gpt_sovits_v3

gpt_sovits_v3

GPT_SoVITS/module/data_utils.py

@@ -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):
     """

GPT_SoVITS/module/models.py

@@ -12,7 +12,7 @@ from torch.nn import functional as F
 from module import commons
 from module import modules
 from module import attentions
-
+from f5_tts.model import DiT
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 from module.commons import init_weights, get_padding
@@ -22,7 +22,7 @@ from module.quantize import ResidualVectorQuantizer
 from text import symbols as symbols_v1
 from text import symbols2 as symbols_v2
 from torch.cuda.amp import autocast
-import contextlib
+import contextlib,random
 
 
 class StochasticDurationPredictor(nn.Module):
@@ -371,6 +371,37 @@ class PosteriorEncoder(nn.Module):
         return z, m, logs, x_mask
 
 
+class Encoder(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 hidden_channels,
+                 kernel_size,
+                 dilation_rate,
+                 n_layers,
+                 gin_channels=0):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        if(g!=None):
+            g = g.detach()
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        return stats, x_mask
+
 class WNEncoder(nn.Module):
     def __init__(
         self,
@@ -1028,3 +1059,218 @@ class SynthesizerTrn(nn.Module):
         ssl = self.ssl_proj(x)
         quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
         return codes.transpose(0, 1)
+class CFM(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,dit
+    ):
+        super().__init__()
+        self.sigma_min = 1e-6
+
+        self.estimator = dit
+
+        self.in_channels = in_channels
+
+        self.criterion = torch.nn.MSELoss()
+
+    @torch.inference_mode()
+    def inference(self, mu, x_lens, prompt, n_timesteps, temperature=1.0, inference_cfg_rate=0):
+        """Forward diffusion"""
+        B, T = mu.size(0), mu.size(1)
+        x = torch.randn([B, self.in_channels, T], device=mu.device,dtype=mu.dtype) * temperature
+        prompt_len = prompt.size(-1)
+        prompt_x = torch.zeros_like(x,dtype=mu.dtype)
+        prompt_x[..., :prompt_len] = prompt[..., :prompt_len]
+        x[..., :prompt_len] = 0
+        mu=mu.transpose(2,1)
+        t = 0
+        d = 1 / n_timesteps
+        for j in range(n_timesteps):
+            t_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * t
+            d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
+            # v_pred = model(x, t_tensor, d_tensor, **extra_args)
+            v_pred = self.estimator(x, prompt_x, x_lens, t_tensor,d_tensor, mu,drop_audio_cond=False,drop_text=False).transpose(2, 1)
+            if inference_cfg_rate>1e-5:
+                neg = self.estimator(x, prompt_x, x_lens, t_tensor, d_tensor, mu, drop_audio_cond=True, drop_text=True).transpose(2, 1)
+                v_pred=v_pred+(v_pred-neg)*inference_cfg_rate
+            x = x + d * v_pred
+            t = t + d
+            x[:, :, :prompt_len] = 0
+        return x
+    def forward(self, x1, x_lens, prompt_lens, mu):
+        b, _, t = x1.shape
+
+        # random timestep
+        t = torch.rand([b], device=mu.device, dtype=x1.dtype)
+        x0 = torch.randn_like(x1,device=mu.device)
+        vt = x1 - x0
+        xt = x0 + t[:, None, None] * vt
+        dt = torch.zeros_like(t,device=mu.device)
+        prompt = torch.zeros_like(x1)
+        for bib in range(b):
+            prompt[bib, :, :prompt_lens[bib]] = x1[bib, :, :prompt_lens[bib]]
+            xt[bib, :, :prompt_lens[bib]] = 0
+        gailv=0.3# if ttime()>1736250488 else 0.1
+        if random.random() < gailv:
+            base = torch.randint(2, 8, (t.shape[0],), device=mu.device)
+            d = 1/torch.pow(2, base)
+            d_input = d.clone()
+            d_input[d_input < 1e-2] = 0
+            # with torch.no_grad():
+            v_pred_1 = self.estimator(xt, prompt, x_lens, t, d_input, mu).transpose(2, 1).detach()
+            # v_pred_1 = self.diffusion(xt, t, d_input, cond=conditioning).detach()
+            x_mid = xt + d[:, None, None] * v_pred_1
+            # v_pred_2 = self.diffusion(x_mid, t+d, d_input, cond=conditioning).detach()
+            v_pred_2 = self.estimator(x_mid, prompt, x_lens, t+d, d_input, mu).transpose(2, 1).detach()
+            vt = (v_pred_1 + v_pred_2) / 2
+            vt = vt.detach()
+            dt = 2*d
+
+        vt_pred = self.estimator(xt, prompt, x_lens, t,dt, mu).transpose(2,1)
+        loss = 0
+
+        # 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')
+        for bib in range(b):
+            loss += self.criterion(vt_pred[bib, :, prompt_lens[bib]:x_lens[bib]], vt[bib, :, prompt_lens[bib]:x_lens[bib]])
+        loss /= b
+
+        return loss#, estimator_out + (1 - self.sigma_min) * z
+
+
+class SynthesizerTrnV3(nn.Module):
+    """
+  Synthesizer for Training
+  """
+
+    def __init__(self,
+                 spec_channels,
+                 segment_size,
+                 inter_channels,
+                 hidden_channels,
+                 filter_channels,
+                 n_heads,
+                 n_layers,
+                 kernel_size,
+                 p_dropout,
+                 resblock,
+                 resblock_kernel_sizes,
+                 resblock_dilation_sizes,
+                 upsample_rates,
+                 upsample_initial_channel,
+                 upsample_kernel_sizes,
+                 n_speakers=0,
+                 gin_channels=0,
+                 use_sdp=True,
+                 semantic_frame_rate=None,
+                 freeze_quantizer=None,
+                 **kwargs):
+
+        super().__init__()
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+
+        self.model_dim=512
+        self.use_sdp = use_sdp
+        self.enc_p = TextEncoder(inter_channels,hidden_channels,filter_channels,n_heads,n_layers,kernel_size,p_dropout)
+        # self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)###回滚。。。
+        self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)###回滚。。。
+        # 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,
+        #                               gin_channels=gin_channels)
+        # 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)
+            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]
+                )
+        with maybe_no_grad:
+            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
+        fea, y_mask_ = self.wns1(fea, mel_lengths, ge)###如果1min微调没问题就不需要微操学习率了
+        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)
+        return cfm_loss
+
+    @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)