
||||
with maybe_no_grad:
|
||||
if self.freeze_quantizer:
|
||||
self.ssl_proj.eval()
|
||||
self.quantizer.eval()
|
||||
ssl = self.ssl_proj(ssl)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(
|
||||
ssl, layers=[0]
|
||||
)
|
||||
quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
|
||||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
|
||||
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=ge)
|
||||
z_p = self.flow(z, y_mask, g=ge)
|
||||
z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
|
||||
o = self.dec(z_slice, g=ge)
|
||||
fea=self.bridge(x)
|
||||
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
|
||||
fea, y_mask_ = self.wns1(fea, mel_lengths, ge)
|
||||
learned_mel = self.linear_mel(fea)
|
||||
B=ssl.shape[0]
|
||||
prompt_len_max = mel_lengths*2/3
|
||||
prompt_len = (torch.rand([B], device=fea.device) * prompt_len_max).floor().to(dtype=torch.long)#
|
||||
minn=min(mel.shape[-1],fea.shape[-1])
|
||||
mel=mel[:,:,:minn]
|
||||
fea=fea[:,:,:minn]
|
||||
cfm_loss= self.cfm(mel, mel_lengths, prompt_len, fea)#fea==cond,y_lengths==target_mel_lengths#ge not need
|
||||
return commit_loss,cfm_loss,F.mse_loss(learned_mel, mel),o, ids_slice, y_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), quantized
|
||||
|
||||
@torch.no_grad()
|
||||
def decode_encp(self, codes,text, refer,ge=None):
|
||||
# print(2333333,refer.shape)
|
||||
# ge=None
|
||||
if(ge==None):
|
||||
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
|
||||
refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
|
||||
ge = self.ref_enc(refer[:,:704] * refer_mask, refer_mask)
|
||||
y_lengths = torch.LongTensor([int(codes.size(2)*2)]).to(codes.device)
|
||||
y_lengths1 = torch.LongTensor([int(codes.size(2)*2.5*1.5)]).to(codes.device)
|
||||
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
|
||||
|
||||
quantized = self.quantizer.decode(codes)
|
||||
if self.semantic_frame_rate == '25hz':
|
||||
quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
|
||||
x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
|
||||
fea=self.bridge(x)
|
||||
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
|
||||
####more wn paramter to learn mel
|
||||
fea, y_mask_ = self.wns1(fea, y_lengths1, ge)
|
||||
return fea,ge
|
||||
|
||||
def extract_latent(self, x):
|
||||
ssl = self.ssl_proj(x)
|
||||
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
|
||||
return codes.transpose(0,1)
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user